From 21861ba013fc76b33eb0c232a5abc3a9669741e1 Mon Sep 17 00:00:00 2001
From: chloebt <chloe.braud@gmail.com>
Date: Thu, 1 Feb 2024 13:18:05 +0100
Subject: [PATCH] TP7 and 8 correct + TP9 and slides 6
---
notebooks/TP7_m2LiTL_RNN_2324_CORRECT.ipynb | 1611 +++++++++++++
...L_transformers_learning_2324_CORRECT.ipynb | 2133 +++++++++++++++++
...sformers_explicabilit\303\251__2324.ipynb" | 933 +++++++
slides/MasterLiTL_2324_Course6_010224 .pdf | Bin 0 -> 3408339 bytes
4 files changed, 4677 insertions(+)
create mode 100644 notebooks/TP7_m2LiTL_RNN_2324_CORRECT.ipynb
create mode 100644 notebooks/TP8_m2LiTL_transformers_learning_2324_CORRECT.ipynb
create mode 100644 "notebooks/TP9_m2LiTL_transformers_explicabilit\303\251__2324.ipynb"
create mode 100644 slides/MasterLiTL_2324_Course6_010224 .pdf
diff --git a/notebooks/TP7_m2LiTL_RNN_2324_CORRECT.ipynb b/notebooks/TP7_m2LiTL_RNN_2324_CORRECT.ipynb
new file mode 100644
index 0000000..0a40cd3
--- /dev/null
+++ b/notebooks/TP7_m2LiTL_RNN_2324_CORRECT.ipynb
@@ -0,0 +1,1611 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU",
+ "gpuClass": "standard"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part1: RNNs: implementing an LSTM with Pytorch\n",
+ "\n",
+ "* Modifications in the PyTorch code to use RNNs for classification\n",
+ "* POS tagging with RNNs\n",
+ "\n",
+ "You need to upload:\n",
+ "* the files from the Allocine corpus\n",
+ "* the file corresponding to the Fasttext embeddings: cc.fr.300.10000.vec\n",
+ "* the file *reader_pytorch_tp5.py* that contains some functions used during the TP (see the import line below). Organizing your code with modules improves readability!"
+ ],
+ "metadata": {
+ "id": "ov8EeZiWBpBC"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tnlfNu2bBm2u"
+ },
+ "outputs": [],
+ "source": [
+ "from reader_pytorch_tp5 import Dataset, load_vectors, load_weights_matrix\n",
+ "#, load_weights_matrix, load_vectors\n",
+ "# torch and torch modules to deal with text data\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torchtext.data.utils import get_tokenizer\n",
+ "from torchtext.vocab import build_vocab_from_iterator\n",
+ "from torch.utils.data import DataLoader\n",
+ "# you can use scikit to print scores\n",
+ "from sklearn.metrics import classification_report"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# CUDA for PyTorch\n",
+ "use_cuda = torch.cuda.is_available()\n",
+ "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
+ "print(device)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KzNS86rVEv-M",
+ "outputId": "2b009384-66e1-4833-9dbb-6b3223477579"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "cuda\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Paths to data:"
+ ],
+ "metadata": {
+ "id": "taGY9N-PJvWS"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Data files\n",
+ "train_file = \"allocine_train.tsv\"\n",
+ "dev_file = \"allocine_dev.tsv\"\n",
+ "test_file = \"allocine_test.tsv\"\n",
+ "# embeddings\n",
+ "embed_file='cc.fr.300.10000.vec'"
+ ],
+ "metadata": {
+ "id": "kGty4hWCJurB"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 0- Read and load the data (code given)\n",
+ "\n",
+ "We are going to make experiments using either an architecture based on:\n",
+ "* an embedding bag layer: word embeddings are summed or averaged.\n",
+ "* an LSTM layer: each sequence of word embeddings goes through the LSTM layer and the resulting vector is used as a new representation of the input sequence.\n",
+ "\n",
+ "**Note on batches:**\n",
+ "* to be able to use batches with the embedding bag layer, we need to use the *offsets* in the *collate_fn* function since the sequences are concatenated.\n",
+ "* but this is different with the LSTM, since we're not concatenating the sequences. However, we need to have all sequences of the same lenght. This is done by *padding*.\n",
+ "\n",
+ "--> We thus should have **2 different *collate_fn* functions, and 2 versions of the training and evaluating functions**, to take into acocunt the *offsets*. **Here, to make things simpler, we don't use batches.**"
+ ],
+ "metadata": {
+ "id": "Wv6H41YoFycw"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the training and development data\n",
+ "trainset = Dataset( train_file )\n",
+ "devset = Dataset( dev_file, vocab=trainset.vocab )\n",
+ "\n",
+ "train_loader = DataLoader(trainset, shuffle=True)\n",
+ "dev_loader = DataLoader(devset, shuffle=False)"
+ ],
+ "metadata": {
+ "id": "2Sqqf4dQJmHB"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load embeddings\n",
+ "vectors = load_vectors( embed_file )\n",
+ "print( 'Version with', len( vectors), 'tokens')\n",
+ "print(vectors.keys() )\n",
+ "\n",
+ "# Compute weights matrix\n",
+ "weights_matrix = load_weights_matrix( trainset, vectors, emb_dim=300 )"
+ ],
+ "metadata": {
+ "id": "a0XTQXaKJ7IH",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "77502cf9-3a33-495b-9d12-660f0c16ebdb"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Originally we have: 2000000 tokens, and vectors of 300 dimensions\n",
+ "Version with 9999 tokens\n",
+ "dict_keys([',', 'de', '.', '</s>', 'la', 'et', ':', 'à', 'le', '\"', 'en', '’', 'les', 'des', ')', '(', 'du', 'est', 'un', \"l'\", \"d'\", 'une', 'pour', '/', '|', 'dans', 'sur', 'que', 'par', 'au', 'a', 'l', 'qui', '-', 'd', 'il', 'pas', '!', 'avec', '_', 'plus', \"'\", 'Le', 'ce', 'ou', 'La', 'ne', 'se', '»', '...', '?', 'vous', 'sont', 'son', '«', 'je', 'Les', 'Il', 'aux', '1', ';', 'mais', \"qu'\", 'on', \"n'\", 'comme', '2', 'sa', 'cette', 'y', 'nous', 'été', 'tout', 'fait', 'En', \"s'\", 'bien', 'ses', 'très', 'ont', 's', 'être', 'votre', 'ai', 'elle', 'n', '3', 'même', \"L'\", 'deux', 'faire', \"c'\", 'aussi', '>', 'leur', '%', 'si', 'entre', 'qu', '€', '&', '4', 'sans', 'Je', \"j'\", 'était', '10', 'autres', 'tous', 'peut', 'France', 'ces', '…', '5', 'lui', 'me', ']', '[', 'où', 'ans', '6', '#', 'après', '+', 'ils', 'dont', 'Pour', '°', '–', 'temps', '*', 'sous', 'Un', 'avoir', 'L', 'A', '}', 'site', 'peu', 'mon', 'encore', '12', 'depuis', '0', 'ça', 'fois', '2017', 'ainsi', 'alors', 'donc', 'notre', 'Ce', '20', '11', 'autre', 'monde', 'non', 'Paris', 'avant', 'Une', 'Elle', '15', 'également', 'Re', 'contre', 'Vous', 'c', 'moins', 'tu', 'suis', '7', 'ville', 'avait', 'vos', 'vers', 'premier', 'vie', 'Et', '2016', '2014', 'jour', '00', '2013', 'leurs', 'Dans', 'soit', '2012', 'toutes', 'nom', '2015', '14', 'De', 'On', '8', 'prix', '18', \"C'\", 'Mais', 'partie', '•', 'nos', 'voir', 'article', '16', 'Plus', '13', 'of', 'chez', 'inscription', 'première', 'quelques', 'toujours', '17', 'Nous', 'plusieurs', 'mai', 'place', 'français', '2011', 'cas', 'puis', 'Cette', 'année', 'ma', 'toute', '2010', 'the', '30', 'suite', 'pays', 'The', 'années', 'lors', 'fin', 'bon', '19', 'À', '21', 'dit', 'trois', 'grand', 'quand', 'partir', 'car', 'sera', '22', 'cet', 'jours', 'C', '2009', 'petit', '=', \"J'\", 'Si', 'maison', 'fut', 'ligne', 'faut', '9', 'nouveau', 'moi', 'lieu', 'mois', '23', 'cours', 'personnes', 'va', 'déjà', 'cela', '2008', 'beaucoup', 'juin', 'groupe', 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'piège', 'énormes', 'nue', '--LE', 'Giuseppe', 'animés', '1884', 'prononcer', 'concession', 'Diane', 'tasse', 'Landes', '1.2', 'sonores', 'saveurs', 'éleveurs', 'bacs', 'Avez-vous', 'inventé', 'urbaines', '1856', \"Quelqu'\", 'Raphaël', 'Read', 'envisage', 'évalué', 'procédés', 'JavaScript', 'PVC', 'demi-grand', 'croyances', 'caméras', 'Bio', 'Gaza', 'visité', 'compatibles', 'botanique', 'singulier', 'Arrivée', 'donnait', 'productivité', 'Buenos', '1-0', 'émet', 'ABC', 'Forêt', 't-il', 'baise', 'fonte', 'Vivre', 'coquine', 'écossais', 'Parcours', 'Toyota', 'IT', 'localités', 'tolérance', 'arrivant', '1846', '10h', 'Nation', 'artificielle', 'crochet', 'navette', 'sacrée', '123', 'sensations', 'réelles', 'disais', 'exprimés', 'ridicule', 'baptisé', 'Seuil', 'renouvelables', 'marches', 'causer', 'débutants', 'view', 'chaussée', 'censure', 'documentaires', 'pénale', 'jolis', 'rapporté', 'atouts', 'dégustation', 'existants', 'interactions', 'manteau', 'Marina', 'MP3', 'déposée', 'sont-ils', 'profils', 'baptême', 'élevées', 'diesel', 'apparente', 'arrestation', 'maxi', 'href', 'évoluant', 'Oliver', '1790', 'Ni', 'informe', 'Charente', 'Navigation', 'Fait', 'posts', 'prévision', 'Secrets', 'Ancienne', 'Bâle', 'indiquent', 'alternatives', 'Figure', 'intermédiaires', 'Vichy', 'grains', 'Hégésippe', 'Cœur', 'dérive', 'quarts', 'manquent', 'créent', 'Agent', 'obstacle', 'Reste', 'médiation', 'petit-déjeuner', 'Autorité', 'Tennis', 'nommer', 'im', 'fiscalité', 'Arles', 'attaqué', '--Annonces', 'Aventure', 'croient', 'plomberie', 'los', 'Thème', 'socle', 'Song', 'Queen', '1840', 'alphabet', 'Accéder', 'tantôt', 'diplômes', 'Bâtiment', 'légères', 'épaules', 'coiffure', 'Original', 'madame', 'commercialisation', 'Ross', 'CS', 'quarantaine', 'commissions', 'caisses', 'Autrement', 'bémol', 'plancher', 'appartenance', 'papillon', 'für', 'XI', 'fêter', 'Feu', 'promesses', 'rapprochement', 'indépendantes', 'Circuit', 'Trophée', 'Raison', 'vanille', 'dépannage', 'équation', 'accordée', 'Train', 'spécialisées', 'approuvé', 'Ciel', 'Victoire', 'oxygène', 'mutation', 'blues', 'signalé', 'Guyane', 'plutot', 'composent', 'Singapour', 'poules', 'câbles', 'préférable', 'répétition', 'Almouggar.com', 'Coeur', 'Disque', 'Géographie', 'chatte', 'Beau', 'Clermont-Ferrand', 'géré', 'Parker', 'Town', 'numero', 'accuse', 'enceintes', 'fondamental', 'Sud-Ouest', 'archéologiques', 'PêcheVoir', 'Anjou', 'Poser', 'Constantinople', 'Cliquer', 'autrui', 'entends', 'manoir', 'Princesse', 'vendue', 'XVIIIe', 'passa', 'duplicate', 'ci-après', '--Espace', 'partiel', 'kms', 'Populaire', 'Utiliser', 'recevez', 'casser', 'médiéval', 'valoir', 'tuto', 'manifestants', 'ad', 'coloniale', 'traversé', 'Paradis', 'sexuelles', 'sévère', 'oubli', 'Créez', 'roche', 'mien', 'admissibilité', 'Sud-Est', 'entame', 'tactile', 'alphabétique', 'solitaire', 'Élisabeth', 'adorable', 'commenter', 'XII', 'introduire', 'éducatif', 'Limousin', 'intellectuels', 'diplomatique', 'Saint-Pétersbourg', 'about', 'consultant', 'Wallonie', 'championne', 'correcte', 'Italien', 'dynamiques', 'Monique', 'accompagnés', 'intéressent', 'Beaux-Arts', 'porc', 'Jerry', 'fonctionnalité', 'amies', 'dénoncer', 'instants', 'encyclopédique', 'Opération', 'mat', 'quotidiens', 'Royale', 'positifs', 'Forums', 'maturité', 'mondiaux', 'enseigner', 'organisateurs', 'détriment', '127', 'PSPsexy', 'Vegas', 'Istanbul', 'pots', 'instances', 'puce', 'Revenir', 'signale', 'minuit', 'quoique', 'généralistes', 'matinée', 'pouce', 'renoncer', 'mental', 'budgétaire', 'essayez', 'imposant', 'Back', '--A', 'remercié', 'Meuse', 'achève', 'angles', 'Special', 'Flandre', 'Mail', 'Rica', 'Azure', 'Alberto', 'Malte', 'Who', 'audit', 'intervalle', '117', 'Stéphanie', 'municipalités', 'annoncée', 'épais', 'espérance', 'fonctionnaire', 'dettes', 'technicien', 'ouvrent', 'Gard', 'orale', 'approfondie', 'tomes', 'dessiner', 'basilique', 'Crédits', 'méchant', 'recommandation', 'Batman', 'tribunaux', 'dira', 'vain', 'Jusqu', '--Histoire', 'PDG', 'Hunter', 'escalade', 'tre', 'minorité', 'Rights', 'améliore', 'Universal', 'Rousseau', 'Faso', '--Questions', 'wikipédia', 'localement', 'Parfait', 'athlètes', 'Puy', 'volets', 'Rachel', 'liaisons', 'Métropole', 'SAS', 'Ingénieur', 'Lorient', 'Chantal', 'acheteur', 'lingerie', 'planètes', 'enregistrée', 'Quelque', 'congé', 'confondre', '1883', 'Caire', 'prétend', 'ranger', 'châssis', 'chaos', 'électoral', 'générer', '19h', 'impasse', 'mutuelle', 'radical', 'Prendre', 'icone', 'renouveler', 'périmètre', 'Bush', 'Conception', 'Téléchargez', 'Pauline', 'interroge', 'carrefour', 'Villeneuve', 'regards', 'scénarios', 'Annecy', 'vous-même', 'digital', 'poètes', 'Fonction', 'Bruges', 'possédait', 'entrent', 'cotisations', 'demandeurs', 'convivial', 'moine', 'barrière', '1863', 'résolu', '26Localisation', 'Stratégie', 'SE', 'nuage', 'Ann', 'détour', 'triangle', 'licences', 'Alexandra', '1Sauter', 'intégrés', 'décida', 'Stockholm', 'négocier', 'empreinte', '2013Age', 'mâles', 'Book', 'Gallery', 'désignation', 'segment', 'Posted', 'pourriez', 'sexualité', 'semblables', 'Actu', 'Professionnel', 'flèche', 'semestre', 'Constantin', 'chaudes', 'toiture', 'confluence', 'Retourner', '6e', 'Explorer', 'compact', 'quotidiennement', 'incarne', 'InvitéInvité', 'oeuf', 'démontré', 'Audio', 'allais', 'Marvel', 'buffet', 'coucou', 'Otto', 'DO', 'finissent', 'réguliers', 'alimenter', 'humides', 'discrimination', 'Micro', 'tribune', '♦', 'Comics', 'démocrate', 'Personnellement', 'Damien', 'franchir', 'vigne', 'para', 'gr', 'blason', 'cycles', 'caoutchouc', 'investi', 'saumon', 'consacrés', '9h', 'étendu', 'Aude', 'Roberto', 'cinquantaine', 'Bons', 'préoccupations', 'pirates', 'Equitation', 'rassembler', 'piliers', 'simulation', 'cinéaste', 'pompiers', '-10,5', 'réplique', 'aéronautique', 'guider', 'végétation', 'Full', 'désordre', 'LG', 'Catalogne', 'envoyée', 'Charme', 'Cologne', 'unis', 'pensait', 'complot', 'israélien', 'dictature', 'details', 'free', 'Chronique', 'Sociétés', 'croisière', 'fun', 'représentés', 'Berne', 'porno', 'appliquent', 'accroître', 'Metal', 'profondes', 'cliniques', 'there', 'prototype', 'vraies', '1865', 'Abs', 'Edouard', 'over', 'enchères', 'priorités', 'VOTRE', 'inégalités', 'séduire', 'injection', 'Effectivement', 'passez', 'éclat', 'Salvador', '1862', 'potable', 'synthétique', '11h', 'confier', 'restés', 'chauffer', 'aborde', 'EDF', 'Troyes', 'ressenti', 'jambe'])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1- Model definition\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "TJghdjf-Leun"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.1 FFNN with an embedding bag layer (code given)\n",
+ "\n",
+ "Below is the code written during TP4 for a classic FeedForward Neural Network using pretrained word embeddings (but without offsets, see the note about batches above)."
+ ],
+ "metadata": {
+ "id": "avYLIUvWMZWL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "class FeedforwardNeuralNetModel(nn.Module):\n",
+ " def __init__(self, hidden_dim, output_dim, weights_matrix):\n",
+ " # calls the init function of nn.Module. Dont get confused by syntax,\n",
+ " # just always do it in an nn.Module\n",
+ " super(FeedforwardNeuralNetModel, self).__init__()\n",
+ "\n",
+ " # Embedding layer\n",
+ " # mode (string, optional) – \"sum\", \"mean\" or \"max\". Default=mean.\n",
+ " self.embedding_bag = nn.EmbeddingBag.from_pretrained(\n",
+ " weights_matrix,\n",
+ " mode='mean')\n",
+ " embed_dim = self.embedding_bag.embedding_dim\n",
+ "\n",
+ " # Linear function\n",
+ " self.fc1 = nn.Linear(embed_dim, hidden_dim)\n",
+ "\n",
+ " # Non-linearity\n",
+ " self.sigmoid = nn.Sigmoid()\n",
+ "\n",
+ " # Linear function (readout)\n",
+ " self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
+ "\n",
+ " def forward(self, text):\n",
+ " # Embedding layer\n",
+ " embedded = self.embedding_bag(text)\n",
+ "\n",
+ " # Linear function\n",
+ " out = self.fc1(embedded)\n",
+ "\n",
+ " # Non-linearity\n",
+ " out = self.sigmoid(out)\n",
+ "\n",
+ " # Linear function (readout)\n",
+ " out = self.fc2(out)\n",
+ " return out"
+ ],
+ "metadata": {
+ "id": "hZKG5VRhJmJw"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.2 Exercise: From FFNN to LSTM\n",
+ "\n",
+ "We want to replace our hidden layer with an LSTM layer: the LSTM layers takes the word embeddings as input and the output will be directly fed to the output layer.\n",
+ "* you thus need to replace the embeddingBag layer with a simple embedding layer taking pretrained word embeddings, see the documentation here (search 'from_pretrained') https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html\n",
+ "* then you need to define an LSTM layer, see the doc here https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html.\n",
+ "\n",
+ "\n",
+ "The *forward* function is given. Note that:\n",
+ "* the LSTM has 2 outputs: ht and ct (hidden and memory state)\n",
+ "* the output of an LSTM, the 'y', is now the last hidden state computed for the entire sequence (classification task)\n",
+ "\n",
+ "In addition, note that in the forward pass, we need to reshape the data using:\n",
+ "```\n",
+ "x = x.view(len(x), 1, -1)\n",
+ "```\n",
+ "\n",
+ "We need to reshape our input data before passing it to the LSTM layer, because it takes a 3D tensor with *(Sequence lenght, Batch size, Input size)*.\n",
+ "This is done with the 'view' method, the pytorch 'reshape' function for tensors. (there's also a format with batch size first, more easy to understand)"
+ ],
+ "metadata": {
+ "id": "Ws0VVWYYMShP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "-------------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "7L7Zw3YS3icG"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "class LSTMModel(nn.Module):\n",
+ " def __init__(self, hidden_dim, output_dim, weights_matrix, batch_first=True ):\n",
+ " super(LSTMModel, self).__init__()\n",
+ "\n",
+ " # Define an embedding layer\n",
+ " # -- SOLUTION\n",
+ " self.embedding = nn.Embedding.from_pretrained( weights_matrix )\n",
+ " embedding_dim = self.embedding.embedding_dim\n",
+ "\n",
+ " # Define an LSTM layer\n",
+ " # -- SOLUTION\n",
+ " self.lstm = nn.LSTM( input_size=embedding_dim,\n",
+ " hidden_size=hidden_dim,\n",
+ " bidirectional=False)\n",
+ "\n",
+ " self.fc2 = nn.Linear(hidden_dim, output_dim) #out, (ht, ct) = self.lstm( embeds )\n",
+ "\n",
+ " def forward(self, text):\n",
+ " embeds = self.embedding(text)\n",
+ " # print( text) # a tensor of indices representing the tokens in a sequence\n",
+ " # print( embeds.shape) # (batch, seq, features) eg 1, 107, 300\n",
+ " # We need: (seq, batch, feature)\n",
+ " x = embeds.view(text.shape[1], 1, -1) # -1 allows to guess a dimension\n",
+ " # print( x.shape) # (seq, batch, features) eg 107, 1, 300\n",
+ " out, (ht, ct) = self.lstm( x ) # <--- here the real 'out' is ht\n",
+ " y = self.fc2(ht[-1]) # <--- we keep only the last hidden state\n",
+ " return y"
+ ],
+ "metadata": {
+ "id": "VV1vVgtmMShQ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2- Running experiments\n",
+ "\n",
+ "The code to train and evaluate your network is given below (again version without offsets)."
+ ],
+ "metadata": {
+ "id": "ykWFAXIFLs3W"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sklearn.metrics import classification_report, accuracy_score\n",
+ "\n",
+ "def train( model, train_loader, optimizer, num_epochs=5, trace=False ):\n",
+ " for epoch in range(num_epochs):\n",
+ " train_loss, total_acc, total_count = 0, 0, 0\n",
+ " for input, label in train_loader:\n",
+ " input = input.to(device)\n",
+ " label = label.to(device)\n",
+ " # Step1. Clearing the accumulated gradients\n",
+ " optimizer.zero_grad()\n",
+ " # Step 2. Forward pass to get output/logits\n",
+ " outputs = model( input )\n",
+ " if trace:\n",
+ " print(input) # <---- call with trace=True to 'see' the input\n",
+ " trace=False\n",
+ " # Step 3. Compute the loss, gradients, and update the parameters by\n",
+ " # calling optimizer.step()\n",
+ " # - Calculate Loss: softmax --> cross entropy loss\n",
+ " loss = criterion(outputs, label)\n",
+ " # - Getting gradients w.r.t. parameters\n",
+ " loss.backward()\n",
+ " # - Updating parameters\n",
+ " optimizer.step()\n",
+ " # Accumulating the loss over time\n",
+ " train_loss += loss.item()\n",
+ " total_acc += (outputs.argmax(1) == label).sum().item()\n",
+ " total_count += label.size(0)\n",
+ " # Compute accuracy on train set at each epoch\n",
+ " print('Epoch: {}. Loss: {}. ACC {} '.format(epoch, train_loss/total_count, total_acc/total_count))\n",
+ " total_acc, total_count = 0, 0\n",
+ " train_loss = 0\n",
+ "\n",
+ "def evaluate( model, dev_loader ):\n",
+ " predictions = []\n",
+ " gold = []\n",
+ " with torch.no_grad():\n",
+ " for input, label in dev_loader:\n",
+ " input = input.to(device)\n",
+ " label = label.to(device)\n",
+ " probs = model(input)\n",
+ " predictions.append( torch.argmax(probs, dim=1).cpu().numpy()[0] )\n",
+ " gold.append(int(label))\n",
+ " print(classification_report(gold, predictions))\n",
+ " return gold, predictions"
+ ],
+ "metadata": {
+ "id": "B5_-75IOJmMY"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.1 Exercise: Experiment with an FFNN with an Embedding bag layer\n",
+ "\n",
+ "Run first experiments with the embedding bag layer architecture, using the values below for the hyper-parameters."
+ ],
+ "metadata": {
+ "id": "w3IbjQfOL29y"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the values of the hyperparameters\n",
+ "hidden_dim = 4\n",
+ "learning_rate = 0.1\n",
+ "num_epochs = 10\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "output_dim = 2"
+ ],
+ "metadata": {
+ "id": "FJ913fY3KTKq"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Initialize the model\n",
+ "model_ffnn = FeedforwardNeuralNetModel( hidden_dim, output_dim, weights_matrix)\n",
+ "optimizer = torch.optim.SGD(model_ffnn.parameters(), lr=learning_rate)\n",
+ "model_ffnn = model_ffnn.to(device)\n",
+ "# Train the model\n",
+ "train( model_ffnn, train_loader, optimizer, num_epochs=num_epochs )\n",
+ "# Evaluate on dev\n",
+ "gold, pred = evaluate( model_ffnn, dev_loader )"
+ ],
+ "metadata": {
+ "id": "SSjMfiIaKTKv",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "e2e2dc68-86b1-4c6f-fd0f-9730850ce042"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch: 0. Loss: 0.7087512138867108. ACC 0.5080564949273921 \n",
+ "Epoch: 1. Loss: 0.676153939716801. ACC 0.58006763477223 \n",
+ "Epoch: 2. Loss: 0.6561484374096667. ACC 0.6138850208872091 \n",
+ "Epoch: 3. Loss: 0.6422408849611562. ACC 0.6280087527352297 \n",
+ "Epoch: 4. Loss: 0.6301603550665704. ACC 0.6512830714143625 \n",
+ "Epoch: 5. Loss: 0.6241167030100238. ACC 0.6536701810224786 \n",
+ "Epoch: 6. Loss: 0.6233362953003695. ACC 0.658444400238711 \n",
+ "Epoch: 7. Loss: 0.6192153244256537. ACC 0.6683906902725284 \n",
+ "Epoch: 8. Loss: 0.6166501782758756. ACC 0.6662025064650885 \n",
+ "Epoch: 9. Loss: 0.615158828818066. ACC 0.6652078774617067 \n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.53 0.89 0.66 230\n",
+ " 1 0.84 0.43 0.57 319\n",
+ "\n",
+ " accuracy 0.62 549\n",
+ " macro avg 0.69 0.66 0.62 549\n",
+ "weighted avg 0.71 0.62 0.61 549\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.2 Exercise: Experiment with an LSTM layer\n",
+ "\n",
+ "Run then experiments using the architecture based on an LSTM layer: Are results better? What about the computation time?\n",
+ "\n",
+ "Try a (very) few hyper-parameter variations to see if you can get better results with each model."
+ ],
+ "metadata": {
+ "id": "p7eoYgQ5MA-y"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Initialization of the model\n",
+ "model_lstm = LSTMModel(hidden_dim, output_dim, weights_matrix)\n",
+ "optimizer = torch.optim.SGD(model_lstm.parameters(), lr=learning_rate)\n",
+ "model = model_lstm.to(device)\n",
+ "\n",
+ "# Train the model\n",
+ "train( model_lstm, train_loader, optimizer, num_epochs=num_epochs )\n",
+ "\n",
+ "# Evaluate on dev\n",
+ "gold, pred = evaluate( model_lstm, dev_loader )"
+ ],
+ "metadata": {
+ "id": "8cl5YdGHJmRx",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "2ff0f393-feb0-45b0-8d52-a9819454629a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch: 0. Loss: 0.7111205904519841. ACC 0.49631987268748756 \n",
+ "Epoch: 1. Loss: 0.7018394312562515. ACC 0.5215834493733837 \n",
+ "Epoch: 2. Loss: 0.6709963797525117. ACC 0.581261189576288 \n",
+ "Epoch: 3. Loss: 0.6263791244822181. ACC 0.6341754525561966 \n",
+ "Epoch: 4. Loss: 0.5816120725545717. ACC 0.6652078774617067 \n",
+ "Epoch: 5. Loss: 0.5538613523110401. ACC 0.6952456733638352 \n",
+ "Epoch: 6. Loss: 0.5202998809223435. ACC 0.718519992042968 \n",
+ "Epoch: 7. Loss: 0.4890909330977199. ACC 0.7485577879450965 \n",
+ "Epoch: 8. Loss: 0.4595529430989381. ACC 0.7579073005768848 \n",
+ "Epoch: 9. Loss: 0.43638200074400607. ACC 0.7768052516411379 \n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.48 0.33 0.39 230\n",
+ " 1 0.61 0.75 0.67 319\n",
+ "\n",
+ " accuracy 0.57 549\n",
+ " macro avg 0.55 0.54 0.53 549\n",
+ "weighted avg 0.56 0.57 0.55 549\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the values of the hyperparameters\n",
+ "hidden_dim = 4\n",
+ "learning_rate = 0.1\n",
+ "num_epochs = 30\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "output_dim = 2"
+ ],
+ "metadata": {
+ "id": "84PEAiufH84d"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Initialize the model\n",
+ "model_ffnn = FeedforwardNeuralNetModel( hidden_dim, output_dim, weights_matrix)\n",
+ "optimizer = torch.optim.SGD(model_ffnn.parameters(), lr=learning_rate)\n",
+ "model_ffnn = model_ffnn.to(device)\n",
+ "# Train the model\n",
+ "train( model_ffnn, train_loader, optimizer, num_epochs=num_epochs )\n",
+ "# Evaluate on dev\n",
+ "gold, pred = evaluate( model_ffnn, dev_loader )"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "117e2608-8a0b-45e9-f9cb-387deca3f4de",
+ "id": "Yn8yW5ErH84e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch: 0. Loss: 0.7117851183236369. ACC 0.5026854983091307 \n",
+ "Epoch: 1. Loss: 0.6981077819724716. ACC 0.5299383330017904 \n",
+ "Epoch: 2. Loss: 0.6631986967287773. ACC 0.5987666600358067 \n",
+ "Epoch: 3. Loss: 0.6470217689844369. ACC 0.6274119753332007 \n",
+ "Epoch: 4. Loss: 0.6318901643314109. ACC 0.6431271135866322 \n",
+ "Epoch: 5. Loss: 0.6286030698260832. ACC 0.645912074796101 \n",
+ "Epoch: 6. Loss: 0.6215241145913573. ACC 0.6566540680326238 \n",
+ "Epoch: 7. Loss: 0.6168083337202123. ACC 0.6588422518400636 \n",
+ "Epoch: 8. Loss: 0.6138973134981666. ACC 0.6614282872488562 \n",
+ "Epoch: 9. Loss: 0.6085967999948163. ACC 0.6612293614481798 \n",
+ "Epoch: 10. Loss: 0.6047262209148936. ACC 0.6664014322657649 \n",
+ "Epoch: 11. Loss: 0.6013051855099216. ACC 0.6697831708772628 \n",
+ "Epoch: 12. Loss: 0.5975609359935437. ACC 0.66938531927591 \n",
+ "Epoch: 13. Loss: 0.5991064651024387. ACC 0.6723692062860553 \n",
+ "Epoch: 14. Loss: 0.595991118228867. ACC 0.6713745772826736 \n",
+ "Epoch: 15. Loss: 0.5936539476583356. ACC 0.6695842450765864 \n",
+ "Epoch: 16. Loss: 0.5891654244551999. ACC 0.6815197931171673 \n",
+ "Epoch: 17. Loss: 0.5906036661002614. ACC 0.6697831708772628 \n",
+ "Epoch: 18. Loss: 0.5874114981673084. ACC 0.6743584642928188 \n",
+ "Epoch: 19. Loss: 0.5853138875479534. ACC 0.6815197931171673 \n",
+ "Epoch: 20. Loss: 0.5858356947329546. ACC 0.6793316093097275 \n",
+ "Epoch: 21. Loss: 0.5799157714815564. ACC 0.6797294609110802 \n",
+ "Epoch: 22. Loss: 0.57856513651593. ACC 0.6741595384921424 \n",
+ "Epoch: 23. Loss: 0.579490848038867. ACC 0.6781380545056693 \n",
+ "Epoch: 24. Loss: 0.5756748403253747. ACC 0.6862940123333996 \n",
+ "Epoch: 25. Loss: 0.5738973663781639. ACC 0.681320867316491 \n",
+ "Epoch: 26. Loss: 0.5746859278863772. ACC 0.6773423513029639 \n",
+ "Epoch: 27. Loss: 0.5699588925799367. ACC 0.6817187189178436 \n",
+ "Epoch: 28. Loss: 0.5732198012591418. ACC 0.6763477222995823 \n",
+ "Epoch: 29. Loss: 0.569670912731041. ACC 0.6795305351104038 \n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.66 0.29 0.40 230\n",
+ " 1 0.64 0.89 0.74 319\n",
+ "\n",
+ " accuracy 0.64 549\n",
+ " macro avg 0.65 0.59 0.57 549\n",
+ "weighted avg 0.64 0.64 0.60 549\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Initialization of the model\n",
+ "model_lstm = LSTMModel(hidden_dim, output_dim, weights_matrix)\n",
+ "optimizer = torch.optim.SGD(model_lstm.parameters(), lr=learning_rate)\n",
+ "model = model_lstm.to(device)\n",
+ "\n",
+ "# Train the model\n",
+ "train( model_lstm, train_loader, optimizer, num_epochs=num_epochs )\n",
+ "\n",
+ "# Evaluate on dev\n",
+ "gold, pred = evaluate( model_lstm, dev_loader )"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "96d64710-e642-4cd2-e890-5808bca6ffb1",
+ "id": "9EpuJBnEH84f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch: 0. Loss: 0.7064150710371017. ACC 0.516610304356475 \n",
+ "Epoch: 1. Loss: 0.6895273971316329. ACC 0.5552019096876865 \n",
+ "Epoch: 2. Loss: 0.6482415009539716. ACC 0.6140839466878855 \n",
+ "Epoch: 3. Loss: 0.6084075201324443. ACC 0.6449174457927194 \n",
+ "Epoch: 4. Loss: 0.5645958882027424. ACC 0.696240302367217 \n",
+ "Epoch: 5. Loss: 0.5392155470126457. ACC 0.709966182613885 \n",
+ "Epoch: 6. Loss: 0.5033203023049426. ACC 0.734036204495723 \n",
+ "Epoch: 7. Loss: 0.4759496562317625. ACC 0.7547244877660633 \n",
+ "Epoch: 8. Loss: 0.46291870424297554. ACC 0.7682514422120549 \n",
+ "Epoch: 9. Loss: 0.42798266820592445. ACC 0.7865526158742789 \n",
+ "Epoch: 10. Loss: 0.42152460734932645. ACC 0.7885418738810424 \n",
+ "Epoch: 11. Loss: 0.40900983143171027. ACC 0.7949074995026855 \n",
+ "Epoch: 12. Loss: 0.371776129203928. ACC 0.8185796697831709 \n",
+ "Epoch: 13. Loss: 0.3770871752133524. ACC 0.8179828923811419 \n",
+ "Epoch: 14. Loss: 0.3805167324169218. ACC 0.8221603341953452 \n",
+ "Epoch: 15. Loss: 0.36161825373854006. ACC 0.82952058882037 \n",
+ "Epoch: 16. Loss: 0.36427690289997755. ACC 0.8331012532325442 \n",
+ "Epoch: 17. Loss: 0.32443881801552843. ACC 0.8458325044758305 \n",
+ "Epoch: 18. Loss: 0.3352295683685041. ACC 0.8442410980704197 \n",
+ "Epoch: 19. Loss: 0.3128053948367803. ACC 0.8589616073204694 \n",
+ "Epoch: 20. Loss: 0.298009600166073. ACC 0.8613487169285856 \n",
+ "Epoch: 21. Loss: 0.29569330546841893. ACC 0.8603540879252038 \n",
+ "Epoch: 22. Loss: 0.30723532522798097. ACC 0.8585637557191168 \n",
+ "Epoch: 23. Loss: 0.311128420169705. ACC 0.8549830913069425 \n",
+ "Epoch: 24. Loss: 0.2895419603432186. ACC 0.8635369007360255 \n",
+ "Epoch: 25. Loss: 0.27214542154540605. ACC 0.8732842649691664 \n",
+ "Epoch: 26. Loss: 0.260997945055503. ACC 0.8828327034016312 \n",
+ "Epoch: 27. Loss: 0.2501558443390086. ACC 0.8886015516212453 \n",
+ "Epoch: 28. Loss: 0.2483478199835197. ACC 0.8846230356077183 \n",
+ "Epoch: 29. Loss: 0.2551806871404551. ACC 0.8812412969962204 \n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.51 0.62 0.56 230\n",
+ " 1 0.68 0.57 0.62 319\n",
+ "\n",
+ " accuracy 0.59 549\n",
+ " macro avg 0.59 0.60 0.59 549\n",
+ "weighted avg 0.61 0.59 0.59 549\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 2: LSTM for POS Tagging\n",
+ "\n",
+ "In the previous part, LSTM is used to encode a sequence, ie as a way to get a better representation than a bag of embeddings.\n",
+ "\n",
+ "RNNs are powerful for sequence labelling task, where the goal is to ouput a label for each token in the input sequence, eg POS tagging, NER, sentence/discourse segmentation (anything annotated with BIO scheme)..."
+ ],
+ "metadata": {
+ "id": "rTsfKWeP7Jdl"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1- Small tutorial\n",
+ "\n",
+ "The code for an LSTM tagger is given below:\n",
+ "* the input is still word embeddings (here intialized randomly)\n",
+ "* the definition of the LSTM layer is the same as previously\n",
+ "* the output layer has (probably) more output dimensions, as given here by 'tagset_size' = here all the possible POS tags\n",
+ "\n",
+ "The difference is in the forward function:\n",
+ "* now we need to keep all the hidden states for each input token in the sequence = tag_space (instead for only outputing the last hidden state)\n",
+ "\n",
+ "Note also that here we apply a softmax function at the end, because the loss used doesn't include it.\n",
+ "\n",
+ "From: https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html"
+ ],
+ "metadata": {
+ "id": "7OOuoZLlIJI2"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "\n",
+ "class LSTMTagger(nn.Module):\n",
+ "\n",
+ " def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):\n",
+ " super(LSTMTagger, self).__init__()\n",
+ " self.hidden_dim = hidden_dim\n",
+ "\n",
+ " self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
+ "\n",
+ " # The LSTM takes word embeddings as inputs, and outputs hidden states\n",
+ " # with dimensionality hidden_dim.\n",
+ " self.lstm = nn.LSTM(embedding_dim, hidden_dim)\n",
+ "\n",
+ " # The linear layer that maps from hidden state space to tag space\n",
+ " self.hidden2tag = nn.Linear(hidden_dim, tagset_size)\n",
+ "\n",
+ " def forward(self, sentence):\n",
+ " embeds = self.word_embeddings(sentence)\n",
+ " #print('embeds.shape', embeds.shape)\n",
+ " #print(embeds.view(len(sentence), 1, -1).shape)\n",
+ " lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))\n",
+ " tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1)) # the whole output, vs output[-1] for classif\n",
+ " tag_scores = F.log_softmax(tag_space, dim=1) # required with nn.NLLLoss()\n",
+ " return tag_scores"
+ ],
+ "metadata": {
+ "id": "7O_41eHi71SO"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.1 Prepare data\n",
+ "\n",
+ "As usual, an important step is to well prepare the data to be given to our model.\n",
+ "Below is some code to prepare a small toy dataset.\n",
+ "\n",
+ "- we need to go over the input sequences (sentences) and retrieve the vocabulary (words) and the tag set (POS).\n",
+ "- we build dictionaries to map words and POS to indices, and transform our data to list of indices."
+ ],
+ "metadata": {
+ "id": "sfyJF7bIJv8M"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Transform a list of tokens into a list of indices using the dict given\n",
+ "def prepare_sequence(seq, to_ix):\n",
+ " '''\n",
+ " - seq: an input sequence of tokens\n",
+ " - to_ix: a dictionary mapping token to indices\n",
+ " output: a tensor of indices representing the input sequence\n",
+ " '''\n",
+ " idxs = [to_ix[w] for w in seq]\n",
+ " return torch.tensor(idxs, dtype=torch.long)\n",
+ "\n",
+ "# Toy dataset\n",
+ "training_data = [\n",
+ " # Tags are: DET - determiner; NN - noun; V - verb\n",
+ " # For example, the word \"The\" is a determiner\n",
+ " (\"The dog ate the apple\".split(), [\"DET\", \"NN\", \"V\", \"DET\", \"NN\"]),\n",
+ " (\"Everybody read that book\".split(), [\"NN\", \"V\", \"DET\", \"NN\"])\n",
+ "]\n",
+ "\n",
+ "# Build the mapping from word to indices\n",
+ "word_to_ix = {}\n",
+ "# For each words-list (sentence) and tags-list in each tuple of training_data\n",
+ "for sent, tags in training_data:\n",
+ " for word in sent:\n",
+ " if word not in word_to_ix: # word has not been assigned an index yet\n",
+ " word_to_ix[word] = len(word_to_ix) # Assign each word with a unique index\n",
+ "print(word_to_ix)\n",
+ "\n",
+ "# Here the mapping for POS tags is given\n",
+ "tag_to_ix = {\"DET\": 0, \"NN\": 1, \"V\": 2} # Assign each tag with a unique index\n",
+ "ix_to_tag = {v: k for k, v in tag_to_ix.items()}"
+ ],
+ "metadata": {
+ "id": "6A5Z-EsT7w_V",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "93f0e789-0cb8-4d12-bf0a-6e78e01b9df6"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'The': 0, 'dog': 1, 'ate': 2, 'the': 3, 'apple': 4, 'Everybody': 5, 'read': 6, 'that': 7, 'book': 8}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.3 Run the model\n",
+ "\n",
+ "Now we can train the POS tagger over the toy dataset."
+ ],
+ "metadata": {
+ "id": "bJiuDcmyK9Ba"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# These will usually be more like 32 or 64 dimensional.\n",
+ "# We will keep them small, so we can see how the weights change as we train.\n",
+ "EMBEDDING_DIM = 6\n",
+ "HIDDEN_DIM = 6\n",
+ "\n",
+ "\n",
+ "model = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix))\n",
+ "loss_function = nn.NLLLoss() # does not include the softmax\n",
+ "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
+ ],
+ "metadata": {
+ "id": "jiSYatB48Brn"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Look at the output of the code below: it corresponds to the scores for each POS (3 possibilities) for each token in the first training sentence (5 words) BEFORE TRAINING."
+ ],
+ "metadata": {
+ "id": "TRY-H1iMLVmM"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# See what the scores are before training\n",
+ "# Note that element i,j of the output is the score for tag j for word i.\n",
+ "# Here we don't need to train, so the code is wrapped in torch.no_grad()\n",
+ "with torch.no_grad():\n",
+ " inputs = prepare_sequence(training_data[0][0], word_to_ix)\n",
+ " tag_scores = model(inputs)\n",
+ " print(tag_scores)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "XdC_GJufLN1d",
+ "outputId": "b90f8cf3-8447-4abe-99ac-a4825f386c23"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tensor([[-1.1145, -1.4033, -0.8530],\n",
+ " [-1.0605, -1.5070, -0.8390],\n",
+ " [-0.9959, -1.5485, -0.8722],\n",
+ " [-1.0142, -1.5663, -0.8475],\n",
+ " [-1.0415, -1.5029, -0.8566]])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now we train the model:"
+ ],
+ "metadata": {
+ "id": "Igkyu3W1LlnM"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "for epoch in range(300): # again, normally you would NOT do 300 epochs, it is toy data\n",
+ " for sentence, tags in training_data:\n",
+ " # Step 1. Remember that Pytorch accumulates gradients.\n",
+ " # We need to clear them out before each instance\n",
+ " model.zero_grad()\n",
+ "\n",
+ " # Step 2. Get our inputs ready for the network, that is, turn them into\n",
+ " # Tensors of word indices.\n",
+ " sentence_in = prepare_sequence(sentence, word_to_ix)\n",
+ " targets = prepare_sequence(tags, tag_to_ix)\n",
+ "\n",
+ " # Step 3. Run our forward pass.\n",
+ " tag_scores = model(sentence_in)\n",
+ "\n",
+ " # Step 4. Compute the loss, gradients, and update the parameters by\n",
+ " # calling optimizer.step()\n",
+ " loss = loss_function(tag_scores, targets)\n",
+ " loss.backward()\n",
+ " optimizer.step()"
+ ],
+ "metadata": {
+ "id": "hW9FiA4dLHRw"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "And we look at the score after training: what do you see?"
+ ],
+ "metadata": {
+ "id": "4JmZd7P4Lp46"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# See what the scores are after training\n",
+ "with torch.no_grad():\n",
+ " inputs = prepare_sequence(training_data[0][0], word_to_ix)\n",
+ " tag_scores = model(inputs)\n",
+ " predictions = torch.argmax(tag_scores, dim=1).cpu().numpy()\n",
+ " print(tag_scores)\n",
+ " print(predictions)\n",
+ " print(training_data[0][0])\n",
+ " print( [ix_to_tag[p] for p in predictions])\n",
+ "\n",
+ " # The sentence is \"the dog ate the apple\". i,j corresponds to score for tag j\n",
+ " # for word i. The predicted tag is the maximum scoring tag.\n",
+ " # Here, we can see the predicted sequence below is 0 1 2 0 1\n",
+ " # since 0 is index of the maximum value of row 1,\n",
+ " # 1 is the index of maximum value of row 2, etc.\n",
+ " # Which is DET NOUN VERB DET NOUN, the correct sequence!\n",
+ " #print(tag_scores)"
+ ],
+ "metadata": {
+ "id": "Q_xgiu1MBAnB",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "420258d1-fb73-487a-a7f7-9d68af59bf38"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tensor([[-0.0449, -3.7969, -3.8403],\n",
+ " [-4.3027, -0.0316, -4.0409],\n",
+ " [-3.5685, -3.9124, -0.0494],\n",
+ " [-0.0427, -3.8332, -3.9049],\n",
+ " [-4.0410, -0.0297, -4.4526]])\n",
+ "[0 1 2 0 1]\n",
+ "['The', 'dog', 'ate', 'the', 'apple']\n",
+ "['DET', 'NN', 'V', 'DET', 'NN']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2- Training a POS tagger on a large set of data"
+ ],
+ "metadata": {
+ "id": "OiMM4pDON_kB"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install torchdata\n",
+ "!pip install portalocker>=2.0.0"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qWakPj_QQwPx",
+ "outputId": "1d65908f-7875-434b-cba3-cd81189ec197"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: torchdata in /usr/local/lib/python3.10/dist-packages (0.7.0)\n",
+ "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.0.7)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.31.0)\n",
+ "Requirement already satisfied: torch==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.1.0+cu121)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (3.13.1)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (4.5.0)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (1.12)\n",
+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (3.2.1)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (3.1.2)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (2023.6.0)\n",
+ "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0->torchdata) (2.1.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (3.3.2)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (3.6)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (2023.11.17)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.1.0->torchdata) (2.1.3)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.1.0->torchdata) (1.3.0)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from torch.utils.data import Dataset\n",
+ "from torchtext import datasets"
+ ],
+ "metadata": {
+ "id": "VyyuXUhmUg5r"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#ud_dataset = torchtext.datasets.UDPOS(root: str = '.data', split: Union[Tuple[str], str] = ('train', 'valid', 'test'))\n",
+ "\n",
+ "train_iter, test_iter = datasets.UDPOS(split=('train', 'valid'))\n"
+ ],
+ "metadata": {
+ "id": "1CszON2uJmUb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "ex = next(iter(train_iter))\n",
+ "print(ex[0])\n",
+ "print(ex[1])\n",
+ "print(ex[2])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "guxlgcFuRRR_",
+ "outputId": "5ff06e59-ea56-47bb-a4a5-b60724130c42"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "['Al', '-', 'Zaman', ':', 'American', 'forces', 'killed', 'Shaikh', 'Abdullah', 'al', '-', 'Ani', ',', 'the', 'preacher', 'at', 'the', 'mosque', 'in', 'the', 'town', 'of', 'Qaim', ',', 'near', 'the', 'Syrian', 'border', '.']\n",
+ "['PROPN', 'PUNCT', 'PROPN', 'PUNCT', 'ADJ', 'NOUN', 'VERB', 'PROPN', 'PROPN', 'PROPN', 'PUNCT', 'PROPN', 'PUNCT', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'PROPN', 'PUNCT', 'ADP', 'DET', 'ADJ', 'NOUN', 'PUNCT']\n",
+ "['NNP', 'HYPH', 'NNP', ':', 'JJ', 'NNS', 'VBD', 'NNP', 'NNP', 'NNP', 'HYPH', 'NNP', ',', 'DT', 'NN', 'IN', 'DT', 'NN', 'IN', 'DT', 'NN', 'IN', 'NNP', ',', 'IN', 'DT', 'JJ', 'NN', '.']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from collections import Counter\n",
+ "from torchtext.vocab import vocab\n",
+ "\n",
+ "# Build vocabulary\n",
+ "training_size = 0\n",
+ "counter = Counter()\n",
+ "for (tokens, tags, _) in train_iter:\n",
+ " training_size += 1\n",
+ " counter.update(tokens)\n",
+ "vocab = vocab(counter, min_freq=10, specials=('<unk>', '<BOS>', '<EOS>', '<PAD>'))\n",
+ "print( \"total number of example in training set:\", training_size)"
+ ],
+ "metadata": {
+ "id": "xdoeEWyPX8pO",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "0e52904e-6907-4435-cd6a-f20c9cd437b5"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/data/datapipes/iter/combining.py:333: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.\n",
+ " warnings.warn(\"Some child DataPipes are not exhausted when __iter__ is called. We are resetting \"\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "total number of example in training set: 12543\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"The length of the new vocab is\", len(vocab))\n",
+ "# token to indices\n",
+ "stoi = vocab.get_stoi()\n",
+ "print(\"The index of '<BOS>' is\", stoi['<BOS>'])\n",
+ "# indice to token\n",
+ "itos = vocab.get_itos()\n",
+ "print(\"The token at index 2 is\", itos[2])\n",
+ "print(\"The token at index 42 is\", itos[42])"
+ ],
+ "metadata": {
+ "id": "P-WrgNL0YCJK",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8b0d68b5-39bc-4637-d7b6-84b8f2d82f15"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The length of the new vocab is 2182\n",
+ "The index of '<BOS>' is 1\n",
+ "The token at index 2 is <EOS>\n",
+ "The token at index 42 is operating\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Transform a list of tokens into a list of indices using the dict given\n",
+ "def prepare_sequence(seq, to_ix):\n",
+ " '''\n",
+ " - seq: an input sequence of tokens\n",
+ " - to_ix: a dictionary mapping token to indices\n",
+ " output: a tensor of indices representing the input sequence\n",
+ " '''\n",
+ " idxs = [to_ix[w] for w in seq]\n",
+ " return torch.tensor(idxs, dtype=torch.long)\n",
+ "\n",
+ "\n",
+ "# Build the mapping from word to indices, and from POS to indices\n",
+ "word_to_ix, tag_to_ix = {}, {}\n",
+ "# For each words-list (sentence) and tags-list in each tuple of training_data\n",
+ "for sent, tags, _ in train_iter:\n",
+ " for word in sent:\n",
+ " if word not in word_to_ix: # word has not been assigned an index yet\n",
+ " word_to_ix[word] = len(word_to_ix) # Assign each word with a unique index\n",
+ " for tag in tags:\n",
+ " if tag not in tag_to_ix:\n",
+ " tag_to_ix[tag] = len(tag_to_ix)\n",
+ "\n",
+ "# Reverse mapping\n",
+ "ix_to_tag = {v: k for k, v in tag_to_ix.items()}\n",
+ "\n",
+ "# Print the mapping dictionaries\n",
+ "print(word_to_ix)\n",
+ "print(tag_to_ix)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Va4-PPpSSPjc",
+ "outputId": "c5e4a7f1-d565-45fb-c4b0-1e03b8235c89"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'Al': 0, '-': 1, 'Zaman': 2, ':': 3, 'American': 4, 'forces': 5, 'killed': 6, 'Shaikh': 7, 'Abdullah': 8, 'al': 9, 'Ani': 10, ',': 11, 'the': 12, 'preacher': 13, 'at': 14, 'mosque': 15, 'in': 16, 'town': 17, 'of': 18, 'Qaim': 19, 'near': 20, 'Syrian': 21, 'border': 22, '.': 23, '[': 24, 'This': 25, 'killing': 26, 'a': 27, 'respected': 28, 'cleric': 29, 'will': 30, 'be': 31, 'causing': 32, 'us': 33, 'trouble': 34, 'for': 35, 'years': 36, 'to': 37, 'come': 38, ']': 39, 'DPA': 40, 'Iraqi': 41, 'authorities': 42, 'announced': 43, 'that': 44, 'they': 45, 'had': 46, 'busted': 47, 'up': 48, '3': 49, 'terrorist': 50, 'cells': 51, 'operating': 52, 'Baghdad': 53, 'Two': 54, 'them': 55, 'were': 56, 'being': 57, 'run': 58, 'by': 59, '2': 60, 'officials': 61, 'Ministry': 62, 'Interior': 63, '!': 64, 'The': 65, 'MoI': 66, 'Iraq': 67, 'is': 68, 'equivalent': 69, 'US': 70, 'FBI': 71, 'so': 72, 'this': 73, 'would': 74, 'like': 75, 'having': 76, 'J.': 77, 'Edgar': 78, 'Hoover': 79, 'unwittingly': 80, 'employ': 81, 'high': 82, 'level': 83, 'members': 84, 'Weathermen': 85, 'bombers': 86, 'back': 87, '1960s': 88, 'third': 89, 'was': 90, 'head': 91, 'an': 92, 'investment': 93, 'firm': 94, 'You': 95, 'wonder': 96, 'if': 97, 'he': 98, 'manipulating': 99, 'market': 100, 'with': 101, 'his': 102, 'bombing': 103, 'targets': 104, 'Ghazaliyah': 105, 'and': 106, 'Jihad': 107, 'districts': 108, 'capital': 109, 'Although': 110, 'announcement': 111, 'probably': 112, 'made': 113, 'show': 114, 'progress': 115, 'identifying': 116, 'breaking': 117, 'terror': 118, 'I': 119, 'do': 120, \"n't\": 121, 'find': 122, 'news': 123, 'Baathists': 124, 'continue': 125, 'penetrate': 126, 'government': 127, 'very': 128, 'hopeful': 129, 'It': 130, 'reminds': 131, 'me': 132, 'too': 133, 'much': 134, 'ARVN': 135, 'officers': 136, 'who': 137, 'secretly': 138, 'working': 139, 'other': 140, 'side': 141, 'Vietnam': 142, 'Guerrillas': 143, 'member': 144, 'Kurdistan': 145, 'Democratic': 146, 'Party': 147, 'after': 148, 'kidnapping': 149, 'him': 150, 'Mosul': 151, 'police': 152, 'commander': 153, 'Ninevah': 154, 'Province': 155, 'bombings': 156, 'declined': 157, '80': 158, 'percent': 159, 'whereas': 160, 'there': 161, 'been': 162, 'big': 163, 'jump': 164, 'number': 165, 'kidnappings': 166, 'On': 167, 'Wednesday': 168, 'guerrillas': 169, 'kidnapped': 170, 'cosmetic': 171, 'surgeon': 172, 'wife': 173, 'while': 174, 'on': 175, 'their': 176, 'way': 177, 'home': 178, 'In': 179, 'Suwayrah': 180, 'Kut': 181, 'two': 182, 'car': 183, 'bombs': 184, 'discovered': 185, 'before': 186, 'could': 187, 'detonated': 188, '(': 189, 'southeastern': 190, 'has': 191, 'overwhelmingly': 192, 'Shiite': 193, 'population': 194, 'are': 195, 'lookout': 196, 'Baathist': 197, 'saboteurs': 198, 'willingly': 199, 'turn': 200, 'willingness': 201, 'main': 202, 'difference': 203, 'south': 204, 'as': 205, 'opposed': 206, 'center': 207, 'north': 208, 'country': 209, ')': 210, 'Kadhim': 211, 'Talal': 212, 'Husain': 213, 'assistant': 214, 'dean': 215, 'School': 216, 'Education': 217, 'Mustansiriyah': 218, 'University': 219, 'assassinated': 220, 'driver': 221, 'Salikh': 222, 'district': 223, 'engineer': 224, 'Asi': 225, 'Ali': 226, 'from': 227, 'Tikrit': 228, 'They': 229, 'also': 230, 'Hamid': 231, \"'Akkab\": 232, 'clan': 233, 'elder': 234, 'branch': 235, 'Dulaim': 236, 'tribe': 237, 'His': 238, 'mother': 239, 'attack': 240, 'leaders': 241, 'have': 242, 'past': 243, 'week': 244, 'half': 245, 'Hawijah': 246, 'launched': 247, 'left': 248, '6': 249, 'dead': 250, 'including': 251, '4': 252, 'soldiers': 253, 'One': 254, 'Jubur': 255, 'deputy': 256, 'garrison': 257, 'hundred': 258, 'Batawi': 259, 'demonstrated': 260, 'Friday': 261, 'protesting': 262, 'Sarhid': 263, 'sons': 264, 'gunmen': 265, 'wearing': 266, 'army': 267, 'uniforms': 268, 'largely': 269, 'Sunni': 270, 'Arab': 271, 'some': 272, 'observers': 273, 'accused': 274, 'elements': 275, 'behind': 276, 'assassination': 277, ';': 278, 'it': 279, 'more': 280, 'likely': 281, 'work': 282, 'punishing': 283, 'cooperating': 284, 'Dec.': 285, '15': 286, 'elections': 287, 'High': 288, 'Electoral': 289, 'Commission': 290, 'denied': 291, 'request': 292, 'Debaathification': 293, 'exclude': 294, '51': 295, 'individuals': 296, 'running': 297, 'party': 298, 'lists': 299, 'grounds': 300, 'sufficiently': 301, 'involved': 302, 'Baath': 303, 'activities': 304, 'warrant': 305, 'excluded': 306, 'civil': 307, 'office': 308, 'said': 309, 'no': 310, 'legal': 311, 'such': 312, 'exclusion': 313, 'item': 314, 'small': 315, 'one': 316, 'easily': 317, 'missed': 318, 'But': 319, 'my': 320, 'view': 321, 'highly': 322, 'significant': 323, 'pushed': 324, 'Ahmad': 325, 'Chalabi': 326, 'National': 327, 'Congress': 328, 'hard': 329, 'many': 330, 'Arabs': 331, 'into': 332, 'arms': 333, 'increasingly': 334, 'marginalized': 335, 'within': 336, 'however': 337, 'despite': 338, 'ties': 339, 'clientelage': 340, 'Washington': 341, 'Tehran': 342, 'He': 343, 'longer': 344, 'dominant': 345, 'list': 346, 'United': 347, 'Alliance': 348, 'wo': 349, 'seats': 350, 'new': 351, 'parliament': 352, 'Some': 353, '2,000': 354, 'junior': 355, 'old': 356, 'recalled': 357, 'duty': 358, 'recent': 359, 'months': 360, 'something': 361, 'blocked': 362, 'Now': 363, 'refusing': 364, 'punish': 365, 'people': 366, 'mere': 367, 'membership': 368, 'situation': 369, 'only': 370, 'going': 371, 'get': 372, 'better': 373, 'If': 374, 'someone': 375, 'committed': 376, 'crime': 377, 'against': 378, 'humanity': 379, 'prosecute': 380, 'person': 381, 'or': 382, 'she': 383, 'did': 384, 'not': 385, 'then': 386, 'should': 387, 'all': 388, 'same': 389, 'rights': 390, 'Iraqis': 391, 'Sharq': 392, 'Awsat': 393, 'reports': 394, 'key': 395, 'eyewitness': 396, 'trial': 397, 'Saddam': 398, 'Hussein': 399, '1982': 400, 'massacre': 401, 'Dujail': 402, 'died': 403, 'A': 404, 'team': 405, 'court': 406, 'managed': 407, 'take': 408, 'deposition': 409, 'begins': 410, 'again': 411, 'Nov.': 412, '28': 413, 'fighting': 414, 'still': 415, 'continues': 416, 'several': 417, 'areas': 418, 'mostly': 419, 'Sadr': 420, 'city': 421, 'Adhamiya': 422, 'Baghdadis': 423, 'venture': 424, 'out': 425, 'neighbourhoods': 426, 'any': 427, 'you': 428, 'never': 429, 'know': 430, 'where': 431, 'might': 432, 'stuck': 433, 'There': 434, 'talk': 435, 'night': 436, 'curfew': 437, 'implemented': 438, 'My': 439, 'neighbourhood': 440, 'surrounded': 441, 'troops': 442, 'three': 443, 'days': 444, 'now': 445, 'helicopters': 446, 'circling': 447, 'over': 448, 'our': 449, 'heads': 450, 'non-stop': 451, 'Fedayeen': 452, 'visible': 453, 'street': 454, 'become': 455, 'bolder': 456, 'than': 457, 'ever': 458, 'Yesterday': 459, 'tens': 460, 'putting': 461, 'road': 462, 'blocks': 463, 'setting': 464, 'mortars': 465, 'open': 466, 'when': 467, 'Americans': 468, 'leave': 469, 'area': 470, 'start': 471, 'firing': 472, 'indiscriminately': 473, 'shooting': 474, 'AK': 475, \"47's\": 476, 'air': 477, 'exact': 478, 'positions': 479, 'during': 480, 'war': 481, 'last': 482, 'year': 483, 'which': 484, 'indicates': 485, 'And': 486, 'nothing': 487, 'we': 488, 'can': 489, 'about': 490, 'really': 491, 'suggesting': 492, 'go': 493, 'fight': 494, 'living': 495, 'dream': 496, 'land': 497, 'Even': 498, 'IP': 499, 'ICDC': 500, 'abandoned': 501, 'those': 502, 'trained': 503, 'armed': 504, 'expect': 505, 'scared': 506, 'civilians': 507, 'anything': 508, 'except': 509, 'hide': 510, 'inside': 511, 'pray': 512, 'helicopter': 513, 'tank': 514, 'does': 515, 'bomb': 516, 'how': 517, 'distinguish': 518, 'brave': 519, 'valiant': 520, '?': 521, 'Everyone': 522, 'apprehensive': 523, 'April': 524, '9th': 525, '10th': 526, 'bloody': 527, 'Most': 528, 'gone': 529, 'few': 530, 'although': 531, 'seems': 532, 'rest': 533, \"'\": 534, 'normal': 535, 'define': 536, 'what': 537, 'rumours': 538, 'preparations': 539, 'slum': 540, 'dwellers': 541, 'another': 542, 'looting': 543, 'spree': 544, 'banks': 545, 'governmental': 546, 'public': 547, 'property': 548, 'similar': 549, 'took': 550, 'place': 551, 'already': 552, 'overheard': 553, 'youngsters': 554, 'joking': 555, 'saying': 556, 'things': 557, '\"': 558, 'time': 559, 'first': 560, 'loot': 561, 'Mosques': 562, 'calling': 563, 'donating': 564, 'blood': 565, 'food': 566, 'medicine': 567, 'Fallujah': 568, 'convoys': 569, 'headed': 570, 'most': 571, 'returned': 572, 'later': 573, 'though': 574, 'What': 575, 'irritates': 576, 'sudden': 577, 'false': 578, 'solidarity': 579, 'between': 580, \"Shi'ite\": 581, 'clerics': 582, 'glad': 583, 'each': 584, 's': 585, 'throats': 586, 'chance': 587, 'Shia': 588, 'describing': 589, 'Fallujan': 590, 'insurgents': 591, \"Ba'athists\": 592, 'Saddamites': 593, 'Wahhabis': 594, 'terrorists': 595, 'just': 596, 'ago': 597, 'So': 598, 'happened': 599, 'guess': 600, \"'s\": 601, 'Me': 602, 'brother': 603, 'cousin': 604, 'enemy': 605, 'friend': 606, 'thing': 607, 'Speaking': 608, 'Jazeera': 609, 'rely': 610, 'sent': 611, 'top': 612, 'reporter': 613, 'Ahmed': 614, 'Mansour': 615, 'spouting': 616, 'kinds': 617, 'propaganda': 618, 'hourly': 619, 'reminding': 620, 'Sahhaf': 621, 'targetting': 622, 'ambulances': 623, 'snipers': 624, 'children': 625, 'pregnant': 626, 'women': 627, 'using': 628, 'cluster': 629, 'hear': 630, 'once': 631, 'make': 632, 'unforgivable': 633, 'error': 634, 'mentioned': 635, 'militants': 636, 'Marines': 637, 'roofs': 638, 'mosques': 639, 'houses': 640, 'Hay': 641, 'Golan': 642, 'but': 643, 'course': 644, 'okay': 645, 'Someone': 646, 'called': 647, 'himself': 648, 'Abu': 649, 'Hafs': 650, 'Ibn': 651, 'Khattab': 652, 'Brigades': 653, 'group': 654, 'enormous': 655, 'casualties': 656, 'among': 657, 'sweared': 658, 'mutilating': 659, 'bodies': 660, 'Over': 661, '300': 662, 'reported': 663, '500': 664, 'wounded': 665, 'alone': 666, 'Iraqiyah': 667, 'tv': 668, 'controlling': 669, 'Ramadi': 670, 'Azzaman': 671, 'newspaper': 672, 'signed': 673, 'Abdul': 674, 'Aziz': 675, 'bin': 676, 'Muqrin': 677, 'Qaeda': 678, 'operative': 679, 'Saudi': 680, 'Arabia': 681, 'Islamic': 682, 'website': 683, 'voice': 684, 'stated': 685, 'originally': 686, 'permitted': 687, 'Islam': 688, 'case': 689, 'allowed': 690, 'Muslims': 691, 'use': 692, 'infidels': 693, 'deter': 694, 'committing': 695, 'criminal': 696, 'actions': 697, 'added': 698, 'America': 699, 'understand': 700, 'language': 701, 'force': 702, 'retaliation': 703, 'kicked': 704, 'Somalia': 705, 'humiliation': 706, 'soldier': 707, 'dragged': 708, 'Mogadishu': 709, 'whole': 710, 'world': 711, 'see': 712, 'day': 713, 'Jews': 714, 'defiled': 715, 'stepped': 716, 'Arabian': 717, 'peninsula': 718, 'together': 719, 'agents': 720, 'supporters': 721, 'Elena': 722, 'motorcycle': 723, 'tour': 724, 'through': 725, 'region': 726, 'around': 727, 'Chernobyl': 728, 'revived': 729, 'interest': 730, 'serious': 731, 'nuclear': 732, 'disasters': 733, 'history': 734, 'We': 735, 'even': 736, 'different': 737, 'versions': 738, 'opinions': 739, 'effect': 740, 'health': 741, 'affected': 742, 'fallout': 743, 'UPDATE': 744, 'write': 745, 'your': 746, 'own': 747, 'story': 748, 'post': 749, 'fault': 750, 'finding': 751, 'assigning': 752, 'blame': 753, 'learn': 754, 'may': 755, 'affect': 756, 'future': 757, 'soothing': 758, 'authoritative': 759, 'UNSCEAR': 760, 'Nations': 761, 'Scientific': 762, 'Committee': 763, 'Effects': 764, 'Atomic': 765, 'Radiation': 766, 'report': 767, '2000': 768, 'effects': 769, 'confirming': 770, 'scientific': 771, 'evidence': 772, 'radiation': 773, 'related': 774, 'exposed': 775, 'heavily': 776, 'promoted': 777, 'Australasian': 778, 'Protection': 779, 'Society': 780, 'press': 781, 'release': 782, 'titled': 783, 'THE': 784, 'MYTHS': 785, 'OF': 786, 'CHERNOBYL': 787, 'contained': 788, 'following': 789, 'widespread': 790, 'myths': 791, 'times': 792, 'reactor': 793, 'accident': 794, '1986': 795, 'caused': 796, 'thousands': 797, 'extra': 798, 'cancer': 799, 'deaths': 800, 'neighbouring': 801, 'regions': 802, 'severely': 803, 'exposure': 804, 'Many': 805, 'believe': 806, 'true': 807, 'Russian': 808, 'Federation': 809, 'Civil': 810, 'Defence': 811, 'Emergencies': 812, 'Elimination': 813, 'Conseguences': 814, 'Natural': 815, 'Disasters': 816, 'EMERCOM': 817, 'Russia': 818, '1996': 819, 'ACCIDENT': 820, 'TEN': 821, 'YEARS': 822, 'ON': 823, 'decade': 824, 'real': 825, 'increase': 826, 'childhood': 827, 'certain': 828, 'extent': 829, 'adult': 830, 'carcinoma': 831, 'thyroid': 832, 'contaminated': 833, 'former': 834, 'Soviet': 835, 'Union': 836, 'Wi940': 837, 'attributed': 838, 'until': 839, 'proven': 840, 'otherwise': 841, 'prestigious': 842, 'IAEA': 843, 'International': 844, 'Energy': 845, 'Agency': 846, 'published': 847, 'early': 848, 'based': 849, 'information': 850, 'sources': 851, 'However': 852, '2001': 853, 'Fifteen': 854, 'Years': 855, 'Accident': 856, 'Lessons': 857, 'learned': 858, 'contradict': 859, 'earlier': 860, 'Here': 861, 'excerpts': 862, 'dramatic': 863, 'induced': 864, 'cancers': 865, 'adolescents': 866, 'Belarus': 867, 'Ukraine': 868, 'observed': 869, 'since': 870, '1991': 871, '...': 872, 'drop': 873, 'birth': 874, 'rate': 875, 'deterioration': 876, 'reproductive': 877, 'complications': 878, 'pregnancy': 879, 'neonatal': 880, '....': 881, 'dynamics': 882, 'change': 883, 'state': 884, 'countries': 885, 'post-accident': 886, 'period': 887, 'characterized': 888, 'persistent': 889, 'negative': 890, 'tendencies': 891, 'morbidity': 892, 'healthy': 893, 'dropping': 894, 'disability': 895, 'increasing': 896, 'As': 897, 'parent': 898, 'well': 899, 'imagine': 900, 'painful': 901, 'must': 902, 'families': 903, 'whose': 904, 'succumbing': 905, 'poisoning': 906, 'lot': 907, 'Being': 908, 'informed': 909, 'give': 910, 'certainty': 911, 'desirable': 912, 'conflicting': 913, 'wealth': 914, 'references': 915, 'Read': 916, 'links': 917, 'draw': 918, 'conclusions': 919, 'These': 920, 'present': 921, 'viewpoints': 922, 'existed': 923, 'exist': 924, 'disaster': 925, 'Report': 926, 'http://www.ibiblio.org/expo/soviet.exhibit/chernobyl.html': 927, 'http://www.ibrae.ac.ru/IBRAE/eng/chernobyl/nat_rep/nat_repe.htm#24': 928, 'http://www.nsrl.ttu.edu/chernobyl/wildlifepreserve.htm': 929, 'http://www.environmentalchemistry.com/yogi/hazmat/articles/chernobyl1.html': 930, 'http://digon_va.tripod.com/Chernobyl.htm': 931, 'http://www.oneworld.org/index_oc/issue196/byckau.html': 932, 'http://www.collectinghistory.net/chernobyl/': 933, 'http://www.ukrainianweb.com/chernobyl_ukraine.htm': 934, 'http://www.bullatomsci.org/issues/1993/s93/s93Marples.html': 935, 'http://www.calguard.ca.gov/ia/Chernobyl-15%20years.htm': 936, 'http://www.infoukes.com/history/chornobyl/gregorovich/index.html': 937, 'http://www.un.org/ha/chernobyl/': 938, 'http://www.tecsoc.org/pubs/history/2002/apr26.htm': 939, 'http://www.chernobyl.org.uk/page2.htm': 940, 'http://www.time.com/time/daily/chernobyl/860901.accident.html': 941, 'http://www.infoukes.com/history/chornobyl/elg/': 942, 'http://www.world-nuclear.org/info/chernobyl/inf07.htm': 943, 'http://www.nea.fr/html/rp/chernobyl/conclusions5.html': 944, 'http://www.nea.fr/html/rp/chernobyl/c01.html': 945, 'http://www.nea.fr/html/rp/chernobyl/c05.html': 946, 'http://www.physics.isu.edu/radinf/chern.htm': 947, 'http://www.chernobyl.info/en': 948, 'http://www.arps.org.au/Chernobyl.htm': 949, 'http://www-formal.stanford.edu/jmc/progress/chernobyl.html': 950, 'http://www.21stcenturysciencetech.com/articles/chernobyl.html': 951, 'interested': 952, 'hearing': 953, 'reached': 954, 'found': 955, 'convincing': 956, 'child': 957, \"50's\": 958, 'glandular': 959, 'problems': 960, 'treated': 961, 'therapy': 962, 'primitive': 963, 'best': 964, 'stopped': 965, 'lesion': 966, 'neck': 967, 'started': 968, 'enlarging': 969, '--': 970, 'CA': 971, 'treatments': 972, 'Remember': 973, 'shoe': 974, 'sizing': 975, 'machines': 976, 'form': 977, 'xray': 978, 'That': 979, 'Do': 980, 'mention': 981, 'done': 982, 'doing': 983, 'Hirsohima': 984, '&': 985, 'Nagaski': 986, 'folks': 987, 'doubts': 988, '.......': 989, 'Children': 990, 'Project': 991, 'http://www.adiccp.org/home/default.asp': 992, 'offers': 993, 'ways': 994, 'help': 995, 'Rest': 996, 'Recuperation': 997, 'Program': 998, 'wherein': 999, 'weeks': 1000, 'summer': 1001, 'little': 1002, 'spent': 1003, 'receiving': 1004, 'wholesome': 1005, 'uncontaminated': 1006, 'good': 1007, 'medical': 1008, 'care': 1009, 'etc.': 1010, 'add': 1011, 'lives': 1012, 'S.': 1013, 'acquaintance': 1014, 'hosted': 1015, 'these': 1016, 'ones': 1017, 'breaks': 1018, 'heart': 1019, 'urge': 1020, 'protect': 1021, 'gather': 1022, 'almost': 1023, 'overwhelming': 1024, 'makes': 1025, 'grateful': 1026, 'blessings': 1027, 'Take': 1028, 'Linda': 1029, \"'m\": 1030, 'sorry': 1031, 'say': 1032, 'revealed': 1033, 'fake': 1034, 'videotape': 1035, 'audio': 1036, 'speeches': 1037, 'Bin': 1038, 'Laden': 1039, 'Ayman': 1040, 'Zawahiri': 1041, 'tell': 1042, 'hopes': 1043, 'remaining': 1044, 'leadership': 1045, 'organization': 1046, 'Because': 1047, 'Pakistan': 1048, 'capture': 1049, 'kill': 1050, '/': 1051, '3s': 1052, '25': 1053, 'commanders': 1054, 'middle': 1055, 'managers': 1056, 'close': 1057, 'contact': 1058, 'tape': 1059, 'signal': 1060, 'priorities': 1061, '1': 1062, 'Assassinate': 1063, 'overthrow': 1064, 'Gen.': 1065, 'Pervez': 1066, 'Musharraf': 1067, 'Pakistani': 1068, 'military': 1069, 'president': 1070, 'coup': 1071, '1999': 1072, 'thrown': 1073, 'States': 1074, 'Taliban': 1075, 'trying': 1076, 'purge': 1077, 'officer': 1078, 'corps': 1079, 'substantial': 1080, 'sympathizers': 1081, 'intelligence': 1082, 'captured': 1083, 'major': 1084, 'figures': 1085, 'Zubayda': 1086, 'Khalid': 1087, 'Shaykh': 1088, 'Muhammad': 1089, 'nearly': 1090, 'operatives': 1091, '400': 1092, 'whom': 1093, 'Pakistanis': 1094, 'turned': 1095, 'held': 1096, 'October': 1097, '2002': 1098, 'right': 1099, 'parties': 1100, '20': 1101, 'went': 1102, 'fundamentalist': 1103, 'religious': 1104, 'coalition': 1105, 'MMA': 1106, 'Northwest': 1107, 'Frontier': 1108, 'shelters': 1109, 'joint': 1110, 'Baluchistan': 1111, 'Afghanistan': 1112, 'instigate': 1113, 'Islamist': 1114, 'hope': 1115, 'catapult': 1116, 'power': 1117, 'political': 1118, 'allies': 1119, 'hosts': 1120, 'thereby': 1121, 'gain': 1122, 'control': 1123, 'base': 1124, 'operations': 1125, 'All': 1126, 'unlikely': 1127, 'crackpot': 1128, 'schemes': 1129, 'mujahidin': 1130, 'virtually': 1131, 'Response': 1132, 'whatever': 1133, 'strengthen': 1134, 'legitimacy': 1135, 'hand': 1136, 'pressure': 1137, 'off': 1138, 'uniform': 1139, 'fair': 1140, 'election': 1141, 'repeal': 1142, 'contentious': 1143, 'Legal': 1144, 'Framework': 1145, 'Order': 1146, 'essentially': 1147, 'perpetuates': 1148, 'dictatorship': 1149, 'restrictions': 1150, 'lifted': 1151, 'mainstream': 1152, 'Muslim': 1153, 'League': 1154, 'N': 1155, 'People': 1156, 'defeat': 1157, 'hogtied': 1158, 'secret': 1159, 'strong': 1160, 'arm': 1161, 'India': 1162, 'final': 1163, 'settlement': 1164, 'Kashmir': 1165, 'issue': 1166, 'attempted': 1167, 'lack': 1168, 'helping': 1169, 'Indian': 1170, 'justification': 1171, 'generates': 1172, 'far': 1173, 'terrorism': 1174, 'threat': 1175, 'Target': 1176, 'Israel': 1177, 'encourage': 1178, 'worst': 1179, 'Palestinians': 1180, 'playing': 1181, 'iron': 1182, 'fist': 1183, 'policies': 1184, 'Sharon': 1185, 'succeeded': 1186, 'politically': 1187, 'isolating': 1188, 'Hamas': 1189, 'process': 1190, 'cutting': 1191, 'its': 1192, 'funding': 1193, 'pull': 1194, 'less': 1195, 'sophisticated': 1196, 'attacks': 1197, 'defanged': 1198, 'simply': 1199, 'means': 1200, 'establishing': 1201, 'general': 1202, 'peace': 1203, 'Bush': 1204, 'administration': 1205, 'finally': 1206, 'apply': 1207, 'effective': 1208, 'stop': 1209, 'outrages': 1210, 'colonization': 1211, 'West': 1212, 'Bank': 1213, 'Gaza': 1214, 'line': 1215, 'worked': 1216, 'tandem': 1217, 'ratchet': 1218, 'tensions': 1219, 'further': 1220, 'spill': 1221, 'serve': 1222, 'recruiting': 1223, 'tool': 1224, 'search': 1225, 'willing': 1226, 'hit': 1227, 'owes': 1228, 'least': 1229, 'crisis': 1230, 'cease': 1231, 'militarily': 1232, 'unnecessary': 1233, 'provocations': 1234, 'establish': 1235, 'genuine': 1236, 'Make': 1237, 'Republican': 1238, 'Right': 1239, 'tactics': 1240, 'actually': 1241, 'hostile': 1242, 'territory': 1243, 'without': 1244, 'succeed': 1245, 'By': 1246, 'moving': 1247, 'quickly': 1248, 'sovereignty': 1249, 'improvement': 1250, 'able': 1251, 'foreigners': 1252, 'Certainly': 1253, 'Shiites': 1254, 'hate': 1255, 'likewise': 1256, 'Kurds': 1257, 'problem': 1258, 'mollifying': 1259, 'Sunnis': 1260, 'solved': 1261, 'avoid': 1262, 'giving': 1263, 'entrée': 1264, 'put': 1265, 'away': 1266, 'free': 1267, 'fetishism': 1268, 'creating': 1269, 'jobs': 1270, 'pumping': 1271, 'money': 1272, 'households': 1273, 'need': 1274, 'FDR': 1275, 'Ronald': 1276, 'Reagan': 1277, 'Of': 1278, 'sooner': 1279, 'withdrawn': 1280, 'favor': 1281, 'provocative': 1282, 'local': 1283, 'international': 1284, 'Getting': 1285, 'Spanish': 1286, 'rallying': 1287, 'cry': 1288, 'getting': 1289, 'British': 1290, 'keep': 1291, 'mind': 1292, 'nationalists': 1293, 'radicals': 1294, 'remain': 1295, 'dangerous': 1296, 'infiltrators': 1297, 'eyes': 1298, 'ball': 1299, 'George': 1300, 'W.': 1301, 'alleged': 1302, 'Thursday': 1303, 'John': 1304, 'Edwards': 1305, 'lacks': 1306, 'experience': 1307, 'necessary': 1308, 'argument': 1309, 'lacked': 1310, 'ran': 1311, 'sort': 1312, 'cheap': 1313, 'shot': 1314, 'hoists': 1315, 'petard': 1316, 'Let': 1317, 'remember': 1318, 'seminal': 1319, 'moment': 1320, 'fails': 1321, 'pop': 1322, 'quiz': 1323, 'November': 1324, '5': 1325, 'Web': 1326, 'posted': 1327, '3:29': 1328, 'p.m.': 1329, 'EST': 1330, '2029': 1331, 'GMT': 1332, 'WASHINGTON': 1333, 'CNN': 1334, 'Texas': 1335, 'Gov.': 1336, 'enduring': 1337, 'sharp': 1338, 'criticism': 1339, 'unable': 1340, 'name': 1341, 'four': 1342, 'current': 1343, 'hot': 1344, 'spots': 1345, 'President': 1346, 'Bill': 1347, 'Clinton': 1348, 'says': 1349, 'pick': 1350, 'names': 1351, 'front': 1352, 'runner': 1353, 'presidential': 1354, 'nomination': 1355, 'faltered': 1356, 'affairs': 1357, 'posed': 1358, 'Andy': 1359, 'Hiller': 1360, 'WHDH': 1361, 'TV': 1362, 'Boston': 1363, 'asked': 1364, 'Chechnya': 1365, 'Taiwan': 1366, 'partial': 1367, 'response': 1368, 'query': 1369, 'leader': 1370, 'referring': 1371, 'Taiwanese': 1372, 'Lee': 1373, 'Teng': 1374, 'hui': 1375, 'others': 1376, 'Can': 1377, 'charge': 1378, 'inquiring': 1379, 'Pervaiz': 1380, 'seized': 1381, '12': 1382, 'Wait': 1383, 'wait': 1384, '50': 1385, 'questions': 1386, 'replied': 1387, 'No': 1388, 'answering': 1389, 'question': 1390, 'elected': 1391, 'guy': 1392, 'appears': 1393, 'bring': 1394, 'stability': 1395, 'think': 1396, 'subcontinent': 1397, 'Gore': 1398, 'released': 1399, 'statement': 1400, 'taking': 1401, 'task': 1402, 'comments': 1403, 'troubling': 1404, 'candidate': 1405, 'oldest': 1406, 'democracy': 1407, 'characterize': 1408, 'takeover': 1409, 'Further': 1410, 'disturbing': 1411, 'nation': 1412, 'tested': 1413, 'weapons': 1414, 'shortly': 1415, 'voicing': 1416, 'opposition': 1417, 'Comprehensive': 1418, 'Test': 1419, 'Ban': 1420, 'Treaty': 1421, 'spokesman': 1422, 'criticized': 1423, 'condone': 1424, 'democratically': 1425, 'governments': 1426, 'David': 1427, 'Leavy': 1428, 'Security': 1429, 'Council': 1430, 'Not': 1431, 'General': 1432, 'confused': 1433, 'making': 1434, 'moreover': 1435, 'suggest': 1436, 'prime': 1437, 'minister': 1438, 'installation': 1439, 'strongest': 1440, 'supporter': 1441, 'part': 1442, 'because': 1443, 'anger': 1444, 'Prime': 1445, 'Minister': 1446, 'Nawaz': 1447, 'Sharif': 1448, 'down': 1449, 'confronting': 1450, 'explicitly': 1451, 'came': 1452, 'warmonger': 1453, 'ca': 1454, 'ominous': 1455, 'performance': 1456, 'interview': 1457, 'stuttering': 1458, 'obviously': 1459, 'idea': 1460, 'talking': 1461, 'ill': 1462, 'fated': 1463, 'instincts': 1464, 'liked': 1465, 'authoritarian': 1466, 'rule': 1467, 'equating': 1468, 'needed': 1469, 'South': 1470, 'Asia': 1471, 'giants': 1472, 'radical': 1473, 'politics': 1474, 'latter': 1475, 'dire': 1476, 'security': 1477, 'becoming': 1478, 'unstable': 1479, 'play': 1480, 'brinkmanship': 1481, 'risking': 1482, 'twice': 1483, 'September': 1484, '11': 1485, 'under': 1486, 'extreme': 1487, 'duress': 1488, 'continued': 1489, 'support': 1490, 'Islamism': 1491, 'recently': 1492, 'implicated': 1493, 'attempts': 1494, 'body': 1495, 'proclaimed': 1496, 'bringing': 1497, 'fall': 1498, 'answer': 1499, 'merit': 1500, 'Another': 1501, 'certainly': 1502, 'knows': 1503, 'foreign': 1504, 'Indeed': 1505, 'given': 1506, 'rampaged': 1507, 'alienating': 1508, 'ignoring': 1509, 'vital': 1510, 'conflicts': 1511, 'potential': 1512, 'blow': 1513, 'argue': 1514, 'campaign': 1515, 'literature': 1516, 'believes': 1517, 'U.S.': 1518, 'active': 1519, 'resolve': 1520, 'reducing': 1521, 'Northern': 1522, 'Ireland': 1523, 'role': 1524, 'promoting': 1525, 'Israelis': 1526, 'From': 1527, 'Daily': 1528, 'Star': 1529, 'Juan': 1530, 'Cole': 1531, 'June': 1532, '04': 1533, '2004': 1534, 'gradually': 1535, 'wearies': 1536, 'begun': 1537, 'worrying': 1538, 'conditions': 1539, 'anti-American': 1540, 'closer': 1541, 'suffer': 1542, 'instability': 1543, 'acutely': 1544, 'Ironically': 1545, 'proponents': 1546, 'Israeli': 1547, 'Ariel': 1548, 'neoconservative': 1549, 'Have': 1550, 'weakened': 1551, 'biggest': 1552, 'faces': 1553, 'conventional': 1554, 'armies': 1555, 'asymmetrical': 1556, 'Palestinian': 1557, 'national': 1558, 'liberation': 1559, 'movements': 1560, 'derailing': 1561, 'Oslo': 1562, 'intifada': 1563, 'encouraged': 1564, 'suicide': 1565, 'discouraged': 1566, 'immigrants': 1567, 'withdrew': 1568, 'Lebanese': 1569, 'May': 1570, 'Hizbullah': 1571, 'mollified': 1572, 'estimated': 1573, '5,000': 1574, 'fighters': 1575, 'pursued': 1576, 'compel': 1577, 'withdraw': 1578, 'Shebaa': 1579, 'Farms': 1580, 'sliver': 1581, 'annexed': 1582, '1967': 1583, 'Any': 1584, 'thorough': 1585, 'assessment': 1586, 'impact': 1587, 'aftermath': 1588, 'environment': 1589, 'therefore': 1590, 'closely': 1591, 'examine': 1592, 'conduct': 1593, 'warfare': 1594, 'often': 1595, 'gave': 1596, 'clear': 1597, 'danger': 1598, 'themselves': 1599, 'end': 1600, 'occupation': 1601, 'Strip': 1602, 'driven': 1603, 'economic': 1604, 'considerations': 1605, 'practical': 1606, 'At': 1607, 'points': 1608, 'late': 1609, '1980s': 1610, 'reportedly': 1611, 'scenes': 1612, 'overtures': 1613, 'arrive': 1614, 'deal': 1615, 'allow': 1616, 'launch': 1617, '1990s': 1618, 'biological': 1619, 'program': 1620, 'destroyed': 1621, 'chemical': 1622, 'stockpiles': 1623, 'Its': 1624, 'ramshackle': 1625, 'collapsed': 1626, 'invasion': 1627, '2003': 1628, 'difficult': 1629, 'menace': 1630, 'bungling': 1631, 'post-war': 1632, 'created': 1633, 'weak': 1634, 'failed': 1635, 'Armed': 1636, 'militias': 1637, 'staffed': 1638, 'men': 1639, 'training': 1640, 'proliferated': 1641, 'chose': 1642, 'ally': 1643, 'itself': 1644, 'groups': 1645, 'Supreme': 1646, 'Revolution': 1647, '15,000': 1648, 'Badr': 1649, 'Corps': 1650, 'paramilitary': 1651, 'Iranian': 1652, 'Revolutionary': 1653, 'Guards': 1654, 'Anti-Israeli': 1655, 'pro-Palestinian': 1656, 'feeling': 1657, 'ideological': 1658, 'currents': 1659, 'generally': 1660, 'follow': 1661, 'theocratic': 1662, 'notions': 1663, 'Iran': 1664, 'Ayatollah': 1665, 'Ruhollah': 1666, 'Khomeini': 1667, 'routinely': 1668, 'chant': 1669, 'demonstrate': 1670, 'vehemently': 1671, 'protested': 1672, 'Sheikh': 1673, 'Yassin': 1674, 'March': 1675, 'Worse': 1676, 'drew': 1677, 'denunciation': 1678, 'moderate': 1679, 'cautious': 1680, 'Grand': 1681, 'Sistani': 1682, 'wields': 1683, 'moral': 1684, 'authority': 1685, 'suppressed': 1686, 'regime': 1687, 'organized': 1688, 'reestablished': 1689, 'historical': 1690, 'inevitable': 1691, 'coreligionists': 1692, 'rich': 1693, 'enough': 1694, 'petroleum': 1695, 'sales': 1696, 'position': 1697, 'bankroll': 1698, 'fundamentalists': 1699, 'deeply': 1700, 'sympathize': 1701, 'deep': 1702, 'inks': 1703, 'Jordan': 1704, 'Palestine': 1705, 'cities': 1706, 'truck': 1707, 'route': 1708, 'Amman': 1709, 'influence': 1710, 'Salafi': 1711, 'movement': 1712, 'popular': 1713, 'Secular': 1714, 'nationalist': 1715, 'universally': 1716, 'post-Saddam': 1717, 'exception': 1718, 'Whereas': 1719, 'ensured': 1720, 'populist': 1721, 'kept': 1722, 'firmly': 1723, 'organize': 1724, 'An': 1725, 'proliferate': 1726, 'inevitably': 1727, 'worry': 1728, 'modicum': 1729, 'normality': 1730, 'citizens': 1731, 'benefit': 1732, 'reserves': 1733, 'private': 1734, 'funneled': 1735, 'aid': 1736, 'interests': 1737, 'served': 1738, 'neighbors': 1739, 'achieved': 1740, 'trading': 1741, 'aggressive': 1742, 'annexation': 1743, 'occupied': 1744, 'indefinite': 1745, 'postponement': 1746, 'unprecedented': 1747, 'rage': 1748, 'violence': 1749, 'spread': 1750, 'throughout': 1751, 'promotion': 1752, 'pro-Zionist': 1753, 'twin': 1754, 'occupations': 1755, 'profoundly': 1756, 'strengthened': 1757, 'www.juancole.com': 1758, 'professor': 1759, 'modern': 1760, 'Middle': 1761, 'East': 1762, 'Michigan': 1763, 'author': 1764, 'Sacred': 1765, 'Space': 1766, 'Holy': 1767, 'War': 1768, 'I.B.': 1769, 'Tauris': 1770, 'DAILY': 1771, 'STAR': 1772, 'publishes': 1773, 'commentary': 1774, 'agreement': 1775, 'Agence': 1776, 'Global': 1777, 'Tamils': 1778, 'feel': 1779, 'proposed': 1780, 'defense': 1781, 'Sri': 1782, 'Lanka': 1783, 'Sinhala': 1784, 'rulers': 1785, 'prepare': 1786, 'abandoning': 1787, 'hints': 1788, 'Indo': 1789, 'drawn': 1790, 'protests': 1791, 'Tamil': 1792, 'TNA': 1793, 'backed': 1794, 'LTTE': 1795, 'Apart': 1796, '1,200': 1797, 'lost': 1798, '1987': 1799, 'peacekeeping': 1800, 'immensely': 1801, 'unpopular': 1802, 'Nadu': 1803, 'Jaffna': 1804, 'Sinhalese': 1805, 'majority': 1806, 'considered': 1807, 'violation': 1808, 'defensive': 1809, 'Ranjit': 1810, 'Devraj': 1811, 'NEW': 1812, 'DELHI': 1813, 'While': 1814, 'ready': 1815, 'enter': 1816, 'cooperation': 1817, 'wary': 1818, 'involvement': 1819, 'island': 1820, 'decades': 1821, 'seen': 1822, 'violent': 1823, 'strife': 1824, 'ethnic': 1825, 'leaving': 1826, '60,000': 1827, 'both': 1828, 'sides': 1829, 'explains': 1830, 'delay': 1831, 'signing': 1832, 'formal': 1833, 'Lankan': 1834, 'Chandrika': 1835, 'Kumaratunga': 1836, 'visit': 1837, 'According': 1838, 'Professor': 1839, 'S': 1840, 'D': 1841, 'Muni': 1842, 'expert': 1843, 'Jawaharal': 1844, 'Nehru': 1845, 'talks': 1846, 'Colombo': 1847, 'Liberation': 1848, 'Tigers': 1849, 'Eelam': 1850, 'stalemated': 1851, 'For': 1852, 'reason': 1853, 'keen': 1854, 'beef': 1855, 'preparedness': 1856, 'want': 1857, 'initiate': 1858, 'conflict': 1859, 'deterring': 1860, 'starting': 1861, 'look': 1862, 'brink': 1863, 'launching': 1864, 'offensive': 1865, 'told': 1866, 'IPS': 1867, 'six': 1868, 'rounds': 1869, 'rebels': 1870, 'abruptly': 1871, 'pulled': 1872, 'negotiations': 1873, 'demanding': 1874, 'recognition': 1875, 'self': 1876, 'proceeding': 1877, 'preceded': 1878, 'Norwegian': 1879, 'Foreign': 1880, 'Jan': 1881, 'Petersen': 1882, 'bid': 1883, 'revive': 1884, 'supposed': 1885, 'ceasefire': 1886, 'successfully': 1887, 'brokered': 1888, 'February': 1889, 'discussions': 1890, 'reclusive': 1891, 'Velupillai': 1892, 'Prabhakaran': 1893, 'rebel': 1894, 'stronghold': 1895, 'Kilinochchi': 1896, 'success': 1897, 'chief': 1898, 'negotiator': 1899, 'Anton': 1900, 'Balasingham': 1901, 'envoy': 1902, 'Erik': 1903, 'Solheim': 1904, 'closed': 1905, 'door': 1906, 'airport': 1907, 'Saturday': 1908, 'effort': 1909, 'salvage': 1910, 'track': 1911, 'diplomatic': 1912, 'intractable': 1913, 'Kalkat': 1914, 'difficulty': 1915, 'lay': 1916, 'fact': 1917, 'de': 1918, 'jure': 1919, 'east': 1920, 'every': 1921, 'aspect': 1922, 'veteran': 1923, 'intervention': 1924, 'implement': 1925, 'Peace': 1926, 'Accord': 1927, 'ambitiously': 1928, 'provided': 1929, 'disarming': 1930, 'formidable': 1931, 'reiteration': 1932, 'older': 1933, 'minus': 1934, 'commitment': 1935, 'currently': 1936, 'chairs': 1937, 'independent': 1938, 'Conflict': 1939, 'Resolution': 1940, 'failure': 1941, 'disarm': 1942, 'subdue': 1943, 'remained': 1944, 'capable': 1945, 'influencing': 1946, 'Norwegians': 1947, 'mean': 1948, 'limited': 1949, 'honest': 1950, 'broker': 1951, 'keenly': 1952, 'aware': 1953, 'unlike': 1954, 'underwrite': 1955, 'arrangement': 1956, 'reluctantly': 1957, 'accepted': 1958, 'accord': 1959, 'Under': 1960, 'northeastern': 1961, 'provincial': 1962, 'council': 1963, 'formed': 1964, 'deployed': 1965, 'peacekeepers': 1966, 'differences': 1967, 'soon': 1968, 'surfaced': 1969, 'led': 1970, 'clashes': 1971, 'Tiger': 1972, 'keeping': 1973, 'About': 1974, 'phase': 1975, '1989': 1976, 'Ranasinghe': 1977, 'Premadasa': 1978, 'critic': 1979, 'mediation': 1980, 'Last': 1981, 'initiative': 1982, 'Japan': 1983, 'persuade': 1984, 'negotiating': 1985, 'table': 1986, 'package': 1987, 'offer': 1988, 'US$': 1989, '4.5': 1990, 'billion': 1991, 'special': 1992, 'Yasushi': 1993, 'Akashi': 1994, 'tangible': 1995, 'visits': 1996, 'frustrated': 1997, 'man': 1998, 'complained': 1999, 'reaching': 2000, 'impasse': 2001, 'ideologue': 2002, 'sniffed': 2003, 'proposal': 2004, 'solution': 2005, 'predetermined': 2006, 'resolutions': 2007, 'declarations': 2008, 'donor': 2009, 'conferences': 2010, 'negotiated': 2011, 'constraints': 2012, 'external': 2013, 'P': 2014, 'Sithamparanathan': 2015, 'quoted': 2016, 'She': 2017, 'brass': 2018, 'Nirmal': 2019, 'Chander': 2020, 'Vij': 2021, 'apprehension': 2022, 'pointed': 2023, 'advised': 2024, 'consider': 2025, 'sentiments': 2026, '45': 2027, 'million': 2028, 'separated': 2029, 'narrow': 2030, 'Palk': 2031, 'Straits': 2032, 'option': 2033, 'circumstances': 2034, 'dirty': 2035, 'New': 2036, 'Delhi': 2037, 'always': 2038, 'counted': 2039, 'render': 2040, 'neighborly': 2041, 'shared': 2042, 'belief': 2043, 'religion': 2044, 'ethnicity': 2045, 'basis': 2046, 'secession': 2047, 'puts': 2048, 'succinctly': 2049, 'Times': 2050, 'Online': 2051, '16/11/2004': 2052, 'Unreported': 2053, 'media': 2054, 'Valley': 2055, 'cultural': 2056, 'genocide': 2057, 'resulted': 2058, 'fleeing': 2059, 'valley': 2060, 'Hindu': 2061, 'human': 2062, 'habitation': 2063, 'recorded': 2064, 'nine': 2065, 'dozen': 2066, 'temples': 2067, 'used': 2068, 'building': 2069, 'material': 2070, 'urinals': 2071, 'Thus': 2072, 'none': 2073, 'busybodies': 2074, 'across': 2075, 'bothered': 2076, 'notice': 2077, 'development': 2078, 'calls': 2079, 'bluff': 2080, 'M.D.': 2081, 'Nalapat': 2082, 'secretaries': 2083, 'save': 2084, 'perhaps': 2085, 'Dean': 2086, 'Rusk': 2087, 'gobbled': 2088, 'credit': 2089, 'outcomes': 2090, 'brazen': 2091, 'Colin': 2092, 'Powell': 2093, 'surprised': 2094, 'alliance': 2095, 'Maulana': 2096, 'Fazlur': 2097, 'Rahman': 2098, 'visited': 2099, 'issued': 2100, 'series': 2101, 'conciliatory': 2102, 'statements': 2103, 'telling': 2104, 'forced': 2105, 'hawkish': 2106, 'precisely': 2107, 'likes': 2108, 'embarrassment': 2109, 'reality': 2110, 'choice': 2111, 'distinction': 2112, 'Realists': 2113, 'includes': 2114, 'policy': 2115, 'establishment': 2116, 'Strobe': 2117, 'Talbott': 2118, 'long': 2119, 'sought': 2120, 'divest': 2121, 'capability': 2122, 'feasible': 2123, 'acceptance': 2124, 'status': 2125, 'quo': 2126, 'keeps': 2127, 'China': 2128, 'gifted': 2129, 'slice': 2130, 'Simultaneously': 2131, 'ensure': 2132, 'degree': 2133, 'autonomy': 2134, 'cut': 2135, 'jihadis': 2136, 'attempting': 2137, 'convert': 2138, 'second': 2139, 'understood': 2140, 'term': 2141, 'yet': 2142, 'affinity': 2143, 'generals': 2144, 'pendulum': 2145, 'swung': 2146, 'toward': 2147, 'quixotic': 2148, 'prize': 2149, 'loose': 2150, 'lobbyist': 2151, 'Christina': 2152, 'Rocca': 2153, 'minimum': 2154, 'accept': 2155, 'deliver': 2156, 'writer': 2157, 'regarded': 2158, 'trifurcation': 2159, 'Jammu': 2160, 'Buddhist': 2161, 'dominated': 2162, 'Ladakh': 2163, 'taxpayers': 2164, 'With': 2165, 'luck': 2166, 'attract': 2167, 'locations': 2168, 'tourist': 2169, 'education': 2170, 'services': 2171, 'haven': 2172, 'happy': 2173, 'outcome': 2174, 'tiny': 2175, 'jihadi': 2176, 'segment': 2177, 'patronized': 2178, 'State': 2179, 'Department': 2180, 'Army': 2181, 'score': 2182, 'catastrophic': 2183, 'Bangladesh': 2184, '1971': 2185, 'filled': 2186, 'tales': 2187, 'atrocities': 2188, 'innocent': 2189, 'Despite': 2190, 'Sept.': 2191, 'supports': 2192, 'jihad': 2193, 'price': 2194, 'paying': 2195, 'Unfortunately': 2196, 'decided': 2197, 'seriously': 2198, 'frequent': 2199, 'boasts': 2200, 'nudging': 2201, 'Indians': 2202, 'gestures': 2203, 'Islamabad': 2204, 'misnamed': 2205, 'tanks': 2206, 'lead': 2207, 'numerous': 2208, 'emerged': 2209, 'prized': 2210, 'version': 2211, 'Ibrahim': 2212, 'Rugova': 2213, 'Yugoslavia': 2214, 'foreigner': 2215, 'afford': 2216, 'ignore': 2217, 'nationalism': 2218, 'struck': 2219, 'conferees': 2220, 'scholars': 2221, 'analysts': 2222, 'endorse': 2223, 'sake': 2224, 'trip': 2225, 'York': 2226, 'Vienna': 2227, 'wrinkles': 2228, 'remains': 2229, 'merely': 2230, 'piece': 2231, 'paper': 2232, 'writing': 2233, 'op': 2234, 'ed': 2235, 'Sonia': 2236, 'Gandhi': 2237, 'Atal': 2238, 'Behari': 2239, 'Vajpayee': 2240, 'instantly': 2241, 'business': 2242, 'community': 2243, 'indulged': 2244, 'empty': 2245, 'bout': 2246, 'saber': 2247, 'rattling': 2248, 'pointing': 2249, 'beneficiary': 2250, 'mythical': 2251, 'perception': 2252, 'especially': 2253, 'corner': 2254, 'emerging': 2255, 'alternative': 2256, 'destination': 2257, 'hence': 2258, 'trusty': 2259, 'supplier': 2260, 'nukes': 2261, 'missiles': 2262, 'scare': 2263, 'involving': 2264, 'flashpoints': 2265, 'North': 2266, 'Korea': 2267, 'Since': 2268, 'talked': 2269, 'signaling': 2270, 'acceptable': 2271, 'action': 2272, 'followed': 2273, 'disputes': 2274, 'eager': 2275, 'indefinitely': 2276, 'carry': 2277, 'lucrative': 2278, 'resolution': 2279, 'challenging': 2280, 'implying': 2281, 'commands': 2282, 'Once': 2283, 'Manmohan': 2284, 'Singh': 2285, 'shows': 2286, 'appetite': 2287, 'face': 2288, 'truth': 2289, 'ramp': 2290, 'insurgency': 2291, 'spawn': 2292, 'fresh': 2293, 'killers': 2294, 'Mumbai': 2295, 'London': 2296, 'Chicago': 2297, 'shown': 2298, 'understands': 2299, 'realities': 2300, 'Hu': 2301, 'Jintao': 2302, 'Beijing': 2303, 'opened': 2304, 'Wen': 2305, 'Jiabao': 2306, 'expected': 2307, 'next': 2308, 'friends': 2309, 'formally': 2310, 'accepting': 2311, 'Sikkim': 2312, 'backing': 2313, 'permanent': 2314, 'U.N.': 2315, 'thus': 2316, 'five': 2317, 'Osama': 2318, 'geopolitics': 2319, 'Manipal': 2320, 'Academy': 2321, 'Higher': 2322, '28/10/2004': 2323, 'Ok': 2324, 'Kerry': 2325, 'Four': 2326, 'advantage': 2327, 'known': 2328, 'invoked': 2329, 'regulation': 2330, 'desert': 2331, 'band': 2332, 'brothers': 2333, 'Dan': 2334, 'Rather': 2335, 'libeled': 2336, 'impugned': 2337, 'service': 2338, 'Air': 2339, 'Guard': 2340, '60': 2341, 'Minutes': 2342, 'II': 2343, 'forged': 2344, 'documents': 2345, 'During': 2346, 'flew': 2347, 'F': 2348, '102': 2349, 'Delta': 2350, 'Dagger': 2351, 'fighter': 2352, 'interceptor': 2353, 'saw': 2354, 'theater': 2355, '1962': 2356, 'December': 2357, '1969': 2358, 'squadrons': 2359, 'Tan': 2360, 'Son': 2361, 'Nhut': 2362, 'Da': 2363, 'Nang': 2364, 'Bien': 2365, 'Hoa': 2366, 'Udorn': 2367, 'Don': 2368, 'Muang': 2369, 'Thailand': 2370, 'Click': 2371, 'here': 2372, 'source': 2373, 'knew': 2374, 'unit': 2375, 'transferred': 2376, 'controversy': 2377, 'anyone': 2378, 'wanted': 2379, 'fly': 2380, 'jets': 2381, 'let': 2382, 'flying': 2383, 'supersonic': 2384, 'anybody': 2385, 'reporters': 2386, 'journalistic': 2387, 'ethics': 2388, 'uncovered': 2389, 'example': 2390, 'considerably': 2391, 'follows': 2392, 'text': 2393, 'e-mail': 2394, 'mine': 2395, 'thanks': 2396, 'Dave': 2397, 'Manzano': 2398, 'facts': 2399, '---------': 2400, '’s': 2401, 'Before': 2402, 'Dems': 2403, '’': 2404, 'spin': 2405, 'n’t': 2406, 'Alabama': 2407, 'physical': 2408, 'daddy': 2409, 'got': 2410, 'News': 2411, 'coverage': 2412, 'tended': 2413, 'focus': 2414, 'brief': 2415, 'portion': 2416, '—': 2417, 'everything': 2418, 'else': 2419, 'record': 2420, 'full': 2421, 'joined': 2422, '1968': 2423, 'Almost': 2424, 'immediately': 2425, 'began': 2426, 'extended': 2427, 'Six': 2428, 'basic': 2429, 'Fifty': 2430, 'flight': 2431, 'Twenty': 2432, 'begin': 2433, 'periods': 2434, 'weekends': 2435, 'After': 2436, 'racking': 2437, 'hundreds': 2438, 'hours': 2439, 'accumulated': 2440, 'requirements': 2441, 'guardsmen': 2442, 'required': 2443, 'accumulate': 2444, 'meet': 2445, 'yearly': 2446, 'obligation': 2447, 'records': 2448, 'earned': 2449, '253': 2450, 'thereafter': 2451, 'measured': 2452, '340': 2453, '1970': 2454, 'words': 2455, 'satisfy': 2456, 'entire': 2457, 'hitch': 2458, 'acd': 2459, '137': 2460, '112': 2461, '1972': 2462, 'numbers': 2463, 'indicate': 2464, 'showed': 2465, 'Did': 2466, 'brings': 2467, '“': 2468, 'deserted': 2469, '”': 2470, 'according': 2471, 'anti-Bush': 2472, 'filmmaker': 2473, 'Michael': 2474, 'Moore': 2475, 'AWOL': 2476, 'Terry': 2477, 'McAuliffe': 2478, 'chairman': 2479, 'permission': 2480, 'Senate': 2481, 'superior': 2482, 'OK': 2483, 'Requests': 2484, 'unusual': 2485, 'retired': 2486, 'Col.': 2487, 'William': 2488, 'Campenni': 2489, 'glut': 2490, 'pilots': 2491, 'winding': 2492, 'Force': 2493, 'desk': 2494, '’72': 2495, '’73': 2496, 'pilot': 2497, 'solve': 2498, '1973': 2499, '56': 2500, 'requirement': 2501, 'Then': 2502, 'plans': 2503, 'Harvard': 2504, 'Business': 2505, 'showing': 2506, 'frequently': 2507, 'July': 2508, '1974': 2509, 'received': 2510, 'honorable': 2511, 'discharge': 2512, 'serving': 2513, 'original': 2514, 'cover': 2515, 'marks': 2516, 'evaluation': 2517, 'clearly': 2518, 'stands': 2519, 'notch': 2520, 'natural': 2521, 'contemporaries': 2522, 'exceptionally': 2523, 'fine': 2524, 'young': 2525, 'continually': 2526, 'flies': 2527, 'intercept': 2528, 'missions': 2529, 'proficiency': 2530, 'exceptional': 2531, 'questioning': 2532, 'Globe': 2533, 'CBS': 2534, 'outlets': 2535, 'Democrats': 2536, 'spitting': 2537, 'mad': 2538, 'Swift': 2539, 'Boat': 2540, 'Veterans': 2541, 'Truth': 2542, 'reasonable': 2543, 'Voters': 2544, 'perfectly': 2545, 'decide': 2546, 'whether': 2547, 'important': 2548, 'camp': 2549, 'blames': 2550, 'boat': 2551, 'veterans': 2552, 'Swifties': 2553, 'gets': 2554, 'sense': 2555, 'entirely': 2556, 'reasons': 2557, 'noted': 2558, 'passing': 2559, 'personally': 2560, 'questioned': 2561, 'word': 2562, 'explain': 2563, 'Earlier': 2564, 'Just': 2565, 'episode': 2566, 'spotlight': 2567, 'someday': 2568, 'Byron': 2569, 'White': 2570, 'House': 2571, 'correspondent': 2572, 'Review': 2573, 'column': 2574, 'Hill': 2575, 'atmosphere': 2576, 'eerie': 2577, 'strikingly': 2578, 'familiar': 2579, 'suspect': 2580, 'streets': 2581, 'looming': 2582, 'doubt': 2583, 'regard': 2584, 'date': 2585, '30': 2586, 'January': 2587, 'renewed': 2588, 'awaiting': 2589, 'dreading': 2590, 'interim': 2591, 'promised': 2592, 'measures': 2593, 'reduce': 2594, 'fail': 2595, 'balloting': 2596, 'centres': 2597, 'voting': 2598, 'attacked': 2599, 'picture': 2600, 'competing': 2601, 'seem': 2602, 'Coalition': 2603, 'Allawi': 2604, 'Iraqiya': 2605, 'Ayad': 2606, 'ministers': 2607, 'quite': 2608, 'expectedly': 2609, 'campaigning': 2610, 'handed': 2611, '100': 2612, 'dollar': 2613, 'gifts': 2614, 'journalists': 2615, 'attending': 2616, 'conference': 2617, 'practice': 2618, 'bad': 2619, 'memories': 2620, 'Naji': 2621, 'Abbudi': 2622, 'affirmed': 2623, 'claims': 2624, 'educating': 2625, 'merits': 2626, 'Emminence': 2627, 'openly': 2628, 'reference': 2629, 'abusing': 2630, 'official': 2631, 'Again': 2632, 'written': 2633, 'allegation': 2634, 'intentional': 2635, 'Hazim': 2636, \"Sha'lan\": 2637, 'engaging': 2638, 'shrill': 2639, \"Ba'athist\": 2640, 'double': 2641, 'agent': 2642, 'CIA': 2643, 'dismisses': 2644, 'thief': 2645, 'stooge': 2646, 'longs': 2647, 'origins': 2648, 'defending': 2649, 'remark': 2650, 'Arabiya': 2651, 'embarrass': 2652, 'viewers': 2653, 'roll': 2654, 'floor': 2655, 'extremely': 2656, 'amusing': 2657, 'watching': 2658, 'looking': 2659, 'smug': 2660, 'amused': 2661, 'contrasted': 2662, 'barely': 2663, 'swearing': 2664, 'Fistfights': 2665, 'please': 2666, 'son': 2667, \"Khaza'il\": 2668, 'Diwaniya': 2669, 'replace': 2670, 'M.S.': 2671, 'Sahaf': 2672, 'nonsensical': 2673, 'passed': 2674, 'jokes': 2675, 'Kurdish': 2676, 'PUK': 2677, 'KDP': 2678, 'outside': 2679, 'act': 2680, 'grab': 2681, 'vote': 2682, 'conservative': 2683, 'surprisingly': 2684, 'rooting': 2685, 'Communist': 2686, 'attempt': 2687, 'counter': 2688, 'Islamists': 2689, 'forthcoming': 2690, 'Assembly': 2691, 'largest': 2692, 'registered': 2693, 'larger': 2694, '***': 2695, 'Several': 2696, 'candidates': 2697, 'targeted': 2698, 'threats': 2699, 'Sectarian': 2700, 'highest': 2701, 'attacking': 2702, 'Husseiniyas': 2703, 'interesting': 2704, 'conversation': 2705, 'aged': 2706, 'taxi': 2707, 'live': 2708, 'relatives': 2709, 'Amiriya': 2710, 'asking': 2711, 'belong': 2712, 'assessing': 2713, 'sectarian': 2714, 'background': 2715, 'hurling': 2716, 'abuses': 2717, 'Persians': 2718, 'Majoos': 2719, 'fire': 2720, 'worshippers': 2721, 'rabid': 2722, 'dogs': 2723, 'handful': 2724, 'descriptions': 2725, 'described': 2726, 'f*ed': 2727, 'horse': 2728, 'dismissed': 2729, 'thieves': 2730, 'traitors': 2731, 'believed': 2732, 'viable': 2733, 'mess': 2734, 'resistance': 2735, 'commonsense': 2736, 'First': 2737, 'driving': 2738, 'submission': 2739, 'contend': 2740, 'impossible': 2741, 'hold': 2742, 'Leaving': 2743, 'aside': 2744, 'views': 2745, 'examples': 2746, 'offered': 2747, 'eventually': 2748, 'leads': 2749, 'opinion': 2750, 'above': 2751, 'planning': 2752, 'timer': 2753, 'claimed': 2754, 'stage': 2755, \"d'etat\": 2756, 'return': 2757, '10': 2758, 'withdrawal': 2759, 'sensing': 2760, 'incredulity': 2761, 'asserted': 2762, 'parts': 2763, 'central': 2764, 'command': 2765, 'seperate': 2766, 'labels': 2767, 'leaderships': 2768, 'regional': 2769, 'concur': 2770, '18': 2771, 'governorates': 2772, 'along': 2773, 'removed': 2774, 'fatal': 2775, 'misconception': 2776, 'harm': 2777, 'anywhere': 2778, 'assumes': 2779, 'realises': 2780, 'call': 2781, 'reconciliation': 2782, 'dissenting': 2783, 'tribal': 2784, 'Sheikhs': 2785, 'rather': 2786, 'upper': 2787, 'effectively': 2788, 'root': 2789, 'midst': 2790, 'promise': 2791, 'sharing': 2792, 'disenfranchised': 2793, 'abused': 2794, 'Very': 2795, 'Anbar': 2796, 'terrible': 2797, 'blunder': 2798, 'accidentally': 2799, 'Razaq': 2800, 'Inad': 2801, \"Gu'ud\": 2802, 'Bu': 2803, 'Nimr': 2804, 'powerful': 2805, 'Dulaym': 2806, 'favoured': 2807, 'terms': 2808, 'family': 2809, 'seat': 2810, 'governor': 2811, 'rose': 2812, 'mid-nineties': 2813, 'execution': 2814, 'Thamir': 2815, 'Madhlum': 2816, 'Dulaymi': 2817, 'belonging': 2818, 'revolt': 2819, 'step': 2820, 'arrest': 2821, 'Hassan': 2822, 'Lihabi': 2823, 'Lihaib': 2824, 'scattered': 2825, 'Salah': 2826, 'Din': 2827, 'Lihaibi': 2828, 'incident': 2829, 'path': 2830, 'forward': 2831, 'vice': 2832, 'versa': 2833, 'Both': 2834, '14': 2835, 'centuries': 2836, 'possible': 2837, 'partition': 2838, 'possibility': 2839, 'borders': 2840, 'Luis': 2841, 'Posada': 2842, 'Carriles': 2843, 'Miami': 2844, 'Herald': 2845, 'relaxed': 2846, 'luxury': 2847, 'condo': 2848, 'yesterday': 2849, 'hid': 2850, 'thought': 2851, 'taken': 2852, 'custody': 2853, 'Immigration': 2854, 'Authorities': 2855, 'whereabouts': 2856, 'shrug': 2857, 'knowledge': 2858, 'entered': 2859, 'comfortable': 2860, 'thugs': 2861, 'overplayed': 2862, 'embarrassed': 2863, 'patron': 2864, \"'d\": 2865, 'faithful': 2866, 'servant': 2867, 'player': 2868, 'cue': 2869, 'bomber': 2870, 'pesky': 2871, 'dark': 2872, 'skinned': 2873, 'extradited': 2874, 'Meiring': 2875, 'inquisitive': 2876, 'journalist': 2877, 'doorstep': 2878, 'single': 2879, 'trail': 2880, 'Philippines': 2881, 'harms': 2882, \"'ll\": 2883, 'am': 2884, 'maybe': 2885, 'saved': 2886, 'either': 2887, 'crimes': 2888, 'wave': 2889, 'flag': 2890, 'Terror': 2891, 'vulnerable': 2892, 'node': 2893, 'agencies': 2894, 'organizations': 2895, 'Ask': 2896, 'Nichols': 2897, 'exacerbated': 2898, 'Philippine': 2899, 'exploit': 2900, 'ancient': 2901, 'Venezuela': 2902, 'answered': 2903, 'Yes': 2904, 'determined': 2905, 'Strong': 2906, 'Man': 2907, 'Hugo': 2908, 'Chavez': 2909, 'guaranteed': 2910, 'post-Chavez': 2911, 'puppet': 2912, 'Caracas': 2913, 'Whether': 2914, 'justice': 2915, 'Venezuelan': 2916, 'vain': 2917, 'instead': 2918, 'embarrassing': 2919, 'killer': 2920, 'strut': 2921, 'gloating': 2922, 'freedom': 2923, 'threatens': 2924, 'consensus': 2925, 'fiction': 2926, 'unspoken': 2927, 'connection': 2928, 'smuggling': 2929, 'dating': 2930, 'book': 2931, 'Disposable': 2932, 'Patriot': 2933, 'Jack': 2934, 'Terrell': 2935, 'asset': 2936, 'recruited': 2937, 'Contra': 2938, 'identifies': 2939, 'recruit': 2940, 'scheme': 2941, 'Oliver': 2942, 'recall': 2943, 'object': 2944, 'smuggle': 2945, 'explosives': 2946, 'http://www.amazon.ca/exec/obidos/ASIN/0915765381/701-3377456-8181939': 2947, \"'ve\": 2948, 'read': 2949, 'memoir': 2950, 'ring': 2951, 'wider': 2952, 'context': 2953, 'confirmed': 2954, 'allegations': 2955, 'drug': 2956, 'connections': 2957, 'target': 2958, 'discrediting': 2959, 'TWIG': 2960, 'counterintelligence': 2961, 'interference': 2962, 'covert': 2963, 'ops': 2964, 'types': 2965, 'Ollie': 2966, 'Robert': 2967, 'Owen': 2968, 'Vince': 2969, 'Cannistaro': 2970, 'Buck': 2971, 'Revell': 2972, 'Peter': 2973, 'Dale': 2974, 'Scott': 2975, 'account': 2976, 'whistleblowing': 2977, 'stifled': 2978, 'Cocaine': 2979, 'Politics': 2980, 'ole': 2981, 'Due': 2982, 'Judge': 2983, '1983': 2984, 'ruling': 2985, 'testify': 2986, 'pseudonym': 2987, 'opening': 2988, 'cross-examination': 2989, 'Wilson': 2990, 'prosecution': 2991, 'convince': 2992, 'jury': 2993, 'relevant': 2994, 'files': 2995, 'claiming': 2996, 'sold': 2997, 'C': 2998, 'Quaddaffi': 2999, 'charging': 3000, 'rogue': 3001, 'contacts': 3002, 'Office': 3003, 'Naval': 3004, 'Intelligence': 3005, 'non-social': 3006, 'retirement': 3007, 'turmoil': 3008, 'Charles': 3009, 'A.': 3010, 'Briggs': 3011, 'rescue': 3012, 'Third': 3013, 'ranking': 3014, 'declaration': 3015, '3rd': 3016, '8th': 3017, 'authorized': 3018, 'pertains': 3019, 'Edwin': 3020, 'P.': 3021, 'various': 3022, 'concerning': 3023, '28th': 3024, 'Declaration': 3025, 'states': 3026, 'directly': 3027, 'indirectly': 3028, 'retiring': 3029, 'http://www.disinfo.com/archive/pages/dossier/id334/pg1/': 3030, 'Federal': 3031, 'District': 3032, 'Lynn': 3033, 'Hughes': 3034, 'Huston': 3035, 'threw': 3036, 'conviction': 3037, 'wrote': 3038, '`': 3039, 'knowingly': 3040, 'concluding': 3041, 'honesty': 3042, 'comes': 3043, 'http://www.bigeye.com/111003.htm': 3044, 'http://www.thekcrachannel.com/news/4503872/detail.html': 3045, 'looks': 3046, 'rearing': 3047, 'Louisianna': 3048, '..': 3049, 'Former': 3050, 'Pastor': 3051, 'Deputy': 3052, 'Implicated': 3053, 'Church': 3054, 'Child': 3055, 'Sex': 3056, 'Abuse': 3057, 'PONCHATOULA': 3058, 'La.': 3059, 'Sheriff': 3060, 'deputies': 3061, 'Louisiana': 3062, 'ongoing': 3063, 'investigation': 3064, 'sexual': 3065, 'abuse': 3066, 'animals': 3067, 'Ponchatoula': 3068, 'church': 3069, 'Austin': 3070, 'Aaron': 3071, 'Bernard': 3072, 'III': 3073, '36': 3074, 'arrested': 3075, 'aggravated': 3076, 'rape': 3077, 'age': 3078, '13': 3079, 'Police': 3080, 'confessed': 3081, 'detectives': 3082, 'sex': 3083, 'girl': 3084, 'admitted': 3085, 'knowing': 3086, 'acts': 3087, 'dog': 3088, 'occurred': 3089, 'Hosanna': 3090, 'Tangipahoa': 3091, 'Parish': 3092, 'sheriff': 3093, 'Christopher': 3094, 'Blair': 3095, 'Labat': 3096, '24': 3097, 'booked': 3098, 'Tuesday': 3099, 'count': 3100, 'nature': 3101, 'Monday': 3102, 'Louis': 3103, 'Lamonica': 3104, 'pastor': 3105, 'counts': 3106, 'walked': 3107, 'Livingston': 3108, 'implicate': 3109, 'unravelling': 3110, 'northwest': 3111, 'murder': 3112, 'missing': 3113, 'purpose': 3114, 'Rummy': 3115, 'send': 3116, 'yeah': 3117, 'evening': 3118, 'featured': 3119, 'television': 3120, 'Germany': 3121, 'ARD': 3122, 'Tagesthemen': 3123, 'newly': 3124, 'papers': 3125, 'complicit': 3126, 'airliner': 3127, 'gandalf': 3128, 'relate': 3129, 'thinking': 3130, 'enjoy': 3131, 'reading': 3132, 'blog': 3133, 'definitely': 3134, 'immigration': 3135, 'Canada': 3136, 'ól': 3137, 'ai': 3138, 'nt': 3139, 'yuor': 3140, 'Please': 3141, 'check': 3142, 'hey': 3143, 'great': 3144, 'site': 3145, 'los': 3146, 'angeles': 3147, 'online': 3148, 'excellent': 3149, 'Keep': 3150, 'pretty': 3151, 'covers': 3152, 'stuff': 3153, 'sure': 3154, 'Hey': 3155, 'love': 3156, 'advice': 3157, 'deals': 3158, 'searching': 3159, 'info': 3160, 'agree': 3161, 'totally': 3162, 'Paul': 3163, 'How': 3164, 'Would': 3165, 'Like': 3166, 'To': 3167, 'Know': 3168, 'YOU': 3169, 'Live': 3170, 'Beautiful': 3171, 'Is': 3172, 'Custom': 3173, 'Designed': 3174, 'YOUR': 3175, 'Specifications': 3176, '.........': 3177, 'NO': 3178, 'COST': 3179, 'CAN': 3180, 'Hard': 3181, 'IF': 3182, 'HOW': 3183, 'Pass': 3184, 'Without': 3185, 'Least': 3186, 'Taking': 3187, 'Look': 3188, 'Only': 3189, 'Catch': 3190, 'HAVE': 3191, 'Income': 3192, 'ABOVE': 3193, '$': 3194, '21,000': 3195, '......': 3196, 'Follow': 3197, 'Easy': 3198, 'Instructions': 3199, 'Provided': 3200, 'Get-A-Free-House.com': 3201, '+': 3202, 'implications': 3203, 'Andaman': 3204, 'Islands': 3205, 'sink': 3206, 'scrutiny': 3207, 'fallibility': 3208, 'evident': 3209, 'inability': 3210, 'foresee': 3211, 'advance': 3212, 'pro-India': 3213, 'Bangladeshi': 3214, 'Hasina': 3215, 'Wazed': 3216, 'August': 3217, '21': 3218, 'rally': 3219, 'Dhaka': 3220, 'Nepalese': 3221, 'King': 3222, 'Gyanendra': 3223, 'dismissing': 3224, 'imposing': 3225, 'virtual': 3226, 'absolute': 3227, 'monarchy': 3228, 'glaring': 3229, 'Trouble': 3230, 'islands': 3231, 'Ramtanu': 3232, 'Maitra': 3233, 'charged': 3234, '34': 3235, 'Arakan': 3236, 'separatists': 3237, 'Myanmar': 3238, 'hiding': 3239, 'Landfall': 3240, 'note': 3241, 'wing': 3242, 'Unity': 3243, 'illegal': 3244, 'entry': 3245, 'deported': 3246, 'tip': 3247, 'iceberg': 3248, 'growing': 3249, 'concern': 3250, 'Reports': 3251, 'circulating': 3252, 'thick': 3253, 'depot': 3254, 'operate': 3255, 'freely': 3256, 'Sea': 3257, 'Information': 3258, 'direction': 3259, 'light': 3260, '26': 3261, 'tsunami': 3262, 'heavy': 3263, 'toll': 3264, 'Andamans': 3265, 'Correspondents': 3266, 'flocked': 3267, 'miserable': 3268, 'surrounding': 3269, 'navy': 3270, 'Far': 3271, 'Eastern': 3272, 'Command': 3273, 'established': 3274, 'blue': 3275, 'water': 3276, 'Deteriorating': 3277, 'deteriorated': 3278, 'Nepal': 3279, 'Bhutan': 3280, 'particularly': 3281, 'Manipur': 3282, 'Nagaland': 3283, 'Tripura': 3284, 'rock': 3285, 'solid': 3286, 'western': 3287, 'developed': 3288, 'withstanding': 3289, 'adventure': 3290, 'sector': 3291, 'eastern': 3292, 'multitude': 3293, 'secessionist': 3294, 'Southeast': 3295, 'conduit': 3296, 'traffic': 3297, '572': 3298, 'large': 3299, 'constitute': 3300, 'Nicobar': 3301, 'transit': 3302, 'drugs': 3303, 'travel': 3304, 'directions': 3305, 'Aceh': 3306, 'coast': 3307, 'Africa': 3308, 'network': 3309, 'unknown': 3310, 'Port': 3311, 'unnamed': 3312, 'permanently': 3313, 'settled': 3314, 'ration': 3315, 'cards': 3316, 'Indonesia': 3317, 'Malaysia': 3318, 'migrated': 3319, 'temporarily': 3320, 'plunder': 3321, 'resources': 3322, 'Havelock': 3323, 'Diglipur': 3324, 'Campbell': 3325, 'Bay': 3326, 'Neil': 3327, 'Rangott': 3328, 'overrun': 3329, 'estimate': 3330, 'suggests': 3331, '50,000': 3332, 'unofficial': 3333, 'higher': 3334, 'Bangladeshis': 3335, 'millions': 3336, 'countrymen': 3337, 'densely': 3338, 'populated': 3339, 'homeland': 3340, 'settle': 3341, 'elsewhere': 3342, 'technical': 3343, 'skills': 3344, 'demand': 3345, 'unlawful': 3346, 'presence': 3347, 'guns': 3348, 'cash': 3349, 'ground': 3350, 'Coast': 3351, 'grossly': 3352, 'unequipped': 3353, 'surge': 3354, 'migrants': 3355, 'naval': 3356, 'Arms': 3357, 'profitable': 3358, 'Considering': 3359, 'huge': 3360, 'strategic': 3361, 'importance': 3362, 'amazing': 3363, 'lax': 3364, 'sit': 3365, 'sea': 3366, 'lanes': 3367, 'Strait': 3368, 'Malacca': 3369, 'tankers': 3370, 'merchant': 3371, 'ships': 3372, 'pass': 3373, 'daily': 3374, 'oil': 3375, 'lapses': 3376, 'dependent': 3377, 'smaller': 3378, 'nations': 3379, 'maintain': 3380, 'Unless': 3381, 'providing': 3382, 'nationals': 3383, 'secessionists': 3384, 'runners': 3385, 'traffickers': 3386, 'generate': 3387, 'confidence': 3388, 'capitals': 3389, 'tend': 3390, 'anti-India': 3391, 'outfits': 3392, 'Inter-': 3393, 'Services': 3394, 'ISI': 3395, 'linked': 3396, 'Maoists': 3397, 'networks': 3398, 'weaken': 3399, 'flank': 3400, 'lackluster': 3401, 'dealing': 3402, 'startling': 3403, 'black': 3404, 'eye': 3405, 'mid-1980s': 3406, 'wide': 3407, 'ranging': 3408, 'operation': 3409, 'inhabited': 3410, 'neighboring': 3411, 'caches': 3412, 'destroy': 3413, 'carried': 3414, 'repeated': 3415, 'requests': 3416, 'addition': 3417, 'activity': 3418, 'maritime': 3419, 'defunct': 3420, 'Asiaweek': 3421, 'shipment': 3422, 'ammunition': 3423, 'purchased': 3424, 'Cambodia': 3425, 'worth': 3426, 'dollars': 3427, 'port': 3428, 'Phuket': 3429, 'aboard': 3430, 'freighter': 3431, 'Comex': 3432, 'Joux': 3433, 'standard': 3434, 'procedure': 3435, 'vessel': 3436, 'changed': 3437, 'Horizon': 3438, 'journey': 3439, 'Bengal': 3440, 'tracked': 3441, 'Orissa': 3442, 'spy': 3443, 'planes': 3444, 'Aviation': 3445, 'Research': 3446, 'Center': 3447, 'intercepted': 3448, 'vessels': 3449, '1997': 3450, 'Thai': 3451, 'interception': 3452, '16': 3453, 'meter': 3454, 'chase': 3455, 'Ranong': 3456, 'confiscation': 3457, 'tons': 3458, 'Among': 3459, 'rocket': 3460, 'propelled': 3461, 'grenade': 3462, 'launchers': 3463, 'assault': 3464, 'rifles': 3465, 'M': 3466, '79': 3467, '10,000': 3468, 'persons': 3469, 'Front': 3470, 'crew': 3471, 'heading': 3472, 'Cox': 3473, 'Bazar': 3474, 'Until': 3475, '1995': 3476, 'maintained': 3477, 'Twante': 3478, 'coat': 3479, 'west': 3480, 'Subsequently': 3481, 'became': 3482, 'backup': 3483, 'born': 3484, 'passport': 3485, 'allegedly': 3486, 'constructing': 3487, 'submarine': 3488, 'shipyard': 3489, 'Sirae': 3490, 'Steady': 3491, 'buildup': 3492, 'steady': 3493, 'ports': 3494, 'conduits': 3495, 'host': 3496, 'militant': 3497, 'steadily': 3498, 'moved': 3499, 'lawlessness': 3500, 'extremist': 3501, 'trafficking': 3502, 'gun': 3503, 'anti-Indian': 3504, 'widely': 3505, 'acknowledged': 3506, 'nurtured': 3507, 'Harkat': 3508, 'ul': 3509, 'Jehad': 3510, 'Islami': 3511, 'Chittagong': 3512, 'northeast': 3513, 'touch': 3514, 'result': 3515, 'coastal': 3516, 'nest': 3517, 'pattern': 3518, 'arrests': 3519, 'seizures': 3520, 'brought': 3521, 'Laos': 3522, 'transported': 3523, 'northward': 3524, 'Kalikhola': 3525, 'passes': 3526, 'northern': 3527, 'Assam': 3528, 'Meghalaya': 3529, 'Note': 3530, 'comprise': 3531, 'lying': 3532, '1,000': 3533, 'kilometers': 3534, 'Stretching': 3535, '750': 3536, 'reach': 3537, 'Sumatra': 3538, 'Ten': 3539, 'Degree': 3540, 'Channel': 3541, 'divides': 3542, 'archipelago': 3543, 'Travel': 3544, 'forbidden': 3545, 'non-Indians': 3546, 'Island': 3547, 'writes': 3548, 'journals': 3549, 'regular': 3550, 'contributor': 3551, 'EIR': 3552, 'Defense': 3553, 'Aakrosh': 3554, 'tied': 3555, 'quarterly': 3556, 'journal': 3557, 'Karzai': 3558, 'rivalry': 3559, 'accuses': 3560, 'consulates': 3561, 'Kandahar': 3562, 'Jalalabad': 3563, 'train': 3564, 'Balochi': 3565, 'Balochistan': 3566, 'claim': 3567, '42': 3568, 'RAW': 3569, 'unless': 3570, 'undermining': 3571, '’’': 3572, 'deny': 3573, '‘’': 3574, 'Should': 3575, 'Afghan': 3576, 'soil': 3577, 'neighbours': 3578, 'Amrullah': 3579, 'Saleh': 3580, 'Directorate': 3581, 'equations': 3582, 'KABUL': 3583, 'landlocked': 3584, 'suffered': 3585, 'constant': 3586, 'Central': 3587, 'Asian': 3588, 'Republics': 3589, 'powers': 3590, 'interfering': 3591, 'signs': 3592, 'reassurance': 3593, 'stable': 3594, 'peaceful': 3595, 'Nobody': 3596, 'looser': 3597, 'English': 3598, 'Nation': 3599, 'publicly': 3600, 'supported': 3601, 'electoral': 3602, 'undercurrents': 3603, 'favourite': 3604, 'contenders': 3605, 'repeatedly': 3606, 'Western': 3607, 'harbouring': 3608, 'extremists': 3609, 'pledged': 3610, 'disrupt': 3611, 'avoided': 3612, 'criticising': 3613, 'catch': 3614, '22': 3615, 'meeting': 3616, 'sidelines': 3617, 'UN': 3618, 'desperately': 3619, 'anxious': 3620, 'secure': 3621, 'non-interference': 3622, 'diplomats': 3623, 'intimately': 3624, 'reigning': 3625, 'peacefully': 3626, 'Where': 3627, 'Mullah': 3628, 'Omar': 3629, 'Usmani': 3630, 'Gulbuddin': 3631, 'Hikmetar': 3632, 'flustered': 3633, 'Hikmetyar': 3634, 'Hizb': 3635, 'e': 3636, 'focused': 3637, 'diplomat': 3638, 'briefed': 3639, 'realise': 3640, 'bigger': 3641, 'pleased': 3642, 'results': 3643, 'cooperate': 3644, 'curbing': 3645, 'angry': 3646, 'categorically': 3647, 'snub': 3648, 'options': 3649, 'insist': 3650, 'decision': 3651, 'unconnected': 3652, 'tripartite': 3653, 'NATO': 3654, 'Kabul': 3655, 'tough': 3656, 'message': 3657, 'instrumental': 3658, 'persuading': 3659, 'restrain': 3660, 'disrupting': 3661, 'crackdown': 3662, 'Lt.': 3663, 'Barno': 3664, 'tactical': 3665, 'goes': 3666, 'adds': 3667, 'Also': 3668, 'crossing': 3669, 'point': 3670, 'Chaman': 3671, 'Pashtun': 3672, 'leading': 3673, 'units': 3674, 'sympathetic': 3675, 'mullahs': 3676, 'JUI': 3677, 'oblivious': 3678, 'facing': 3679, 'Waziristan': 3680, 'anti-Americanism': 3681, 'anti-army': 3682, 'actionable': 3683, 'presenting': 3684, 'suspected': 3685, 'Moreover': 3686, 'try': 3687, 'quietly': 3688, 'isolated': 3689, 'facilitated': 3690, 'happen': 3691, 'push': 3692, 'win': 3693, 'demotion': 3694, 'Fahim': 3695, 'warlord': 3696, 'Ismail': 3697, 'Khan': 3698, 'fear': 3699, 'reprisals': 3700, 'Their': 3701, 'removal': 3702, 'provide': 3703, 'increased': 3704, 'motivation': 3705, 'welcomed': 3706, 'appointment': 3707, 'Ashfaq': 3708, 'Kiyani': 3709, 'Kayani': 3710, 'delegation': 3711, 'meetings': 3712, 'issues': 3713, 'assured': 3714, 'adverse': 3715, 'acted': 3716, 'upon': 3717, 'swiftly': 3718, 'Senior': 3719, 'warned': 3720, 'shift': 3721, 'reflecting': 3722, 'traditionally': 3723, 'Tajik': 3724, 'strenuous': 3725, 'efforts': 3726, 'Younis': 3727, 'Qanooni': 3728, 'strike': 3729, 'oppose': 3730, 'particular': 3731, 'feared': 3732, 'refused': 3733, 'fellow': 3734, 'Panjsheri': 3735, 'Tajiks': 3736, 'urged': 3737, 'stand': 3738, 'proved': 3739, 'critical': 3740, 'Hazara': 3741, 'Mohammed': 3742, 'Mohaqeq': 3743, 'posing': 3744, 'stabilise': 3745, 'reduced': 3746, 'hardliners': 3747, 'undermine': 3748, 'strategy': 3749, 'deepen': 3750, 'rift': 3751, 'moderates': 3752, 'concerned': 3753, 'Shindand': 3754, 'massive': 3755, 'era': 3756, 'airbase': 3757, 'kilometres': 3758, 'enhanced': 3759, 'ousting': 3760, 'Governor': 3761, 'Herat': 3762, 'province': 3763, 'month': 3764, 'objections': 3765, 'ouster': 3766, 'warlords': 3767, 'heightened': 3768, 'neo-conservatives': 3769, 'aggressively': 3770, 'Iranians': 3771, 'listening': 3772, 'spying': 3773, 'facility': 3774, 'pad': 3775, 'Special': 3776, 'Forces': 3777, 'insists': 3778, 'poses': 3779, 'Nevertheless': 3780, 'places': 3781, 'sensitive': 3782, 'relations': 3783, 'scenario': 3784, 'abundantly': 3785, 'nor': 3786, 'Pashtuns': 3787, 'move': 3788, 'tribes': 3789, 'Quetta': 3790, 'resisted': 3791, 'voted': 3792, 'rapidly': 3793, 'changing': 3794, 'demise': 3795, 'fluid': 3796, 'reconsider': 3797, 'unlimited': 3798, 'sanctuary': 3799, 'Link': 3800, '16/10/2004': 3801, 'debate': 3802, 'multi-millionnaires': 3803, 'cleverly': 3804, 'manufactured': 3805, 'absurd': 3806, 'charges': 3807, 'vets': 3808, 'flip': 3809, 'flopped': 3810, 'contradicting': 3811, 'Or': 3812, 'eyewitnesses': 3813, 'address': 3814, 'substance': 3815, 'Big': 3816, 'Lie': 3817, 'risk': 3818, 'falling': 3819, 'logic': 3820, 'absurdity': 3821, 'appreciated': 3822, 'bravery': 3823, 'life': 3824, 'lived': 3825, 'zone': 3826, 'wounds': 3827, 'superficial': 3828, 'shrapnel': 3829, 'forearm': 3830, 'minor': 3831, 'wound': 3832, 'brain': 3833, 'demonstrates': 3834, 'mortal': 3835, 'absent': 3836, 'yourself': 3837, 'medal': 3838, 'youth': 3839, 'drinking': 3840, 'fish': 3841, 'wee': 3842, 'risked': 3843, 'slack': 3844, 'efficiently': 3845, 'alcoholism': 3846, 'possibly': 3847, 'speaks': 3848, 'character': 3849, 'addictive': 3850, 'personality': 3851, 'erratic': 3852, 'alarming': 3853, 'explosive': 3854, 'temper': 3855, 'provoked': 3856, 'disastrous': 3857, 'siege': 3858, 'spring': 3859, '600': 3860, 'revenge': 3861, 'civilian': 3862, 'mercenaries': 3863, 'African': 3864, 'Newsweek': 3865, 'commanded': 3866, 'cabinet': 3867, 'sadistic': 3868, 'streak': 3869, 'enjoyed': 3870, 'executions': 3871, 'delight': 3872, 'seemed': 3873, 'prospect': 3874, 'executing': 3875, 'wrong': 3876, 'doers': 3877, 'enjoying': 3878, 'scale': 3879, 'Drug': 3880, 'ability': 3881, 'emotions': 3882, 'empathy': 3883, 'pickling': 3884, 'nervous': 3885, 'system': 3886, 'toxic': 3887, 'substances': 3888, 'damaged': 3889, 'goods': 3890, 'abstain': 3891, 'visual': 3892, 'spatial': 3893, 'abilities': 3894, 'abstraction': 3895, 'solving': 3896, 'short': 3897, 'memory': 3898, 'slowest': 3899, 'recover': 3900, 'wagon': 3901, 'pretzel': 3902, 'laudable': 3903, 'suffers': 3904, 'severe': 3905, 'suffering': 3906, 'absenting': 3907, 'senate': 3908, 'Winton': 3909, 'Red': 3910, 'Blount': 3911, 'stint': 3912, 'unclear': 3913, 'slacked': 3914, 'nephew': 3915, 'Murph': 3916, 'Archibald': 3917, 'appeared': 3918, 'Public': 3919, 'Radio': 3920, 'Things': 3921, 'Considered': 3922, 'gives': 3923, 'devastating': 3924, 'insight': 3925, '8:00': 3926, 'PM': 3927, 'ET': 3928, 'NPR': 3929, 'season': 3930, 'fulfilled': 3931, 'obligations': 3932, 'lieutenant': 3933, '1970s': 3934, 'scoured': 3935, 'remembered': 3936, 'seeing': 3937, 'Mr.': 3938, 'headquarters': 3939, 'Montgomery': 3940, 'Wade': 3941, 'Goodwyn': 3942, 'WADE': 3943, 'GOODWYN': 3944, 'reporting': 3945, 'Baba': 3946, 'Groom': 3947, 'smart': 3948, 'funny': 3949, 'woman': 3950, 'smack': 3951, 'dab': 3952, 'exciting': 3953, 'scheduler': 3954, 'job': 3955, 'hub': 3956, 'wheel': 3957, 'her': 3958, 'handsome': 3959, 'remembers': 3960, '32': 3961, 'Ms.': 3962, 'BABA': 3963, 'GROOM': 3964, 'Campaign': 3965, 'Worker': 3966, 'wear': 3967, 'khaki': 3968, 'trousers': 3969, 'jacket': 3970, 'phone': 3971, 'accent': 3972, 'melded': 3973, 'everybody': 3974, 'gotten': 3975, 'construction': 3976, 'Prominent': 3977, 'Southern': 3978, 'Republicans': 3979, 'rare': 3980, 'breed': 3981, 'appointed': 3982, 'Richard': 3983, 'Nixon': 3984, 'postmaster': 3985, 'tennis': 3986, 'partners': 3987, 'father': 3988, 'Congressman': 3989, 'Lieutenant': 3990, 'urging': 3991, 'county': 3992, 'chairpersons': 3993, '67': 3994, 'counties': 3995, 'Back': 3996, 'Deep': 3997, 'rural': 3998, 'apparatus': 3999, 'Texan': 4000, 'supplies': 4001, 'helped': 4002, 'statewide': 4003, 'marriage': 4004, 'coming': 4005, 'infantry': 4006, 'dedicated': 4007, 'workers': 4008, 'MURPH': 4009, 'ARCHIBALD': 4010, 'Nephew': 4011, 'Well': 4012, 'morning': 4013, 'mid-evenings': 4014, 'Ordinarily': 4015, 'noon': 4016, 'ordinarily': 4017, '5:30': 4018, '6:00': 4019, \"'72\": 4020, 'manager': 4021, 'materials': 4022, 'concerns': 4023, 'overall': 4024, 'impression': 4025, 'somebody': 4026, 'figured': 4027, 'broached': 4028, 'subject': 4029, 'unpleasant': 4030, 'DC': 4031, 'dated': 4032, 'beautiful': 4033, 'evenings': 4034, 'house': 4035, 'rented': 4036, 'disrepair': 4037, 'damage': 4038, 'walls': 4039, 'chandelier': 4040, 'owned': 4041, 'grumble': 4042, 'unpaid': 4043, 'repair': 4044, 'bill': 4045, 'friendly': 4046, 'stories': 4047, 'kind': 4048, 'starter': 4049, \"'re\": 4050, 'kids': 4051, 'football': 4052, 'weather': 4053, 'gambit': 4054, 'frequency': 4055, 'discussed': 4056, 'mid-20s': 4057, 'conversations': 4058, 'senatorial': 4059, 'frankly': 4060, 'inappropriate': 4061, 'sometimes': 4062, 'Yale': 4063, 'Haven': 4064, 'whenever': 4065, 'grandson': 4066, 'Connecticut': 4067, 'senator': 4068, 'class': 4069, 'boy': 4070, 'Democrat': 4071, 'law': 4072, 'enforcement': 4073, 'Prescott': 4074, 'laugh': 4075, 'dutifully': 4076, 'notes': 4077, 'drunk': 4078, 'guys': 4079, 'girls': 4080, 'buddy': 4081, 'impress': 4082, 'behavior': 4083, 'untouched': 4084, 'Our': 4085, 'Republic': 4086, 'hands': 4087, 'When': 4088, 'burning': 4089, 'flesh': 4090, 'death': 4091, 'abiding': 4092, 'citizen': 4093, 'Bob': 4094, 'Dylan': 4095, 'Afraid': 4096, 'today': 4097, 'discuss': 4098, 'attention': 4099, 'Mirror': 4100, 'headline': 4101, '200,000': 4102, 'AK47s': 4103, 'Fallen': 4104, 'Into': 4105, 'Hands': 4106, 'Terrorists': 4107, 'thread': 4108, 'RI': 4109, 'discussion': 4110, 'board': 4111, 'smuggled': 4112, '99': 4113, 'tonne': 4114, 'cache': 4115, 'flown': 4116, 'Bosnia': 4117, 'planeloads': 4118, 'vanished': 4119, 'Orders': 4120, 'ahead': 4121, 'contracted': 4122, 'via': 4123, 'complex': 4124, 'web': 4125, 'traders': 4126, 'Moldovan': 4127, 'airline': 4128, 'transport': 4129, 'blasted': 4130, 'Liberia': 4131, 'Amnesty': 4132, 'separate': 4133, 'probe': 4134, 'meant': 4135, 'Mike': 4136, 'Blakemore': 4137, 'unbelievable': 4138, 'terrifying': 4139, 'defence': 4140, 'chiefs': 4141, 'hired': 4142, '90s': 4143, 'Bosnian': 4144, 'controllers': 4145, 'flights': 4146, 'supposedly': 4147, '2005': 4148, 'purchases': 4149, 'Nato': 4150, 'voiced': 4151, 'fears': 4152, 'Swiss': 4153, 'firms': 4154, 'tracking': 4155, 'mechanism': 4156, 'siphoned': 4157, 'UK': 4158, 're-routed': 4159, 'Aerocom': 4160, 'yes': 4161, 'Victor': 4162, 'Bout': 4163, 'bang': 4164, 'wall': 4165, 'incompetence': 4166, 'theory': 4167, 'receives': 4168, 'Administration': 4169, 'harshest': 4170, 'critics': 4171, 'misdirection': 4172, 'representative': 4173, 'privatized': 4174, 'global': 4175, 'gangland': 4176, 'billions': 4177, 'vanish': 4178, 'Pentagon': 4179, 'contracts': 4180, 'untouchable': 4181, 'stature': 4182, 'sinks': 4183, 'beneath': 4184, 'frothing': 4185, 'triviality': 4186, 'Meanwhile': 4187, 'Toledo': 4188, 'priest': 4189, 'Gerald': 4190, 'Robinson': 4191, 'guilty': 4192, 'Rev.': 4193, 'stony': 4194, 'faced': 4195, 'verdict': 4196, 'blinked': 4197, 'glanced': 4198, 'lawyers': 4199, 'handcuffs': 4200, 'sacristy': 4201, 'adjoining': 4202, 'hospital': 4203, 'chapel': 4204, 'downtown': 4205, 'Easter': 4206, '1980': 4207, 'Investigators': 4208, 'nun': 4209, 'Margaret': 4210, 'Ann': 4211, 'Pahl': 4212, '71': 4213, 'strangled': 4214, 'stabbed': 4215, 'chest': 4216, 'forming': 4217, 'shape': 4218, 'inverted': 4219, 'cross': 4220, 'recognized': 4221, 'Satanic': 4222, 'symbol': 4223, 'altar': 4224, 'cloth': 4225, 'draped': 4226, 'naked': 4227, 'sexually': 4228, 'assaulted': 4229, 'humiliate': 4230, 'prosecutor': 4231, 'Mandros': 4232, 'closing': 4233, 'arguments': 4234, 'everyone': 4235, 'God': 4236, 'sentencing': 4237, 'tearful': 4238, 'Claudia': 4239, 'Vercellotti': 4240, 'Survivors': 4241, 'Network': 4242, 'Those': 4243, 'Abused': 4244, 'Priests': 4245, 'SNAP': 4246, 'reopen': 4247, 'rot': 4248, 'hell': 4249, 'More': 4250, 'prosecutors': 4251, 'tools': 4252, 'horrific': 4253, 'defendants': 4254, 'seemingly': 4255, 'institutions': 4256, 'victims': 4257, 'witnesses': 4258, 'stay': 4259, 'silent': 4260, 'changes': 4261, 'speak': 4262, 'protected': 4263, 'weapon': 4264, 'letter': 4265, 'opener': 4266, 'Finally': 4267, 'email': 4268, 'reopening': 4269, 'Atlanta': 4270, 'Murders': 4271, 'Dekalb': 4272, 'County': 4273, 'Chief': 4274, 'Graham': 4275, 'reopened': 4276, 'investigations': 4277, 'mysteriously': 4278, 'stepping': 4279, 'replacement': 4280, 'Brown': 4281, 'supervisor': 4282, 'PD': 4283, 'murders': 4284, 'subsequent': 4285, 'matter': 4286, 'individually': 4287, 'institutionally': 4288, 'domain': 4289, 'lots': 4290, 'jeff': 4291, 'thank': 4292, 'god': 4293, 'souls': 4294, 'congress': 4295, 'CRACKDOWN': 4296, 'POLYGAMY': 4297, 'GROUP': 4298, 'Small': 4299, 'polygamous': 4300, 'southwestern': 4301, 'watchful': 4302, 'fairly': 4303, 'benign': 4304, 'sect': 4305, 'Fundamentalist': 4306, 'Latter': 4307, 'Day': 4308, 'Saints': 4309, 'FLDS': 4310, 'Mormonism': 4311, '1890': 4312, 'Warren': 4313, 'Jeffs': 4314, 'Wanted': 4315, 'Fugitives': 4316, 'caps': 4317, 'approach': 4318, 'practices': 4319, 'grew': 4320, 'followers': 4321, 'communities': 4322, 'assets': 4323, '110': 4324, 'school': 4325, 'intertwined': 4326, 'ex-members': 4327, 'mainly': 4328, 'underage': 4329, 'marry': 4330, 'secluded': 4331, 'compounds': 4332, 'federal': 4333, 'crack': 4334, 'vigorously': 4335, 'Specifically': 4336, 'Utah': 4337, 'Arizona': 4338, 'arranging': 4339, 'spiritual': 4340, 'marriages': 4341, 'weekend': 4342, 'Salt': 4343, 'Lake': 4344, 'City': 4345, 'Phoenix': 4346, 'traveling': 4347, 'bodyguards': 4348, 'apocalyptic': 4349, 'Christian': 4350, 'Identity': 4351, 'philosophies': 4352, 'Aryan': 4353, 'neo-Nazi': 4354, 'Creativity': 4355, 'Movement': 4356, 'preached': 4357, 'racism': 4358, 'race': 4359, 'devil': 4360, 'evil': 4361, 'unto': 4362, 'earth': 4363, 'cited': 4364, 'Poverty': 4365, 'Law': 4366, 'editorial': 4367, 'Deseret': 4368, 'Morning': 4369, 'unwillingness': 4370, 'stir': 4371, 'hornet': 4372, 'contributed': 4373, 'kid': 4374, 'glove': 4375, 'lawmakers': 4376, 'http://www.csmonitor.com/2006/0509/p02s01-ussc.html?s=t5': 4377, 'Maybe': 4378, 'exactly': 4379, 'Loose': 4380, \"AK47's\": 4381, '?!?!?': 4382, 'Never': 4383, 'regularly': 4384, 'scheduled': 4385, 'programming': 4386, 'Raw': 4387, 'Story': 4388, 'i': 4389, 'headlines': 4390, 'Funny': 4391, 'squirelled': 4392, 'meaning': 4393, 'hint': 4394, 'conspiracy': 4395, 'abusers': 4396, 'satanic': 4397, 'underground': 4398, 'judge': 4399, 'bench': 4400, 'obeying': 4401, \"it's\": 4402, 'feet': 4403, 'Personally': 4404, 'damn': 4405, 'adults': 4406, 'invovled': 4407, 'marrying': 4408, 'enfurates': 4409, 'cults': 4410, 'multimillions': 4411, 'poverty': 4412, 'cult': 4413, 'reassigns': 4414, 'wives': 4415, 'husband': 4416, 'displeases': 4417, 'reward': 4418, 'Husbands': 4419, 'mothers': 4420, 'teen': 4421, 'daughters': 4422, 'retardation': 4423, 'genetic': 4424, 'needs': 4425, 'gene': 4426, 'parents': 4427, 'vegetable': 4428, 'doctor': 4429, 'begs': 4430, 'intermarrying': 4431, 'pure': 4432, 'fumarase': 4433, 'deficiency': 4434, 'kingston': 4435, 'convicted': 4436, 'beating': 4437, 'daughter': 4438, 'unconcious': 4439, 'shit': 4440, 'shoot': 4441, 'juicy': 4442, 'profits': 4443, 'evaporate': 4444, 'comparison': 4445, 'contractors': 4446, 'awash': 4447, 'tax': 4448, 'milking': 4449, 'angle': 4450, 'whoopsie': 4451, 'daisy': 4452, 'huh': 4453, 'Christ': 4454, 'bit': 4455, 'tired': 4456, 'treating': 4457, 'gullible': 4458, 'idiots': 4459, 'suppose': 4460, 'lucky': 4461, 'GOP': 4462, 'outsourcing': 4463, 'bypassing': 4464, 'channels': 4465, 'turning': 4466, 'unruly': 4467, 'cast': 4468, 'characters': 4469, 'order': 4470, 'create': 4471, 'preparation': 4472, 'http://www.rawstory.com/news/2006/US_outsourcing_special_operations_intelligence_gathering_0413.html': 4473, 'deserves': 4474, 'MEK': 4475, 'unquestionably': 4476, 'boast': 4477, 'Mujahedeen': 4478, 'affair': 4479, \"''\": 4480, 'generations': 4481, 'martyrs': 4482, 'grandmothers': 4483, 'grown': 4484, 'schools': 4485, 'Ashraf': 4486, 'Family': 4487, 'nights': 4488, 'Fridays': 4489, 'invaded': 4490, 'Kuwait': 4491, 'France': 4492, 'Denmark': 4493, 'England': 4494, 'raised': 4495, 'guardians': 4496, 'usually': 4497, '19': 4498, 'fill': 4499, 'ranks': 4500, 'youngest': 4501, 'generation': 4502, 'Though': 4503, 'boys': 4504, 'taught': 4505, 'blindly': 4506, 'Every': 4507, 'beginning': 4508, 'poster': 4509, 'Massoud': 4510, 'Maryam': 4511, 'salute': 4512, 'shout': 4513, 'praises': 4514, 'Nadereh': 4515, 'Afshari': 4516, 'believer': 4517, 'responsible': 4518, 'gulf': 4519, 'German': 4520, 'tried': 4521, 'absorb': 4522, 'Rajavis': 4523, 'brainwash': 4524, 'Which': 4525, 'empowerment': 4526, 'enlightenment': 4527, 'sacrifice': 4528, 'inspired': 4529, 'wisdom': 4530, 'bright': 4531, 'fellows': 4532, 'set': 4533, 'Jeff': 4534, 'SPLOID.com': 4535, 'topic': 4536, 'http://www.sploid.com/news/2006/05/evil_priest_gui.php': 4537, 'Umm': 4538, 'Question': 4539, 'Mark': 4540, 'Jimmy': 4541, 'Plant': 4542, 'Blogshares': 4543, 'fantasy': 4544, 'blogosphere': 4545, 'game': 4546, 'pretend': 4547, 'Satan': 4548, 'http://www.laweekly.com/general/features/satan-loves-you/13454/': 4549, 'metal': 4550, 'strippers': 4551, 'dope': 4552, 'Okay': 4553, 'partly': 4554, 'evolves': 4555, 'music': 4556, 'belongs': 4557, 'weirder': 4558, 'guardian': 4559, 'morality': 4560, 'Praise': 4561, 'Given': 4562, 'void': 4563, 'educational': 4564, 'establishments': 4565, 'religions': 4566, 'guiding': 4567, 'righteousness': 4568, 'fallen': 4569, 'Hmmmmmm': 4570, '....................': 4571, 'trauma': 4572, 'controlled': 4573, 'slaves': 4574, 'wikipedia': 4575, 'stub': 4576, 'waiting': 4577, 'http://en.wikipedia.org/wiki/Aerocom': 4578, 'hates': 4579, ':)': 4580, 'chants': 4581, 'Manson': 4582, 'tees': 4583, 'bearing': 4584, 'Kill': 4585, 'Columbine': 4586, 'acting': 4587, 'indescriminately': 4588, 'murdered': 4589, 'classmates': 4590, 'brainwashed': 4591, 'fans': 4592, 'literally': 4593, 'fulfilling': 4594, 'paradigm': 4595, 'er': 4596, 'phoney': 4597, 'thy': 4598, 'neighbor': 4599, 'REAL': 4600, 'MORALITY': 4601, '?????': 4602, 'hmmmm': 4603, 'indeed': 4604, 'detect': 4605, 'hissing': 4606, 'lisp': 4607, 'serpent': 4608, 'article': 4609, 'deathly': 4610, 'inversion': 4611, 'ala': 4612, 'satanism': 4613, 'brainwashing': 4614, 'Perhaps': 4615, 'phenomenon': 4616, 'invitation': 4617, 'couter-cultural': 4618, 'hypocrisy': 4619, 'percentage': 4620, 'Morality': 4621, 'crowd': 4622, 'lip': 4623, 'loving': 4624, 'beyond': 4625, 'congregation': 4626, 'checked': 4627, 'Satanists': 4628, 'destruction': 4629, 'dealt': 4630, 'Christians': 4631, 'Irony': 4632, 'Long': 4633, 'editor': 4634, 'simple': 4635, 'entering': 4636, 'previously': 4637, 'unexplored': 4638, 'socio-political': 4639, 'require': 4640, 'examination': 4641, 'introspection': 4642, 'Kevin': 4643, 'Coogan': 4644, 'publication': 4645, 'previous': 4646, 'quote': 4647, 'Imaginary': 4648, 'romantic': 4649, 'varied': 4650, 'gloomy': 4651, 'monotonous': 4652, 'barren': 4653, 'boring': 4654, 'marvelous': 4655, 'intoxicating': 4656, 'Simone': 4657, 'Weil': 4658, 'Starroute': 4659, 'due': 4660, 'appartently': 4661, 'cognitively': 4662, 'functional': 4663, 'senseless': 4664, 'incompetent': 4665, 'sign': 4666, 'ulterior': 4667, 'hidden': 4668, 'motives': 4669, 'trust': 4670, 'befuddled': 4671, 'wilt': 4672, 'http://youtube.com/watch?v=d46_ctqDmI4': 4673, 'buying': 4674, 'BS': 4675, 'specifically': 4676, 'tidbit': 4677, 'waaaaaaaaaaaaay': 4678, 'definitions': 4679, 'http://news.bbc.co.uk/2/hi/programmes/this_world/4446342.stm': 4680, 'relentless': 4681, 'uncover': 4682, 'shocking': 4683, 'Italy': 4684, 'tale': 4685, 'gripped': 4686, '1998': 4687, 'Fabio': 4688, 'Tollis': 4689, 'Chiara': 4690, 'Marino': 4691, 'disappeared': 4692, 'pub': 4693, 'Midnight': 4694, 'centre': 4695, 'scene': 4696, 'Milan': 4697, 'Michele': 4698, 'attend': 4699, 'concerts': 4700, 'festivals': 4701, 'Europe': 4702, 'handing': 4703, 'leaflets': 4704, 'quizzing': 4705, 'forms': 4706, 'obsessed': 4707, 'images': 4708, 'collection': 4709, 'paraphernalia': 4710, 'bedroom': 4711, 'convinced': 4712, 'disappearance': 4713, 'inseparable': 4714, 'Yeah': 4715, 'posts': 4716, 'wholly': 4717, 'expand': 4718, 'include': 4719, 'freaky': 4720, 'bozos': 4721, 'Talk': 4722, 'beat': 4723, 'Smartwolf': 4724, 'principle': 4725, 'employed': 4726, 'deliberately': 4727, 'GWB': 4728, 'neocons': 4729, 'Jesus': 4730, 'Nazareth': 4731, 'ignorant': 4732, 'why': 4733, 'phonies': 4734, 'appellation': 4735, 'imposters': 4736, 'Everything': 4737, 'lie': 4738, 'serves': 4739, 'purposes': 4740, 'identified': 4741, 'fully': 4742, 'subterfuge': 4743, 'magician': 4744, 'rigorous': 4745, 'perceive': 4746, 'See': 4747, 'avant': 4748, 'guard': 4749, 'art': 4750, '20th': 4751, 'Century': 4752, 'Italian': 4753, 'Futurism': 4754, 'labeled': 4755, 'degenerate': 4756, 'Socialists': 4757, 'Adolf': 4758, 'Hitler': 4759, 'exhibition': 4760, '1937': 4761, 'curating': 4762, 'parallel': 4763, 'Expressionist': 4764, 'quick': 4765, 'overview': 4766, 'Entartete': 4767, 'Kunst': 4768, 'Degenerate': 4769, 'Art': 4770, 'exhibit': 4771, 'http://en.wikipedia.org/wiki/Degenerate_art': 4772, 'notion': 4773, 'appropriated': 4774, 'Nazis': 4775, 'traced': 4776, 'Jewish': 4777, 'intellectual': 4778, 'Max': 4779, 'Nordau': 4780, '1892': 4781, 'authored': 4782, 'Degeneration': 4783, 'co': 4784, 'founder': 4785, 'World': 4786, 'Zionist': 4787, 'Organization': 4788, 'apparently': 4789, 'Are': 4790, 'adherent': 4791, 'Crowleyan': 4792, 'Thelema': 4793, 'ilk': 4794, 'appropriating': 4795, 'appropriators': 4796, 'redirect': 4797, 'energy': 4798, 'Does': 4799, 'reappropriation': 4800, 'originate': 4801, 'Seems': 4802, 'imaginary': 4803, 'Whore': 4804, 'Babalon': 4805, 'Babylon': 4806, 'accurate': 4807, 'valid': 4808, 'socio': 4809, 'conundrum': 4810, 'Confused': 4811, 'Good': 4812, 'Why': 4813, 'undecided': 4814, 'answers': 4815, 'Balance': 4816, 'Coil': 4817, 'wish': 4818, 'Wikipedia': 4819, 'easier': 4820, 'spending': 4821, 'obscure': 4822, 'detailed': 4823, 'http://en.wikipedia.org/wiki/John_Balance': 4824, 'http://www.guardian.co.uk/obituaries/story/0,3604,1371372,00.html': 4825, 'blended': 4826, 'systems': 4827, 'Shamanism': 4828, 'Christianity': 4829, 'Buddhism': 4830, 'Paganism': 4831, 'Hermeticism': 4832, 'Gnosticism': 4833, 'imbued': 4834, 'vast': 4835, 'output': 4836, 'magickal': 4837, 'designed': 4838, 'functionally': 4839, 'transcendent': 4840, 'listeners': 4841, 'kg': 4842, 'per': 4843, '1000': 4844, 'tonnes': 4845, 'containers': 4846, 'load': 4847, '20,000': 4848, 'E@tG': 4849, 'formulate': 4850, 'acquiring': 4851, 'Reporting': 4852, 'Susan': 4853, 'Polk': 4854, 'bay': 4855, 'farmer': 4856, 'event': 4857, 'Lo': 4858, 'behold': 4859, 'Mormons': 4860, 'booth': 4861, 'delivering': 4862, 'Mormon': 4863, 'genealogy': 4864, 'amazed': 4865, 'spiel': 4866, 'delivered': 4867, 'Kindly': 4868, 'utterly': 4869, 'programmed': 4870, 'watch': 4871, 'Has': 4872, 'purportedly': 4873, 'Ahmadinejad': 4874, 'http://www.thetruthseeker.co.uk/article.asp?id=4503': 4875, 'verified': 4876, 'succintly': 4877, 'succint': 4878, 'regardless': 4879, 'processes': 4880, 'W': 4881, 'contrast': 4882, 'whoever': 4883, 'vs.': 4884, 'Bring': 4885, 'condition': 4886, 'farce': 4887, 'definately': 4888, 'Change': 4889, 'Alex': 4890, 'Jones': 4891, 'Rio': 4892, 'Grande': 4893, 'Blood': 4894, 'Important': 4895, 'Kos': 4896, 'Diary': 4897, 'bush': 4898, 'gathering': 4899, 'storm': 4900, 'scandals': 4901, 'http://www.dailykos.com/story/2006/5/12/232746/857': 4902, '=': 4903, 'Executive': 4904, 'creepy': 4905, 'scary': 4906, 'accumulating': 4907, 'basically': 4908, 'sieze': 4909, 'feels': 4910, 'minute': 4911, '==': 4912, 'revoke': 4913, '13011': 4914, 'exmearden': 4915, 'Fri': 4916, '2006': 4917, '08:27:46': 4918, 'PDT': 4919, 'scanning': 4920, 'whitehouse.gov': 4921, 'noticed': 4922, 'Amendments': 4923, '11030': 4924, '13279': 4925, '13339': 4926, '13381': 4927, '13389': 4928, 'Revocation': 4929, 'jumping': 4930, 'fray': 4931, 'blogger': 4932, 'TroubleFunk': 4933, 'Tronicus': 4934, 'Simply': 4935, 'permeate': 4936, 'Mystery': 4937, 'Tradition': 4938, 'confluence': 4939, 'spirituality': 4940, 'alchemical': 4941, 'initially': 4942, 'appropriation': 4943, 'Similarly': 4944, 'invoking': 4945, 'Egyptian': 4946, 'gods': 4947, 'goddesses': 4948, 'reaffirmation': 4949, 'oppressive': 4950, 'hierarchical': 4951, 'compartmentalize': 4952, 'personal': 4953, 'weirdness': 4954, 'hardcore': 4955, 'factions': 4956, 'integrate': 4957, 'synthesis': 4958, 'regards': 4959, 'vacuous': 4960, 'perspective': 4961, 'culture': 4962, 'Shall': 4963, 'censor': 4964, 'works': 4965, 'poet': 4966, 'Baudelaire': 4967, 'artists': 4968, 'checking': 4969, 'knee': 4970, 'jerk': 4971, 'reaction': 4972, 'apologize': 4973, 'over-generalizations': 4974, 'literalist': 4975, 'True': 4976, 'believers': 4977, 'hung': 4978, 'pyrimidal': 4979, 'structure': 4980, 'cloak': 4981, 'motivations': 4982, 'masses': 4983, 'boggles': 4984, 'ANYONE': 4985, 'value': 4986, 'christian': 4987, 'shall': 4988, 'ye': 4989, 'hackles': 4990, 'haul': 4991, 'Rock': 4992, 'Roll': 4993, 'Whie': 4994, 'subscribe': 4995, 'listen': 4996, 'patently': 4997, 'rediculous': 4998, 'bother': 4999, 'specific': 5000, 'pin': 5001, 'Sub-cultures': 5002, 'lable': 5003, 'implies': 5004, 'develope': 5005, 'vacuum': 5006, 'egos': 5007, 'metaphors': 5008, 'literaly': 5009, 'wether': 5010, 'banging': 5011, 'Canibal': 5012, 'Corpse': 5013, 'humming': 5014, 'Onward': 5015, 'Soldiers': 5016, 'mixing': 5017, 'militarism': 5018, 'respect': 5019, 'adhear': 5020, 'non-violence': 5021, 'forgiveness': 5022, 'charity': 5023, 'Smartwolves': 5024, 'monopoly': 5025, 'lifestyle': 5026, 'hissy': 5027, 'fit': 5028, 'wanting': 5029, 'buy': 5030, 'wants': 5031, 'sus': 5032, 'located': 5033, 'enroute': 5034, 'storage': 5035, 'stooges': 5036, 'pretext': 5037, 'Thanks': 5038, 'Nick': 5039, 'Keck': 5040, 'speech': 5041, 'gentleman': 5042, 'advisory': 5043, 'multi-nation': 5044, 'corporation': 5045, 'worthwhile': 5046, 'View': 5047, 'Eye': 5048, 'Storm': 5049, 'Haim': 5050, 'Harari': 5051, 'Advisory': 5052, 'Board': 5053, 'multi-national': 5054, 'technological': 5055, 'entertainment': 5056, 'occasion': 5057, 'Chairman': 5058, 'suggested': 5059, 'events': 5060, 'Government': 5061, 'privileged': 5062, '200': 5063, 'proverbial': 5064, 'fascinating': 5065, 'thoughts': 5066, 'prefer': 5067, 'devote': 5068, 'remarks': 5069, 'broader': 5070, 'refer': 5071, 'Morocco': 5072, 'predominantly': 5073, 'Moslem': 5074, 'non-Arab': 5075, 'non-Moslem': 5076, 'minorities': 5077, 'immediate': 5078, 'neighborhood': 5079, 'spite': 5080, 'upheaval': 5081, 'mass': 5082, 'happening': 5083, 'Sudan': 5084, 'massacring': 5085, 'Algeria': 5086, 'village': 5087, 'Algerians': 5088, 'invade': 5089, 'endanger': 5090, 'butcher': 5091, 'Egypt': 5092, 'poison': 5093, 'gas': 5094, 'Yemen': 5095, \"60's\": 5096, 'Assad': 5097, 'Father': 5098, 'El': 5099, 'Hamma': 5100, 'Syria': 5101, 'Libyan': 5102, 'blowing': 5103, 'Pan': 5104, 'Am': 5105, 'dysfunctional': 5106, 'league': 5107, 'Mauritania': 5108, 'Gulf': 5109, 'total': 5110, 'EU': 5111, 'expansion': 5112, 'combined': 5113, 'GDP': 5114, 'Netherlands': 5115, 'plus': 5116, 'Belgium': 5117, 'equal': 5118, 'California': 5119, 'Within': 5120, 'meager': 5121, 'gaps': 5122, 'poor': 5123, 'succeeding': 5124, 'corrupt': 5125, 'social': 5126, 'below': 5127, '150': 5128, 'Human': 5129, 'grotesque': 5130, 'Libya': 5131, 'Chair': 5132, 'Rights': 5133, 'commission': 5134, 'prepared': 5135, 'committee': 5136, 'intellectuals': 5137, 'auspices': 5138, 'books': 5139, 'translated': 5140, 'Greece': 5141, 'translates': 5142, 'publications': 5143, 'Birth': 5144, 'rates': 5145, 'decline': 5146, 'wealthy': 5147, 'advanced': 5148, 'cultures': 5149, 'creates': 5150, 'breeding': 5151, 'cruel': 5152, 'dictators': 5153, 'fanaticism': 5154, 'incitement': 5155, 'Civilization': 5156, 'Judaism': 5157, 'decent': 5158, 'devout': 5159, 'Moslems': 5160, 'develops': 5161, 'Islamophobia': 5162, 'accomplices': 5163, 'omission': 5164, 'applies': 5165, 'afraid': 5166, 'express': 5167, 'amplified': 5168, 'rampant': 5169, 'acknowledges': 5170, 'pillars': 5171, 'undeclared': 5172, '*': 5173, 'element': 5174, 'Suicide': 5175, 'invention': 5176, 'expression': 5177, 'lately': 5178, 'potent': 5179, 'psychological': 5180, 'direct': 5181, 'relatively': 5182, 'accidents': 5183, 'quantitatively': 5184, 'lethal': 5185, 'earthquakes': 5186, 'die': 5187, 'AIDS': 5188, 'Russians': 5189, 'murderers': 5190, 'fuss': 5191, 'killings': 5192, 'spectacular': 5193, 'frightening': 5194, 'dismembered': 5195, 'horrible': 5196, 'lifelong': 5197, 'injuries': 5198, 'detail': 5199, 'hysterical': 5200, 'tourism': 5201, 'industry': 5202, 'Bali': 5203, 'Turkey': 5204, 'undisputed': 5205, 'preventive': 5206, 'murderer': 5207, 'penetrated': 5208, 'constantly': 5209, 'improving': 5210, 'arrange': 5211, 'plane': 5212, 'explode': 5213, 'Who': 5214, 'crowded': 5215, 'detector': 5216, 'lines': 5217, 'counters': 5218, 'busy': 5219, 'Put': 5220, 'station': 5221, 'Spain': 5222, 'buses': 5223, 'Protect': 5224, 'movie': 5225, 'theaters': 5226, 'concert': 5227, 'halls': 5228, 'supermarkets': 5229, 'shopping': 5230, 'malls': 5231, 'hospitals': 5232, 'guards': 5233, 'hall': 5234, 'somewhat': 5235, 'vulnerability': 5236, 'strict': 5237, 'controls': 5238, 'eliminate': 5239, 'Money': 5240, 'cold': 5241, 'blooded': 5242, 'murderous': 5243, 'fanatic': 5244, 'beliefs': 5245, 'blown': 5246, 'politician': 5247, 'relative': 5248, 'influential': 5249, 'truly': 5250, 'supreme': 5251, 'fervor': 5252, 'benefits': 5253, 'Heaven': 5254, 'Instead': 5255, 'outcast': 5256, 'naive': 5257, 'retarded': 5258, 'incited': 5259, 'hotheads': 5260, 'delights': 5261, 'pay': 5262, 'handsomely': 5263, 'performed': 5264, 'despair': 5265, 'poorest': 5266, 'happens': 5267, 'desperate': 5268, 'continents': 5269, 'Desperation': 5270, 'reconnaissance': 5271, 'transportation': 5272, 'Bremmer': 5273, 'exploded': 5274, 'vicious': 5275, 'inhuman': 5276, 'cynical': 5277, 'funded': 5278, 'affluent': 5279, 'hunger': 5280, 'identical': 5281, 'pirates': 5282, 'seas': 5283, 'crucial': 5284, 'united': 5285, 'pyramid': 5286, 'arresting': 5287, 'dealer': 5288, 'tolerate': 5289, 'thrive': 5290, 'humble': 5291, 'trains': 5292, 'Istanbul': 5293, 'unity': 5294, 'Civilized': 5295, 'horror': 5296, 'absolutely': 5297, 'indispensable': 5298, 'wakes': 5299, 'ingredient': 5300, 'lies': 5301, 'Words': 5302, 'politicians': 5303, 'professional': 5304, 'norms': 5305, 'diplomacy': 5306, 'childish': 5307, 'deliberate': 5308, 'fabrications': 5309, 'heights': 5310, 'incredible': 5311, 'provocation': 5312, 'plot': 5313, 'Mouhamad': 5314, 'Said': 5315, 'Disinformation': 5316, 'tactic': 5317, 'preposterous': 5318, 'ridiculed': 5319, 'milieu': 5320, 'icon': 5321, 'jester': 5322, 'respectable': 5323, 'newspapers': 5324, 'prevent': 5325, 'credence': 5326, 'liars': 5327, 'anti-Semite': 5328, 'subtle': 5329, 'holocaust': 5330, 'temple': 5331, 'Jerusalem': 5332, 'occurrence': 5333, 'finance': 5334, 'dispatch': 5335, 'condemn': 5336, 'cameras': 5337, 'audience': 5338, 'routine': 5339, 'opposite': 5340, 'Arabic': 5341, 'Incitement': 5342, 'accompanied': 5343, 'pictures': 5344, 'mutilated': 5345, 'distort': 5346, 'Little': 5347, 'hatred': 5348, 'admiration': 5349, 'sets': 5350, 'tuned': 5351, 'soap': 5352, 'operas': 5353, 'recommend': 5354, 'demonstration': 5355, 'Berlin': 5356, 'carrying': 5357, 'banners': 5358, 'supporting': 5359, 'featuring': 5360, 'babies': 5361, 'dressed': 5362, 'defined': 5363, 'Arafat': 5364, 'activists': 5365, 'walks': 5366, 'restaurant': 5367, 'mid-day': 5368, 'eats': 5369, 'observes': 5370, 'eating': 5371, 'lunch': 5372, 'adjacent': 5373, 'tables': 5374, 'pays': 5375, 'blows': 5376, 'herself': 5377, 'rolling': 5378, 'martyr': 5379, 'activist': 5380, 'European': 5381, 'Dignitaries': 5382, 'bereaved': 5383, 'flows': 5384, 'actual': 5385, 'equips': 5386, 'sends': 5387, 'Orwellian': 5388, 'nomenclature': 5389, 'realize': 5390, 'emotional': 5391, 'infrastructure': 5392, 'Joseph': 5393, 'Goebbels': 5394, 'repeat': 5395, 'outperformed': 5396, 'successors': 5397, 'Huge': 5398, 'amounts': 5399, 'channeled': 5400, 'concentric': 5401, 'spheres': 5402, 'inner': 5403, 'circle': 5404, 'funds': 5405, 'hideouts': 5406, 'soft': 5407, 'circles': 5408, 'primarily': 5409, 'financed': 5410, 'regimes': 5411, 'Authority': 5412, 'safe': 5413, 'havens': 5414, 'wholesale': 5415, 'vendors': 5416, 'planners': 5417, 'preachers': 5418, 'welfare': 5419, 'feed': 5420, 'hungry': 5421, 'schooling': 5422, 'ignorance': 5423, 'operates': 5424, 'madrasas': 5425, 'inciting': 5426, 'electronic': 5427, 'printed': 5428, 'inferior': 5429, 'unthinkable': 5430, 'minimal': 5431, 'blaming': 5432, 'miseries': 5433, 'outer': 5434, 'donations': 5435, 'Governments': 5436, \"NGO's\": 5437, 'goals': 5438, 'noble': 5439, 'infested': 5440, 'exploited': 5441, 'victim': 5442, 'Saudis': 5443, 'financing': 5444, 'Figuratively': 5445, 'speaking': 5446, 'inwards': 5447, 'blackmail': 5448, 'horrifying': 5449, 'factor': 5450, 'Half': 5451, 'receptive': 5452, 'guaranteeing': 5453, 'blind': 5454, 'comfortably': 5455, 'camps': 5456, 'join': 5457, 'packaged': 5458, 'tours': 5459, 'hotspots': 5460, 'ski': 5461, 'Switzerland': 5462, 'Mrs.': 5463, 'Paris': 5464, 'bankrupt': 5465, 'typical': 5466, 'ringleader': 5467, 'Aksa': 5468, 'brigade': 5469, 'payment': 5470, 'couple': 5471, 'performing': 5472, 'retail': 5473, 'fourth': 5474, 'laws': 5475, 'civilized': 5476, 'liberties': 5477, 'fashioned': 5478, 'habits': 5479, 'respecting': 5480, 'sites': 5481, 'symbols': 5482, 'avoiding': 5483, 'mutilation': 5484, 'shields': 5485, 'Nazi': 5486, 'disregard': 5487, 'observe': 5488, 'student': 5489, 'science': 5490, 'debates': 5491, 'anti-democratic': 5492, 'winning': 5493, 'democratic': 5494, 'abolishing': 5495, 'Other': 5496, 'aspects': 5497, 'society': 5498, 'limitations': 5499, 'policeman': 5500, 'dealers': 5501, 'penalty': 5502, 'multiple': 5503, 'dilemmas': 5504, 'raid': 5505, 'priests': 5506, 'hostages': 5507, 'ambulance': 5508, 'strip': 5509, 'pretended': 5510, 'belly': 5511, 'standing': 5512, 'mental': 5513, 'arch-murderer': 5514, 'moves': 5515, 'location': 5516, 'dilemma': 5517, 'Suppose': 5518, 'Teheran': 5519, 'atrocity': 5520, 'responsibility': 5521, 'promising': 5522, 'interviews': 5523, 'condemnations': 5524, 'invite': 5525, 'functions': 5526, 'treat': 5527, 'dignitary': 5528, 'homework': 5529, 'figure': 5530, 'illusions': 5531, 'lawless': 5532, 'ice': 5533, 'hockey': 5534, 'sending': 5535, 'ballerina': 5536, 'skater': 5537, 'knock': 5538, 'heavyweight': 5539, 'boxer': 5540, 'chess': 5541, 'cannibals': 5542, 'handle': 5543, 'throw': 5544, 'stones': 5545, 'shoots': 5546, 'immunity': 5547, 'sheltered': 5548, 'royally': 5549, 'pretends': 5550, 'crooks': 5551, 'protection': 5552, 'criminals': 5553, 'repeating': 5554, 'temporary': 5555, 'evolution': 5556, 'adapted': 5557, 'punishment': 5558, 'rules': 5559, 'twilight': 5560, 'educate': 5561, 'eliminated': 5562, 'financial': 5563, 'starvation': 5564, 'organizing': 5565, 'elite': 5566, 'counter-propaganda': 5567, 'boycott': 5568, 'access': 5569, 'internet': 5570, 'Above': 5571, 'determination': 5572, 'Allow': 5573, 'depart': 5574, 'malignant': 5575, 'tumor': 5576, 'remove': 5577, 'surgically': 5578, 'starve': 5579, 'preventing': 5580, 'expanding': 5581, 'comment': 5582, 'justified': 5583, 'pre-war': 5584, 'map': 5585, 'Lebanon': 5586, 'colony': 5587, 'conquest': 5588, 'territories': 5589, 'unfriendly': 5590, 'encircled': 5591, 'republics': 5592, 'surprising': 5593, 'incite': 5594, 'uprising': 5595, 'plan': 5596, 'encircle': 5597, 'resulting': 5598, 'ambitions': 5599, 'ideology': 5600, 'supremacy': 5601, 'ruthless': 5602, 'execute': 5603, 'elaborate': 5604, 'traces': 5605, 'Embassies': 5606, 'develop': 5607, 'conservatives': 5608, 'virtuoso': 5609, 'cop': 5610, 'versus': 5611, 'sponsors': 5612, 'Hezbollah': 5613, 'Uzbekistan': 5614, 'consortium': 5615, 'players': 5616, 'trade': 5617, 'appease': 5618, 'refuse': 5619, 'signals': 5620, 'dry': 5621, 'conglomerate': 5622, 'pointless': 5623, 'enterprises': 5624, 'collaborate': 5625, 'beautifully': 5626, 'fertile': 5627, 'monitor': 5628, 'finances': 5629, 'relief': 5630, 'react': 5631, 'forceful': 5632, 'decisively': 5633, 'naivety': 5634, 'surrender': 5635, 'yielded': 5636, 'matters': 5637, 'won': 5638, 'surely': 5639, 'costly': 5640, 'expelling': 5641, 'forbidding': 5642, 'veils': 5643, 'functioning': 5644, 'judicial': 5645, 'equality': 5646, 'ideas': 5647, 'racial': 5648, 'defamation': 5649, 'avoidance': 5650, 'regarding': 5651, 'worship': 5652, 'inflammatory': 5653, 'carefully': 5654, 'transition': 5655, 'paving': 5656, 'prevail': 5657, 'takes': 5658, 'landscape': 5659, 'victory': 5660, 'understandable': 5661, 'recoil': 5662, 'wars': 5663, 'horrors': 5664, 'cost': 5665, 'additional': 5666, 'tide': 5667, 'associated': 5668, 'faction': 5669, 'Vanguards': 5670, 'Conquest': 5671, 'seeking': 5672, 'recreate': 5673, 'mecca': 5674, 'struggle': 5675, 'acquired': 5676, 'anthrax': 5677, 'strain': 5678, 'Veterinary': 5679, 'Medical': 5680, 'Diagnostic': 5681, 'Lab': 5682, 'senior': 5683, 'abroad': 5684, 'islamists': 5685, 'available': 5686, 'requested': 5687, 'Anthrax': 5688, 'Means': 5689, 'Motive': 5690, 'Modus': 5691, 'Operandi': 5692, 'Opportunity': 5693, 'Homeland': 5694, 'disclosed': 5695, 'Atta': 5696, 'Zacarias': 5697, 'Moussaoui': 5698, 'inquiries': 5699, 'cropdusters': 5700, 'contemplated': 5701, 'dispersing': 5702, 'included': 5703, 'rumor': 5704, 'setback': 5705, 'KSM': 5706, 'Rawalpindi': 5707, 'Panel': 5708, 'experts': 5709, 'concluded': 5710, 'restrained': 5711, 'difficulties': 5712, '9/11': 5713, 'Staff': 5714, 'ambitious': 5715, 'advances': 5716, 'produce': 5717, 'prior': 5718, 'Director': 5719, 'Tenet': 5720, '9': 5721, 'Spring': 5722, 'named': 5723, 'Shukrijumah': 5724, 'Jafar': 5725, 'Pilot': 5726, 'casing': 5727, 'Photographs': 5728, 'computer': 5729, 'disc': 5730, 'locks': 5731, 'passengers': 5732, 'bulletin': 5733, 'surveillance': 5734, 'disperse': 5735, 'mailings': 5736, 'bureaucratically': 5737, 'impolite': 5738, 'contest': 5739, 'lone': 5740, 'scientist': 5741, 'argued': 5742, 'Fall': 5743, 'warning': 5744, 'Princeton': 5745, 'islamist': 5746, 'scholar': 5747, 'Lewis': 5748, 'explained': 5749, 'disagree': 5750, 'innocents': 5751, 'sanctioned': 5752, 'biochemical': 5753, 'Prophet': 5754, 'guidance': 5755, 'Scheuer': 5756, 'analyst': 5757, 'warn': 5758, 'letters': 5759, 'branches': 5760, 'D.C.': 5761, 'symbolic': 5762, 'detention': 5763, 'sheik': 5764, 'Abdel': 5765, 'Trade': 5766, '1993': 5767, 'Handwritten': 5768, 'laptop': 5769, '#': 5770, 'production': 5771, 'spray': 5772, 'dryer': 5773, 'addressed': 5774, 'recruitment': 5775, 'expertise': 5776, 'Salama': 5777, 'Mabruk': 5778, 'disk': 5779, 'confiscated': 5780, 'Azerbaijan': 5781, 'underestimate': 5782, 'research': 5783, 'stages': 5784, '5th': 5785, 'Capitol': 5786, 'Others': 5787, 'airliners': 5788, 'directed': 5789, '31': 5790, 'indicating': 5791, 'crop': 5792, 'dusting': 5793, 'Ramzi': 5794, 'Binalshibh': 5795, '14,000': 5796, 'conspirator': 5797, 'mission': 5798, 'dusters': 5799, 'biochemist': 5800, 'Malaysian': 5801, 'Yazid': 5802, 'Sufaat': 5803, 'sharpest': 5804, 'shed': 5805, 'superiors': 5806, 'unreliable': 5807, 'filing': 5808, 'Jew': 5809, 'sympathizer': 5810, 'Hambali': 5811, 'supervised': 5812, 'Hindi': 5813, 'Britani': 5814, 'NYC': 5815, 'studied': 5816, 'virulent': 5817, 'Ames': 5818, 'guide': 5819, 'Dr.': 5820, 'quest': 5821, 'weaponize': 5822, 'confidante': 5823, 'prison': 5824, 'Emails': 5825, 'Atef': 5826, 'USAMRIID': 5827, 'koran': 5828, 'instructed': 5829, 'jihadist': 5830, 'crusader': 5831, 'enemies': 5832, 'OBL': 5833, 'Wall': 5834, 'Street': 5835, 'Journal': 5836, 'contains': 5837, 'memo': 5838, 'seek': 5839, 'talent': 5840, 'beneficial': 5841, 'easy': 5842, 'specialists': 5843, 'greatly': 5844, 'Christmas': 5845, 'audiotape': 5846, 'orange': 5847, 'alert': 5848, 'yellow': 5849, 'brigades': 5850, 'banner': 5851, 'paradise': 5852, 'somewhere': 5853, 'spotted': 5854, 'Wherever': 5855, 'traceable': 5856, 'profilers': 5857, 'draft': 5858, 'Sheik': 5859, 'Bojinka': 5860, 'threatened': 5861, 'dates': 5862, 'Use': 5863, 'detentions': 5864, 'Patrick': 5865, 'Retired': 5866, 'Assistant': 5867, 'Secretary': 5868, 'Analysis': 5869, 'testified': 5870, 'interrogations': 5871, 'nonconventional': 5872, 'notably': 5873, 'WTC': 5874, 'briefing': 5875, 'steps': 5876, 'raw': 5877, 'seed': 5878, 'product': 5879, 'unweaponized': 5880, 'Moro': 5881, 'MILF': 5882, 'obtaining': 5883, 'intended': 5884, 'shura': 5885, 'sworn': 5886, 'lengthy': 5887, 'confession': 5888, 'attorney': 5889, 'resort': 5890, 'possessed': 5891, 'extradition': 5892, 'associate': 5893, 'recipe': 5894, 'unprocessed': 5895, 'weight': 5896, 'accounts': 5897, 'distribution': 5898, 'transfers': 5899, 'Significantly': 5900, 'individual': 5901, 'quarter': 5902, 'century': 5903, 'contacted': 5904, 'necessarily': 5905, 'copy': 5906, 'Ft.': 5907, 'Detrick': 5908, 'Senator': 5909, 'Leahy': 5910, 'Congressional': 5911, 'collected': 5912, 'MSNBC': 5913, 'relying': 5914, 'spokesperson': 5915, 'narrowed': 5916, 'pool': 5917, 'labs': 5918, 'match': 5919, 'overseas': 5920, 'techniques': 5921, 'efficient': 5922, 'bioweaponeer': 5923, 'Kenneth': 5924, 'Alibek': 5925, 'optimal': 5926, 'method': 5927, 'electrostatic': 5928, 'dominance': 5929, 'spores': 5930, 'trillion': 5931, 'spore': 5932, 'concentration': 5933, 'clumps': 5934, '40': 5935, 'microns': 5936, 'Spores': 5937, 'inhalable': 5938, 'extraordinary': 5939, 'simpler': 5940, 'achieve': 5941, 'grams': 5942, 'industrial': 5943, 'processing': 5944, 'sponsored': 5945, 'lab': 5946, 'mistaken': 5947, 'theories': 5948, 'sponsorship': 5949, 'indicated': 5950, 'Dugway': 5951, 'undermines': 5952, 'liberals': 5953, 'Steve': 5954, 'Hatfill': 5955, 'biodefense': 5956, 'useful': 5957, 'USDA': 5958, 'employee': 5959, 'Johnelle': 5960, 'Bryant': 5961, 'sensational': 5962, 'purchasing': 5963, 'retrofitting': 5964, 'cropduster': 5965, 'interrogators': 5966, 'manufacturing': 5967, 'details': 5968, 'aerial': 5969, 'dispersal': 5970, 'consistent': 5971, 'opportunity': 5972, 'met': 5973, 'plotters': 5974, 'hijackers': 5975, 'Jemaah': 5976, 'Islamiah': 5977, 'JI': 5978, 'company': 5979, 'Green': 5980, 'Laboratory': 5981, 'Medicine': 5982, 'items': 5983, 'symbolizes': 5984, 'holy': 5985, 'manual': 5986, 'stayed': 5987, 'condominium': 5988, 'lessons': 5989, 'marketing': 5990, 'Infocus': 5991, 'Technologies': 5992, '35,000': 5993, 'traveled': 5994, 'Brigade': 5995, 'realizing': 5996, 'perceiving': 5997, 'Filipino': 5998, 'bragging': 5999, 'manipulate': 6000, 'thwarted': 6001, 'logistical': 6002, 'Various': 6003, 'doctors': 6004, 'Qadoos': 6005, 'bacteriologist': 6006, 'Aafia': 6007, 'Siddiqui': 6008, 'PhD': 6009, 'Karachi': 6010, 'Microbiologist': 6011, 'harboring': 6012, 'fugitives': 6013, 'cardiac': 6014, 'granted': 6015, 'pre-arrest': 6016, 'bail': 6017, 'Yet': 6018, 'stipend': 6019, 'officially': 6020, 'low': 6021, 'IQ': 6022, 'Brandeis': 6023, 'neurology': 6024, 'nabbed': 6025, 'Understandably': 6026, 'Amerithrax': 6027, 'confidential': 6028, 'rarely': 6029, 'grant': 6030, 'manhunt': 6031, 'Van': 6032, 'Harp': 6033, 'classified': 6034, 'releasing': 6035, 'Attorney': 6036, 'Ashcroft': 6037, 'Mueller': 6038, 'Her': 6039, 'Ismat': 6040, 'grandchildren': 6041, 'minicab': 6042, 'uncle': 6043, 'detained': 6044, 'Chemical': 6045, 'Wire': 6046, 'Group': 6047, 'lawyer': 6048, 'advises': 6049, 'airfare': 6050, 'tickets': 6051, 'heard': 6052, 'translation': 6053, 'phrase': 6054, 'english': 6055, 'chemicals': 6056, 'appear': 6057, 'pursuit': 6058, 'Floridian': 6059, 'Adnan': 6060, 'assisting': 6061, 'nickname': 6062, 'DOJ': 6063, 'commercial': 6064, 'Summer': 6065, 'Hamilton': 6066, 'Maati': 6067, 'faulty': 6068, 'analysis': 6069, 'preconceptions': 6070, 'testimony': 6071, 'millennium': 6072, 'Ressam': 6073, 'hijacker': 6074, 'Alhaznawi': 6075, 'cutaneous': 6076, 'bumping': 6077, 'suitcase': 6078, 'relating': 6079, 'Prague': 6080, 'conclusion': 6081, 'obtained': 6082, 'Ken': 6083, 'assisted': 6084, 'mobile': 6085, 'biolab': 6086, 'foisted': 6087, 'Curveball': 6088, 'divined': 6089, 'design': 6090, 'entourage': 6091, 'birthday': 6092, 'Mukhabarat': 6093, 'rejected': 6094, 'suggestion': 6095, 'preferring': 6096, 'pursue': 6097, 'concept': 6098, 'scientists': 6099, 'code': 6100, 'Charlie': 6101, 'Alpha': 6102, 'Kay': 6103, 'survey': 6104, 'hunt': 6105, 'WMD': 6106, 'innovations': 6107, 'milling': 6108, 'drying': 6109, 'motive': 6110, 'Senators': 6111, 'Daschle': 6112, 'tending': 6113, 'simplistically': 6114, 'receive': 6115, 'appropriations': 6116, 'pursuant': 6117, 'prevented': 6118, 'achieving': 6119, 'interferes': 6120, 'expressed': 6121, 'sentiment': 6122, 'exchange': 6123, 'plotter': 6124, 'Yousef': 6125, 'Judiciary': 6126, 'overseeing': 6127, 'Appropriations': 6128, 'Subcommittee': 6129, 'blanket': 6130, 'waiver': 6131, 'lift': 6132, 'pursuing': 6133, 'imprisonment': 6134, 'Mubarak': 6135, 'foremost': 6136, 'height': 6137, 'sentence': 6138, 'Albanian': 6139, 'returnees': 6140, 'retrial': 6141, 'debt': 6142, 'loan': 6143, 'guarantees': 6144, 'pale': 6145, 'Knights': 6146, 'Banner': 6147, 'secular': 6148, 'somehow': 6149, 'creation': 6150, 'Anwar': 6151, 'Sadat': 6152, 'Camp': 6153, 'treaty': 6154, 'underscore': 6155, 'liberal': 6156, 'democrats': 6157, 'profile': 6158, 'overlook': 6159, 'uses': 6160, 'domestic': 6161, 'educated': 6162, 'AMI': 6163, 'publisher': 6164, 'Enquirer': 6165, 'goofy': 6166, 'Jennifer': 6167, 'Lopez': 6168, 'enclosing': 6169, 'proposing': 6170, 'Disease': 6171, 'Control': 6172, 'employees': 6173, 'Leonard': 6174, 'containing': 6175, 'Sun': 6176, 'Bobby': 6177, 'Bender': 6178, 'recalls': 6179, 'modus': 6180, 'operandi': 6181, 'signature': 6182, 'offices': 6183, 'Gamaa': 6184, 'Islamiya': 6185, 'treatment': 6186, 'imprisoned': 6187, 'casualty': 6188, 'outstanding': 6189, 'rewards': 6190, 'explanation': 6191, 'ten': 6192, 'mailed': 6193, 'Sound': 6194, 'Leavenworth': 6195, 'defendant': 6196, 'Parole': 6197, 'Officer': 6198, 'mid-February': 6199, 'Along': 6200, 'considerable': 6201, 'jail': 6202, 'tension': 6203, 'controversial': 6204, 'Feith': 6205, 'summarized': 6206, 'purported': 6207, 'assistance': 6208, 'Hayat': 6209, 'deadly': 6210, 'missive': 6211, 'Brian': 6212, 'Jenkins': 6213, 'sender': 6214, 'purporting': 6215, 'cyanide': 6216, 'Zealand': 6217, 'ingredients': 6218, 'nerve': 6219, 'chapter': 6220, 'Poisonous': 6221, 'Letter': 6222, 'Eagle': 6223, 'stamp': 6224, 'green': 6225, 'hearts': 6226, 'birds': 6227, 'codes': 6228, 'masterminds': 6229, 'Jenny': 6230, 'representing': 6231, 'symbolism': 6232, 'Birds': 6233, 'video': 6234, 'Martyrs': 6235, 'FAQ': 6236, 'Azzam': 6237, 'Publications': 6238, 'Hearts': 6239, 'refers': 6240, 'mailer': 6241, 'Greendale': 6242, 'revealing': 6243, 'correspondence': 6244, 'color': 6245, 'perp': 6246, 'cute': 6247, 'naming': 6248, 'river': 6249, 'i.e.': 6250, 'Cairo': 6251, 'announcing': 6252, 'merger': 6253, 'Darunta': 6254, 'recruits': 6255, 'washed': 6256, 'Hadith': 6257, 'Messenger': 6258, 'Allah': 6259, 'wherever': 6260, 'Paradise': 6261, 'seldom': 6262, 'relates': 6263, 'sleepers': 6264, 'spadework': 6265, 'fundraising': 6266, 'charitable': 6267, 'causes': 6268, 'alias': 6269, 'sergeant': 6270, 'Whatever': 6271, 'persuasion': 6272, 'deserve': 6273, 'sufficient': 6274, 'Media': 6275, 'approximation': 6276, 'Second': 6277, 'hindsight': 6278, 'Efrem': 6279, 'Zimbalist': 6280, 'Jr.': 6281, 'striking': 6282, 'appropriate': 6283, 'balance': 6284, 'exhausted': 6285, 'accusing': 6286, 'Stephen': 6287, 'dubious': 6288, 'suspicion': 6289, 'founded': 6290, 'premises': 6291, 'reliable': 6292, 'guilt': 6293, 'fixation': 6294, 'rumored': 6295, 'stemmed': 6296, 'careers': 6297, 'staff': 6298, 'predisposition': 6299, 'winger': 6300, '7': 6301, '8': 6302, 'pending': 6303, 'libel': 6304, 'uncertain': 6305, 'suit': 6306, 'columnist': 6307, 'Nicholas': 6308, 'Kristof': 6309, 'agreed': 6310, 'apparent': 6311, 'compromise': 6312, 'permit': 6313, 'discovery': 6314, 'proceed': 6315, 'file': 6316, 'Answer': 6317, 'Complaint': 6318, 'Theory': 6319, 'ironically': 6320, 'coincidental': 6321, 'adding': 6322, 'regrettable': 6323, 'leaks': 6324, 'leak': 6325, 'enthusiasm': 6326, 'dropped': 6327, 'proves': 6328, 'Berry': 6329, 'residences': 6330, 'prove': 6331, 'gasp': 6332, 'insider': 6333, 'searches': 6334, 'excluding': 6335, 'Maureen': 6336, 'Andre': 6337, 'curves': 6338, 'FX': 6339, 'inflation': 6340, 'CPI': 6341, 'PPI': 6342, 'valuation': 6343, 'determine': 6344, 'index': 6345, 'Typically': 6346, 'savings': 6347, 'eliminating': 6348, 'nonessential': 6349, 'specifics': 6350, 'Bolivia': 6351, 'Jamaica': 6352, 'Guatemala': 6353, 'Columbia': 6354, 'Puerto': 6355, 'Rico': 6356, 'data': 6357, 'Panama': 6358, 'Brazil': 6359, 'Euro': 6360, 'Underwriting': 6361, 'IV': 6362, 'Cindy': 6363, 'revalue': 6364, 'Philip': 6365, 'regrets': 6366, 'commitments': 6367, 'Kaminski': 6368, 'Dear': 6369, 'inviting': 6370, 'Risk': 6371, 'Australia': 6372, 'Sydney': 6373, 'aiming': 6374, 'programme': 6375, 'Hong': 6376, 'Kong': 6377, 'Tel': 6378, '+852': 6379, '2545': 6380, '2710': 6381, 'Kind': 6382, 'Annesley': 6383, 'Conference': 6384, 'Producer': 6385, 'Waters': 6386, '+44': 6387, '7484': 6388, '9866': 6389, '9800': 6390, 'www.risk-conferences.com/risk2001aus': 6391, 'Beth': 6392, 'reservations': 6393, 'Round': 6394, 'Table': 6395, '5/18': 6396, 'Sullivan': 6397, 'Steak': 6398, '6:30': 6399, 'ling': 6400, 'overdue': 6401, 'Paula': 6402, 'Will': 6403, 'drive': 6404, 'FYI': 6405, 'Anne': 6406, 'Sarah': 6407, 'mail': 6408, 'Roberts': 6409, 'hire': 6410, 'Sara': 6411, 'Woody': 6412, 'MBA': 6413, 'grad': 6414, 'Rice': 6415, 'compared': 6416, 'title': 6417, 'admin': 6418, 'coordinator': 6419, 'honestly': 6420, 'Admin': 6421, \"coordinator's\": 6422, 'administrative': 6423, 'compare': 6424, 'duties': 6425, 'Sr.': 6426, 'Spec.': 6427, 'Parkhill': 6428, 'Sevil': 6429, 'scope': 6430, 'responsibilities': 6431, 'broad': 6432, 'specialist': 6433, 'salary': 6434, 'range': 6435, '33': 6436, '66': 6437, 'K': 6438, 'advise': 6439, 'Clayton': 6440, 'appreciate': 6441, 'Martin': 6442, 'box': 6443, 'wonderfully': 6444, 'keys': 6445, 'nice': 6446, 'Enron': 6447, 'refund': 6448, 'server': 6449, 'Iris': 6450, 'Congratulations': 6451, 'paperwork': 6452, 'unique': 6453, 'opportunities': 6454, 'advisors': 6455, 'consisted': 6456, 'mutual': 6457, 'fund': 6458, 'invest': 6459, 'Home': 6460, 'Depot': 6461, 'Coke': 6462, 'biased': 6463, 'valuable': 6464, 'equity': 6465, 'hedge': 6466, 'growth': 6467, 'correlation': 6468, 'S@P': 6469, 'diversification': 6470, 'dinner': 6471, 'markets': 6472, 'Gapinski': 6473, 'Account': 6474, 'Vice': 6475, 'Emery': 6476, 'Financial': 6477, 'PaineWebber': 6478, 'Inc.': 6479, '713-654-0365': 6480, '800-553-3119': 6481, 'x365': 6482, 'Fax': 6483, '713-654-1281': 6484, 'Cell': 6485, '281-435-0295': 6486, 'Appreciate': 6487, 'ENE': 6488, 'bound': 6489, 'forgo': 6490, '%': 6491, 'premium': 6492, 'lighten': 6493, 'stategy': 6494, 'approved': 6495, 'sell': 6496, 'unexercised': 6497, 'vested': 6498, 'pullback': 6499, 'stock': 6500, 'bounce': 6501, '@': 6502, '75': 6503, '73': 6504, 'Call': 6505, 'Notice': 6506, 'Regarding': 6507, 'Entry': 6508, 'transmit': 6509, 'orders': 6510, 'instructions': 6511, 'transmitted': 6512, 'Privacy': 6513, 'Confidentiality': 6514, 'review': 6515, 'content': 6516, 'communications': 6517, 'cares': 6518, '??????': 6519, 'Sean.Cooper@ElPaso.com': 6520, 'rescheduled': 6521, 'Your': 6522, 'Beau': 6523, 'invited': 6524, 'thursday': 6525, 'NYMEX': 6526, 'cocktail': 6527, 'hour': 6528, 'oh': 6529, 'agenda': 6530, \"Tuesday's\": 6531, '3:30': 6532, 'Houston': 6533, 'completely': 6534, 'update': 6535, 'weekly': 6536, \"Monday's\": 6537, '???????????????': 6538, 'employment': 6539, 'respond': 6540, 'Lavorato': 6541, 'attached': 6542, 'allocation': 6543, 'amongst': 6544, 'categories': 6545, 'Real': 6546, 'Time': 6547, 'Traders': 6548, 'presentation': 6549, 'tomorrow': 6550, 'x': 6551, 'comparisons': 6552, 'lose': 6553, '65': 6554, 'k': 6555, 'hiring': 6556, 'math': 6557, 'stretch': 6558, 'ASAP': 6559, 'reviewing': 6560, 'renewal': 6561, 'Laurent': 6562, 'communicated': 6563, 'ask': 6564, 'Current': 6565, 'Salary': 6566, '47,500': 6567, 'Job': 6568, 'Specialist': 6569, 'YE': 6570, 'PRC': 6571, 'Rating': 6572, 'Satisfactory': 6573, 'Original': 6574, 'Proposition': 6575, 'Base': 6576, 'Year': 6577, 'Agreement': 6578, 'Revised': 6579, 'Proposal': 6580, '55': 6581, 'Case': 6582, 'approximately': 6583, 'consideration': 6584, 'rated': 6585, 'satisfactory': 6586, 'room': 6587, 'Secondly': 6588, 'performers': 6589, 'respectively': 6590, 'eg.': 6591, 'Thomas': 6592, 'Jason': 6593, 'Choate': 6594, 'Todd': 6595, 'DeCook': 6596, 'Makkai': 6597, 'Listing': 6598, 'Maria': 6599, 'Valdes': 6600, '62,500': 6601, '55,008': 6602, '60,008': 6603, '42,008': 6604, 'Oxley': 6605, 'Transmission': 6606, 'Expansion': 6607, 'Systems': 6608, 'Transition': 6609, 'Feb.': 6610, 'Florida': 6611, 'OVERVIEW': 6612, 'regulatory': 6613, 'challenges': 6614, 'electric': 6615, 'transmission': 6616, 'capacity': 6617, 'demands': 6618, 'competitive': 6619, 'FERC': 6620, 'RTO': 6621, 'initiatives': 6622, 'parameters': 6623, 'solutions': 6624, 'technology': 6625, 'models': 6626, 'analyze': 6627, 'proposals': 6628, 'pricing': 6629, 'alternatives': 6630, 'traditional': 6631, 'methods': 6632, 'legislative': 6633, 'ensuring': 6634, 'grid': 6635, 'brochure': 6636, 'Workshops': 6637, 'clicking': 6638, 'link': 6639, '<': 6640, 'http://www.euci.com/pdf/trans_expn.pdf': 6641, '>': 6642, '<<': 6643, '>>': 6644, 'Electricity': 6645, 'Market': 6646, 'Design': 6647, 'Georgia': 6648, '969': 6649, 'Essie': 6650, 'Sonya': 6651, '07/30/2001': 6652, '05:17': 6653, 'Embedded': 6654, 'Picture': 6655, 'Device': 6656, 'Independent': 6657, 'Bitmap': 6658, 'WHO': 6659, 'WHAT': 6660, 'Happy': 6661, 'Hour': 6662, 'Suarez': 6663, 'WHEN': 6664, 'Today': 6665, 'pm': 6666, 'WHERE': 6667, 'Porch': 6668, '217': 6669, 'Gray': 6670, 'St.': 6671, '713': 6672, '571-9571': 6673, 'WHY': 6674, 'EBS': 6675, 'NOT': 6676, 'Leon': 6677, 'xferring': 6678, 'Co.': 6679, '1691': 6680, 'Regards': 6681, 'Vicsandra': 6682, 'decisions': 6683, 'helpful': 6684, 'Patty': 6685, 'Corporate': 6686, 'Tax': 6687, 'x35172': 6688, 'EB': 6689, '1774': 6690, 'red': 6691, 'companies': 6692, 'indication': 6693, 'rank': 6694, 'entities': 6695, 'assigned': 6696, '18T': 6697, 'EI': 6698, 'Operations': 6699, 'LLC': 6700, 'entity': 6701, 'TIS': 6702, 'SAP': 6703, 'Hyperion': 6704, 'income': 6705, 'liabilities': 6706, 'corporate': 6707, 'sheet': 6708, 'inactive': 6709, '86M': 6710, 'Net': 6711, 'Works': 6712, 'MTM': 6713, 'Per': 6714, 'financials': 6715, 'expenses': 6716, 'reorg': 6717, '80Y': 6718, 'Broadband': 6719, 'Acquisition': 6720, 'Inc': 6721, 'acquistion': 6722, 'WarpSpeed': 6723, 'Communications': 6724, '83N': 6725, 'dissolved': 6726, 'completion': 6727, 'Company': 6728, 'amount': 6729, 'I/S': 6730, '1579': 6731, 'Division': 6732, '17H': 6733, 'Networks': 6734, 'Holding': 6735, 'Administrative': 6736, 'Companies': 6737, 'Same': 6738, 'EPI': 6739, 'Set': 6740, 'centralize': 6741, 'Broke': 6742, '1179': 6743, 'Commodity': 6744, 'Richards': 6745, 'Mary': 6746, 'Fischer': 6747, '1307': 6748, 'EBIC': 6749, 'Apache': 6750, 'Rolls': 6751, 'Cherokee': 6752, 'Finance': 6753, 'VOF': 6754, 'CFC': 6755, 'Glen': 6756, 'Walloch': 6757, 'Walker': 6758, '1689': 6759, 'Ventures': 6760, 'Branom': 6761, 'Analyst': 6762, '345-8702': 6763, 'leon.branom@enron.com': 6764, 'Virginia': 6765, 'Hello': 6766, 'Cross': 6767, 'Signac': 6768, 'impressionist': 6769, 'lithograph': 6770, 'gallery': 6771, 'Weston': 6772, 'Lichtenstein': 6773, 'hanging': 6774, 'magnificent': 6775, 'Appel': 6776, 'Actually': 6777, 'depending': 6778, 'listed': 6779, 'favorites': 6780, 'purchase': 6781, 'numbered': 6782, 'artist': 6783, 'outrageously': 6784, 'expensive': 6785, 'Rothko': 6786, 'Kline': 6787, 'Bonnard': 6788, 'colorful': 6789, 'Lautrec': 6790, 'Suerat': 6791, 'lithos': 6792, 'Braque': 6793, 'Arp': 6794, 'Rouault': 6795, 'Modrian': 6796, 'Motherwell': 6797, 're-looking': 6798, 'Picasso': 6799, 'cubist': 6800, '1920s': 6801, 'technique': 6802, 'wa': 6803, 'pronunciation': 6804, 'Vs': 6805, 'proper': 6806, 'spelling': 6807, 'sale': 6808, 'galleries': 6809, 'SF': 6810, 'Matisse': 6811, 'burner': 6812, 'mike': 6813, 'Huskers': 6814, 'drool': 6815, 'Sooners': 6816, 'Rice@ENRON': 6817, 'COMMUNICATIONS': 6818, '01/19/2001': 6819, '09:14': 6820, 'AM': 6821, 'McConnell@ECT': 6822, '01/19/01': 6823, '07:55': 6824, 'm': 6825, '07:24': 6826, 'postpone': 6827, 'Presentation': 6828, 'sunday': 6829, 'cancel': 6830, 'PS': 6831, 'bowl': 6832, 'games': 6833, 'galleryfurniture.com': 6834, 'Shreveport': 6835, '))': 6836, 'realized': 6837, 'sick': 6838, 'college': 6839, 'fan': 6840, 'intial': 6841, 'document': 6842, 'additions': 6843, 'wording': 6844, 'calendars': 6845, 'Too': 6846, 'Compaq': 6847, 'San': 6848, 'Antonio': 6849, 'wow': 6850, 'restaurants': 6851, 'Ric': 6852, 'Menger': 6853, 'Mann@ENRON': 6854, '09/20/2000': 6855, '04:18': 6856, 'Francis': 6857, 'TRY': 6858, '445': 6859, 'ENA': 6860, 'orientation': 6861, '430': 6862, '27': 6863, 'vacation': 6864, 'Francisco': 6865, 'Enterprise': 6866, 'spouses': 6867, 'Figures': 6868, 'fyi': 6869, 'wrap': 6870, 'LOI': 6871, 'licensed': 6872, 'Fuel': 6873, 'equipment': 6874, 'intend': 6875, 'manufacturer': 6876, 'generic': 6877, 'attach': 6878, 'Development': 6879, 'agreeing': 6880, 'substantially': 6881, 'optionality': 6882, 'bug': 6883, 'bite': 6884, 'backside': 6885, 'ONE': 6886, 'EPC': 6887, 'contract': 6888, 'confidentiality': 6889, 'Bart': 6890, 'handy': 6891, 'speed': 6892, 'Forget': 6893, 'project': 6894, '170': 6895, 'paid': 6896, 'expecting': 6897, 'attributable': 6898, 'CRRA': 6899, 'FuelCell': 6900, 'meaningful': 6901, 'listing': 6902, 'ONSI': 6903, 'Hence': 6904, 'attachment': 6905, 'mark': 6906, 'assumption': 6907, 'Based': 6908, 'reacts': 6909, 'ultimate': 6910, 'Performance': 6911, 'tests': 6912, 'Remarkably': 6913, 'Sent': 6914, 'Ben': 6915, 'Jacoby@ECT': 6916, 'Mom': 6917, 'tommorow': 6918, 'Chris': 6919, 'beers': 6920, 'McDermott': 6921, 'Michael.McDermott@spectrongroup.com': 6922, '06/01/2000': 6923, '06:00:56': 6924, 'Yo': 6925, 'Mama': 6926, '`s': 6927, 'fat': 6928, 'mama': 6929, 'hauls': 6930, 'ass': 6931, 'trips': 6932, 'dances': 6933, 'skip': 6934, 'diagnosed': 6935, 'disease': 6936, 'mayonnaise': 6937, 'aspirin': 6938, '<-': 6939, 'winner': 6940, 'cereal': 6941, 'lifeguard': 6942, 'zoo': 6943, 'elephants': 6944, 'peanuts': 6945, 'graduation': 6946, 'photograph': 6947, 'license': 6948, 'pants': 6949, 'driveway': 6950, 'pack': 6951, 'Maximum': 6952, 'Occupancy': 6953, '240': 6954, 'Patrons': 6955, 'OR': 6956, 'milk': 6957, 'carton': 6958, 'Levis': 6959, '501': 6960, 'jeans': 6961, 'wears': 6962, 'Levi`s': 6963, '1002`s': 6964, 'elevator': 6965, 'HAS': 6966, 'silver': 6967, 'shovel': 6968, 'mouth': 6969, 'orbiting': 6970, 'shade': 6971, ':-)': 6972, 'Done': 6973, 'Expect': 6974, 'tonight': 6975, 'Poll': 6976, 'co-workers': 6977, 'cd': 6978, 'waste': 6979, 'arthritis': 6980, 'formality': 6981, 'Microwave': 6982, 'fixed': 6983, 'Jai': 6984, 'Hawker': 6985, '974-6721': 6986, 'forget': 6987, 'model': 6988, 'Kathy': 6989, 'forgot': 6990, 'Thanx': 6991, 'reminder': 6992, 'currency': 6993, 'Calgary': 6994, 'reply': 6995, 'Peters': 6996, 'Give': 6997, 'NX3': 6998, 'NX1': 6999, '11:30': 7000, 'CD': 7001, '3-1663': 7002, 'deed': 7003, 'Find': 7004, 'resume': 7005, 'Hopefully': 7006, 'spel': 7007, 'anyting': 7008, 'incorrectly': 7009, 'worker': 7010, 'afternoon': 7011, 'Kelowna': 7012, 'golfing': 7013, 'Cheers': 7014, 'resond': 7015, 'Dawn': 7016, 'Corp.': 7017, 'Suite': 7018, '1100': 7019, '70': 7020, 'Toronto': 7021, 'Ontario': 7022, 'M5J': 7023, '1S9': 7024, '416-865-3700': 7025, 'DeVries': 7026, '416-865-3703': 7027, 'Manager': 7028, '416-865-3704': 7029, 'Attached': 7030, 'forecast': 7031, 'X': 7032, 'NWP': 7033, 'PGT': 7034, 'Sushi': 7035, 'Ryan': 7036, 'Watt': 7037, '26/09/2000': 7038, '14:14': 7039, 'Fernley': 7040, 'Sally': 7041, 'Off': 7042, 'Jackie': 7043, 'Gentle': 7044, 'page': 7045, 'communication': 7046, 'fundamental': 7047, 'standards': 7048, 'summary': 7049, 'introduce': 7050, 'inclusion': 7051, 'Globalflash': 7052, 'newsletter': 7053, 'Late': 7054, 'sounds': 7055, 'Meagan': 7056, 'Charity': 7057, 'dance': 7058, 'Bearkadette': 7059, 'Ball': 7060, 'winter': 7061, 'Cotillion': 7062, 'row': 7063, 'Sunday': 7064, 'laundry': 7065, '!!': 7066, 'Hi': 7067, 'Talked': 7068, 'shower': 7069, 'grand': 7070, 'mid-January': 7071, 'sound': 7072, 'Mother': 7073, 'timing': 7074, 'delivery': 7075, 'intent': 7076, 'introduction': 7077, 'applied': 7078, 'commodity': 7079, 'engage': 7080, 'worldwide': 7081, 'professionals': 7082, 'mitigated': 7083, 'management': 7084, 'consistency': 7085, 'enable': 7086, 'Brent': 7087, 'drafted': 7088, 'circulated': 7089, 'Shona': 7090, 'reviewed': 7091, 'belittle': 7092, 'meat': 7093, 'Rick': 7094, 'Causey': 7095, 'defining': 7096, 'regularity': 7097, 'identify': 7098, 'compiling': 7099, 'commissioned': 7100, 'Moscoso': 7101, 'James': 7102, 'publish': 7103, 'Controller': 7104, 'hopefully': 7105, 'Sorry': 7106, 'interrupted': 7107, 'responding': 7108, 'input': 7109, 'Delainey@ECT': 7110, '11/10/2000': 7111, '01:04': 7112, 'ie': 7113, '13.9': 7114, '11.5': 7115, 'flat': 7116, 'goal': 7117, 'Delainey': 7118, 'speaker': 7119, 'Sue': 7120, 'enjoys': 7121, 'utility': 7122, 'bashing': 7123, 'respects': 7124, 'panel': 7125, 'thanked': 7126, 'intervening': 7127, 'Miller': 7128, 'godsend': 7129, 'Dasovich': 7130, '12:33': 7131, 'offended': 7132, 'quoting': 7133, 'CEO': 7134, 'regulated': 7135, 'attributing': 7136, '----': 7137, 'decidely': 7138, 'downhill': 7139, 'Best': 7140, 'modulate': 7141, '12:21': 7142, 'politely': 7143, 'participate': 7144, 'actively': 7145, 'Amen': 7146, 'IBM': 7147, 'Erin': 7148, '11:08': 7149, 'IT': 7150, 'backpedalling': 7151, 'overcome': 7152, 'TIBCO': 7153, 'Terminal': 7154, 'Server': 7155, 'commit': 7156, 'transferring': 7157, 'messages': 7158, 'domains': 7159, 'initiated': 7160, 'Corp': 7161, 'EES': 7162, 'agrees': 7163, 'proprietary': 7164, 'WebSphere': 7165, 'WebLogic': 7166, 'BackWeb': 7167, 'HR': 7168, 'user': 7169, 'responses': 7170, 'anonymous': 7171, 'CONFIRMIT': 7172, 'surveys': 7173, 'Messages': 7174, 'simultaneously': 7175, 'users': 7176, 'administrator': 7177, 'expire': 7178, 'disappear': 7179, 'retrieve': 7180, 'miss': 7181, 'expired': 7182, 'plain': 7183, 'pushing': 7184, 'hoping': 7185, 'foot': 7186, 'test': 7187, 'implementing': 7188, 'Steven': 7189, 'J': 7190, 'Kean': 7191, '11/09/2000': 7192, '11:35': 7193, 'eg': 7194, 'Tokyo': 7195, 'unreachable': 7196, 'Courtney': 7197, 'Votaw': 7198, '11/08/2000': 7199, '11:02': 7200, 'Document': 7201, 'calendar': 7202, 'Kaufman@ECT': 7203, 'Lysa': 7204, 'Akin@ECT': 7205, '05:59': 7206, 'Kaufman': 7207, 'Meeting': 7208, 'Date': 7209, '13th': 7210, '1:30': 7211, '2:30': 7212, '888-422-7132': 7213, 'Pin': 7214, '411507': 7215, 'ONLY': 7216, '362416': 7217, '503/464-7927': 7218, 'Akin': 7219, \"Ass't.\": 7220, '814014': 7221, 'Marketing': 7222, '3,500': 7223, 'mmbtu': 7224, '30th': 7225, '2.975': 7226, 'invoicing': 7227, 'invoice': 7228, 'Sitara': 7229, 'Darla': 7230, 'Express': 7231, 'card': 7232, 'expiration': 7233, '3/03': 7234, 'Otherwise': 7235, 'assume': 7236, 'Thank': 7237, 'non': 7238, 'bondad': 7239, 'Jane': 7240, '763736': 7241, '01': 7242, 'Southwest': 7243, 'Gas': 7244, 'discrepancy': 7245, 'reflects': 7246, 'SJ': 7247, 'Bondad': 7248, 'SWG': 7249, 'Non-Bondad': 7250, 'confirm': 7251, 'correct': 7252, 'Laurie': 7253, 'Ellis': 7254, 'Client': 7255, 'Phone': 7256, '345-9945': 7257, '646-8420': 7258, 'Email': 7259, 'laurie.ellis@enron.com': 7260, 'flooding': 7261, 'Hope': 7262, 'survived': 7263, 'ok': 7264, 'V02': 7265, 'Anderson': 7266, 'flowed': 7267, 'Nancy': 7268, 'ENRON.XLS': 7269, 'File': 7270, 'enrongss.xls': 7271, 'volumes': 7272, 'flowing': 7273, 'gda': 7274, 'blanco': 7275, '759933': 7276, 'nonbondad': 7277, 'SW': 7278, 'honro': 7279, 'reconfirm': 7280, 'trader': 7281, 'enclosed': 7282, 'Volumes': 7283, 'PGE': 7284, 'CityGate': 7285, 'delvery': 7286, '11/1/01': 7287, 'spreadsheet': 7288, 'Indexed': 7289, 'Basis': 7290, 'patience': 7291, 'walk': 7292, 'thru': 7293, 'Des': 7294, 'Moines': 7295, 'dthat': 7296, 'Joe': 7297, 'Cester': 7298, 'NetMeeting': 7299, 'Deb': 7300, 'Tholt': 7301, 'M.': 7302, 'theraphy': 7303, 'updates': 7304, 'Jared': 7305, 'BUNCH': 7306, 'plate': 7307, 'busier': 7308, 'cool': 7309, 'Ruth': 7310, 'objects': 7311, 'maam': 7312, 'UBS': 7313, 'Judy': 7314, 'Franklin': 7315, 'Transportation': 7316, 'Work': 7317, '832.676.3177': 7318, '832.676.1329': 7319, 'Pager': 7320, '1.888.509.3736': 7321, '******************************************************************': 7322, 'ElPaso': 7323, 'Corporation': 7324, 'solely': 7325, 'notify': 7326, 'SHE': 7327, 'IS': 7328, 'ALWAYS': 7329, 'PHONE': 7330, 'bet': 7331, 'Shemin': 7332, '!!!': 7333, 'Dominion': 7334, 'Davis': 7335, 'myself': 7336, 'Savier': 7337, 'sp': 7338, 'tabs': 7339, 'monthly': 7340, 'schedule': 7341, 'Tennessee': 7342, 'NET': 7343, '284': 7344, 'approve': 7345, 'Ed': 7346, 'approval': 7347, 'Cullen': 7348, 'Dykman': 7349, 'copies': 7350, 'Melanie': 7351, 'minutes': 7352, 'revised': 7353, 'Stipulation': 7354, 'modified': 7355, 'faxed': 7356, 'Toni': 7357, 'Donohue': 7358, 'deleted': 7359, 'assuming': 7360, 'revision': 7361, 'Gotschal': 7362, 'T.': 7363, 'Metcalfe': 7364, 'LLP': 7365, '177': 7366, 'Montague': 7367, 'Brooklyn': 7368, '11201': 7369, '718-780-0046': 7370, 'fax': 7371, '718-780-0276': 7372, 'dmetcalfe@cullenanddykman.com': 7373, '____________________________________________________': 7374, 'contain': 7375, 'sole': 7376, 'recipient': 7377, 'reliance': 7378, 'forwarding': 7379, 'strictly': 7380, 'prohibited': 7381, 'delete': 7382, '-ECT-KEDNE': 7383, 're': 7384, 'IGTS': 7385, 'Cap': 7386, 'Releases': 7387, '-FINAL.doc': 7388, 'BLACKLINE': 7389, '-Stip': 7390, '-2-F.doc': 7391, 'heartwarming': 7392, 'Barbara': 7393, 'Walters': 7394, 'gender': 7395, 'roles': 7396, 'customarily': 7397, 'husbands': 7398, 'yards': 7399, 'approached': 7400, 'enabled': 7401, 'reversal': 7402, 'Land': 7403, 'mines': 7404, 'Kuwaiti': 7405, 'attorneys': 7406, 'Tenn': 7407, 'Iroq': 7408, 'space': 7409, 'bottom': 7410, 'fun': 7411, '10:30': 7412, 'REALLY': 7413, 'DO': 7414, 'TO': 7415, 'GO': 7416, 'KNOW': 7417, 'LOT': 7418, 'GOING': 7419, 'WITH': 7420, 'AND': 7421, 'LITTLE': 7422, 'WOMAN': 7423, 'HANDLE': 7424, 'TRACTOR': 7425, 'ALONG': 7426, 'FOR': 7427, 'COMPANY': 7428, 'BUT': 7429, 'NECESSARY': 7430, 'PLATE': 7431, 'DAD': 7432, 'Chris.Germany@enron.com': 7433, '01/25/2002': 7434, '03:13:58': 7435, 'garage': 7436, 'annoyed': 7437, 'notified': 7438, '10:00': 7439, 'GRRRRRRR': 7440, 'twinkies': 7441, 'Correction': 7442, 'welch': 7443, 'GE': 7444, 'GM': 7445, 'eThink': 7446, 'Team': 7447, 'eSpeak': 7448, 'tremendous': 7449, 'cases': 7450, 'creative': 7451, 'Britney': 7452, 'Spears': 7453, '61': 7454, 'colleagues': 7455, 'sampling': 7456, 'suggestions': 7457, 'eSpeakers': 7458, 'Welch': 7459, 'Motors': 7460, 'McNeally': 7461, 'Microsystems': 7462, 'Satisfied': 7463, 'Customers': 7464, 'Covey': 7465, 'Seven': 7466, 'Habits': 7467, 'Highly': 7468, 'Effective': 7469, 'Oprah': 7470, 'Winfrey': 7471, 'talkshow': 7472, 'Joint': 7473, 'Chiefs': 7474, 'U.S.A': 7475, 'Alan': 7476, 'Greenspan': 7477, 'Reserve': 7478, 'Gates': 7479, 'Microsoft': 7480, 'promises': 7481, 'guests': 7482, 'guest': 7483, 'speakers': 7484, 'ethink@enron.com': 7485, 'Everybody': 7486, 'plenty': 7487, 'internal': 7488, 'scheduling': 7489, 'participation': 7490, 'Confirmed': 7491, 'Hyatt': 7492, 'Regency': 7493, 'Downtown': 7494, 'perfect': 7495, 'lobby': 7496, '7:00': 7497, 'breakfast': 7498, 'Smith': 7499, '800': 7500, 'convenient': 7501, '945': 7502, 'patient': 7503, 'Nesbitt': 7504, 'ekrapels@esaibos.com': 7505, 'correction': 7506, 'spend': 7507, 'pre-meeting': 7508, 'cruise': 7509, 'EDT': 7510, '2:300': 7511, 'www.weathereffects.com': 7512, 'electricity': 7513, 'EOL': 7514, 'quality': 7515, 'Hedging': 7516, 'Trading': 7517, 'deadline': 7518, 'pressures': 7519, 'intrigued': 7520, 'competition': 7521, 'platforms': 7522, 'astonished': 7523, 'Goldman': 7524, 'Morgan': 7525, 'BP': 7526, 'Shell': 7527, 'compete': 7528, 'yours': 7529, 'shotgun': 7530, 'proud': 7531, 'password': 7532, 'WSI': 7533, 'seasonal': 7534, 'PJM': 7535, 'NEPOOL': 7536, 'ESAI': 7537, 'contributes': 7538, 'forecasts': 7539, 'judgments': 7540, 'herding': 7541, 'bore': 7542, 'e.g.': 7543, 'Nepool': 7544, 'onpeak': 7545, '43': 7546, '46': 7547, '4:00': 7548, 'P.S.': 7549, 'Caroline': 7550, 'Abramo@ENRON': 7551, '03/02/2001': 7552, '10:46': 7553, 'Joe_Lardy@cargill.com': 7554, '10:39:03': 7555, 'Currently': 7556, 'Cargill': 7557, 'MM': 7558, 'collateral': 7559, 'threshold': 7560, 'cleanest': 7561, 'biz': 7562, 'master': 7563, 'isda': 7564, 'mutually': 7565, 'agreeable': 7566, 'annex': 7567, '20,000,000': 7568, 'referenced': 7569, 'Schedule': 7570, 'B': 7571, 'POA': 7572, 'distinct': 7573, 'measure': 7574, 'mill': 7575, 'gross': 7576, 'workable': 7577, 'relationships': 7578, 'priority': 7579, 'regading': 7580, 'allocate': 7581, 'limit': 7582, 'yourselves': 7583, 'Dee': 7584, 'Shackleton': 7585, 'Bailey': 7586, '02/27/2001': 7587, '08:23': 7588, 'blacklined': 7589, 'b': 7590, 'Paragraph': 7591, 'ISDA': 7592, 'Master': 7593, '11/8/2000': 7594, '19th': 7595, 'Cordially': 7596, 'transactions': 7597, 'Jorge': 7598, 'Garcia@ENRON': 7599, '03/01/2001': 7600, '01:35': 7601, 'Afternoon': 7602, 'Confirmations': 7603, 'trades': 7604, 'Laurel': 7605, 'Edison': 7606, 'Swap': 7607, 'Ltd.': 7608, 'PG&E': 7609, 'Calculation': 7610, 'Floating': 7611, 'Amount': 7612, 'payable': 7613, 'Payment': 7614, 'calculated': 7615, 'Notional': 7616, 'Quantity': 7617, 'Period': 7618, 'Price': 7619, 'USD': 7620, '38,000': 7621, 'Provisions': 7622, 'fee': 7623, '{': 7624, 'netting': 7625, 'provisions': 7626, '02:10': 7627, 'prepares': 7628, 'Singapore': 7629, 'distant': 7630, 'Houson': 7631, 'departments': 7632, 'philosophy': 7633, 'omnibus': 7634, 'ps': 7635, 'Congrats': 7636, 'offsite': 7637, 'shake': 7638, 'Carlos': 7639, 'Alatorre@ENRON': 7640, '06:07': 7641, 'Prod': 7642, 'Description': 7643, 'fro': 7644, 'LME': 7645, 'Product': 7646, 'Settlement': 7647, 'Transaction': 7648, 'Curr': 7649, 'Spot': 7650, 'Mar': 7651, 'JPY': 7652, 'Limited': 7653, 'EEFTL': 7654, 'Management': 7655, 'Counterparty': 7656, 'submits': 7657, 'Currency': 7658, 'volume': 7659, 'submitted': 7660, 'Website': 7661, 'multiplied': 7662, 'Metal': 7663, 'Exchange': 7664, 'fixing': 7665, 'Reuters': 7666, 'MTLE': 7667, 'transaction': 7668, 'correspond': 7669, 'forth': 7670, 'description': 7671, 'Dollar': 7672, 'Dollars': 7673, 'insurance': 7674, 'mom': 7675, 'dad': 7676, 'surgery': 7677, 'p.m': 7678, '1.5': 7679, 'ICU': 7680, '48': 7681, 'Term': 7682, 'Worth': 7683, 'anyway': 7684, 'accessibility': 7685, 'manage': 7686, 'client': 7687, 'expectations': 7688, 'accordingly': 7689, 'wedding': 7690, 'Wars': 7691, 'Museum': 7692, 'Fine': 7693, 'Arts': 7694, '++++++': 7695, 'CONFIDENTIALITY': 7696, 'NOTICE': 7697, '+++++': 7698, 'hereby': 7699, 'dissemination': 7700, 'copying': 7701, 'attachments': 7702, 'herein': 7703, 'Mann': 7704, '03/15/2001': 7705, '04:03': 7706, 'Lorie': 7707, 'Leigh': 7708, 'ECT': 7709, '04:17': 7710, 'Plus': 7711, 'hosting': 7712, '22nd': 7713, '1-800-991-9019': 7714, 'passcode': 7715, '6871082#': 7716, '11:00': 7717, 'CST': 7718, '3143C': 7719, 'Turbine': 7720, 'Purchase': 7721, '713-853-1696': 7722, 'carotid': 7723, 'artery': 7724, 'transports': 7725, 'circulatory': 7726, 'Kathleen': 7727, '04:11': 7728, 'worse': 7729, 'Origination': 7730, 'ours': 7731, 'rows': 7732, '49': 7733, '53': 7734, 'columns': 7735, 'E': 7736, '04:44': 7737, 'titles': 7738, 'verify': 7739, 'formatted': 7740, 'print': 7741, 'size': 7742, 'org': 7743, 'chart': 7744, 'submit': 7745, 'imperative': 7746, 'Louise': 7747, 'Kitchen': 7748, 'revisions': 7749, 'blackline': 7750, 'correctly': 7751, 'sample': 7752, 'availability': 7753, 'provision': 7754, 'redvepco.doc': 7755, 'SAMPLE.DOC': 7756, '46093': 7757, '02/13/2001': 7758, '04:13': 7759, 'Peggy': 7760, 'Banczak': 7761, 'handles': 7762, 'Mexico': 7763, 'Schwartzenburg@ENRON_DEVELOPMENT': 7764, '03/16/2001': 7765, '09:22': 7766, 'bust': 7767, 'structuring': 7768, 'Laidlaw': 7769, 'Harry': 7770, 'Okabayashi': 7771, 'tasking': 7772, 'procure': 7773, 'engineering': 7774, 'Wholesale': 7775, 'ABB': 7776, 'Renee': 7777, 'Alfaro': 7778, 'JWVS': 7779, 'Janice': 7780, 'significantly': 7781, 'watched': 7782, 'deteriorate': 7783, 'ultimately': 7784, 'thankful': 7785, 'privilege': 7786, 'homes': 7787, 'Sounds': 7788, 'Eric': 7789, 'SMSU': 7790, 'joining': 7791, 'delighted': 7792, 'obtain': 7793, 'Elizabeth': 7794, 'married': 7795, 'Buenos': 7796, 'Aires': 7797, 'Argentina': 7798, 'honeymoon': 7799, 'returning': 7800, 'sixth': 7801, 'grandchild': 7802, 'lb.': 7803, 'oz.': 7804, 'inch': 7805, 'grandsons': 7806, 'granddaughters': 7807, 'Ginger': 7808, 'Rees': 7809, 'Copeland': 7810, 'Lay': 7811, 'Tori': 7812, 'L.': 7813, 'Wells': 7814, 'Rosalee': 7815, '3:00': 7816, 'crystallizes': 7817, 'Arthur': 7818, 'Levitt': 7819, 'brainstorm': 7820, 'generated': 7821, 'sees': 7822, '5:00': 7823, '8:30': 7824, 'Could': 7825, '___________': 7826, 'Garten': 7827, 'Kitty': 7828, 'Armada': 7829, 'M306': 7830, 'ultra': 7831, 'portable': 7832, 'notebook': 7833, 'enterprise': 7834, 'equip': 7835, 'latest': 7836, 'laptops': 7837, 'consumer': 7838, 'M300': 7839, 'lightest': 7840, \"E500's\": 7841, 'heavier': 7842, 'http://www.compaq.com/products/notebooks/index.html': 7843, 'Compaq.com': 7844, 'notebook.url': 7845, 'Jose': 7846, 'participating': 7847, 'Fleming': 7848, 'RE': 7849, '???': 7850, 'Reception': 7851, 'Recently': 7852, 'Dewhurst': 7853, 'reception': 7854, 'Wed.': 7855, '6/14': 7856, 'Petroleum': 7857, 'Club': 7858, '.?': 7859, 'greater': 7860, 'Reform': 7861, 'Caucus': 7862, 'educators': 7863, 'common': 7864, 'approaches': 7865, '14th': 7866, 'Kent': 7867, 'Grusendorf': 7868, 'Representative': 7869, '94': 7870, 'o.k.': 7871, 'kenneth.lay@enron.com': 7872, 'WPO': 7873, 'Forum': 7874, 'Everett': 7875, 'Joy': 7876, '713/871-5119': 7877, '===================================================': 7878, 'NOTE': 7879, 'legally': 7880, 'disseminate': 7881, 'virus': 7882, 'defect': 7883, 'affiliates': 7884, 'loss': 7885, 'arising': 7886, '====================================================': 7887, 'expense': 7888, 'Olson': 7889, 'Katsof': 7890, 'Tco': 7891, 'Pool': 7892, 'Leach': 7893, 'swap': 7894, 'TCO': 7895, 'Index': 7896, '.04': 7897, '36647': 7898, 'Demand': 7899, 'speadsheet': 7900, 'matching': 7901, 'Brenda': 7902, 'Pasquallie': 7903, 'Jr': 7904, 'PO': 7905, 'Box': 7906, 'Cedar': 7907, 'Lane': 7908, '77415-0027': 7909, 'Had': 7910, '33,000': 7911, 'miles': 7912, 'Ram': 7913, '2500': 7914, 'ton': 7915, '360': 7916, 'Magnum': 7917, 'Motor': 7918, 'Infinity': 7919, 'stereo': 7920, 'bucket': 7921, 'nerf': 7922, 'bars': 7923, 'bed': 7924, 'liner': 7925, 'camper': 7926, 'tow': 7927, 'Oh': 7928, 'dueled': 7929, 'catalytic': 7930, 'converter': 7931, 'Flow': 7932, 'Masters': 7933, 'Makes': 7934, 'fuel': 7935, 'consumption': 7936, 'tolerable': 7937, 'Debbie': 7938, '16,900': 7939, 'clean': 7940, 'Sandalwood': 7941, 'Driftwood': 7942, 'gray': 7943, 'Jerry': 7944, 'dodge': 7945, 'Yep': 7946, 'articulating': 7947, 'dirt': 7948, 'plant': 7949, 'ST': 7950, 'Augustine': 7951, 'Grass': 7952, 'rain': 7953, 'economy': 7954, 'faltering': 7955, 'Dow': 7956, 'Stock': 7957, 'Hooray': 7958, 'hoorah': 7959, 'Needs': 7960, '41': 7961, 'spoke': 7962, 'bunch': 7963, 'Dad': 7964, 'lock': 7965, 'gate': 7966, 'steers': 7967, 'heifers': 7968, 'tractor': 7969, 'Reggie': 7970, '11/01/01': 7971, 'Be': 7972, 'sweety': 7973, 'balcony': 7974, 'gaze': 7975, 'moon': 7976, 'pasture': 7977, 'shuttle': 7978, 'hickies': 7979, 'burns': 7980, 'jealous': 7981, 'wonderful': 7982, 'grades': 7983, 'reassured': 7984, 'graduate': 7985, 'scholarship': 7986, 'kinesiologist': 7987, 'sports': 7988, 'injury': 7989, 'therapist': 7990, 'legs': 7991, '11/03': 7992, 'Jaime': 7993, 'Jgerma5@aol.com': 7994, 'careful': 7995, 'listened': 7996, 'Maw': 7997, 'shut': 7998, 'zipped': 7999, 'alright': 8000, 'Shucks': 8001, 'Sherrar': 8002, 'Rise': 8003, 'Rebellion': 8004, 'dudes': 8005, '76': 8006, 'saga': 8007, 'borrow': 8008, 'style': 8009, 'split': 8010, '120': 8011, 'shares': 8012, 'Powder': 8013, 'Dry': 8014, 'Bro': 8015, '2301': 8016, 'Brazosport': 8017, 'Blvd': 8018, '3611': 8019, 'Investment': 8020, 'Recovery': 8021, 'Freeport': 8022, '77541': 8023, '451': 8024, '0491': 8025, '0448': 8026, '979': 8027, '238': 8028, '2102': 8029, '548': 8030, '7034': 8031, 'brand': 8032, 'Must': 8033, 'wheeler': 8034, 'divy': 8035, 'cashout': 8036, 'buring': 8037, 'dough': 8038, 'ck': 8039, 'Oglethorpe': 8040, 'Dec': 8041, 'Doug': 8042, 'invoiced': 8043, 'phones': 8044, '713-853-4743': 8045, 'Ernie': 8046, 'emails': 8047, 'anwser': 8048, 'responds': 8049, 'prices': 8050, 'Simien': 8051, '08/01/2001': 8052, 'Mary.Ellenberger@enron.com': 8053, '05/03/2001': 8054, '04:06:52': 8055, 'cc': 8056, 'Subject': 8057, 'Re': 8058, 'TGPL': 8059, 'LA': 8060, 'Z1': 8061, '9.95': 8062, 'Feb': 8063, '6.25': 8064, '4.98': 8065, '5.37': 8066, 'esimien@nisource.com': 8067, '01:32:35': 8068, 'favour': 8069, 'Apr': 8070, 'Gregg': 8071, 'Penman': 8072, '10/23/2000': 8073, '12:12': 8074, 'circulate': 8075, 'Hodge': 8076, 'slightly': 8077, 'Peoples': 8078, 'audit': 8079, 'm.nordstrom@pecorp.com': 8080, '10/20/2000': 8081, '11:32': 8082, 'enovate': 8083, 'Section': 8084, 'XIII': 8085, 'Audit': 8086, 'MEH-risk': 8087, 'Oct': 8088, '20.doc': 8089, 'machine': 8090, 'Laura': 8091, 'initial': 8092, 'pages': 8093, 'Portland': 8094, '10/26/2000': 8095, '11:07': 8096, 'spirit': 8097, 'jointly': 8098, 'L.L.C': 8099, 'slight': 8100, 'Highlighting': 8101, 'designated': 8102, 'optimistic': 8103, 'Activity': 8104, 'picking': 8105, 'dramatically': 8106, 'blurred': 8107, 'Therefore': 8108, 'mid': 8109, 'prompt': 8110, 'complicated': 8111, 'whomever': 8112, 'Janet': 8113, 'comfort': 8114, 'conscientious': 8115, 'initials': 8116, 'Either': 8117, 'coordinate': 8118, '11:26': 8119, 'FCE': 8120, 'Thanksgiving': 8121, 'Craig': 8122, 'Danelia': 8123, 'roasted': 8124, 'turkey': 8125, 'roast': 8126, 'joy': 8127, 'cooking': 8128, 'elsewise': 8129, 'played': 8130, 'Uno': 8131, 'fast': 8132, 'physically': 8133, 'meek': 8134, 'Alena': 8135, 'memorized': 8136, 'messy': 8137, 'embarrased': 8138, 'apartment': 8139, 'baby': 8140, 'lease': 8141, 'roaches': 8142, 'crawling': 8143, 'sometime': 8144, 'upstairs': 8145, 'suite': 8146, 'neat': 8147, 'bathroom': 8148, 'cozy': 8149, 'Uncle': 8150, 'thrilled': 8151, 'stilted': 8152, 'XMAS': 8153, '21st': 8154, '27th': 8155, 'chatting': 8156, 'Kyle.Jones@radianz.com': 8157, '11/29/2000': 8158, '05:07': 8159, 'Tana': 8160, 'log': 8161, 'Anyway': 8162, 'Series': 8163, 'Thanksgiv8ing': 8164, 'Dinners': 8165, 'gon': 8166, 'na': 8167, 'swell': 8168, 'Ma': 8169, 'r': 8170, 'woud': 8171, 'wierd': 8172, 'botn': 8173, 'appartment': 8174, 'ocnversation': 8175, 'Unlce': 8176, 'shocked': 8177, 'Xmas': 8178, 'Got': 8179, 'ta': 8180, 'Love': 8181, 'ya': 8182, 'Kyle': 8183, 'Harrison': 8184, 'Technical': 8185, 'Solutions': 8186, 'Engineer': 8187, 'Radianz': 8188, '1251': 8189, 'Avenue': 8190, 'Americas': 8191, '7th': 8192, 'Floor': 8193, 'NY': 8194, '10016': 8195, 'USA': 8196, '+1': 8197, '212': 8198, '899-4425': 8199, '899-4310': 8200, '917': 8201, '859-7187': 8202, 'kyle.jones@radianz.com': 8203, 'http://www.radianz.com': 8204, '|--------+----------------------->': 8205, '|': 8206, 'Tana.Jones@en': 8207, 'ron.com': 8208, '04:34': 8209, '>----------------------------------------------------------------------------|': 8210, 'Computer': 8211, 'sister': 8212, '?!': 8213, 'Kyle.Jones@ra': 8214, 'Return': 8215, 'Receipt': 8216, 'AMERICAS': 8217, 'Equant': 8218, '01:00:51': 8219, 'Taffy': 8220, 'Milligan': 8221, 'Patel@ENRON': 8222, '04:49': 8223, 'GCP': 8224, 'Signoffs': 8225, 'Approvals': 8226, '11/29/00': 8227, 'marked': 8228, 'Patel': 8229, 'Bradley': 8230, 'Diebner@ECT': 8231, '04:31': 8232, 'bd': 8233, 'Breslau': 8234, 'countersignature': 8235, 'Thinking': 8236, 'w/o': 8237, 'Adding': 8238, 'Specified': 8239, 'Entities': 8240, 'default': 8241, 'defaults': 8242, 'trigger': 8243, 'component': 8244, 'Sage': 8245, '11/30/2000': 8246, '10:52': 8247, 'ECCL': 8248, 'Deutsche': 8249, '\"\"': 8250, 'DB': 8251, 'hesitant': 8252, 'potentially': 8253, 'swaps': 8254, 'structured': 8255, 'Bradford': 8256, 'Credit': 8257, '11/8/00': 8258, 'paralegal': 8259, 'Denis': 8260, \"O'Connell\": 8261, '11/22/2000': 8262, '06:05': 8263, 'counterparties': 8264, 'behalf': 8265, 'Tks': 8266, 'Deutsched': 8267, 'AG': 8268, 'Taylor': 8269, 'co-signing': 8270, 'TAGG': 8271, 'counterparty': 8272, 'assess': 8273, 'Russell': 8274, 'properly': 8275, 'updated': 8276, 'Seoul': 8277, 'planned': 8278, 'Ted': 8279, 'laid': 8280, 'etc': 8281, 'harder': 8282, 'Travis': 8283, 'Chuck': 8284, 'dragging': 8285, 'U.T.': 8286, 'Kelley': 8287, 'Parks': 8288, 'Wild': 8289, 'Card': 8290, 'gang': 8291, 'warm': 8292, 'Emily': 8293, 'musical': 8294, 'Rent': 8295, 'Diego': 8296, 'Labor': 8297, 'Hoot': 8298, 'Julie': 8299, 'Needless': 8300, 'excited': 8301, 'girlie': 8302, 'birdie': 8303, 'separately': 8304, 'exploring': 8305, 'considering': 8306, '__________________________________________________': 8307, 'Yahoo!': 8308, 'Send': 8309, 'instant': 8310, 'alerts': 8311, 'http://im.yahoo.com/': 8312, 'Tonto': 8313, 'a.k.a': 8314, 'kick': 8315, 'Lone': 8316, 'Ranger': 8317, 'forgotten': 8318, 'songs': 8319, 'incredibly': 8320, 'voices': 8321, 'poignant': 8322, 'caught': 8323, 'Corey': 8324, 'Jeopardy': 8325, 'occasions': 8326, 'detract': 8327, 'enjoyment': 8328, 'Trivial': 8329, 'Pursuit': 8330, 'obliterating': 8331, 'thesis': 8332, 'Trivia': 8333, 'Kori': 8334, 'Night': 8335, 'introduced': 8336, 'spell': 8337, 'Darren': 8338, 'Shorty': 8339, 'Noble': 8340, '08/17/2000': 8341, '10:05': 8342, 're-run': 8343, 'Smart': 8344, 'LW': 8345, 'adhering': 8346, 'faithfully': 8347, 'pinning': 8348, 'shirt': 8349, 'remind': 8350, 'confident': 8351, 'colors': 8352, 'failing': 8353, 'elephant': 8354, 'Thought': 8355, 'Dixie': 8356, 'Chicks': 8357, '08/16/2000': 8358, '10:56': 8359, 'AAAAAGGGHHHHHH': 8360, 'TX': 8361, 'exam': 8362, 'rode': 8363, 'organizational': 8364, 'pleasure': 8365, 'Bowen': 8366, '07/14/2000': 8367, '03:58': 8368, 'Out': 8369, 'requires': 8370, 'GILBERGD@sullcrom.com': 8371, '02:18': 8372, 'disclosure': 8373, 'EnronOnline': 8374, 'assist': 8375, 'T1': 8376, 'Direct': 8377, 'Bockius': 8378, 'Ted.Bockius@ivita.com': 8379, '07/17/2000': 8380, '02:21': 8381, 'emergency': 8382, 'Taub': 8383, 'visitors': 8384, '??': 8385, 'Amy': 8386, 'Cornell': 8387, 'CPCG': 8388, '281-518-9526': 8389, '281-518-1081': 8390, 'amy.cornell@compaq.com': 8391, 'mailto:amy.cornell@compaq.com': 8392, 'Jann': 8393, 'phoned': 8394, 'Riggins': 8395, 'bicycle': 8396, 'cycling': 8397, 'injured': 8398, 'Hospital': 8399, '1504': 8400, 'Loop': 8401, '713-793-2000': 8402, 'flowers': 8403, 'e-mailed': 8404, 'prayers': 8405, 'Rosario': 8406, 'Gonzales': 8407, 'ESSG': 8408, 'CCA-15': 8409, '150301': 8410, '153B09': 8411, '281-514-3183': 8412, 'rosario.gonzales@compaq.com': 8413, 'mailto:rosario.gonzales@compaq.com': 8414, 'provisional': 8415, 'application': 8416, 'patent': 8417, 'placed': 8418, 'copyright': 8419, 'roughly': 8420, 'Forster@ENRON': 8421, '06:15': 8422, 'Patent': 8423, 'Pending': 8424, 'homepage': 8425, 'guidelines': 8426, 'Hansen@ENRON': 8427, '04:28': 8428, 'planing': 8429, 'Hoston': 8430, 'implementation': 8431, 'Forster': 8432, '02:34': 8433, 'taxes': 8434, 'mid-August': 8435, 'Leonardo': 8436, 'Pacheco': 8437, '08:50': 8438, 'ETA': 8439, 'PA': 8440, 'ext.': 8441, '39938': 8442, 'documentation': 8443, 'calculating': 8444, 'termination': 8445, 'Amsterdam': 8446, 'attractive': 8447, 'developments': 8448, 'Pickle': 8449, 'Haedicke': 8450, '07/18/2000': 8451, '08:58': 8452, 'CFTC': 8453, 'deregulation': 8454, 'EEI': 8455, 'slides': 8456, 'Plan': 8457, 'Days': 8458, 'Oct.': 8459, 'Leave': 8460, 'Thur.': 8461, 'Arrv.': 8462, 'a.m.': 8463, 'transfer': 8464, 'Nice': 8465, 'rent': 8466, 'hotel': 8467, 'collapse': 8468, 'Fri.': 8469, 'Stay': 8470, 'Monaco': 8471, 'Sat.': 8472, '23': 8473, 'Drive': 8474, 'Tropez': 8475, 'Cannes': 8476, 'towns': 8477, 'Sun.': 8478, 'Mon.': 8479, 'Tue.': 8480, 'Versailles': 8481, 'Fontainbleu': 8482, '29': 8483, 'Giverny': 8484, 'Monet': 8485, 'gardens': 8486, 'Eurostar': 8487, 'arrv.': 8488, 'CLH': 8489, 'Tues.': 8490, 'Fly': 8491, 'lv.': 8492, '4:30': 8493, 'confusing': 8494, 'Taylors': 8495, 'forwarded': 8496, 'EI.London': 8497, 'Ladies': 8498, 'Enclosed': 8499, 'worksheet': 8500, 'Ineos': 8501, 'Acrylics': 8502, 'thresholds': 8503, 'MACS': 8504, 'Pounds': 8505, 'Troy': 8506, 'Black': 8507, 'CP': 8508, 'Ineos.xls': 8509, 'Robbi': 8510, 'looked': 8511, 'UEComm': 8512, 'AUD': 8513, 'Marie': 8514, 'Uecomm': 8515, 'Suttle': 8516, \"OK'd\": 8517, 'Anthony': 8518, 'w': 8519, 'r.': 8520, 't.': 8521, 'CSA': 8522, 'executable': 8523, 'Fred': 8524, 'Pat': 8525, 'inserted': 8526, 'amended': 8527, 'timeframes': 8528, 'Support': 8529, 'Annex': 8530, 'Clauses': 8531, 'ease': 8532, 'amendmnets': 8533, 'hesitate': 8534, 'Sill': 8535, 'Counsel': 8536, '126': 8537, 'Trenerry': 8538, 'Crescent': 8539, 'Abbotsford': 8540, 'VIC': 8541, '3067': 8542, 'Ph.': 8543, '03': 8544, '9221': 8545, '4101': 8546, 'Fax.': 8547, '4193': 8548, 'Mob.': 8549, '0417': 8550, '575': 8551, '920': 8552, 'UC': 8553, '0308.doc': 8554, 'Turn': 8555, 'Reminder': 8556, 'ConEd': 8557, '1:00': 8558, '3801A': 8559, 'handling': 8560, 'Deemed': 8561, 'Ispat': 8562, 'Mexicana': 8563, 'S.A.': 8564, 'C.V': 8565, 'omitted': 8566, 'x33907': 8567, 'Elliotts': 8568, 'CONGRATULATIONS': 8569, '!!!!!!!': 8570, 'everyday': 8571, 'Babies': 8572, 'Elliott': 8573, 'arrived': 8574, 'Glad': 8575, 'storing': 8576, 'sleep': 8577, 'cause': 8578, 'congratulations': 8579, 'gentlemen': 8580, 'Emma': 8581, '6th': 8582, 'triplets': 8583, 'Benjamin': 8584, '12.45': 8585, 'lbs': 8586, 'ozs': 8587, 'Toby': 8588, '12.46': 8589, 'Hannah': 8590, '12.48': 8591, 'lowest': 8592, 'weights': 8593, 'OOPS': 8594, 'Nylon': 8595, 'Polykron': 8596, 'ISDAs': 8597, 'Rebecca': 8598, 'attaching': 8599, 'authorizing': 8600, 'brokerage': 8601, 'revise': 8602, 'clearer': 8603, 'Jim': 8604, 'Armogida': 8605, 'revising': 8606, 'Liz': 8607, 'Heard': 8608, 'Marquez': 8609, 'guaranty': 8610, 'Pemex': 8611, 'Lucy': 8612, 'Ortiz': 8613, 'Pinto': 8614, 'Leite': 8615, 'reflect': 8616, '713/345-7942': 8617, 'francisco.pinto.leite@enron.com': 8618, 'Tanya': 8619, 'Niagara': 8620, 'Mohawk': 8621, 'drafts': 8622, 'Para.': 8623, 'Having': 8624, 'spoken': 8625, 'Dennis': 8626, 'Goldmann': 8627, 'Guaranties': 8628, 'sum': 8629, 'Holdings': 8630, 'f': 8631, 'o': 8632, 'respective': 8633, 'raise': 8634, 'modify': 8635, 'existing': 8636, 'understanding': 8637, '315-460-3344': 8638, '315-460-3349': 8639, 'Snyder': 8640, 'ENRON': 8641, 'SCHEDULE': 8642, 'nmemdrft8-7-01': 8643, '.doc': 8644, 'Para13': 8645, \"GTC's\": 8646, 'Tracy': 8647, 'Hai': 8648, 'adjustment': 8649, 'incorrect': 8650, 'liquidations': 8651, 'P&L': 8652, \"#'s\": 8653, 'adjustments': 8654, 'swing': 8655, 'adjusted': 8656, 'Karim': 8657, 'Evidently': 8658, 'Stacey': 8659, 'coding': 8660, 'delta': 8661, 'messed': 8662, 'liq': 8663, 'liquidation': 8664, 'flipping': 8665, 'PUTS': 8666, 'Marcelo': 8667, 'Meira': 8668, 'Sr': 8669, 'Developer': 8670, '345-3436': 8671, 'id': 8672, '11608': 8673, 'values': 8674, 'positive': 8675, 'CORRECT': 8676, 'till': 8677, 'adjust': 8678, '2nd': 8679, 'offering': 8680, 'informally': 8681, '11:45': 8682, 'max': 8683, 'gearing': 8684, '02': 8685, 'improved': 8686, 'til': 8687, '2:00': 8688, 'eat': 8689, 'hook': 8690, 'videoconference': 8691, 'Videoconference': 8692, 'Room': 8693, 'Associate': 8694, 'Recruiting': 8695, '26th': 8696, 'conferenced': 8697, 'Nella': 8698, 'Fagan': 8699, 'propose': 8700, 'confusion': 8701, 'responded': 8702, 'TCA': 8703, 'Tabors': 8704, 'Alberta': 8705, 'Export': 8706, '050901.doc': 8707, 'Rob': 8708, 'redlined': 8709, 'Access': 8710, 'Pricing': 8711, 'Analysis_0712': 8712, 'Article': 8713, 'section': 8714, '5.1': 8715, 'outage': 8716, 'TAU': 8717, 'Katie': 8718, 'Kaplan': 8719, 'kaplan@iepa.com': 8720, '10/27/2000': 8721, '06:20': 8722, 'kaplan': 8723, 'Greetings': 8724, 'IEP': 8725, 'historic': 8726, 'Julia': 8727, 'Sacramento': 8728, 'targeting': 8729, '100,000': 8730, 'contributions': 8731, 'respondents': 8732, 'COB': 8733, 'Policy': 8734, '916': 8735, '448-9499': 8736, 'heat': 8737, 'Hoecker': 8738, 'chats': 8739, 'post-call': 8740, 'regroup': 8741, 'Jeremy': 8742, 'Meier': 8743, 'jermeier@earthlink.net': 8744, '10/29/2000': 8745, '11:48': 8746, 'instructive': 8747, 'Robbie': 8748, 'et': 8749, 'MSA': 8750, 'certification': 8751, 'await': 8752, 'tariff': 8753, 'CPUC': 8754, 'addresses': 8755, 'Rossi': 8756, 'Michelle': 8757, 'Melissa': 8758, 'Lloyd': 8759, 'Blumenfeld': 8760, 'Cohen': 8761, 'Cal': 8762, 'Markets': 8763, 'Lara': 8764, 'Leibman@ENRON': 8765, '10/31/2000': 8766, '03:48': 8767, 'Angie': 8768, 'Buis': 8769, '10/31/00': 8770, '02:17': 8771, '11/2': 8772, 'dial': 8773, '1-877-331-6867': 8774, 'participants': 8775, 'prompted': 8776, '600-480': 8777, 'Wayne': 8778, '4434': 8779, 'dialing': 8780, '857-771': 8781, 'operator': 8782, 'duration': 8783, '3.5': 8784, 'terminate': 8785, 'x-37097': 8786, 'filed': 8787, 'Industry': 8788, 'Restructuring': 8789, 'Strategy': 8790, 'Motion': 8791, 'Approval': 8792, 'thirty': 8793, 'signatories': 8794, 'appendices': 8795, 'electronically': 8796, 'shipped': 8797, 'Additional': 8798, 'participated': 8799, 'devoted': 8800, 'ratepayer': 8801, 'advocates': 8802, 'customers': 8803, 'generators': 8804, 'marketers': 8805, 'shippers': 8806, 'providers': 8807, ',?': 8808, 'suppliers': 8809, 'producers': 8810, 'utilities': 8811, 'aggregators': 8812, 'pipeline': 8813, 'municipalities': 8814, 'accomplishment': 8815, 'Assignments': 8816, 'resting': 8817, 'laurels': 8818, 'ld2d-#69366-1.DOC': 8819, 'ld2d-#69345-1.DOC': 8820, 'ld2d-#69397-1.DOC': 8821, 'ld2d-#69396-1.DOC': 8822, 'ld2d-#69377-1.XLS': 8823, 'ld2d-#69381-1.DOC': 8824, 'ld2d-#69336-1.XLS': 8825, 'Primary': 8826, '4-12': 8827, 'ld2d-#69334-1.DOC': 8828, 'Severin': 8829, 'Borenstein': 8830, 'E.T.': 8831, 'Grether': 8832, 'Haas': 8833, 'U.C.': 8834, 'Institute': 8835, '2539': 8836, 'Channing': 8837, 'Way': 8838, 'Berkeley': 8839, '94720-1900': 8840, '94720-5180': 8841, 'p': 8842, '510-642-3689': 8843, '510-642-5145': 8844, '707-885-2508': 8845, 'http://www.ucei.berkeley.edu/ucei': 8846, 'borenste@haas.berkeley.edu': 8847, 'WWW': 8848, 'http://haas.berkeley.edu/~borenste': 8849, 'rough': 8850, 'CEM': 8851, 'distributed': 8852, 'GA': 8853, 'Steffes': 8854, 'Topic': 8855, 'PUC': 8856, 'Priorities': 8857, 'Goal': 8858, 'refute': 8859, 'Loretta': 8860, 'Lynch': 8861, 'Carl': 8862, 'Woods': 8863, 'assertions': 8864, 'deregulate': 8865, 'clock': 8866, 'alter': 8867, 'Comments': 8868, 'Laird': 8869, 'S.D.': 8870, 'shaped': 8871, '8:35': 8872, 'southwest': 8873, 'Sandi': 8874, 'sez': 8875, 'renewable': 8876, '03/08/2000': 8877, '09:45': 8878, 'Fyi': 8879, 'Justin': 8880, 'Marly': 8881, 'suffix': 8882, 'ONLINE': 8883, 'purely': 8884, 'descriptive': 8885, 'registration': 8886, 'incorporates': 8887, 'owner': 8888, 'adequate': 8889, 'core': 8890, 'logo': 8891, 'Jonathan': 8892, 'Goddard': 8893, '08/03/2000': 8894, 'X37047': 8895, 'proceeded': 8896, 'TM': 8897, 'affords': 8898, 'akin': 8899, 'McDonald': 8900, 'trademarks': 8901, 'Mc': 8902, '01:50': 8903, 'trademarking': 8904, 'covered': 8905, 'EnronEAuction': 8906, 'register': 8907, '____________________________________________________________': 8908, 'mistake': 8909, 'disclose': 8910, 'contents': 8911, 'Edmund': 8912, 'non-approved': 8913, 'Austria': 8914, 'Croatia': 8915, 'Czech': 8916, 'Poland': 8917, 'Portugal': 8918, 'Romania': 8919, 'Slovenia': 8920, 'Below': 8921, 'Jurisdictions': 8922, 'derivatives': 8923, 'Finland': 8924, 'Norway': 8925, 'Sweden': 8926, 'U.K': 8927, 'Gibraltar': 8928, 'Scotland': 8929, 'incorporated': 8930, 'jurisdiction': 8931, 'U.K.': 8932, 'Cappelletto': 8933, 'compliance': 8934, 'OTC': 8935, 'Derivatives': 8936, 'Contracts': 8937, 'Qualified': 8938, 'Securities': 8939, 'Act': 8940, 'annual': 8941, 'comply': 8942, 'Canadian': 8943, 'provinces': 8944, 'eligible': 8945, 'participant': 8946, 'equivalant': 8947, 'Molly': 8948, 'Harris': 8949, '05:37': 8950, '3/8/00': 8951, '...?!!!': 8952, 'Crawfish': 8953, 'Boil': 8954, 'Garden': 8955, 'Heights': 8956, '3926': 8957, 'Feagan': 8958, 'celebration': 8959, 'anniversary': 8960, 'Nat': 8961, 'futures': 8962, 'Shankman': 8963, 'Arnold': 8964, 'amendment': 8965, 'BC': 8966, 'executed': 8967, 'Representation': 8968, 'rep': 8969, 'confirms': 8970, 'ECC': 8971, 'Much': 8972, 'Amendment': 8973, 'Subscription': 8974, 'WeatherTrade': 8975, 'amends': 8976, 'Fee': 8977, 'subparagraph': 8978, 'c': 8979, 'Copies': 8980, 'Updating': 8981, 'B.': 8982, 'mergers': 8983, 'cpys': 8984, 'Tom': 8985, 'freeze': 8986, 'button': 8987, 'clicked': 8988, 'popped': 8989, 'unfreeze': 8990, 'scoop': 8991, 'Wow': 8992, 'Carol': 8993, 'Kuykendall': 8994, '03/09/2000': 8995, '03:51': 8996, 'hi': 8997, 'Arco': 8998, 'Products': 8999, 'adn': 9000, 'Torrey': 9001, 'ARCO': 9002, 'thorny': 9003, 'e-commerce': 9004, 'contracting': 9005, 'ETA_revision0307.doc': 9006, 'representation': 9007, 'Confirmation': 9008, 'Desk': 9009, '05:39': 9010, '3/9/00': 9011, 'Probably': 9012, 'Frank': 9013, 'Australian': 9014, '03/10/2000': 9015, '3/10/00': 9016, '04/04/2001': 9017, '09:27': 9018, 'Merchanting': 9019, 'Metals': 9020, 'substantiation': 9021, 'allocated': 9022, 'monitored': 9023, 'signoff': 9024, '03/04/2001': 9025, '16:29': 9026, 'intention': 9027, 'documented': 9028, 'robust': 9029, 'tonnage': 9030, 'timely': 9031, 'debtors': 9032, 'creditor': 9033, 'balances': 9034, 'OBSF': 9035, 'transactional': 9036, 'barclays': 9037, 'intercompany': 9038, 'continuing': 9039, 'AS400': 9040, 'inconsistency': 9041, 'useability': 9042, 'outright': 9043, 'screen': 9044, 'enquiry': 9045, 'AA': 9046, 'macro': 9047, 'transparent': 9048, 'accounting': 9049, 'Consequently': 9050, 'inform': 9051, 'misstated': 9052, 'simplifications': 9053, 'reconciling': 9054, 'analyses': 9055, 'enquiries': 9056, 'timetable': 9057, 'outline': 9058, 'reprioritised': 9059, 'rescoped': 9060, 'MO': 9061, 'incentive': 9062, 'awards': 9063, 'unsociable': 9064, 'objectives': 9065, 'headcount': 9066, 'duplicate': 9067, 'replacing': 9068, 'Traffic': 9069, 'mm': 9070, 'exists': 9071, 'DPR': 9072, 'p&l': 9073, 'investigate': 9074, 'misstatements': 9075, 'timeframe': 9076, 'retention': 9077, 'economically': 9078, 'reconciled': 9079, 'repeatable': 9080, 'instigated': 9081, 'prioritised': 9082, 'circularisation': 9083, '23rd': 9084, 'returns': 9085, 'co-ordination': 9086, 'requiring': 9087, 'download': 9088, 'vanilla': 9089, 'Barclays': 9090, 'recalculation': 9091, 'utilising': 9092, 'manually': 9093, 'creditors': 9094, 'needing': 9095, '21/03/2001': 9096, '18:20': 9097, 'Complexities': 9098, 'Sheet': 9099, 'Facility': 9100, 'commenced': 9101, 'uncertainty': 9102, 'revocation': 9103, 'bugs': 9104, 'functionality': 9105, 'compounded': 9106, 'operational': 9107, 'burden': 9108, 'amend': 9109, 'renegotiation': 9110, 'developer': 9111, 'resigned': 9112, 'gardening': 9113, 'lower': 9114, 'usage': 9115, 'irrespective': 9116, 'parochial': 9117, 'mitigating': 9118, 'levels': 9119, 'assurance': 9120, 'accuracy': 9121, 'Q1': 9122, 'delayed': 9123, 'decoupled': 9124, 'Brokerage': 9125, 'developers': 9126, 'AR': 9127, 'AP': 9128, 'reviews': 9129, 'businesses': 9130, 'components': 9131, 'covering': 9132, 'summarised': 9133, 'contingencies': 9134, 'build': 9135, 'VBA': 9136, 'excel': 9137, 'downloads': 9138, 'User': 9139, 'commence': 9140, 'resubstantiation': 9141, 'completed': 9142, 'inventory': 9143, 'Inspection': 9144, 'inspectors': 9145, 'expectation': 9146, 'replies': 9147, 'Substantive': 9148, 'checks': 9149, 'priced': 9150, 'unpriced': 9151, 'spot': 9152, 'Full': 9153, 'substantiating': 9154, 'existence': 9155, 'applicable': 9156, 'invoices': 9157, 'substantive': 9158, 'x34703': 9159, 'disappointed': 9160, 'TEXAS': 9161, 'Graduate': 9162, 'felt': 9163, 'experiences': 9164, 'recommendations': 9165, 'extracurricular': 9166, 'compensate': 9167, 'Professors': 9168, 'Titman': 9169, 'Ronn': 9170, 'Jemison': 9171, 'courses': 9172, 'Admissions': 9173, 'retaking': 9174, 'GMAT': 9175, 'monumental': 9176, 'successful': 9177, 'Sincerely': 9178, 'Rogers': 9179, 'favorable': 9180, 'chances': 9181, 'UT': 9182, 'learning': 9183, 'retake': 9184, 'commitee': 9185, 'extrcurricular': 9186, 'committment': 9187, 'dialague': 9188, 'Private': 9189, 'Equity': 9190, 'Fund': 9191, 'banking': 9192, 'Teco': 9193, 'O&M': 9194, 'costs': 9195, 'depicts': 9196, 'valuing': 9197, 'booking': 9198, 'curve': 9199, 'Hub': 9200, 'converting': 9201, 'MWh': 9202, 'volatilities': 9203, 'intra-day': 9204, 'blend': 9205, 'blending': 9206, 'formula': 9207, 'vols': 9208, '15th': 9209, 'twenty': 9210, 'deducting': 9211, 'SPRDOPT': 9212, 'Exotic': 9213, 'Options': 9214, 'function': 9215, 'VOM': 9216, 'stream': 9217, 'P&I': 9218, 'principal': 9219, 'Yvan': 9220, 'probability': 9221, 'misunderstanding': 9222, 'Furthermore': 9223, 'reiterate': 9224, 'RAC': 9225, 'reserve': 9226, 'methodology': 9227, 'ii': 9228, 'inherent': 9229, 'matches': 9230, 'enables': 9231, 'appropriately': 9232, 'Randy': 9233, 'Gallup': 9234, 'Compression': 9235, 'ECS': 9236, 'obligates': 9237, 'faith': 9238, 'CDEC': 9239, 'automated': 9240, 'automatically': 9241, 'alerted': 9242, 'peak': 9243, 'loading': 9244, 'complete': 9245, 'obligated': 9246, 'Transwestern': 9247, 'developing': 9248, 'accomplish': 9249, 'harmless': 9250, 'mentions': 9251, 'managing': 9252, 'mitigate': 9253, 'tilt': 9254, 'TW': 9255, 'flexibiltiy': 9256, 'absence': 9257, 'communicate': 9258, 'teh': 9259, 'info.': 9260, 'excuse': 9261, 'DF': 9262, 'Centilli': 9263, '12/14/2000': 9264, '02:49': 9265, 'Nemec': 9266, 'Service': 9267, 'Contract': 9268, 'Load': 9269, 'compressor': 9270, '200,987.33': 9271, 'incurred': 9272, 'remainder': 9273, 'averaging': 9274, '79,000': 9275, 'bidders': 9276, 'bidding': 9277, 'chunk': 9278, 'primary': 9279, 'deemed': 9280, 'alternate': 9281, 'primaries': 9282, 'alternates': 9283, 'None': 9284, 'importantly': 9285, 'memorializes': 9286, 'nailed': 9287, 'underlying': 9288, 'pro': 9289, 'forma': 9290, 'discount': 9291, 'Topack': 9292, 'Needles': 9293, '01/11/2001': 9294, '02:42': 9295, 'Dynegy': 9296, 'agreements': 9297, 'pls': 9298, 'df': 9299, 'format': 9300, 'kinda': 9301, 'topics': 9302, 'asap': 9303, 'MK': 9304, 'whasssup': 9305, 'rehearing': 9306, 'Stojic': 9307, 'Kelly': 9308, 'DENISE': 9309, 'LAGESSE': 9310, '01/12/2001': 9311, '10:34': 9312, 'Forgot': 9313, 'Drew': 9314, 'Fossum': 9315, '04:37': 9316, 'Lou': 9317, 'Huber': 9318, 'chair': 9319, 'Norma': 9320, '01:14': 9321, 'authorization': 9322, 'Aeron': 9323, '47': 9324, 'type': 9325, 'surplus': 9326, '567.77': 9327, 'lou': 9328, '01/13/2001': 9329, 'Sommer': 9330, 'MKM': 9331, 'spies': 9332, 'Stan': 9333, 'Dari': 9334, 'vented': 9335, 'aggravation': 9336, 'drafting': 9337, 'Gibson': 9338, 'complaining': 9339, 'duplicity': 9340, 'tempers': 9341, 'cooled': 9342, 'strongly': 9343, 'coordinated': 9344, 'orally': 9345, 'phonne': 9346, 'Yea': 9347, 'hoss': 9348, 'Ca': 9349, '14721': 9350, 'Evan': 9351, 'completing': 9352, 'CEC': 9353, 'CAEM': 9354, 'Directors': 9355, 'Hotel': 9356, 'conjunction': 9357, 'DISCO': 9358, 'Future': 9359, 'www.caem.org': 9360, 'Following': 9361, 'session': 9362, 'convention': 9363, 'unfortunately': 9364, 'reserved': 9365, 'block': 9366, 'rooms': 9367, 'nearby': 9368, 'Wyndham': 9369, 'lodging': 9370, 'hotels': 9371, 'Duncan': 9372, '202.739.0134': 9373, 'Mangold': 9374, '703.729.2710': 9375, 'H': 9376, 'NW': 9377, '20001': 9378, '202.582.1234': 9379, '1.800.233.1234': 9380, '202.637.4781': 9381, 'http://washington.hyatt.com/wasgh/index.html': 9382, '1400': 9383, '20005': 9384, '202.429.1700': 9385, '1.877.999.3223': 9386, '202.785.0786': 9387, 'http://www.wyndham.com/Washington_DC/default.cfm': 9388, 'Lora': 9389, 'Sullivan@ENRON': 9390, '07/19/2001': 9391, '03:31': 9392, 'Ace': 9393, 'Reporters': 9394, 'transcripts': 9395, 'Robertson': 9396, 'Affairs': 9397, '1775': 9398, '20006': 9399, '202-466-9142': 9400, '202-828-3372': 9401, 'lora.sullivan@enron.com': 9402, 'Ursula': 9403, 'BRADLEY': 9404, 'JR': 9405, 'ROBERT': 9406, 'L': 9407, 'Evaluation': 9408, 'Associates': 9409, 'Analysts': 9410, 'blank': 9411, 'retrieved': 9412, 'PEP': 9413, 'evaluations': 9414, 'Evaluations': 9415, 'BRENNER': 9416, 'URSULA': 9417, 'J.doc': 9418, 'Jean': 9419, 'Thane': 9420, 'communicating': 9421, 'commissioners': 9422, 'Ercot': 9423, 'responsive': 9424, 'ignored': 9425, 'complaints': 9426, 'complain': 9427, 'PUCT': 9428, 'Wood': 9429, 'Noel': 9430, 'legacy': 9431, 'recognizes': 9432, 'repercussions': 9433, 'Sibley': 9434, 'Wolens': 9435, 'legislature': 9436, 'engaged': 9437, 'apprised': 9438, 'Electric': 9439, 'forum': 9440, 'invitees': 9441, 'Commissioners': 9442, 'Sam': 9443, 'probing': 9444, 'a.m': 9445, 'PRS': 9446, '9:30': 9447, 'budget': 9448, 'Walking': 9449, 'Presto': 9450, 'ERCOT': 9451, 'reflective': 9452, 'ISO': 9453, 'fixes': 9454, 'messaging': 9455, 'constructive': 9456, 'FORMAL': 9457, 'PLAN': 9458, 'leverage': 9459, 'Becky': 9460, 'outgrowth': 9461, 'Leslie': 9462, 'Hunter': 9463, 'summaries': 9464, 'Fundamentals': 9465, 'intranet': 9466, 'envision': 9467, 'meantime': 9468, 'Kaminski@ECT': 9469, '04/30/2001': 9470, '02:28': 9471, 'Percell': 9472, 'extensive': 9473, 'modeling': 9474, 'Science': 9475, 'Advance': 9476, 'Mathematics': 9477, 'optimization': 9478, 'numerical': 9479, 'improve': 9480, 'efficiency': 9481, 'introducing': 9482, 'executives': 9483, 'arrangements': 9484, 'percell@swbell.net': 9485, '11:16:58': 9486, 'briefly': 9487, 'afterwards': 9488, 'SIAM': 9489, 'Workshop': 9490, 'simulation': 9491, 'software': 9492, 'products': 9493, 'utilize': 9494, 'mathematical': 9495, 'confines': 9496, 'qualifications': 9497, 'addendum': 9498, 'academic': 9499, 'consulting': 9500, '10030': 9501, 'Doliver': 9502, '77042-2016': 9503, '532-3836': 9504, 'Percell,': 9505, 'Resume': 9506, 'Only.doc': 9507, 'Exp.doc': 9508, 'Contact': 9509, 'Stark': 9510, 'Nasim': 9511, 'Khan@TRANSREDES': 9512, '12:42': 9513, 'assignment': 9514, 'NK': 9515, 'Stanley': 9516, 'Horton@ENRON': 9517, '12:35': 9518, '09:24': 9519, '11th': 9520, 'posting': 9521, 'belonged': 9522, 'Danny': 9523, 'Danny_Jones%ENRON@eott.com': 9524, '01:39:56': 9525, 'Horton': 9526, '0000108806': 9527, 'department': 9528, 'aviation': 9529, 'hangar': 9530, 'attendant': 9531, 'enron': 9532, 'persue': 9533, 'feild': 9534, 'respectfully': 9535, 'recommendation': 9536, 'referral': 9537, 'ETS': 9538, 'Anything': 9539, 'Respectfully': 9540, '281-443-3744': 9541, 'smithjones@ev1.net': 9542, '05/01/2001': 9543, 'Fehl': 9544, 'Hannon': 9545, 'WNBA': 9546, 'Comets': 9547, '104': 9548, 'Row': 9549, 'Seats': 9550, 'Preseason': 9551, 'Game': 9552, '7:30': 9553, 'SOL': 9554, 'Detroit': 9555, 'SHOCK': 9556, 'Los': 9557, 'Angeles': 9558, 'SPARKS': 9559, 'FIRE': 9560, '17': 9561, 'STARZZ': 9562, 'MYSTICS': 9563, 'MONARCHS': 9564, 'Indiana': 9565, 'FEVER': 9566, 'Cleveland': 9567, 'ROCKERS': 9568, '12:30': 9569, 'LIBERTY': 9570, 'Seattle': 9571, 'STORM': 9572, 'Orlando': 9573, 'MIRACLE': 9574, 'MERCURY': 9575, 'Minnesota': 9576, 'LYNX': 9577, 'listings': 9578, '713/853-6197': 9579, 'Mailing': 9580, 'iis': 9581, 'Stephen.Dyer@bakerbotts.com': 9582, '08:50:01': 9583, 'Took': 9584, 'Adam': 9585, 'SD': 9586, 'Billy': 9587, 'Dorsey@ENRON_DEVELOPMENT': 9588, '05/02/2001': 9589, '12:03': 9590, 'Weekly': 9591, 'CDT': 9592, 'Location': 9593, '50th': 9594, 'Boardroom': 9595, 'Video': 9596, 'Connections': 9597, 'remote': 9598, 'Conf': 9599, 'AT&T': 9600, 'Sherri': 9601, 'Sera': 9602, '713/853-5984': 9603, 'Dorsey': 9604, '713/646-6505': 9605, 'medium': 9606, '_______': 9607, '12:00': 9608, '4th': 9609, 'committments': 9610, 'divisions': 9611, 'Password': 9612, 'Application': 9613, 'applicant': 9614, 'database': 9615, 'Parent': 9616, 'ID': 9617, 'provides': 9618, 'downstream': 9619, 'Stephanie': 9620, 'Ferrous': 9621, 'preference': 9622, 'division': 9623, 'Samuel': 9624, 'Schott': 9625, '03/21/2001': 9626, '01:58': 9627, 'highlighted': 9628, 'Attn.': 9629, 'GCP_London': 9630, 'Rgds': 9631, 'x3-9890': 9632, 'ENW_GCP': 9633, 'Cass': 9634, '04/16/2001': 9635, '04:52': 9636, 'Datamanager': 9637, 'Steel': 9638, 'Hot': 9639, 'Rolled': 9640, 'Plate': 9641, 'Phy': 9642, 'Short': 9643, 'Type': 9644, 'Stl': 9645, 'Plt': 9646, 'Reference': 9647, 'Termination': 9648, 'rolled': 9649, 'steel': 9650, 'thickness': 9651, 'width': 9652, '72': 9653, 'inches': 9654, 'F.O.B.': 9655, 'Marine': 9656, 'Ill': 9657, 'Metro': 9658, 'Area': 9659, 'Seller': 9660, 'freight': 9661, 'unloading': 9662, 'warehouse': 9663, 'Buyer': 9664, 'quantity': 9665, 'lesser': 9666, 'allowance': 9667, 'Each': 9668, 'Dispatch': 9669, 'guarantee': 9670, 'supply': 9671, 'receipt': 9672, 'outlined': 9673, 'Terms': 9674, 'Conditions': 9675, 'Contractual': 9676, 'telegraphic': 9677, 'Unit': 9678, 'Measure': 9679, 'net': 9680, 'STEPS': 9681, 'APPROVAL': 9682, 'click': 9683, 'START': 9684, 'select': 9685, 'PROGRAMS': 9686, 'TEST': 9687, 'APPLICATIONS': 9688, 'ENRONONLINE': 9689, 'CLUSTER': 9690, 'PROD': 9691, 'PROCEED': 9692, 'USUAL': 9693, 'LOGIN': 9694, 'PASSWORD': 9695, 'Production': 9696, 'Cluster': 9697, 'EnronOnLine': 9698, 'Types': 9699, 'Awaiting': 9700, 'Partially': 9701, 'Approved': 9702, 'mouse': 9703, 'properties': 9704, 'APPROVE': 9705, 'Hare': 9706, '05:51': 9707, 'Incorporated': 9708, 'setups': 9709, 'confirmation': 9710, 'reads': 9711, 'compromising': 9712, 'customer': 9713, 'Applications': 9714, 'transact': 9715, 'understandably': 9716, 'upset': 9717, '03:11': 9718, '03/28/2001': 9719, '01:09': 9720, 'System': 9721, 'Sub': 9722, 'Cheryl': 9723, 'Johnson': 9724, 'Name': 9725, '_': 9726, 'Data': 9727, 'Tradename': 9728, 'broken': 9729, 'defeats': 9730, 'Doc': 9731, 'eve.': 9732, 'MHC': 9733, 'voicemail': 9734, 'Butcher': 9735, 'handled': 9736, 'sessions': 9737, 'Kriste': 9738, 'K.': 9739, '4861': 9740, '853-7557': 9741, '646-5847': 9742, '08:00': 9743, 'Pacific': 9744, 'Tijuana': 9745, 'Conf.': 9746, '*~*~*~*~*~*~*~*~*~*': 9747, 'PST': 9748, '800/711-8000': 9749, 'Passcode': 9750, '4153030': 9751, 'Henderson': 9752, 'Employment': 9753, 'Cash': 9754, 'Brad': 9755, 'Seleznov': 9756, 'herewith': 9757, 'intentions': 9758, 'Dealbench': 9759, 'Word': 9760, 'Tobias': 9761, 'Munk': 9762, 'Teresa': 9763, 'Lund': 9764, 'Bridgeline': 9765, 'non-compete': 9766, 'formatting': 9767, 'SSD': 9768, '220b': 9769, 'dg': 9770, 'Services.doc': 9771, '220a': 9772, 'DG': 9773, 'Services.DOC': 9774, 'Agreements': 9775, 'Diane': 9776, 'Goode': 9777, 'groan': 9778, 'GIS': 9779, 'ids': 9780, 'Eid': 9781, 'migrate': 9782, 'Brandee': 9783, 'Sanborn': 9784, 'I.S.C.': 9785, 'Customer': 9786, 'Care': 9787, 'Process': 9788, 'http://isc.enron.com/site': 9789, 'OLE': 9790, 'Object': 9791, 'Ngoc': 9792, 'Luan': 9793, 'Do@ENRON_DEVELOPMENT': 9794, '08/07/2001': 9795, '12:36': 9796, 'Whom': 9797, 'Concern': 9798, 'Through': 9799, 'identity': 9800, 'fraud': 9801, 'stolen': 9802, 'identification': 9803, 'IDs': 9804, 'SS': 9805, 'safer': 9806, 'bus': 9807, 'databases': 9808, 'sourcing': 9809, 'badge': 9810, 'safeguard': 9811, 'Points': 9812, '3.30': 9813, '5.30': 9814, '46C1': 9815, 'Leads': 9816, 'Sunjay': 9817, 'Arya': 9818, 'Gary': 9819, 'Khymberly': 9820, 'Booth': 9821, 'Mecole': 9822, 'Tim': 9823, \"O'Rourke\": 9824, 'Cashion': 9825, 'Sheila': 9826, 'Walton': 9827, 'Wendy': 9828, 'Fincher': 9829, 'Knudsen': 9830, 'Karen': 9831, 'Phillips': 9832, 'Davies': 9833, 'Skinner': 9834, 'Shanna': 9835, 'Funkhouser': 9836, 'Peer': 9837, 'gathered': 9838, 'Upon': 9839, 'contacting': 9840, 'projects': 9841, 'rotation': 9842, 'contribution': 9843, 'x54667': 9844, 'occur': 9845, \"Ya'll\": 9846, 'Sandra': 9847, 'McMahon': 9848, 'Affirmative': 9849, 'Action': 9850, 'demographic': 9851, 'workforce': 9852, 'origin': 9853, 'nationality': 9854, 'Skilling': 9855, 'HBS': 9856, 'study': 9857, 'Modern': 9858, 'Giants': 9859, 'shadow': 9860, 'morph': 9861, 'earliest': 9862, 'convience': 9863, 'Try': 9864, 'bob': 9865, 'NEPCO': 9866, 'picketing': 9867, 'pipe': 9868, 'fitters': 9869, 'picket': 9870, 'interfere': 9871, 'Olgletree': 9872, 'Stubley': 9873, 'Indivero': 9874, 'jobsite': 9875, 'Construction': 9876, 'Osler': 9877, 'visiting': 9878, 'Building': 9879, 'Planning': 9880, 'Zoning': 9881, 'dept': 9882, 'pipefitters': 9883, 'inquired': 9884, 'Apparently': 9885, 'Payne': 9886, 'Creek': 9887, 'counsel': 9888, 'Croall': 9889, 'Galen': 9890, 'Torneby': 9891, '11831': 9892, 'Parkway': 9893, 'Bothell': 9894, 'WA': 9895, '98011': 9896, '425-415-3052': 9897, '425-922-0475': 9898, '425-415-3098': 9899, 'mailto:galent@nepco.com': 9900, 'mailto:galen.torneby@nepco.com': 9901, 'Hopkinson@ENRON_DEVELOPMENT': 9902, '09/16/99': 9903, '10:07': 9904, 'invitaion': 9905, 'Shackleton@ECT': 9906, '08:55': 9907, 'wise': 9908, 'Felix': 9909, 'Araujo': 9910, 'Cintra': 9911, 'Tozzini': 9912, 'derivative': 9913, 'Sao': 9914, 'Paulo': 9915, 'Sept': 9916, 'BA': 9917, 'Marval': 9918, '10:16': 9919, '___________________________________________': 9920, 'Warning': 9921, 'Em-enro2.doc': 9922, 'cancelled': 9923, 'Andrea': 9924, 'Bertone@ENRON_DEVELOPMENT': 9925, '09/15/99': 9926, '04:41': 9927, '08:15': 9928, 'Daniel': 9929, 'R': 9930, 'Castagnola@ENRON_DEVELOPMENT': 9931, '05/17/99': 9932, '01:47': 9933, 'Looks': 9934, '11:23': 9935, 'extension': 9936, '35620': 9937, 'luncheon': 9938, 'comprehensive': 9939, 'pertinent': 9940, 'reschedule': 9941, 'Obviously': 9942, 'LA.': 9943, 'eligibility': 9944, '09/03/99': 9945, '01:57': 9946, 'sided': 9947, 'laminated': 9948, 'Lunch': 9949, 'L/C': 9950, 'Manogue': 9951, 'doc': 9952, 'lined': 9953, 'Fedexed': 9954, 'overnight': 9955, 'S&S': 9956, 'gtee': 9957, 'l/c': 9958, 'drops': 9959, 'bank': 9960, 'refuses': 9961, 'docs': 9962, \"one's\": 9963, 'issuing': 9964, 'owe': 9965, 'Phoebe': 9966, 'Chloe': 9967, 'N.O.': 9968, 'Memphis': 9969, 'Eleuthra': 9970, 'Petersburg': 9971, 'Amelia': 9972, 'Plantation': 9973, '380': 9974, 'SE': 9975, 'scaring': 9976, 'usual': 9977, 'selling': 9978, 'burgers': 9979, 'franchise': 9980, 'SARA': 9981, 'SamChawk@aol.com': 9982, '09/11/99': 9983, '07:48:44': 9984, 'stranger': 9985, 'SAM': 9986, 'tells': 9987, 'Cone': 9988, '.....': 9989, 'iii': 9990, 'definitive': 9991, 'discretion': 9992, 'Quick': 9993, '09/17/99': 9994, '04:50': 9995, 'Nordic': 9996, 'tks': 9997, '09:48': 9998, 'JMB': 9999, 'JBennett@GMSSR.com': 10000, '12/22/2000': 10001, '06:28': 10002, 'Parties': 10003, 'procedural': 10004, 'hearings': 10005, '12/27': 10006, '12/28': 10007, 'moments': 10008, 'holidays': 10009, 'Angela': 10010, 'Minkin': 10011, '1%P701!.doc': 10012, 'Lindh': 10013, 'FRL3@pge.com': 10014, '12/21/2000': 10015, '07:50': 10016, 'Confidential': 10017, 'Rule': 10018, 'Participants': 10019, 'workshops': 10020, 'Maintains': 10021, '2007': 10022, 'Offers': 10023, 'Provides': 10024, 'vintaged': 10025, 'Redwood': 10026, '7.5': 10027, 'cent': 10028, 'dth': 10029, 'minimizing': 10030, 'bypass': 10031, 'Adopts': 10032, 'reliability': 10033, 'escalator': 10034, 'adjustable': 10035, 'Preserves': 10036, 'differential': 10037, 'Baja': 10038, 'paths': 10039, 'Proposes': 10040, 'aggregation': 10041, 'anticipates': 10042, 'Core': 10043, 'Procurement': 10044, 'Incentive': 10045, 'Mechanism': 10046, 'CPIM': 10047, 'holdings': 10048, 'anticipated': 10049, 'increases': 10050, 'feedback': 10051, 'Attachment': 10052, 'workpapers': 10053, 'extend': 10054, 'wishes': 10055, 'holiday': 10056, 'Ray': 10057, 'Williams': 10058, '415': 10059, '973-2776': 10060, '973-3634': 10061, '12-20-00.doc': 10062, 'Proposed': 10063, '27.doc': 10064, 'COS': 10065, 'Rates': 10066, 'Workpapers': 10067, '12-20-2000': 10068, 'Proposal.xls': 10069, 'stabilization': 10070, 'Jeffrey': 10071, 'Few': 10072, 'fleshing': 10073, 'pager': 10074, '888.916.7184': 10075, '415.782.7822': 10076, '415.621.8317': 10077, 'finalize': 10078, '800.713.8600': 10079, 'Code': 10080, '80435': 10081, '12/26/2000': 10082, '03:15': 10083, 'Going': 10084, 'wild': 10085, 'ride': 10086, 'Kingerski': 10087, '03:33': 10088, 'acknowledge': 10089, 'modest': 10090, 'reasoned': 10091, 'whim': 10092, 'overly': 10093, 'prescriptive': 10094, 'excellently': 10095, 'perishable': 10096, 'observations': 10097, 'OBSERVATION': 10098, 'finger': 10099, 'gouging': 10100, 'bent': 10101, 'spikes': 10102, 'pose': 10103, 'risks': 10104, 'refunds': 10105, 'Reliant': 10106, 'Duke': 10107, 'Barton': 10108, 'democrat': 10109, 'Filner': 10110, 'locked': 10111, 'Bilbray': 10112, '---': 10113, 'lauded': 10114, 'Wolak': 10115, 'emphatically': 10116, 'culprit': 10117, 'screwed': 10118, 'incentives': 10119, 'embedded': 10120, 'IMPLICATION': 10121, 'prudent': 10122, 'fingered': 10123, 'clamoring': 10124, 'subsides': 10125, 'stunk': 10126, 'profited': 10127, 'consumers': 10128, 'w/out': 10129, 'declaring': 10130, 'fashion': 10131, 'finesse': 10132, 'specify': 10133, 'Shawna': 10134, 'Johnson@ENRON': 10135, '02/22/2001': 10136, 'Programs': 10137, 'Penn': 10138, 'undergraduate': 10139, 'campus': 10140, 'interviewing': 10141, 'interns': 10142, 'interviewers': 10143, '1st': 10144, 'interviewer': 10145, 'Campus': 10146, 'Place': 10147, 'Pennsylvania': 10148, 'Interviews': 10149, 'Inn': 10150, 'Pen': 10151, 'Interviewers': 10152, 'McGowan': 10153, 'Jen': 10154, 'Fraser': 10155, 'Rhee': 10156, 'Hilgert': 10157, 'referrals': 10158, 'VP': 10159, 'ext': 10160, '58369': 10161, 'Coordinator': 10162, 'Kushnick': 10163, 'skush@swbell.net': 10164, '01/26/2001': 10165, '12:48': 10166, '01/25/2001': 10167, '09:40': 10168, 'Byargeon': 10169, 'Loch': 10170, 'offshore': 10171, 'drilling': 10172, 'supplement': 10173, 'VPP': 10174, '1/31': 10175, 'Medusa': 10176, 'involves': 10177, 'Murphy': 10178, 'Agip': 10179, 'Callon': 10180, 'Mississippi': 10181, 'Canyon': 10182, 'Blocks': 10183, '538': 10184, '582': 10185, 'reservoir': 10186, 'barrels': 10187, 'sour': 10188, 'crude': 10189, '40,000': 10190, 'bpd': 10191, '1Q': 10192, 'Quality': 10193, 'Mars': 10194, 'API': 10195, 'Gravity': 10196, '2.0': 10197, 'sulfur': 10198, 'Plans': 10199, 'Equilon': 10200, '143': 10201, 'platform': 10202, 'onshore': 10203, 'WD': 10204, 'pumps': 10205, 'floating': 10206, 'Platt': 10207, '95,000': 10208, 'Meraux': 10209, 'refinery': 10210, 'notional': 10211, 'binding': 10212, 'Amanda': 10213, 'Huble@ENRON': 10214, '01/24/2001': 10215, '01:02': 10216, 'typically': 10217, 'Hoog': 10218, '09:37': 10219, 'burdensome': 10220, 'muni': 10221, 'ended': 10222, 'downside': 10223, 'loses': 10224, 'hedging': 10225, 'collecting': 10226, 'payout': 10227, 'losing': 10228, 'combine': 10229, 'liability': 10230, 'RusAmArts@aol.com': 10231, '11:57': 10232, 'highlight': 10233, 'MMC': 10234, 'EAST': 10235, 'WHOLESALE': 10236, 'RETAIL': 10237, 'COMM': 10238, 'ENERGY': 10239, 'SERVICES': 10240, 'Resource': 10241, 'Rina': 10242, 'ENRONR~1.DOC': 10243, 'Yikes': 10244, 'Alma': 10245, 'Martinez@ENRON': 10246, '11:36': 10247, '7:35': 10248, 'lady': 10249, 'Nicki': 10250, 'yrs.': 10251, 'waited': 10252, '8:15': 10253, 'grandmother': 10254, 'Pretty': 10255, 'strange': 10256, 'aye': 10257, 'runs': 10258, 'UofH': 10259, 'accompany': 10260, 'lecture': 10261, 'departure': 10262, 'relieved': 10263, 'EGM': 10264, 'Jana': 10265, 'Giovannini': 10266, '10:51': 10267, 'volunteered': 10268, '11:42': 10269, 'recognize': 10270, 'anticipate': 10271, 'fulltime': 10272, 'Cycle': 10273, 'interviewed': 10274, 'Managers': 10275, 'round': 10276, 'buttons': 10277, '1.65': 10278, 'Great': 10279, '02:02': 10280, 'deviation': 10281, 'Eco': 10282, 'reactions': 10283, 'favourable': 10284, 'Perry@ENRON_DEVELOPMENT': 10285, '25/01/2001': 10286, '00:20': 10287, 'LNG': 10288, '272,000': 10289, '0.10': 10290, '3,300,000': 10291, 'share': 10292, '1,650,000': 10293, 'counting': 10294, 'margin': 10295, '1,922,000': 10296, 'cargo': 10297, 'Marianne': 10298, '1.1': 10299, 'parentheses': 10300, '3.1': 10301, 'proviso': 10302, 'insert': 10303, '6.1': 10304, 'semicolon': 10305, 'clause': 10306, '6.2.1': 10307, '6.3': 10308, 'reworded': 10309, 'Capitalize': 10310, 'definition': 10311, 'Average': 10312, 'Annual': 10313, 'Factor': 10314, 'Delete': 10315, 'Costs': 10316, 'Govt': 10317, 'GTC': 10318, 'ECP': 10319, 'EESI': 10320, 'reps': 10321, 'Collateral': 10322, 'comma': 10323, 'deposit': 10324, 'granting': 10325, 'perfection': 10326, 'Events': 10327, 'Default': 10328, 'dispute': 10329, 'consolidation': 10330, 'Remedies': 10331, 'UCC': 10332, 'Disclaimer': 10333, 'warranties': 10334, 'setoff': 10335, 'Clair': 10336, '3889': 10337, '713-853-3989': 10338, '713-646-3393': 10339, 'carol.st.clair@enron.com': 10340, 'Castano@EES': 10341, '05/30/2001': 10342, '11:05': 10343, 'incorporate': 10344, 'Con': 10345, 'billing': 10346, 'midmarket': 10347, 'collect': 10348, 'T&D': 10349, 'Setoff': 10350, 'affiliate': 10351, 'differently': 10352, 'harsh': 10353, 'majeure': 10354, 'soften': 10355, 'GTCs': 10356, 'calculation': 10357, 'saving': 10358, 'hybrid': 10359, 'Dietrich': 10360, 'Hillegonds': 10361, 'hiatus': 10362, 'pocket': 10363, '7/16': 10364, 'mid-July': 10365, 'Bond': 10366, 'St': 10367, '05/31/2001': 10368, '09:01': 10369, 'Phyllis': 10370, 'LC': 10371, 'transferable': 10372, 'lemelpe@NU.COM': 10373, '03:00': 10374, 'LOC': 10375, 'Mid': 10376, 'Power': 10377, 'Form': 10378, 'Christie': 10379, 'Rhonda': 10380, 'Lepore': 10381, 'Select': 10382, 'Settlements': 10383, 'reconcile': 10384, '860-665-2368': 10385, 'leporjj@selectenergy.com': 10386, 'Denton': 10387, '05/25/2001': 10388, '12:45': 10389, '5/1/01': 10390, '12:01': 10391, '6/1/01': 10392, '10:44': 10393, 'Valerie': 10394, 'Mooney': 10395, 'Sacks': 10396, 'discussing': 10397, 'tuning': 10398, 'Bruce': 10399, 'Val': 10400, 'finalizing': 10401, 'finished': 10402, 'coordinating': 10403, 'Lemell': 10404, '06/01/2001': 10405, '03:20': 10406, 'Deal': 10407, 'No.': 10408, '74419': 10409, 'Enpower': 10410, '295870': 10411, 'chnages': 10412, '06/04/01': 10413, '08:38': 10414, 'transferability': 10415, 'ICC': 10416, 'governs': 10417, 'Guaranty': 10418, 'Were': 10419, 'Interconnect': 10420, 'Questar': 10421, 'reimbursable': 10422, 'interconnect': 10423, 'payments': 10424, 'CIAC': 10425, 'ourselves': 10426, 'Para': 10427, '3.2': 10428, 'protects': 10429, 'financially': 10430, 'IRS': 10431, 'Guthrie': 10432, 'Kim': 10433, '713-853-3098': 10434, 'Trails': 10435, '02-05-02.doc': 10436, 'Earl': 10437, 'metering': 10438, 'facilities': 10439, 'ROW': 10440, 'ft': 10441, 'specifications': 10442, 'helps': 10443, 'Theirs': 10444, 'Chanley': 10445, '505-625-8031': 10446, '3.': 10447, 'departing': 10448, 'Newark': 10449, 'Rome': 10450, 'Ticket': 10451, '1,183': 10452, 'ticket': 10453, 'Janell': 10454, 'leg': 10455, 'X33098': 10456, 'telephony': 10457, 'Hotline': 10458, 'extensions': 10459, 'greetings': 10460, 'Kowalke': 10461, 'Reps.': 10462, 'notification': 10463, 'Popup': 10464, 'Critical': 10465, 'Buchanan': 10466, 'TMS': 10467, '713-853-3044': 10468, 'TK': 10469, 'hurry': 10470, 'wondering': 10471, 'Top': 10472, 'Shippers': 10473, 'Revenues': 10474, 'Browncover': 10475, 'Total': 10476, '180.9': 10477, 'revenues': 10478, 'ledger': 10479, '165.9': 10480, '10.0': 10481, 'Sempra': 10482, 'Richardson': 10483, 'Astr': 10484, '2.7': 10485, 'SoCal': 10486, '1.8': 10487, 'prepayment': 10488, 'x36709': 10489, 'richard': 10490, 'Operational': 10491, 'Accountabilities': 10492, 'interpersonal': 10493, 'mature': 10494, 'Daugherty': 10495, 'Spiritual': 10496, 'Methodist': 10497, 'Health': 10498, '6565': 10499, 'Fannin': 10500, '77030-2707': 10501, '713-793-1429': 10502, 'Page': 10503, '281-735-5919': 10504, '713-819-2784': 10505, '713-790-2605': 10506, 'Mansoor': 10507, 'attain': 10508, 'bankruptcy': 10509, 'reluctant': 10510, 'ship': 10511, 'prepaid': 10512, 'Daniels': 10513, 'Fisher': 10514, 'Rosemont': 10515, 'instrumentation': 10516, 'chromatograph': 10517, 'prepayments': 10518, 'mode': 10519, 'I/C': 10520, 'UltraSonic': 10521, 'testing': 10522, 'Kansas': 10523, 'realistic': 10524, 'Listening': 10525, 'w/': 10526, 'investors': 10527, \"'02\": 10528, 'Dunn': 10529, 'heckuvalot': 10530, 'mischief': 10531, 'detrimental': 10532, 'Investors': 10533, 'intends': 10534, 'QFs': 10535, 'haircuts': 10536, 'Figured': 10537, 'schedulers': 10538, 'shop': 10539, 'attendance': 10540, './': 10541, 'windows': 10542, 'OFOs': 10543, 'holding': 10544, 'Probing': 10545, 'palace': 10546, 'focuses': 10547, 'hikes': 10548, 'KIMBERLY': 10549, 'KINDY': 10550, 'Orange': 10551, 'Register': 10552, 'SACRAMENTO': 10553, 'subpoenas': 10554, 'investigative': 10555, 'Sen.': 10556, 'Santa': 10557, 'Ana': 10558, 'focusing': 10559, 'Shakespearean': 10560, 'twists': 10561, 'intrigue': 10562, 'Lawmakers': 10563, 'allege': 10564, 'fueled': 10565, 'Winter': 10566, 'Operator': 10567, 'defied': 10568, 'concerted': 10569, 'e-mails': 10570, 'memos': 10571, 'Maviglio': 10572, 'betrayed': 10573, 'mounting': 10574, 'Fishman': 10575, 'lights': 10576, 'megawatt': 10577, 'limits': 10578, 'seven': 10579, 'grinned': 10580, 'beared': 10581, 'cap': 10582, 'investigating': 10583, 'broke': 10584, 'Records': 10585, 'Regulatory': 10586, 'represented': 10587, 'dozens': 10588, 'sow': 10589, 'threaten': 10590, 'stifle': 10591, 'generating': 10592, 'Hall': 10593, 'coordination': 10594, 'aim': 10595, 'collusion': 10596, 'fix': 10597, 'violate': 10598, '250': 10599, 'profit': 10600, 'withhold': 10601, 'declared': 10602, 'Stage': 10603, 'braced': 10604, 'blackouts': 10605, 'narrowly': 10606, 'averted': 10607, 'pivotal': 10608, 'abolish': 10609, 'Final': 10610, 'lifting': 10611, 'rested': 10612, 'Neither': 10613, 'representatives': 10614, 'retrospect': 10615, 'regulators': 10616, 'justifying': 10617, 'sat': 10618, 'advisers': 10619, 'poker': 10620, 'accelerated': 10621, 'Prices': 10622, 'jumped': 10623, 'average': 10624, '249': 10625, '700': 10626, 'overcharges': 10627, 'exceeded': 10628, 'hurt': 10629, 'Californians': 10630, 'spike': 10631, 'skyrocketing': 10632, 'plants': 10633, 'Smutney': 10634, 'executive': 10635, 'director': 10636, 'represents': 10637, 'consult': 10638, 'unaware': 10639, 'consulted': 10640, 'Producers': 10641, 'chaos': 10642, 'defended': 10643, 'motion': 10644, 'transpired': 10645, 'clues': 10646, 'resurrect': 10647, 'recreating': 10648, 'dig': 10649, 'Vicki': 10650, 'DA': 10651, 'UDCs': 10652, 'indifferent': 10653, 'adopted': 10654, 'SCE': 10655, 'SDG&E': 10656, 'teams': 10657, 'scramble': 10658, 'golf': 10659, 'wager': 10660, 'Montavano': 10661, 'Shapiro': 10662, 'switch': 10663, 'Neal': 10664, 'Manne': 10665, 'NMANNE@SusmanGodfrey.com': 10666, '11/28/2000': 10667, '06:16': 10668, 'negotiation': 10669, 'Particularly': 10670, 'entreaty': 10671, 'RNR': 10672, 'conformity': 10673, 'arbitrators': 10674, 'ineffective': 10675, 'Accordingly': 10676, 'arbitration': 10677, 'choose': 10678, 'hardship': 10679, 'Rance@ENRON': 10680, 'percentages': 10681, 'Perkins': 10682, 'Chumley': 10683, 'contractor': 10684, 'Veronica': 10685, 'Parra': 10686, 'Nedre': 10687, 'Strambler': 10688, 'Fran': 10689, 'Mayes': 10690, 'Felicia': 10691, 'Solis': 10692, 'Carter': 10693, 'McCullough': 10694, 'Lisa': 10695, 'Mellencamp': 10696, 'Shelia': 10697, 'Tweed': 10698, 'Sanders': 10699, 'Deffner': 10700, 'Brain': 10701, 'Kerrigan': 10702, 'Worthy': 10703, 'Structuring': 10704, 'Technology': 10705, 'Alford': 10706, 'Coffman': 10707, 'Keeney': 10708, 'Insurance': 10709, 'Marshall': 10710, 'Hooser': 10711, '11:34': 10712, 'H.': 10713, 'Klimberg': 10714, 'LIPA': 10715, 'Anyone': 10716, 'Blaine@ENRON_DEVELOPMENT': 10717, '07:35': 10718, 'actins': 10719, 'manages': 10720, 'litgation': 10721, 'litigation': 10722, 'insurers': 10723, 'sued': 10724, 'Casualty': 10725, 'mb': 10726, 'steer': 10727, 'sensitivity': 10728, 'proceedings': 10729, 'Beachcrofts': 10730, 'DPC': 10731, 'Address': 10732, 'Robin': 10733, 'Gibbs': 10734, 'Montgomery@ENRON': 10735, '08:22': 10736, 'Russ': 10737, '3-5297': 10738, 'brianp@aiglincoln.com': 10739, 'Jacobs': 10740, 'rsjacobs@Encoreacq.com': 10741, '11:25': 10742, 'Rich': 10743, 'Kinga': 10744, 'Patterson': 10745, 'harass': 10746, 'pre': 10747, 'Chanukah': 10748, 'C.DTF': 10749, 'crushed': 10750, 'Williams@ENRON_DEVELOPMENT': 10751, '09:46': 10752, 'fold': 10753, 'AAA': 10754, 'atty': 10755, '11/27/2000': 10756, '10:25': 10757, '3,202.61': 10758, '11-20-2000': 10759, 'Nuria_R_Ibarra@calpx.com': 10760, '04/28/2000': 10761, '11:15:11': 10762, 'Patricia': 10763, 'Gillman': 10764, '4_28_00.doc': 10765, '09:56': 10766, 'melanie.gray@weil.com': 10767, '11/13/2000': 10768, '03:43': 10769, 'pleadings': 10770, 'A&K': 10771, 'Trustee': 10772, 'nonenforcement': 10773, 'remedies': 10774, 'pulling': 10775, 'Court': 10776, 'trustee': 10777, 'oppositon': 10778, 'Nov': 10779, '**********': 10780, 'reader': 10781, 'telephone': 10782, '713-546-5000': 10783, 'Gerry': 10784, 'Strathmann': 10785, 'gstrathmann@mediaone.net': 10786, '02:19': 10787, 'eCommerce': 10788, 'Dispute': 10789, 'Protocol': 10790, 'enthusiastic': 10791, 'Pitney': 10792, 'Bowes': 10793, 'Daimler': 10794, 'Chrysler': 10795, 'focal': 10796, 'aspirational': 10797, 'Arbitration': 10798, 'Association': 10799, 'B2B': 10800, 'validate': 10801, 'assumptions': 10802, 'refine': 10803, 'offerings': 10804, 'Initially': 10805, 'consultants': 10806, 'Ideally': 10807, 'consist': 10808, 'cross-functional': 10809, 'marketplaces': 10810, 'underway': 10811, '978-376-9004': 10812, '->': 10813, '978': 10814, '376-9004': 10815, '303-294-4499': 10816, '09:52': 10817, 'tel': 10818, 'Palmer@ENRON': 10819, '10:45': 10820, 'Leopold': 10821, 'Jason.Leopold@dowjones.com': 10822, '10:40': 10823, 'Energyphiles': 10824, 'anti-trust': 10825, 'assembled': 10826, 'battered': 10827, 'lawsuit': 10828, '550': 10829, 'b/t': 10830, '1810': 10831, 'Offices': 10832, 'Levine': 10833, 'Steinberg': 10834, 'Huver': 10835, '619-231-9449': 10836, 'complaint': 10837, 'UCAN': 10838, '***********************************': 10839, 'teacher': 10840, 'kills': 10841, 'students': 10842, 'Shames': 10843, 'Utility': 10844, 'Consumers': 10845, '1717': 10846, 'Kettner': 10847, 'Blvd.': 10848, '105': 10849, '92101': 10850, '619-696-6966': 10851, 'mshames@ucan.org': 10852, 'Marriage': 10853, 'Kids': 10854, 'suck': 10855, 'abomination': 10856, 'numbing': 10857, 'trailor': 10858, 'trash': 10859, 'cretins': 10860, 'procreating': 10861, 'springer': 10862, 'crapfest': 10863, 'hippie': 10864, 'monkey': 10865, 'fuck': 10866, 'grow': 10867, 'Politically': 10868, 'permissive': 10869, 'bullshit': 10870, 'noone': 10871, 'fucked': 10872, 'likley': 10873, 'smoking': 10874, 'fucking': 10875, 'perv': 10876, 'lazy': 10877, 'waht': 10878, 'sympathy': 10879, 'Fuck': 10880, 'Stop': 10881, 'pawn': 10882, 'brats': 10883, 'break': 10884, 'mommy': 10885, 'madea': 10886, 'Tough': 10887, 'drag': 10888, 'singles': 10889, 'segueway': 10890, 'instances': 10891, 'Mommies': 10892, 'childcare': 10893, 'insidious': 10894, 'Mommism': 10895, 'stamped': 10896, 'http://www.newsday.com/news/opinion/ny-vpnasa054135614feb05,0,5979821.story?coll=ny-editorials-headlines': 10897, 'Newsday.com': 10898, 'NASA': 10899, 'engineers': 10900, 'astronauts': 10901, 'giddy': 10902, 'fever': 10903, 'tentative': 10904, 'Discovery': 10905, 'technically': 10906, 'safety': 10907, 'features': 10908, 'procedures': 10909, 'agency': 10910, 'errors': 10911, 'Challenger': 10912, 'sacrificed': 10913, 'image': 10914, 'puffing': 10915, 'Seeing': 10916, 'woke': 10917, 'cosmos': 10918, 'imagination': 10919, 'species': 10920, 'shuttles': 10921, 'preserve': 10922, 'defects': 10923, 'encouraging': 10924, 'overlooked': 10925, 'losses': 10926, 'Clearly': 10927, 'tackling': 10928, 'superb': 10929, 'minded': 10930, 'Kennedy': 10931, 'boost': 10932, 'morale': 10933, 'deliveries': 10934, 'thermal': 10935, 'protective': 10936, 'tile': 10937, 'orbit': 10938, 'hunks': 10939, 'insulation': 10940, 'PR': 10941, 'hurdles': 10942, 'lovers': 10943, 'redefine': 10944, 'rekindle': 10945, 'Selah': 10946, 'Posted': 10947, 'Hidden': 10948, 'Nook': 10949, '2/7/2005': 10950, '01:09:32': 10951, 'Healing': 10952, 'Collective': 10953, 'Body': 10954, 'Orwell': 10955, 'novel': 10956, '1984': 10957, 'doublethink': 10958, 'device': 10959, 'totalitarian': 10960, 'irreconcilable': 10961, 'duality': 10962, 'cherished': 10963, 'dualities': 10964, 'autonomous': 10965, 'destinies': 10966, 'separation': 10967, 'beings': 10968, 'tress': 10969, 'rocks': 10970, 'sky': 10971, 'healing': 10972, 'collective': 10973, 'soul': 10974, 'Think': 10975, 'choir': 10976, 'strength': 10977, 'harmony': 10978, 'dischord': 10979, 'insult': 10980, 'Living': 10981, 'synch': 10982, 'tributaries': 10983, 'sickness': 10984, 'weakest': 10985, 'sickest': 10986, 'lesson': 10987, 'teenager': 10988, 'scorn': 10989, 'intolerance': 10990, 'Herpes': 10991, 'climate': 10992, 'shame': 10993, 'indignity': 10994, 'hurting': 10995, 'escape': 10996, 'ocean': 10997, 'swim': 10998, 'haunt': 10999, 'consequences': 11000, 'rejection': 11001, 'embrace': 11002, 'affects': 11003, 'shapes': 11004, 'destiny': 11005, 'healthier': 11006, 'collectively': 11007, 'Fortunately': 11008, 'behaviour': 11009, 'choices': 11010, 'planet': 11011, 'crash': 11012, 'burn': 11013, 'conflagration': 11014, 'arrogance': 11015, 'rise': 11016, 'prejudice': 11017, 'kicking': 11018, 'screaming': 11019, 'Sunshine': 11020, 'Scipio': 11021, 'Homeopath': 11022, 'Herbalist': 11023, 'Holistic': 11024, 'Viral': 11025, 'Simple': 11026, 'Idea': 11027, 'Freedom': 11028, 'downsizing': 11029, 'debts': 11030, 'Agel': 11031, 'vehicle': 11032, 'uniquely': 11033, 'positioned': 11034, 'giant': 11035, 'category': 11036, 'Imagine': 11037, 'innovation': 11038, 'Innovative': 11039, 'introduces': 11040, 'Gelceuticals': 11041, 'innovative': 11042, 'nutritional': 11043, 'Gel': 11044, 'Suspension': 11045, 'packets': 11046, 'absorption': 11047, 'vitamins': 11048, 'minerals': 11049, 'nutrients': 11050, 'maximized': 11051, 'consuming': 11052, 'Consider': 11053, 'convenience': 11054, 'factors': 11055, 'tablets': 11056, 'capsules': 11057, 'juice': 11058, 'bottle': 11059, 'glass': 11060, 'Single': 11061, 'packages': 11062, 'capabilities': 11063, 'Perfect': 11064, 'swallowing': 11065, 'Faster': 11066, 'Learn': 11067, 'Compensation': 11068, 'compensation': 11069, 'revolutionary': 11070, 'binary': 11071, 'breakaway': 11072, 'unilevel': 11073, 'matrix': 11074, 'viability': 11075, 'longevity': 11076, 'residual': 11077, 'principles': 11078, 'unsure': 11079, 'discover': 11080, 'possibilities': 11081, 'feature': 11082, 'field': 11083, 'Jensen': 11084, 'smoke': 11085, '·': 11086, 'Create': 11087, 'Residual': 11088, 'Vehicle': 11089, 'Dreams': 11090, 'Amazing': 11091, 'Breakthrough': 11092, 'Behind': 11093, 'Quadra': 11094, 'Builds': 11095, 'Bigger': 11096, 'Bonus': 11097, 'Checks': 11098, 'Help': 11099, 'Build': 11100, 'Stronger': 11101, 'Secret': 11102, 'Lock': 11103, 'Legacy': 11104, 'Position': 11105, '620-294-4000': 11106, '620-294-3000': 11107, '5107': 11108, 'DATE': 11109, 'TIME': 11110, 'Standard': 11111, 'LOCATION': 11112, 'PT': 11113, '**': 11114, 'MT': 11115, 'CT': 11116, 'Fillmore': 11117, '612-205-9814': 11118, '620-294-1909': 11119, 'Dharmadeva': 11120, 'dharmad...@gmail.com': 11121, 'Namaskar': 11122, 'kaoshikii': 11123, 'yoga': 11124, 'postures': 11125, 'males': 11126, 'females': 11127, 'Dharma': 11128, 'Kaoshikii': 11129, 'invented': 11130, '1978': 11131, 'Shrii': 11132, \"A'nandamu'rti\": 11133, 'psycho-spiritual': 11134, 'exercise': 11135, 'benefiting': 11136, 'stamina': 11137, 'ward': 11138, 'cure': 11139, 'diseases': 11140, 'youthful': 11141, 'childbirth': 11142, 'Sanskrit': 11143, 'kosa': 11144, 'shell': 11145, 'layer': 11146, 'innermost': 11147, 'layers': 11148, 'kosas': 11149, 'blossoming': 11150, 'microcosm': 11151, 'Macrocosm': 11152, 'Cosmic': 11153, 'Consciousness': 11154, 'normally': 11155, 'referred': 11156, 'mysticism': 11157, 'Benefits': 11158, 'Exercises': 11159, 'glands': 11160, 'limbs': 11161, 'toes': 11162, 'Increases': 11163, 'spine': 11164, 'flexible': 11165, 'Arthritis': 11166, 'waist': 11167, 'joints': 11168, 'Gout': 11169, 'becomes': 11170, 'Irregularities': 11171, 'menstruation': 11172, 'cured': 11173, 'Glandular': 11174, 'secretions': 11175, 'Troubles': 11176, 'bladder': 11177, 'urethra': 11178, 'Gives': 11179, 'Adds': 11180, 'charm': 11181, 'shine': 11182, 'skin': 11183, 'Removes': 11184, 'lethargy': 11185, 'Cures': 11186, 'insomnia': 11187, 'hysteria': 11188, 'Fear': 11189, 'complexes': 11190, 'Hopelessness': 11191, 'Helps': 11192, 'potentiality': 11193, 'Spinal': 11194, 'pain': 11195, 'piles': 11196, 'hernia': 11197, 'hydrocele': 11198, 'kidney': 11199, 'gall': 11200, 'troubles': 11201, 'gastric': 11202, 'dyspepsia': 11203, 'acidity': 11204, 'dysentery': 11205, 'obesity': 11206, 'liver': 11207, 'Dance': 11208, 'eighteen': 11209, 'rhythmically': 11210, 'dancer': 11211, 'dhin': 11212, \"ta'\": 11213, 'dancers': 11214, 'placing': 11215, 'toe': 11216, 'heel': 11217, 'danced': 11218, 'Ideation': 11219, 'ideation': 11220, 'upraised': 11221, 'folded': 11222, 'represent': 11223, 'Parama': 11224, \"Purus'a\": 11225, 'Bending': 11226, 'fulfil': 11227, 'backward': 11228, 'obstacles': 11229, 'O': 11230, 'Lord': 11231, 'cosmic': 11232, 'rhythm': 11233, 'Quebecker': 11234, 'wins': 11235, 'award': 11236, 'Pittsburgh': 11237, 'Quebec': 11238, 'awarded': 11239, 'Carnegie': 11240, 'Medal': 11241, 'polar': 11242, 'bear': 11243, 'knife': 11244, 'Baffin': 11245, 'Fortier': 11246, 'Gatineau': 11247, 'Que.': 11248, 'Arctic': 11249, 'Circle': 11250, 'mauled': 11251, 'bears': 11252, 'Soper': 11253, 'River': 11254, 'canoeing': 11255, 'orthodontist': 11256, 'leaning': 11257, 'tent': 11258, 'seconds': 11259, 'girlfriend': 11260, 'paw': 11261, 'ripping': 11262, 'ceiling': 11263, 'considers': 11264, 'humans': 11265, 'snack': 11266, 'screamed': 11267, 'frighten': 11268, 'metres': 11269, 'ripped': 11270, 'mauling': 11271, 'Alain': 11272, 'Parenteau': 11273, 'screams': 11274, 'grabbed': 11275, 'glasses': 11276, 'unzipped': 11277, 'dwarfed': 11278, 'knocking': 11279, 'distracting': 11280, 'allowing': 11281, 'Doyon': 11282, 'tripped': 11283, 'beside': 11284, 'jaw': 11285, 'fur': 11286, 'centimetre': 11287, 'blade': 11288, 'badly': 11289, 'bleeding': 11290, 'lashed': 11291, 'canoes': 11292, 'paddled': 11293, 'eight': 11294, 'travelled': 11295, 'nearest': 11296, 'airlifted': 11297, 'gash': 11298, 'jugular': 11299, 'bronze': 11300, 'Crawford': 11301, 'Aug.': 11302, 'defend': 11303, 'sandwich': 11304, 'robber': 11305, 'wielding': 11306, 'punches': 11307, 'assailant': 11308, 'industrialist': 11309, 'Andrew': 11310, 'hero': 11311, '1904': 11312, '181': 11313, 'Apple': 11314, 'Computers': 11315, 'favorite': 11316, 'fruit': 11317, 'Jobs': 11318, \"O'clock\": 11319, 'CISCO': 11320, 'acronym': 11321, 'popularly': 11322, 'COMp': 11323, 'PAQ': 11324, 'denote': 11325, 'integral': 11326, 'Corel': 11327, 'derived': 11328, 'Cowpland': 11329, 'COwpland': 11330, 'REsearch': 11331, 'Google': 11332, 'joke': 11333, 'boasting': 11334, 'engine': 11335, 'Googol': 11336, 'zeros': 11337, 'founders': 11338, 'Stanford': 11339, 'Sergey': 11340, 'Brin': 11341, 'Larry': 11342, 'presented': 11343, 'angel': 11344, 'investor': 11345, 'cheque': 11346, 'Hotmail': 11347, 'Founder': 11348, 'accessing': 11349, 'Sabeer': 11350, 'Bhatia': 11351, 'ending': 11352, 'hotmail': 11353, 'html': 11354, 'HoTMaiL': 11355, 'selective': 11356, 'uppercasing': 11357, 'Hewlett': 11358, 'Packard': 11359, 'tossed': 11360, 'coin': 11361, 'Intel': 11362, 'Noyce': 11363, 'Gordon': 11364, 'trademarked': 11365, 'chain': 11366, 'INTegrated': 11367, 'ELectronics': 11368, 'Lotus': 11369, 'Notes': 11370, 'Mitch': 11371, 'Kapor': 11372, 'Padmasana': 11373, 'Transcendental': 11374, 'Meditation': 11375, 'Maharishi': 11376, 'Mahesh': 11377, 'Yogi': 11378, 'Coined': 11379, 'MICROcomputer': 11380, 'SOFTware': 11381, 'Originally': 11382, 'christened': 11383, 'Micro': 11384, 'Soft': 11385, 'Motorola': 11386, 'Galvin': 11387, 'radios': 11388, 'cars': 11389, 'radio': 11390, 'Victrola': 11391, 'ORACLE': 11392, 'Ellison': 11393, 'Oats': 11394, 'Oracle': 11395, 'SQL': 11396, 'terminated': 11397, 'finish': 11398, 'RDBMS': 11399, 'Later': 11400, 'Sony': 11401, 'originated': 11402, 'Latin': 11403, 'sonus': 11404, 'sonny': 11405, 'slang': 11406, 'youngster': 11407, 'SUN': 11408, 'Founded': 11409, 'buddies': 11410, 'Andreas': 11411, 'Bechtolsheim': 11412, 'built': 11413, 'microcomputer': 11414, 'Vinod': 11415, 'Khosla': 11416, 'McNealy': 11417, 'manufacture': 11418, 'computers': 11419, 'UNIX': 11420, 'OS': 11421, 'Gulliver': 11422, 'Travels': 11423, 'repulsive': 11424, 'appearance': 11425, 'Founders': 11426, 'Yang': 11427, 'Filo': 11428, 'selected': 11429, 'yahoos': 11430, 'Yahoo': 11431, 'mayur...@yahoo.com': 11432, 'SMS': 11433, '919819602175': 11434, 'forums': 11435, 'Guild': 11436, 'accessible': 11437, 'suddenly': 11438, 'Usually': 11439, 'bandwidth': 11440, 'maintenance': 11441, 'Humanpixel': 11442, 'Aye': 11443, 'Mate': 11444, 'projected': 11445, 'Blizzard': 11446, 'preorder': 11447, '2/3': 11448, 'Febuary': 11449, 'feb': 11450, '05': 11451, 'calender': 11452, 'retailers': 11453, 'websites': 11454, 'firewalls': 11455, 'lol': 11456, ';)': 11457, 'curious': 11458, 'hav': 11459, 'Check': 11460, 'multiplayer': 11461, 'graphics': 11462, 'amoung': 11463, 'gaming': 11464, 'MUD': 11465, 'beta': 11466, 'preview': 11467, 'Warm': 11468, 'GW': 11469, 'Lady': 11470, 'Kingel': 11471, 'hail': 11472, 'Legit': 11473, 'Gaming': 11474, 'Guilds': 11475, 'legit': 11476, 'guilds': 11477, 'DI': 11478, 'straightforward': 11479, 'greet': 11480, 'guild': 11481, 'ULGG': 11482, 'wander': 11483, 'Announce': 11484, 'Arrival': 11485, 'greeted': 11486, 'feast': 11487, 'paste': 11488, 'browser': 11489, 'friendship': 11490, 'honor': 11491, 'Come': 11492, 'hello': 11493, 'warmly': 11494, 'moreso': 11495, 'Judges': 11496, 'Lawyers': 11497, 'Policemen': 11498, 'Counselors': 11499, 'Taxpayers': 11500, 'et.': 11501, 'al.': 11502, 'downtrodden': 11503, 'dispossesed': 11504, 'torturing': 11505, 'convicts': 11506, 'alcohol': 11507, 'addicts': 11508, 'unemployed': 11509, 'unemployable': 11510, 'ex-cons': 11511, 'uninsured': 11512, 'homeless': 11513, 'tongues': 11514, 'Crime': 11515, 'budgets': 11516, 'militarize': 11517, 'Bullet': 11518, 'proof': 11519, 'vests': 11520, 'automatic': 11521, 'robots': 11522, 'testosterone': 11523, 'oozing': 11524, 'prisons': 11525, 'sentences': 11526, 'tighten': 11527, 'belt': 11528, 'spartan': 11529, 'courts': 11530, 'Unemployent': 11531, 'stays': 11532, 'oversees': 11533, 'dregs': 11534, 'rabble': 11535, 'deeper': 11536, 'humane': 11537, 'cohesive': 11538, 'Us': 11539, 'Them': 11540, 'gated': 11541, 'gentrified': 11542, 'Independence': 11543, 'Constitution': 11544, 'phrases': 11545, 'convict': 11546, 'addict': 11547, 'prostitute': 11548, 'darkest': 11549, 'ruts': 11550, '22s': 11551, 'crafting': 11552, 'instilling': 11553, 'burrowed': 11554, 'surface': 11555, 'succumbed': 11556, 'incarcerated': 11557, 'probation': 11558, 'parole': 11559, 'interface': 11560, 'travelling': 11561, 'grim': 11562, 'failures': 11563, 'bravely': 11564, 'struggling': 11565, 'insights': 11566, 'Solidarity': 11567, 'Reilly': 11568, 'Bruha': 11569, 'P.O.': 11570, '8274': 11571, 'Cranston': 11572, '02920': 11573, 'conceptualize': 11574, 'guerilla': 11575, 'promote': 11576, 'Ideas': 11577, 'welcome': 11578, 'Collaboration': 11579, 'prayed': 11580, 'annotated': 11581, 'D.': 11582, 'Singmaster': 11583, 'Mathematical': 11584, 'Intelligencer': 11585, 'v': 11586, 'pp': 11587, '69': 11588, '---------------------------------------------------------------------------': 11589, '-----': 11590, 'postings': 11591, 'comp.mail.maps': 11592, 'E.g.': 11593, 'pathalias': 11594, 'smail': 11595, 'comp.sources.unix': 11596, 'archives': 11597, 'archive': 11598, 'moderator': 11599, 'indices': 11600, 'comp.sources.d': 11601, 'NSA': 11602, 'eater': 11603, 'Usenet': 11604, 'keywords': 11605, 'Peanutjake': 11606, 'peanutjak...@usa.com': 11607, 'Groups': 11608, 'misc.consumers': 11609, 'IMMEDIATE': 11610, 'ATTENTION': 11611, 'NEEDED': 11612, 'HIGHLY': 11613, 'CONFIDENTIAL': 11614, 'URGENT': 11615, 'ASSISTANCE': 11616, 'FROM': 11617, 'GEORGE': 11618, 'WALKER': 11619, 'BUSH': 11620, '202.456.1414': 11621, '202.456.1111': 11622, 'FAX': 11623, '202.456.2461': 11624, 'Sir': 11625, 'Madam': 11626, 'Herbert': 11627, 'surprise': 11628, 'neither': 11629, 'reputable': 11630, 'maximum': 11631, 'presently': 11632, 'trapped': 11633, 'republic': 11634, 'solicit': 11635, 'extraction': 11636, 'nineteen': 11637, 'eighties': 11638, 'regain': 11639, 'revenue': 11640, 'unsuccessful': 11641, 'partner': 11642, 'acquire': 11643, 'emirate': 11644, 'subsidiary': 11645, 're-secured': 11646, 'sixty': 11647, '61,000,000,000': 11648, '36,000,000,000': 11649, 'supplied': 11650, 'Kingdom': 11651, 'Persian': 11652, 'monarchies': 11653, 'sixteen': 11654, '16,000,000,000': 11655, 'Japanese': 11656, 'urgent': 11657, 'removing': 11658, 'shoulder': 11659, 'upcoming': 11660, '100,000,000,000': 11661, '200,000,000,000': 11662, 'acquisition': 11663, 'urgently': 11664, 'gracious': 11665, 'distinguished': 11666, 'sitting': 11667, 'vice-president': 11668, 'Cheney': 11669, 'Halliburton': 11670, 'Condoleeza': 11671, 'dedication': 11672, 'Chevron': 11673, 'tanker': 11674, 'beseech': 11675, 'equaling': 11676, 'Internal': 11677, 'Revenue': 11678, 'trusted': 11679, 'intermediary': 11680, 'fifteenth': 11681, 'magnitude': 11682, 'worried': 11683, 'assuring': 11684, 'bold': 11685, 'regretted': 11686, 'assure': 11687, 'co-operate': 11688, 'plight': 11689, 'forever': 11690, 'Switchboard': 11691, 'presid...@whitehouse.gov': 11692, 'battles': 11693, 'Watch': 11694, 'airlift': 11695, 'Emergency': 11696, 'southern': 11697, 'battling': 11698, 'survivors': 11699, 'Hurricane': 11700, 'Katrina': 11701, 'destructive': 11702, 'Hundreds': 11703, 'Orleans': 11704, 'flooded': 11705, 'mayor': 11706, 'rescuers': 11707, 'Amid': 11708, 'worsening': 11709, 'evacuate': 11710, 'stadium': 11711, 'shelter': 11712, 'Superdome': 11713, 'toilets': 11714, 'overflowing': 11715, 'Map': 11716, 'Blanco': 11717, 'directive': 11718, 'resident': 11719, 'escaping': 11720, 'hollering': 11721, 'flashing': 11722, 'Kioka': 11723, 'Associated': 11724, 'Press': 11725, 'hack': 11726, 'beauty': 11727, 'flood': 11728, 'waters': 11729, 'Flooding': 11730, 'Walls': 11731, 'breached': 11732, '1,350': 11733, '3,000': 11734, 'lb': 11735, 'sandbags': 11736, 'concrete': 11737, 'barriers': 11738, 'Biloxi': 11739, 'roof': 11740, 'barn': 11741, 'oak': 11742, 'trees': 11743, 'pines': 11744, 'letting': 11745, 'Natalie': 11746, 'McVeigh': 11747, 'Oakley': 11748, 'Blogging': 11749, 'Nagin': 11750, 'submerged': 11751, 'sections': 11752, 'Engineers': 11753, 'evacuees': 11754, 'BBC': 11755, 'Alastair': 11756, 'Leithead': 11757, 'panic': 11758, 'Heavily': 11759, 'impose': 11760, 'martial': 11761, 'stem': 11762, 'outbreaks': 11763, 'looters': 11764, 'stealing': 11765, 'non-essential': 11766, 'Casinos': 11767, 'housed': 11768, 'barges': 11769, 'hurled': 11770, 'onto': 11771, 'beach': 11772, 'Tens': 11773, 'restored': 11774, 'Hiroshima': 11775, 'Haley': 11776, 'Barbour': 11777, 'viewing': 11778, 'brunt': 11779, 'slammed': 11780, 'Gulfport': 11781, 'inland': 11782, '54': 11783, 'flats': 11784, 'municipal': 11785, 'Vincent': 11786, 'Creel': 11787, 'Mobilising': 11788, 'residents': 11789, 'prayer': 11790, 'mobilised': 11791, 'volunteers': 11792, 'dispatched': 11793, 'donate': 11794, 'organisations': 11795, 'Damage': 11796, 'estimates': 11797, 'bn': 11798, '70.85': 11799, 'barrel': 11800, 'fields': 11801, 'secretary': 11802, 'MOON': 11803, 'LANDING': 11804, 'HOAX': 11805, 'CHURCH': 11806, 'TECHNOLOGY': 11807, 'GOVERNMENT': 11808, 'Shuttle': 11809, 'Carried': 11810, 'Aircraft': 11811, 'Q': 11812, 'WASTE': 11813, 'Answered': 11814, 'Category': 11815, 'Astronomy': 11816, 'Asked': 11817, 'yheggy': 11818, 'ga': 11819, 'List': 11820, '2.00': 11821, '18:32': 11822, 'Expires': 11823, '17:32': 11824, '420072': 11825, 'googlenut': 11826, '19:14': 11827, '16.379': 11828, 'fiscal': 11829, 'Release': 11830, 'Aeronautics': 11831, 'FY04': 11832, 'enacted': 11833, 'reduction': 11834, '665': 11835, '4.319': 11836, 'Station': 11837, '1.6': 11838, 'reduces': 11839, 'ISS': 11840, 'FY05': 11841, 'Moon': 11842, 'vision': 11843, 'Crew': 11844, 'Exploration': 11845, 'CEV': 11846, '268': 11847, 'lunar': 11848, 'exploration': 11849, 'Centennial': 11850, 'Challenges': 11851, 'Neighborhood': 11852, 'Reinvestment': 11853, 'NRC': 11854, '115': 11855, 'Spacetoday.net': 11856, '16.4': 11857, 'clarification': 11858, 'rating': 11859, 'Googlenut': 11860, 'Search': 11861, 'nasa': 11862, 'Log': 11863, 'saem_aero': 11864, '18:11': 11865, 'Excellent': 11866, 'webpage': 11867, 'grade': 11868, 'crossed': 11869, 'bridge': 11870, 'airplane': 11871, 'recieved': 11872, 'mathematics': 11873, 'drove': 11874, 'cellular': 11875, 'reaped': 11876, 'outreach': 11877, 'jaqamofino': 11878, '06:02': 11879, 'HUGE': 11880, 'payers': 11881, 'wastes': 11882, 'payer': 11883, 'monies': 11884, 'Endowment': 11885, 'NEA': 11886, 'healthcare': 11887, 'housing': 11888, 'trillions': 11889, 'invent': 11890, 'cellfone': 11891, 'bridges': 11892, 'cents': 11893, 'desparate': 11894, 'dear': 11895, 'hoax': 11896, 'actully': 11897, 'Beyond': 11898, 'sponoring': 11899, '5000': 11900, 'explaining': 11901, 'votes': 11902, 'humanatarian': 11903, 'validity': 11904, 'Rockin': 11905, 'worthy': 11906, 'Abby': 11907, 'beloved': 11908, 'bless': 11909, 'flyer': 11910, 'Benefit': 11911, 'raiser': 11912, 'Roy': 11913, 'raising': 11914, 'sponsoring': 11915, 'distributing': 11916, 'Grant': 11917, 'prognosis': 11918, 'recipients': 11919, 'grows': 11920, 'exponentially': 11921, 'contribute': 11922, 'caring': 11923, 'BENEFIT': 11924, 'AUCTION': 11925, 'RAFFLE': 11926, 'ABBY': 11927, 'FREEMAN': 11928, 'JUNE': 11929, 'P.M.': 11930, 'A.M.': 11931, 'MAIN': 11932, 'STREET': 11933, 'SALOON': 11934, 'ADMISSION': 11935, 'DONATIONS': 11936, 'AT': 11937, 'DOOR': 11938, 'Freeman': 11939, 'non-Hodgkin': 11940, 'lymphoma': 11941, 'bands': 11942, \"cookin'\": 11943, 'prizes': 11944, 'Bands': 11945, 'Band': 11946, 'Contest': 11947, 'Festival': 11948, 'Cuyahoga': 11949, 'Falls': 11950, 'logging': 11951, 'Cast': 11952, 'Donations': 11953, 'Charter': 11954, 'Kendel': 11955, 'Bunnell': 11956, 'Spencer': 11957, 'Filed': 11958, '19/11/2004': 11959, 'insatiable': 11960, 'prompting': 11961, 'collisions': 11962, 'globe': 11963, 'seeks': 11964, 'intrusion': 11965, 'territorial': 11966, 'Chinese': 11967, 'soaring': 11968, 'Increased': 11969, 'petrol': 11970, 'preferably': 11971, 'apologised': 11972, 'mouthpiece': 11973, 'commented': 11974, 'prelude': 11975, 'arena': 11976, 'pariah': 11977, 'veto': 11978, 'sanctions': 11979, 'Darfur': 11980, 'invested': 11981, 'oilfield': 11982, 'exporter': 11983, 'expanded': 11984, 'overtook': 11985, 'boosted': 11986, 'ownership': 11987, 'polluting': 11988, 'coal': 11989, 'Shortages': 11990, 'cuts': 11991, 'ordered': 11992, 'avail': 11993, 'clash': 11994, 'furious': 11995, 'outbid': 11996, 'battle': 11997, 'enabling': 11998, 'overland': 11999, 'hostilities': 12000, 'Increasingly': 12001, 'Claude': 12002, 'Mandil': 12003, 'hardly': 12004, 'thinks': 12005, 'Eurasia': 12006, 'inflated': 12007, 'falters': 12008, 'plummet': 12009, 'Kindopp': 12010, 'fraction': 12011, 'aggression': 12012, 'greatest': 12013, 'wield': 12014, 'petition': 12015, 'Britain': 12016, 'Visualizations': 12017, '-----------------------------------------------': 12018, 'visualizations': 12019, 'transcendence': 12020, 'Visualisations': 12021, 'meditation': 12022, 'stick': 12023, 'intense': 12024, 'beginners': 12025, 'MP3': 12026, 'visualisations': 12027, 'lap': 12028, 'straight': 12029, 'tongue': 12030, 'breathing': 12031, 'calm': 12032, 'nose': 12033, 'Spend': 12034, 'concentrating': 12035, 'breath': 12036, 'nostrils': 12037, 'Visualization': 12038, 'Continue': 12039, 'breathe': 12040, 'slowly': 12041, 'muscles': 12042, 'lighter': 12043, 'motor': 12044, 'sensory': 12045, 'organs': 12046, 'withdrawing': 12047, 'quietness': 12048, 'melting': 12049, 'naturally': 12050, 'tropical': 12051, 'basking': 12052, 'glow': 12053, 'sun': 12054, 'Visualize': 12055, 'vivid': 12056, 'colours': 12057, 'Feel': 12058, 'warmth': 12059, 'golden': 12060, 'penetrates': 12061, 'warmer': 12062, 'surrounds': 12063, 'rays': 12064, 'familiarity': 12065, 'vastness': 12066, 'merge': 12067, 'universal': 12068, 'Mind': 12069, 'ideate': 12070, 'desire': 12071, 'Nam': 12072, 'Kevalam': 12073, 'mantra': 12074, 'serenely': 12075, 'restfully': 12076, 'limp': 12077, 'sinking': 12078, 'sand': 12079, 'earthly': 12080, 'relaxation': 12081, 'sunlight': 12082, 'warming': 12083, 'gently': 12084, 'sensed': 12085, 'merging': 12086, 'infinite': 12087, 'bathed': 12088, 'absorbing': 12089, 'ray': 12090, 'glowing': 12091, 'radiantly': 12092, 'gentle': 12093, 'breeze': 12094, 'swirls': 12095, 'relaxes': 12096, 'Open': 12097, 'Mountain': 12098, 'mountain': 12099, 'happiness': 12100, 'NAM': 12101, 'KEVALAM': 12102, 'Infinite': 12103, 'everywhere': 12104, 'wordy': 12105, 'translate': 12106, 'internally': 12107, 'intuitively': 12108, 'Touching': 12109, 'builds': 12110, 'intuition': 12111, 'synthetic': 12112, 'grapple': 12113, 'meanings': 12114, 'analytical': 12115, 'endless': 12116, 'consciousness': 12117, 'dot': 12118, 'practicing': 12119, 'miracle': 12120, 'ordinary': 12121, 'NORTH': 12122, 'CAROLINA': 12123, 'RELIGIOUS': 12124, 'COALITION': 12125, 'MARRIAGE': 12126, 'EQUALITY': 12127, 'conclude': 12128, 'gay': 12129, 'Carolina': 12130, 'Religious': 12131, 'Equality': 12132, 'extending': 12133, 'couples': 12134, 'NCRC4ME': 12135, 'signatures': 12136, 'Leaders': 12137, 'NC': 12138, 'presents': 12139, 'resolved': 12140, 'equally': 12141, 'ban': 12142, 'defeated': 12143, 'reintroduced': 12144, 'pro-same': 12145, 'CHS': 12146, 'Vestry': 12147, 'unanimously': 12148, 'Rector': 12149, 'Deacon': 12150, 'Clare': 12151, 'Supporters': 12152, 'Sign': 12153, 'postal': 12154, 'signers': 12155, 'Raleigh': 12156, 'Spirit': 12157, 'Dudley': 12158, 'Cate': 12159, '828-296-8466': 12160, 'jodud...@aol.com': 12161, '---------------------------': 12162, 'necessities': 12163, 'clothing': 12164, 'affection': 12165, 'supportive': 12166, 'liberty': 12167, 'posterity': 12168, 'affirms': 12169, 'inalienable': 12170, 'fruits': 12171, 'labor': 12172, 'Throughout': 12173, 'tyrants': 12174, 'denying': 12175, 'oppressed': 12176, 'peoples': 12177, 'nurture': 12178, 'shameful': 12179, 'slavery': 12180, 'injustice': 12181, 'denial': 12182, 'interracial': 12183, 'discriminatory': 12184, 'Denial': 12185, 'inequities': 12186, 'protest': 12187, 'clergy': 12188, 'mandated': 12189, 'civic': 12190, 'sacred': 12191, 'texts': 12192, 'traditions': 12193, 'affirm': 12194, 'coerce': 12195, 'bar': 12196, 'consenting': 12197, 'differing': 12198, 'genders': 12199, 'lets': 12200, 'scriptural': 12201, 'theological': 12202, 'liturgical': 12203, 'conscience': 12204, 'officiate': 12205, 'Likewise': 12206, 'dishonors': 12207, 'convictions': 12208, 'visibility': 12209, 'Dempsey': 12210, 'Breeze': 12211, 'FL': 12212, '850-748-0740': 12213, 'McGinnis': 12214, 'bmc...@patriot.net': 12215, 'Reason': 12216, 'Oppose': 12217, 'Nomination': 12218, 'Alito': 12219, 'Tear': 12220, 'Down': 12221, 'Balances': 12222, 'Giving': 12223, 'Fellow': 12224, 'nominated': 12225, 'vacancy': 12226, 'Consent': 12227, 'http://loveallpeople.org/usconstitutiona.txt': 12228, 'qualified': 12229, 'evaluate': 12230, 'intellect': 12231, 'graduated': 12232, 'Appeals': 12233, 'encyclopedic': 12234, 'recite': 12235, 'Judicial': 12236, 'Temperament': 12237, 'courteous': 12238, 'mild': 12239, 'mannered': 12240, 'respectful': 12241, 'meticulous': 12242, 'judges': 12243, 'loves': 12244, 'outnumber': 12245, 'Jan.': 12246, 'fifty': 12247, 'judging': 12248, 'haste': 12249, 'repent': 12250, 'leisure': 12251, 'Sometimes': 12252, 'regret': 12253, 'tragic': 12254, 'flaw': 12255, 'unacceptable': 12256, 'Justice': 12257, 'tearing': 12258, 'Unitary': 12259, 'vastly': 12260, 'Presidential': 12261, 'Constitutional': 12262, 'Legislative': 12263, 'crazy': 12264, 'Branch': 12265, 'Agencies': 12266, 'Military': 12267, 'Commander': 12268, 'boss': 12269, 'Administrator': 12270, 'Decisions': 12271, 'rendered': 12272, 'Laws': 12273, 'Dictator': 12274, 'unrestrained': 12275, 'Free': 12276, 'Brave': 12277, 'Dictatorship': 12278, 'Fascist': 12279, 'THAT': 12280, 'ALL': 12281, 'AMERICANS': 12282, 'SHOULD': 12283, 'OPPOSE': 12284, 'NOMINATION': 12285, 'SAMUEL': 12286, 'ALITO': 12287, 'Testimony': 12288, 'http://judiciary.senate.gov/testimony.cfm?id=1725&wit_id=4905': 12289, 'Descriptions': 12290, 'Links': 12291, 'http://www.UnitaryExecutive.net': 12292, 'Blessings': 12293, 'http://www.LoveAllPeople.org': 12294, 'http://www.InternetchurchOfChrist.org': 12295, 'http://www.loveallpeople.org/theonereasonwhy.html': 12296, 'http://www.loveallpeople.org/theonereasonwhy.txt': 12297, '###': 12298, 'CONTACT': 12299, '1908': 12300, 'Mt': 12301, 'Vernon': 12302, 'Ave': 12303, '2543': 12304, 'Alexandria': 12305, 'VA': 12306, '22301': 12307, '7037686710': 12308, 'Reply': 12309, 'Diplomacy': 12310, 'Trying': 12311, 'Landing': 12312, 'Hoax': 12313, 'strategies': 12314, 'relation': 12315, 'gleaned': 12316, 'underneath': 12317, 'Country': 12318, 'Numero': 12319, 'assert': 12320, 'mesh': 12321, 'economical': 12322, 'thawed': 12323, 'Dependant': 12324, 'unify': 12325, 'aims': 12326, 'landing': 12327, '2018': 12328, 'shifting': 12329, 'towards': 12330, 'programs': 12331, 'launches': 12332, 'manned': 12333, 'spaceflight': 12334, 'Secretive': 12335, 'outsiders': 12336, 'MARK': 12337, 'CARREAU': 12338, 'Copyright': 12339, 'Chronicle': 12340, 'Shenzhou': 12341, 'VI': 12342, 'spacecraft': 12343, 'Gansu': 12344, 'pair': 12345, 'embarked': 12346, 'hurtling': 12347, 'span': 12348, 'Fei': 12349, 'Junlong': 12350, 'Nie': 12351, 'Haishen': 12352, 'Jiuquan': 12353, 'Satellite': 12354, 'Launch': 12355, 'solo': 12356, 'Liwei': 12357, 'vaulted': 12358, 'communist': 12359, 'spacefaring': 12360, 'sustain': 12361, 'secretive': 12362, 'explorers': 12363, 'Joan': 12364, 'Freese': 12365, 'College': 12366, 'Newport': 12367, 'R.I': 12368, 'rush': 12369, 'buster': 12370, 'Economics': 12371, 'determiner': 12372, 'worries': 12373, 'technologies': 12374, 'delegate': 12375, 'roundtable': 12376, 'destinations': 12377, 'applaud': 12378, 'achievements': 12379, 'Allard': 12380, 'Beutel': 12381, 'stressed': 12382, 'partnership': 12383, 'multi-compartment': 12384, 'module': 12385, 'fliers': 12386, 'Earth': 12387, 'spacewalks': 12388, 'demonstrations': 12389, 'docking': 12390, 'undocking': 12391, 'assembly': 12392, 'E.': 12393, 'Pike': 12394, 'Va.': 12395, 'GlobalSecurity.org': 12396, 'jam': 12397, 'numero': 12398, 'uno': 12399, 'Cold': 12400, 'scored': 12401, 'Apollo': 12402, 'Soviets': 12403, 'V': 12404, 'Hainan': 12405, 'assemble': 12406, 'mark.carr...@chron.com': 12407, 'righter': 12408, 'righ...@sonic.net': 12409, 'alt.animals.rights.promotion': 12410, 'alt.animals': 12411, 'alt.animals.cat': 12412, 'alt.animals.ethics.vegetarian': 12413, 'alt.animals.lion': 12414, 'alt.animals.tiger': 12415, 'alt.animals.felines.diseases': 12416, 'alt.animals.dog': 12417, 'alt.animals.horses.breeding': 12418, 'alt.animals.felines.snowleopards': 12419, 'animal': 12420, 'nightmare': 12421, 'Civet': 12422, 'Cats': 12423, 'losers': 12424, 'sheds': 12425, 'degrees': 12426, 'fires': 12427, 'periodically': 12428, 'scrape': 12429, 'sweat': 12430, 'genitals': 12431, 'corporations': 12432, 'prolong': 12433, 'scent': 12434, 'perfumes': 12435, 'demons': 12436, 'GREEDY': 12437, 'cats': 12438, 'mercy': 12439, 'pasted': 12440, 'plays': 12441, 'song': 12442, 'cat': 12443, 'Meowing': 12444, 'vocals': 12445, 'furnaces': 12446, 'feelings': 12447, 'dying': 12448, 'troubled': 12449, 'slow': 12450, 'agonizing': 12451, 'Cat': 12452, 'Holocaust': 12453, 'DOING': 12454, 'judgment': 12455, 'emailed': 12456, 're-read': 12457, 'imagined': 12458, 'hellish': 12459, 'creatures': 12460, 'sweet': 12461, 'minds': 12462, 'torture': 12463, 'starters': 12464, 'uploaded': 12465, 'relaxing': 12466, 'cooked': 12467, 'Holocaust-esque': 12468, 'chambers': 12469, 't...@sonic.net': 12470, 'Thu': 12471, '02:39:27': 12472, '0800': 12473, 'SPAR': 12474, 's...@sonic.net': 12475, 'Rachels': 12476, 'fitting': 12477, 'abundance': 12478, 'greed': 12479, 'civet': 12480, 'Hunger': 12481, 'Moral': 12482, 'Obligation': 12483, 'articles': 12484, 'Singer': 12485, 'utilitarianism': 12486, 'non-human': 12487, 'entitled': 12488, 'Vegetarianism': 12489, 'Weight': 12490, 'Problem': 12491, 'intelligent': 12492, 'sociable': 12493, 'cages': 12494, 'darkened': 12495, 'temperature': 12496, 'confined': 12497, 'justifies': 12498, 'mistreatment': 12499, 'misfortune': 12500, 'perfume': 12501, 'Musk': 12502, 'scraped': 12503, 'surv': 12504, 'ive': 12505, 'musk': 12506, 'Kant': 12507, 'Animals': 12508, 'vengeance': 12509, 'trivial': 12510, 'tormented': 12511, 'effected': 12512, 'url': 12513, 'http://www.sonic.net/~fsjob/TragiCore-TheCivetCat.mp3': 12514, 'lyrics': 12515, 'audible': 12516, 'vegetarian': 12517, 'vegan': 12518, 'Farewell': 12519, 'icq': 12520, 'uin': 12521, '5249025': 12522, 'Wigner': 12523, 'Heisenberg': 12524, 'Shrodinger': 12525, '-------------------': 12526, '.:': 12527, 'VEGAN': 12528, ':.': 12529, 'MAKE': 12530, 'THOUSANDS': 12531, 'THIS': 12532, 'SCAM': 12533, 'useless': 12534, 'pre-fabricated': 12535, 'crap': 12536, 'pre-fab': 12537, 'WELL': 12538, 'GUESS': 12539, '25.00': 12540, '1000.00': 12541, '10,000.00': 12542, '42,000.00': 12543, '6.00': 12544, 'stamps': 12545, 'lottery': 12546, '..........': 12547, 'NOW': 12548, 'Suggestion': 12549, 'Print': 12550, 'postage': 12551, 'IMPORTANT': 12552, 'rip': 12553, 'adhered': 12554, 'dividends': 12555, 'PLEASE': 12556, 'EXACTLY': 12557, 'integrity': 12558, 'Mail': 12559, 'Lists': 12560, 'bucks': 12561, 'mailing': 12562, 'secondary': 12563, 'STEP': 12564, 'Get': 12565, 'pieces': 12566, 'PUT': 12567, 'ME': 12568, 'MAILING': 12569, 'LIST': 12570, '1.00': 12571, 'bills': 12572, 'EACH': 12573, 'envelope': 12574, 'thievery': 12575, 'Next': 12576, 'envelopes': 12577, 'seal': 12578, 'sealed': 12579, 'stating': 12580, 'ABSOLUTELY': 12581, 'LEGAL': 12582, '!!!!': 12583, 'requesting': 12584, 'legitimate': 12585, 'skeptical': 12586, 'Post': 12587, '1-800-238-5355': 12588, 'G.': 12589, 'Burrows': 12590, '264': 12591, 'Tor': 12592, 'Toowoomba': 12593, 'QLD': 12594, '4350': 12595, 'Luest': 12596, '366': 12597, 'Grove': 12598, '92842': 12599, 'V.': 12600, 'Bourret': 12601, 'Broome': 12602, '11222': 12603, 'R.': 12604, 'Ansems': 12605, 'Foulkesstraat': 12606, '4641': 12607, 'BW': 12608, 'Ossendrecht': 12609, 'Brumbley': 12610, '4632': 12611, 'Hilton': 12612, 'Columbus': 12613, 'Ohio': 12614, '43228': 12615, 'TWO': 12616, 'NAME': 12617, 'THREE': 12618, '24,000': 12619, 'Title': 12620, 'Sec.': 12621, '1302': 12622, '1341': 12623, 'Postal': 12624, 'Lottery': 12625, 'REMEMBER': 12626, 'retain': 12627, 'VERIFIES': 12628, 'theft': 12629, 'downloaded': 12630, 'reimbursed': 12631, 'geometrically': 12632, 'reaches': 12633, 'CASH': 12634, '*****': 12635, 'DIRECTIONS': 12636, 'POST': 12637, 'NEWS': 12638, 'GROUPS': 12639, 're-type': 12640, 'cursor': 12641, 'edit': 12642, 'menu': 12643, 'notepad': 12644, 'Save': 12645, 'settings': 12646, 'FOUR': 12647, 'Visit': 12648, 'boards': 12649, 'highlighting': 12650, 'selecting': 12651, 'Fill': 12652, 'header': 12653, 'scroll': 12654, '!!!!!!': 12655, \"'S\": 12656, 'Really': 12657, 'Wishes': 12658, 'Blue': 12659, 'Planet': 12660, 'Bad': 12661, 'Wolf': 12662, 'Whipple': 12663, 'UNITED': 12664, 'PRESS': 12665, 'INTERNATIONAL': 12666, 'Boulder': 12667, 'CO': 12668, 'UPI': 12669, 'Ever': 12670, 'ate': 12671, 'Riding': 12672, 'Hood': 12673, 'grandma': 12674, 'blew': 12675, 'thirds': 12676, 'Three': 12677, 'Pigs': 12678, 'persistently': 12679, 'reputation': 12680, 'wolf': 12681, 'exterminated': 12682, 'remnant': 12683, 'Isle': 12684, 'Royale': 12685, 'Park': 12686, 'Superior': 12687, 'Yellowstone': 12688, '1943': 12689, 'sheep': 12690, 'cattle': 12691, 'ranchers': 12692, 'extermination': 12693, 'conducted': 12694, 'gratefully': 12695, 'dusted': 12696, 'graze': 12697, 'cows': 12698, 'ruminate': 12699, 'discouraging': 12700, 'offs': 12701, 'wolves': 12702, 'Alaska': 12703, 'legend': 12704, 'UFOs': 12705, 'Sasquatch': 12706, 'fleeting': 12707, 'Wyoming': 12708, 'park': 12709, 'imported': 12710, 'captive': 12711, 'Rumor': 12712, 'renegade': 12713, 'biologist': 12714, 'caged': 12715, 'reestablish': 12716, 'caper': 12717, 'rehabilitate': 12718, 'Eventually': 12719, 'trickle': 12720, 'predator': 12721, 'Rocky': 12722, 'Mountains': 12723, 'Fish': 12724, 'Wildlife': 12725, 'albeit': 12726, 'packs': 12727, 'flourished': 12728, '850': 12729, 'roaming': 12730, 'Rockies': 12731, 'compelling': 12732, 'reintroduction': 12733, 'keystone': 12734, 'populations': 12735, 'ecology': 12736, 'variety': 12737, 'Oregon': 12738, 'OSU': 12739, 'forestry': 12740, 'professors': 12741, 'Ripple': 12742, 'Beschta': 12743, 'unfortunate': 12744, 'smacks': 12745, 'elk': 12746, 'browse': 12747, 'unmolested': 12748, 'aspen': 12749, 'willow': 12750, 'streams': 12751, 'saplings': 12752, 'erosion': 12753, 'munching': 12754, 'photographs': 12755, 'Bangs': 12756, 'recovery': 12757, 'bald': 12758, 'billiard': 12759, 'triggered': 12760, 'veritable': 12761, 'ecological': 12762, 'cascade': 12763, 'streamside': 12764, 'habitat': 12765, 'beaver': 12766, 'instance': 12767, 'uninteresting': 12768, 'willows': 12769, 'cottonwoods': 12770, 'critters': 12771, 'beavers': 12772, 'avian': 12773, 'berry': 12774, 'producing': 12775, 'shrubs': 12776, 'Plants': 12777, 'wildland': 12778, 'ecosystems': 12779, 'herd': 12780, 'markedly': 12781, 'suspicious': 12782, 'generalizations': 12783, 'cautioned': 12784, 'Wolves': 12785, 'endangered': 12786, 'altogether': 12787, 'regulations': 12788, 'conservationists': 12789, 'Colorado': 12790, 'vacated': 12791, 'FWS': 12792, 'downgrade': 12793, 'Regardless': 12794, 'distances': 12795, 'highway': 12796, 'dynamic': 12797, '31,000': 12798, 'lions': 12799, 'Pound': 12800, 'pound': 12801, 'livestock': 12802, 'occasionally': 12803, 'Brothers': 12804, 'Grimm': 12805, 'accommodate': 12806, 'courtesy': 12807, 'arriving': 12808, 'rumors': 12809, '300,000': 12810, 'hybrids': 12811, 'pets': 12812, 'survive': 12813, 'ranger': 12814, 'Glacier': 12815, 'equipped': 12816, 'DNA': 12817, 'descendants': 12818, 'examining': 12819, 'relationship': 12820, 'environmental': 12821, 'E-mail': 12822, 'sciencem...@upi.com': 12823, 'Climate': 12824, 'Humans': 12825, 'Scientists': 12826, 'Celsius': 12827, 'Fahrenheit': 12828, 'trend': 12829, 'portends': 12830, 'meanwhile': 12831, 'insects': 12832, 'altering': 12833, 'patterns': 12834, 'turns': 12835, 'surest': 12836, 'barometers': 12837, 'biologists': 12838, 'biosphere': 12839, 'acknowledging': 12840, 'Nina': 12841, 'ecologist': 12842, 'conservation': 12843, 'pioneer': 12844, 'Aldo': 12845, 'Proceedings': 12846, 'Sciences': 12847, 'uninterrupted': 12848, 'phenological': 12849, 'Phenology': 12850, 'studies': 12851, 'organism': 12852, 'cycle': 12853, 'pulse': 12854, 'phenophases': 12855, 'latitudes': 12856, 'migrations': 12857, 'flowered': 12858, 'phoebe': 12859, 'forest': 12860, 'phlox': 12861, 'blooming': 12862, 'mid-May': 12863, 'cuckoo': 12864, 'bird': 12865, 'laying': 12866, 'eggs': 12867, 'findings': 12868, 'determining': 12869, 'geographical': 12870, 'Observed': 12871, 'Impacts': 12872, 'Pew': 12873, 'researchers': 12874, 'affecting': 12875, 'biology': 12876, 'causal': 12877, 'Camille': 12878, 'Parmesan': 12879, 'occurring': 12880, 'Across': 12881, 'edge': 12882, 'career': 12883, 'studying': 12884, 'checkerspot': 12885, 'butterfly': 12886, 'migrates': 12887, 'localized': 12888, 'extinctions': 12889, 'Empirical': 12890, 'extinction': 12891, 'prematurely': 12892, 'snowstorm': 12893, 'Tree': 12894, 'swallows': 12895, 'nesting': 12896, 'Tropical': 12897, 'Bird': 12898, 'watchers': 12899, 'Caribbean': 12900, 'Sufficient': 12901, 'detectable': 12902, 'Such': 12903, 'contraction': 12904, 'ranges': 12905, 'composition': 12906, 'alterations': 12907, 'ecosystem': 12908, 'carbon': 12909, 'Alaskan': 12910, 'tundra': 12911, 'switched': 12912, 'dioxide': 12913, 'releases': 12914, 'CO2': 12915, 'stored': 12916, 'winters': 12917, 'decompose': 12918, 'coincided': 12919, 'Iceland': 12920, 'Impact': 12921, 'Assessment': 12922, 'ACIA': 12923, 'impacts': 12924, 'Corell': 12925, 'experiencing': 12926, 'rapid': 12927, 'temperatures': 12928, 'predicting': 12929, 'Ocean': 12930, 'predictions': 12931, 'Appraisal': 12932, 'falls': 12933, 'IPN': 12934, 'predict': 12935, 'rises': 12936, 'exceed': 12937, 'diminish': 12938, 'solar': 12939, 'Atlantic': 12940, 'fisheries': 12941, 'stocks': 12942, 'predicted': 12943, 'cod': 12944, 'haddock': 12945, 'saithe': 12946, 'herring': 12947, 'whiting': 12948, 'flatfish': 12949, 'crustaceans': 12950, 'lobster': 12951, 'shrimp': 12952, 'capelin': 12953, 'Greenland': 12954, 'halibut': 12955, 'varieties': 12956, 'pile': 12957, 'examined': 12958, 'chasms': 12959, 'dividing': 12960, 'examines': 12961, 'neglects': 12962, 'non-commercial': 12963, 'seals': 12964, 'butterflies': 12965, 'unconsumables': 12966, 'transcend': 12967, 'assumed': 12968, 'unspecified': 12969, 'rosy': 12970, 'tug': 12971, 'dandelions': 12972, 'Wisconsin': 12973, 'pastures': 12974, 'Sand': 12975, 'Almanac': 12976, 'Sit': 12977, 'tussock': 12978, 'cock': 12979, 'ears': 12980, 'bedlam': 12981, 'meadowlarks': 12982, 'redwings': 12983, 'upland': 12984, 'plover': 12985, 'Argentine': 12986, 'Preamble': 12987, 'gamut': 12988, '---->===}*{===<----': 12989, 'lasting': 12990, 'learnt': 12991, 'Relief': 12992, 'displaced': 12993, 'vowed': 12994, 'restore': 12995, 'breakdown': 12996, 'commentators': 12997, 'profound': 12998, 'perceived': 12999, 'inquiry': 13000, 'altered': 13001, 'Bashers': 13002, 'appalled': 13003, 'podium': 13004, 'downfalls': 13005, 'socialism': 13006, 'revolted': 13007, 'chaps': 13008, 'sight': 13009, 'detractors': 13010, 'scoring': 13011, 'Mac': 13012, 'Nottingham': 13013, 'Warming': 13014, 'Distances': 13015, 'Juggernaut': 13016, 'irrelevant': 13017, 'Windermere': 13018, 'anytime': 13019, 'depiction': 13020, 'Bulgaria': 13021, 'communistic': 13022, 'remnants': 13023, 'Believe': 13024, 'sown': 13025, 'stricken': 13026, 'Dallas': 13027, 'Fort': 13028, 'Metroplex': 13029, 'diversity': 13030, 'vacationed': 13031, 'minority': 13032, 'white': 13033, 'Arkansas': 13034, 'Montana': 13035, 'Idaho': 13036, 'workplace': 13037, 'Nashua': 13038, 'Hampshire': 13039, 'pockets': 13040, 'diverse': 13041, 'Additionally': 13042, 'standings': 13043, 'stakes': 13044, 'filmed': 13045, 'reflection': 13046, 'perceptions': 13047, 'evacuated': 13048, '1.25': 13049, 'stadiums': 13050, 'centers': 13051, 'populate': 13052, 'Galveston': 13053, 'Sure': 13054, 'shy': 13055, 'snow': 13056, 'experienced': 13057, 'booming': 13058, 'influx': 13059, 'buyers': 13060, 'household': 13061, 'rebuilding': 13062, 'Linna': 13063, 'slanted': 13064, 'admit': 13065, 'credibility': 13066, \"who's\": 13067, 'aunt': 13068, 'straightened': 13069, 'cousins': 13070, 'Aunt': 13071, 'Mayor': 13072, 'mumbo': 13073, 'jumbo': 13074, 'flogging': 13075, 'burst': 13076, 'laughing': 13077, 'mirth': 13078, 'smirk': 13079, 'trotting': 13080, 'schtick': 13081, 'resounding': 13082, 'trot': 13083, 'shunted': 13084, 'radar': 13085, 'stumping': 13086, 'refineries': 13087, 'pour': 13088, 'OPEC': 13089, 'touting': 13090, 'FREEDOM': 13091, 'polution': 13092, 'dependency': 13093, 'theirs': 13094, 'puke': 13095, 'Aries': 13096, 'Flexibility': 13097, 'motto': 13098, 'adopt': 13099, 'flow': 13100, 'ups': 13101, 'downs': 13102, 'natives': 13103, 'belts': 13104, 'curb': 13105, 'seize': 13106, 'societal': 13107, 'trends': 13108, 'creativity': 13109, 'eloquent': 13110, 'persuasive': 13111, 'enthusiastically': 13112, 'overtime': 13113, 'career-wise': 13114, 'aspiring': 13115, 'climb': 13116, 'Taurus': 13117, 'Tenacity': 13118, 'Diligence': 13119, 'aspiration': 13120, 'progresses': 13121, 'impressive': 13122, 'newfound': 13123, 'stimulating': 13124, 'Gemini': 13125, 'Success': 13126, 'knocks': 13127, 'reaping': 13128, 'wake': 13129, 'leaps': 13130, 'bounds': 13131, 'concepts': 13132, 'combines': 13133, 'earning': 13134, 'Cancer': 13135, 'Finding': 13136, 'outward': 13137, 'fits': 13138, 'starts': 13139, 'alternating': 13140, 'Enjoy': 13141, 'restful': 13142, 'hectic': 13143, 'happier': 13144, 'dissatisfaction': 13145, 'fades': 13146, 'mountains': 13147, 'Leo': 13148, 'Abundance': 13149, 'Pursuing': 13150, 'advantages': 13151, 'deciding': 13152, 'advancement': 13153, 'thin': 13154, 'Romance': 13155, 'envy': 13156, 'tight': 13157, 'inconvenient': 13158, 'advancements': 13159, 'sheer': 13160, 'Virgo': 13161, 'Perseverance': 13162, 'smoothly': 13163, 'grindstone': 13164, 'reaffirmed': 13165, 'hopeless': 13166, 'prosperity': 13167, 'accomplishments': 13168, 'Libra': 13169, 'Flex': 13170, 'beneficent': 13171, 'Jupiter': 13172, 'Happiness': 13173, 'wed': 13174, 'theme': 13175, 'satisfaction': 13176, 'Scorpio': 13177, 'Transmutation': 13178, 'transformation': 13179, 'dreamed': 13180, 'residence': 13181, 'horizons': 13182, 'cling': 13183, 'Sagittarius': 13184, 'Empowerment': 13185, 'Pluto': 13186, 'punch': 13187, 'packed': 13188, 'combination': 13189, 'effectiveness': 13190, 'gaining': 13191, 'binds': 13192, 'hindered': 13193, 'Overall': 13194, 'Capricorn': 13195, 'awaited': 13196, 'partnerships': 13197, 'Financially': 13198, 'lean': 13199, 'unbearable': 13200, 'stronger': 13201, 'wiser': 13202, 'Aquarius': 13203, 'Magic': 13204, 'intuitive': 13205, 'psychic': 13206, 'mysteries': 13207, 'universe': 13208, 'mystical': 13209, 'awareness': 13210, 'brimming': 13211, 'infuse': 13212, 'peaks': 13213, 'Pisces': 13214, 'Self': 13215, 'surprises': 13216, 'Uranus': 13217, 'unexpected': 13218, 'windfall': 13219, 'unexpectedly': 13220, 'retreats': 13221, 'regenerate': 13222, 'unpredictable': 13223, 'Remain': 13224, 'flourish': 13225, 'dictionary': 13226, 'Mayur': 13227, 'Mails': 13228, 'Everyday': 13229, 'Share': 13230, 'mailto:mayur...@yahoo.com': 13231, '09819602175': 13232, 'ymsgr:sendIM?mayursha&__Hi+Mayur...': 13233, 'offline': 13234, 'online?u=mayursha&m=g&t=1': 13235, 'Download': 13236, '-------------------------------------------------------------': 13237, 'mysterious': 13238, 'RAPHAEL': 13239, 'Holinshed': 13240, 'Neoplatonic': 13241, 'painter': 13242, 'Sanzio': 13243, 'painted': 13244, 'Fighting': 13245, 'Dragon': 13246, '06': 13247, '----------------------------------------------------------------------': 13248, 'Shakspere': 13249, 'Bridget': 13250, '-----------------------------------------------------------------------': 13251, 'ARCHILOCHUS': 13252, 'eclipse': 13253, '648': 13254, 'Koran': 13255, 'descends': 13256, '610': 13257, 'AD': 13258, 'CLEMENT': 13259, 'Methodius': 13260, 'dies': 13261, '884': 13262, 'Petrarch': 13263, 'meets': 13264, 'LAURA': 13265, '1327': 13266, 'DURER': 13267, '1528': 13268, 'BRIDGET': 13269, 'Vere': 13270, '1584': 13271, 'Walsingham': 13272, '1590': 13273, 'native': 13274, 'Crete': 13275, 'EL': 13276, 'GRECO': 13277, '1614': 13278, 'LUCIO': 13279, 'PAINT': 13280, 'Pompey': 13281, 'ha': 13282, 'MfM': 13283, 'Sc.': 13284, '}': 13285, 'plague': 13286, '1348': 13287, '1483': 13288, '1520': 13289, 'Hobbes': 13290, '1588': 13291, 'Start': 13292, 'SOUND': 13293, 'FURY': 13294, '1928': 13295, 'EARTHQUAKE': 13296, '1580': 13297, 'Historian': 13298, 'Stow': 13299, '1605': 13300, 'Sat': 13301, 'Wed': 13302, 'Alexander': 13303, 'conquered': 13304, 'Darius': 13305, 'fortunate': 13306, 'Potidea': 13307, 'JOHN': 13308, 'AUBREY': 13309, 'F.R.S.': 13310, '---------------------------------------------------------------------': 13311, 'ANTONIO': 13312, 'PEREZ': 13313, '1535': 13314, '1611': 13315, 'Labour': 13316, 'Lost': 13317, 'Adriana': 13318, 'Armatho': 13319, 'bombast': 13320, 'refugee': 13321, '1593': 13322, 'acquainted': 13323, 'ANTHONY': 13324, 'Bacon': 13325, 'intimacy': 13326, 'Spaniard': 13327, 'affectation': 13328, '1594': 13329, 'PEREGRINO': 13330, 'Holofernes': 13331, 'ridiculing': 13332, 'traveler': 13333, 'picked': 13334, 'spruce': 13335, 'odd': 13336, 'PEREGRINate': 13337, 'Scene': 13338, 'unmistakable': 13339, 'Nathaniel': 13340, 'singular': 13341, 'epithet': 13342, 'enters': 13343, 'parody': 13344, 'sobriquet': 13345, '----------------------------------------------------------------': 13346, 'Shakespeare': 13347, 'Electronic': 13348, 'Vol.': 13349, '0832': 13350, 'MGr...@usa.pipeline.com': 13351, '10:57:32': 13352, '0400': 13353, 'Facts': 13354, 'Purpose': 13355, \"1590's\": 13356, 'Essex': 13357, 'physician': 13358, 'Leicester': 13359, 'socially': 13360, 'ailment': 13361, 'Perez': 13362, 'conversos': 13363, 'undoubtedly': 13364, 'ethnically': 13365, 'Doctor': 13366, 'RODERIGO': 13367, 'LOPEZ': 13368, '1525': 13369, 'Portugese': 13370, 'Queen': 13371, 'summarily': 13372, 'quartered': 13373, 'Tyburn': 13374, '1560': 13375, 'certificate': 13376, 'Robsart': 13377, 'stairs': 13378, 'RUY': 13379, 'handily': 13380, 'comers': 13381, 'tournament': 13382, '1561': 13383, 'Libro': 13384, 'la': 13385, 'invencion': 13386, 'y': 13387, 'arte': 13388, 'del': 13389, 'Juego': 13390, 'Acedraz': 13391, 'classic': 13392, 'Chess': 13393, 'openings': 13394, '-------------------------------------------------------------------': 13395, '1530': 13396, '1647': 13397, 'Ruy': 13398, 'b.': 13399, 'Zafra': 13400, '1533': 13401, 'Atahualpa': 13402, 'inca': 13403, 'emperor': 13404, 'peru': 13405, 'learns': 13406, '1542': 13407, 'LEONARDO': 13408, 'Giovanni': 13409, 'a.k.a.': 13410, 'Il': 13411, 'Puttino': 13412, 'Boy': 13413, 'Calaria': 13414, '1550': 13415, 'Valdiviesco': 13416, 'Bishop': 13417, 'Nicaragua': 13418, '1551': 13419, 'Ivan': 13420, 'bans': 13421, '1555': 13422, 'Castling': 13423, 'castling': 13424, 'proposes': 13425, 'Introduces': 13426, 'Alcala': 13427, '1562': 13428, 'reformer': 13429, 'writings': 13430, '1570': 13431, 'Gianutto': 13432, 'della': 13433, 'Mantia': 13434, 'Horatio': 13435, 'Author': 13436, '1572': 13437, 'eminent': 13438, '1574': 13439, 'Boi': 13440, 'Ceron': 13441, 'Phillip': 13442, '1575': 13443, 'beats': 13444, 'Madrid': 13445, 'Plague': 13446, 'Cremona': 13447, 'banned': 13448, '1576': 13449, 'prisoner': 13450, 'Catherine': 13451, 'Medici': 13452, 'lopez': 13453, 'Terrible': 13454, 'Tarsia': 13455, 'Venice': 13456, 'curate': 13457, 'divert': 13458, 'idle': 13459, 'CHESS': 13460, 'fives': 13461, 'billiards': 13462, 'diversion': 13463, 'obliged': 13464, 'supposition': 13465, 'nobody': 13466, 'Quixote': 13467, 'comedy': 13468, 'emperors': 13469, 'popes': 13470, 'ends': 13471, 'strips': 13472, 'garments': 13473, 'grave': 13474, 'Sancho': 13475, 'lasts': 13476, 'mixed': 13477, 'jumbled': 13478, 'shaken': 13479, 'stowed': 13480, 'bag': 13481, 'Thou': 13482, 'doltish': 13483, 'shrewd': 13484, 'Ay': 13485, 'shrewdness': 13486, 'sticks': 13487, 'yield': 13488, 'dung': 13489, 'Freemason': 13490, 'sponsered': 13491, 'revolutions': 13492, 'French': 13493, 'Bolivar': 13494, 'Garibaldi': 13495, 'blatantly': 13496, 'Masonic': 13497, 'Nostromo': 13498, 'Tale': 13499, 'Seaboard': 13500, 'Decoud': 13501, 'exotic': 13502, 'dandy': 13503, 'Parisian': 13504, 'boulevard': 13505, 'sanded': 13506, 'cafe': 13507, 'Albergo': 13508, 'Giorgio': 13509, 'Viola': 13510, 'companion': 13511, 'coloured': 13512, 'Faithful': 13513, 'Hero': 13514, 'dimly': 13515, 'candle': 13516, 'sensations': 13517, 'Looking': 13518, 'window': 13519, 'darkness': 13520, 'impenetrable': 13521, 'buildings': 13522, 'harbour': 13523, 'obscurity': 13524, 'Placid': 13525, 'spreading': 13526, 'dumb': 13527, 'expressionless': 13528, 'motionless': 13529, 'stare': 13530, 'emptiness': 13531, 'depth': 13532, 'abyss': 13533, 'uneasy': 13534, 'overtopped': 13535, 'Juste': 13536, 'prominent': 13537, 'eyelids': 13538, 'wreathed': 13539, 'solemnity': 13540, 'dense': 13541, 'cloud': 13542, '------------------------------------------------------------------': 13543, 'Wisteria': 13544, 'Lodge': 13545, 'Murillo': 13546, 'precaution': 13547, 'satellite': 13548, 'Lucas': 13549, 'greatness': 13550, 'slept': 13551, 'avenger': 13552, 'prearranged': 13553, 'doors': 13554, 'postponed': 13555, 'crept': 13556, 'sprang': 13557, 'traitress': 13558, 'plunged': 13559, 'knives': 13560, 'rid': 13561, 'Garcia': 13562, 'gagged': 13563, 'twisted': 13564, 'swear': 13565, 'sleeve': 13566, 'gorse': 13567, 'bushes': 13568, 'winds': 13569, 'detected': 13570, 'burglar': 13571, '------------------------------------------------------': 13572, 'Neuendorffer': 13573, 'revolution': 13574, 'Sixties': 13575, 'Beast': 13576, 'Liverpool': 13577, 'Titanic': 13578, 'arrogant': 13579, 'captain': 13580, 'orchestra': 13581, 'allows': 13582, 'recording': 13583, 'studio': 13584, 'homosexuals': 13585, 'lunatics': 13586, 'Beatles': 13587, 'king': 13588, 'Enrique': 13589, 'VIII': 13590, 'adulterated': 13591, 'Bible': 13592, 'divorce': 13593, 'rein': 13594, 'divorces': 13595, 'murdering': 13596, 'fingers': 13597, 'Boleyn': 13598, 'nicknamed': 13599, 'Devil': 13600, 'Piccadilly': 13601, 'Circus': 13602, 'brothel': 13603, 'Piccadilla': 13604, 'Sin': 13605, 'nowadays': 13606, 'antichrist': 13607, 'Lennon': 13608, 'puppets': 13609, 'disintegration': 13610, 'mankind': 13611, 'Stuart': 13612, 'Sutcliffe': 13613, 'bass': 13614, 'club': 13615, 'Hamburg': 13616, 'haemorrhage': 13617, 'bruises': 13618, 'stardom': 13619, 'shrink': 13620, 'Pope': 13621, 'GOD': 13622, 'CURSE': 13623, 'fell': 13624, 'WEEK': 13625, 'LATER': 13626, 'Epstein': 13627, 'forger': 13628, 'Beatle': 13629, 'overdose': 13630, 'preach': 13631, 'magic': 13632, 'Rosemary': 13633, 'Roman': 13634, 'Polansky': 13635, 'upholstered': 13636, 'silk': 13637, 'Came': 13638, 'LSD': 13639, 'schizophrenic': 13640, 'Walrus': 13641, 'incoherent': 13642, 'expositions': 13643, 'Sympathy': 13644, 'pact': 13645, 'Rolling': 13646, 'Stones': 13647, 'guitarist': 13648, 'Mick': 13649, 'Jagger': 13650, 'assassin': 13651, 'Antichrist': 13652, 'beard': 13653, 'bogus': 13654, 'Yoko': 13655, 'Ono': 13656, 'proclaiming': 13657, 'ridiculized': 13658, 'admonished': 13659, 'ballad': 13660, 'crucify': 13661, 'distresses': 13662, 'Stone': 13663, 'magazine': 13664, 'condensed': 13665, 'Cabalah': 13666, 'tradition': 13667, 'arrogantly': 13668, 'Henley': 13669, 'Thames': 13670, 'divinity': 13671, 'throat': 13672, 'metastasis': 13673, 'McCartney': 13674, 'toying': 13675, 'planets': 13676, 'deluded': 13677, 'ego': 13678, 'sweeper': 13679, 'produces': 13680, 'advertising': 13681, 'loved': 13682, 'Heather': 13683, 'Mills': 13684, 'intimate': 13685, 'bought': 13686, 'mourning': 13687, 'promotional': 13688, 'waning': 13689, 'popularity': 13690, 'masquerade': 13691, 'pretending': 13692, 'afore': 13693, 'insecure': 13694, 'womanish': 13695, 'effeminate': 13696, 'manners': 13697, 'ugly': 13698, 'virility': 13699, 'repressed': 13700, 'homosexuality': 13701, 'film': 13702, 'uneasiness': 13703, 'sings': 13704, 'traumas': 13705, 'Asher': 13706, 'manliness': 13707, 'inferiority': 13708, 'handicapped': 13709, 'conscious': 13710, 'superiority': 13711, 'virile': 13712, 'musician': 13713, 'anymore': 13714, 'fame': 13715, 'Besides': 13716, 'intercourse': 13717, 'homosexual': 13718, 'Cavern': 13719, 'recovered': 13720, 'collar': 13721, 'tie': 13722, 'crippled': 13723, 'wore': 13724, 'boots': 13725, 'manifestations': 13726, 'studios': 13727, 'filming': 13728, 'Swedish': 13729, 'parade': 13730, 'despective': 13731, 'manner': 13732, 'ah': 13733, 'alike': 13734, 'bundle': 13735, 'greeting': 13736, 'entrance': 13737, 'Friar': 13738, 'overbearing': 13739, 'receptionist': 13740, 'Hand': 13741, 'cinema': 13742, 'despotism': 13743, 'pedantry': 13744, 'worshiped': 13745, 'chauvinisms': 13746, 'hated': 13747, 'Oasis': 13748, 'untalented': 13749, 'musicians': 13750, 'elementary': 13751, 'guitar': 13752, 'chords': 13753, 'sounded': 13754, 'poorly': 13755, 'Satanism': 13756, 'deceive': 13757, 'disguise': 13758, 'deceiving': 13759, 'ghostly': 13760, 'weakening': 13761, 'proclaimers': 13762, 'aversion': 13763, 'dissipation': 13764, 'illness': 13765, 'antisocialism': 13766, 'apprentice': 13767, 'mentally': 13768, 'manifest': 13769, 'anti': 13770, 'feminism': 13771, 'rebellion': 13772, 'addiction': 13773, 'outlines': 13774, 'youths': 13775, 'hierarchies': 13776, 'convulsed': 13777, 'misanthropy': 13778, 'misogyny': 13779, 'paedophilia': 13780, 'irrational': 13781, 'feeble': 13782, 'lasciviousness': 13783, 'existent': 13784, 'aberrations': 13785, 'manoeuvre': 13786, 'marionettes': 13787, 'misery': 13788, 'curse': 13789, 'L.P': 13790, 'CURSED': 13791, 'BY': 13792, 'throwing': 13793, 'HIGH': 13794, 'COURT': 13795, 'Five': 13796, 'yamwhatiyam': 13797, 'pop...@spinach.eat': 13798, 'alt.consumers': 13799, 'ba.consumers': 13800, 'misc.consumers.frugal-living': 13801, 'Attack': 13802, 'Looming': 13803, 'Folly': 13804, 'Rivers': 13805, 'Pitt': 13806, 't': 13807, 'u': 13808, 'h': 13809, 'Perspective': 13810, '09': 13811, 'wires': 13812, 'preparing': 13813, 'reinforcing': 13814, 'Turkish': 13815, 'Der': 13816, 'Tagesspiegel': 13817, 'mullah': 13818, 'ramifications': 13819, 'Blowback': 13820, 'amalgam': 13821, 'religiously': 13822, 'principally': 13823, 'Dawa': 13824, 'SCIRI': 13825, 'umbilical': 13826, 'essence': 13827, 'owns': 13828, 'undertake': 13829, 'twelve': 13830, 'Hawk': 13831, 'aimed': 13832, 'disgruntled': 13833, 'sham': 13834, 'probable': 13835, 'taxed': 13836, 'reprisal': 13837, 'loyal': 13838, 'escalation': 13839, 'bunker': 13840, 'bases': 13841, 'seething': 13842, 'cauldron': 13843, 'Armaments': 13844, 'Unlike': 13845, 'fifteen': 13846, 'worn': 13847, 'grueling': 13848, 'heyday': 13849, 'armaments': 13850, 'nonetheless': 13851, 'Strategic': 13852, 'Studies': 13853, '540,000': 13854, '350,000': 13855, '120,000': 13856, '1,613': 13857, '21,600': 13858, 'armored': 13859, 'vehicles': 13860, '3,200': 13861, 'artillery': 13862, '306': 13863, 'combat': 13864, 'aircraft': 13865, 'submarines': 13866, '59': 13867, 'combatants': 13868, 'amphibious': 13869, 'CSIS': 13870, 'asymmetric': 13871, 'proliferation': 13872, 'missile': 13873, 'MILNET': 13874, 'astride': 13875, 'Tanker': 13876, 'squarely': 13877, 'bullseye': 13878, 'shipping': 13879, 'Navy': 13880, 'modernizing': 13881, 'cites': 13882, 'modernization': 13883, 'troublesome': 13884, 'anti-shipping': 13885, 'anti-ship': 13886, 'patrol': 13887, 'craft': 13888, 'midget': 13889, 'arrayed': 13890, 'exit': 13891, 'Hormuz': 13892, 'mountainous': 13893, 'coastline': 13894, 'holds': 13895, 'Missile': 13896, 'batteries': 13897, 'havoc': 13898, 'fleet': 13899, 'armament': 13900, 'Sunburn': 13901, 'fastest': 13902, 'Mach': 13903, 'altitude': 13904, '2.2': 13905, 'faster': 13906, 'Harpoon': 13907, 'manufacturers': 13908, 'cripple': 13909, 'destroyer': 13910, 'Exocet': 13911, 'Recall': 13912, 'Exocets': 13913, 'USS': 13914, 'shreds': 13915, '37': 13916, 'sailors': 13917, 'carrier': 13918, 'Theodore': 13919, 'Roosevelt': 13920, '7,000': 13921, 'Sailing': 13922, 'Tarawa': 13923, 'Expeditionary': 13924, 'Strike': 13925, 'Pearl': 13926, 'Harbor': 13927, 'detection': 13928, 'chooses': 13929, 'retaliate': 13930, 'muscle': 13931, 'fired': 13932, 'grenades': 13933, 'roadside': 13934, 'vaunted': 13935, '2,210': 13936, 'guerrilla': 13937, 'lasted': 13938, 'thousand': 13939, 'decides': 13940, '23,000': 13941, 'unspeakably': 13942, 'Connection': 13943, 'condemnation': 13944, 'Rafik': 13945, 'Hariri': 13946, 'Damascus': 13947, 'conceivably': 13948, 'scoff': 13949, 'Virtually': 13950, 'credible': 13951, 'counterweight': 13952, '215,000': 13953, '4,700': 13954, 'comprised': 13955, 'eleven': 13956, 'totaling': 13957, '650': 13958, 'possesses': 13959, 'arsenals': 13960, 'ballistic': 13961, 'SCUD': 13962, 'Compounding': 13963, 'Economy': 13964, 'loom': 13965, 'engagement': 13966, 'exploding': 13967, 'hampered': 13968, 'thirst': 13969, 'inked': 13970, 'liquefied': 13971, 'Yadavaran': 13972, '150,000': 13973, 'Caspian': 13974, 'Kazakhstan': 13975, 'imperil': 13976, 'confrontation': 13977, 'palm': 13978, 'Conservative': 13979, 'surpluses': 13980, 'accumulation': 13981, 'sums': 13982, 'dumping': 13983, 'provoke': 13984, 'showdown': 13985, 'teaching': 13986, 'hegemonic': 13987, 'superpower': 13988, 'arose': 13989, 'hardest': 13990, 'ceases': 13991, 'undervalued': 13992, 'shoppers': 13993, 'Wal': 13994, 'Mart': 13995, 'shelves': 13996, 'Neiman': 13997, 'Marcus': 13998, 'incomes': 13999, 'coincides': 14000, 'rising': 14001, 'Depression': 14002, 'squeeze': 14003, 'extends': 14004, 'realm': 14005, 'Preparedness': 14006, 'overwhelm': 14007, 'foe': 14008, 'battlefield': 14009, 'cemented': 14010, 'sapped': 14011, 'decrease': 14012, 'deployments': 14013, 're-enlist': 14014, 'stretched': 14015, 'economists': 14016, 'Nobel': 14017, 'Prize': 14018, 'nationally': 14019, 'renowned': 14020, 'analyzed': 14021, 'tag': 14022, 'Bilmes': 14023, 'Laureate': 14024, 'Stiglitz': 14025, 'surpassing': 14026, 'envelops': 14027, 'involve': 14028, 'shatter': 14029, 'Add': 14030, 'implicit': 14031, 'disruption': 14032, 'Mideast': 14033, 'undersized': 14034, 'resupply': 14035, 'carriers': 14036, 'minted': 14037, 'imperiled': 14038, 'Conclusion': 14039, 'Possible': 14040, 'maniac': 14041, 'fraught': 14042, 'peril': 14043, 'catastrophe': 14044, 'envelop': 14045, 'cocked': 14046, 'hat': 14047, 'Vladimir': 14048, 'Putin': 14049, 'bluntly': 14050, 'endeavor': 14051, 'aiding': 14052, 'dangers': 14053, 'offset': 14054, 'gains': 14055, 'bubble': 14056, 'encased': 14057, 'zealot': 14058, '1600': 14059, 'consistently': 14060, 'undertaken': 14061, 'garner': 14062, 'lobbed': 14063, 'damaging': 14064, 'Karl': 14065, 'Rove': 14066, 'advisor': 14067, 'notoriously': 14068, 'ballot': 14069, 'midterms': 14070, 'gained': 14071, 'Abramoff': 14072, 'scandal': 14073, 'subsume': 14074, 'fought': 14075, 'Logic': 14076, 'insane': 14077, 'unavoidable': 14078, 'Graydon': 14079, 'excepted': 14080, '68.4': 14081, 'Number': 14082, 'Endangered': 14083, 'Threatened': 14084, 'Species': 14085, '0': 14086, 'voluntarily': 14087, '408': 14088, 'extinct': 14089, '2050': 14090, 'pollution': 14091, 'mentioning': 14092, 'paragraphs': 14093, 'EPA': 14094, 'Draft': 14095, 'Environment': 14096, '68': 14097, 'ratify': 14098, 'Kyoto': 14099, 'greenhouse': 14100, 'gases': 14101, '5.2': 14102, '1990': 14103, '2012': 14104, 'emissions': 14105, 'Percentage': 14106, 'reneged': 14107, 'regulate': 14108, '44': 14109, 'fossil': 14110, 'timber': 14111, 'mining': 14112, 'industries': 14113, 'rollbacks': 14114, 'downgrading': 14115, 'appointees': 14116, 'alumni': 14117, 'Approximate': 14118, 'injurious': 14119, 'relay': 14120, 'widest': 14121, 'Environmental': 14122, 'polluters': 14123, 'penalties': 14124, 'historically': 14125, 'valued': 14126, 'conducting': 14127, '3.7': 14128, '62': 14129, '63': 14130, 'Task': 14131, 'environmentalists': 14132, 'investigated': 14133, 'Estimated': 14134, 'premature': 14135, 'Clear': 14136, 'Skies': 14137, 'Clean': 14138, 'Water': 14139, 'violations': 14140, 'mountaintop': 14141, '750,000': 14142, 'Tons': 14143, 'polluter': 14144, '3.8': 14145, 'Superfund': 14146, 'fees': 14147, 'uncommitted': 14148, '270': 14149, 'citing': 14150, 'Negligence': 14151, 'unheeded': 14152, 'screened': 14153, 'Ground': 14154, 'Zero': 14155, '78': 14156, 'lung': 14157, 'ailments': 14158, '88': 14159, 'ear': 14160, 'Asbestos': 14161, 'Libby': 14162, 'Grace': 14163, 'produced': 14164, \"Qa'ida\": 14165, '101': 14166, '83': 14167, 'painting': 14168, 'Library': 14169, 'Prince': 14170, 'Bandar': 14171, 'ambassador': 14172, '1,700': 14173, 'visas': 14174, 'Visa': 14175, '140': 14176, 'Naturalisation': 14177, 'INS': 14178, 'legalised': 14179, 'gambling': 14180, 'linguists': 14181, 'mid-October': 14182, 'Nearly': 14183, 'Reward': 14184, 'Dick': 14185, 'Donald': 14186, 'Rumsfeld': 14187, 'Wolfowitz': 14188, 'Perle': 14189, 'Representatives': 14190, 'Newspaper': 14191, 'GI': 14192, 'toys': 14193, 'Ambitious': 14194, 'warrior': 14195, '130': 14196, '191': 14197, 'recognised': 14198, 'spends': 14199, '401.3': 14200, 'Saviour': 14201, 'spurs': 14202, 'gift': 14203, '2.5': 14204, '237': 14205, 'Minimum': 14206, 'misleading': 14207, 'Henry': 14208, 'Waxman': 14209, 'simultaneous': 14210, 'Actual': 14211, 'cement': 14212, 'factory': 14213, '80,000': 14214, 'delays': 14215, '4.7': 14216, '680': 14217, 'reconstruction': 14218, 'Bechtel': 14219, '2.8': 14220, 'Value': 14221, '35': 14222, 'suspended': 14223, 'Criminal': 14224, '92': 14225, 'urban': 14226, 'potable': 14227, '1945': 14228, 'Death': 14229, 'coffins': 14230, 'photographed': 14231, 'memorial': 14232, 'attended': 14233, 'Interceptor': 14234, 'outfitting': 14235, 'masks': 14236, 'investigators': 14237, 'autumn': 14238, '90': 14239, 'detectors': 14240, 'defective': 14241, '87': 14242, 'Humvees': 14243, 'armour': 14244, 'stopping': 14245, 'protecting': 14246, 'landmines': 14247, 'Making': 14248, '3.29': 14249, 'Nationwide': 14250, 'grants': 14251, '94.40': 14252, 'Samoa': 14253, '5.87': 14254, '77.92': 14255, 'alma': 14256, 'mater': 14257, '215': 14258, 'surveyed': 14259, 'Mayors': 14260, 'dime': 14261, 'airports': 14262, 'screening': 14263, 'baggage': 14264, '22,600': 14265, 'unscreened': 14266, 'passenger': 14267, '95': 14268, 'subjected': 14269, 'inspection': 14270, '5.5': 14271, 'budgeted': 14272, 'vaccines': 14273, 'pathogens': 14274, 'Centres': 14275, 'advantaged': 14276, '10.9': 14277, 'unaffected': 14278, 'sweeping': 14279, '42,000': 14280, 'Skull': 14281, 'Bones': 14282, 'McCallum': 14283, 'SEC': 14284, 'Donaldson': 14285, '189': 14286, '113': 14287, 'Pioneer': 14288, 'bundling': 14289, 'cheques': 14290, 'Pioneers': 14291, 'bankruptcies': 14292, 'recession': 14293, '489': 14294, 'deficit': 14295, '5.6': 14296, 'Projected': 14297, '7.22': 14298, 'mid-2004': 14299, 'cutter': 14300, '39': 14301, 'phased': 14302, '30,858': 14303, 'tsar': 14304, '9.3': 14305, '2.3': 14306, 'Friend': 14307, '34.6': 14308, '6.8': 14309, 'defines': 14310, 'subsidies': 14311, 'richest': 14312, '1e': 14313, 'Loss': 14314, 'stockholders': 14315, '205': 14316, '59,339': 14317, 'jet': 14318, 'Length': 14319, 'Kenny': 14320, 'Lawman': 14321, '57': 14322, 'Aids': 14323, 'abstinence': 14324, 'programmes': 14325, 'libertarian': 14326, 'admits': 14327, 'Guantánamo': 14328, 'Cuba': 14329, 'nationalities': 14330, 'detainees': 14331, 'Guantanamo': 14332, 'prisoners': 14333, 'handcuffed': 14334, 'shackled': 14335, 'surgical': 14336, 'earmuffs': 14337, 'blindfolds': 14338, 'mid-2003': 14339, 'psychiatrists': 14340, '43.6': 14341, '2.4': 14342, 'Image': 14343, 'booster': 14344, '2,500': 14345, 'Rank': 14346, 'Attitudes': 14347, '1949': 14348, '23.8': 14349, '85': 14350, 'Indonesians': 14351, 'unfavourable': 14352, 'endorsements': 14353, 'longest': 14354, 'Record': 14355, 'holder': 14356, 'Briefing': 14357, 'Determined': 14358, 'Targets': 14359, 'ranch': 14360, 'retreat': 14361, 'Kennebunkport': 14362, 'Maine': 14363, 'fool': 14364, 'trick': 14365, 'Factors': 14366, '52': 14367, 'midterm': 14368, 'Election': 14369, 'Software': 14370, 'Economist': 14371, '185': 14372, 're-election': 14373, 'expects': 14374, 'raisers': 14375, '187': 14376, '64.2': 14377, 'Rangers': 14378, 'WMDs': 14379, 'describe': 14380, 'Evangelicals': 14381, 'saviour': 14382, 'interpret': 14383, 'voters': 14384, 'Ugh': 14385, 'stupid': 14386, 'sisters': 14387, 'ohh': 14388, 'dads': 14389, 'lonely': 14390, 'forgive': 14391, 'Equine': 14392, 'collages': 14393, 'uk': 14394, 'NEED': 14395, 'HELP': 14396, '!!!?': 14397, 'foundation': 14398, 'Horse': 14399, 'psychology': 14400, 'employs': 14401, 'Hartpury': 14402, 'Merrist': 14403, 'NVQ': 14404, 'equine': 14405, 'HND': 14406, 'UCAS': 14407, 'Woking': 14408, 'Surrey': 14409, 'Moreton': 14410, 'Morrell': 14411, 'Coventry': 14412, 'Warwickshire': 14413, 'Gloucestershire': 14414, 'Go': 14415, 'universities': 14416, 'Pacquiao': 14417, 'Philipines': 14418, 'famous': 14419, 'Cheap': 14420, 'hookers': 14421, 'intimidating': 14422, 'blush': 14423, 'japanese': 14424, 'dress': 14425, 'nasty': 14426, 'insulting': 14427, 'undesirable': 14428, 'puttagenius': 14429, 'caloy': 14430, 'gro': 14431, 'irene': 14432, 'filipinos': 14433, 'plunge': 14434, 'pit': 14435, 'expulse': 14436, 'rotten': 14437, 'overs': 14438, 'dinasaurs': 14439, 'texting': 14440, 'Limerick': 14441, 'pros': 14442, 'cons': 14443, 'L.I.T': 14444, 'Moyross': 14445, 'ppl': 14446, 'sh*t': 14447, 'loads': 14448, 'spoilt': 14449, 'Orla': 14450, 'directing': 14451, \"AREA'S\": 14452, 'limerick': 14453, 'nowhere': 14454, 'university': 14455, 'myTouch': 14456, '4G': 14457, 'crashes': 14458, 'custom': 14459, 'ROMs': 14460, 'install': 14461, 'MIUI': 14462, 'newest': 14463, 'Cyanogen': 14464, 'Mod': 14465, 'voided': 14466, 'warrenty': 14467, 'installing': 14468, 'roms': 14469, 'ROM': 14470, 'cynangon': 14471, 'mod': 14472, 'clockwork': 14473, 'Cynagon': 14474, 'rom': 14475, 'bugless': 14476, 'beast': 14477, 'baked': 14478, 'snake': 14479, 'launcher': 14480, 'adw': 14481, 'mytouch': 14482, '4g': 14483, 'moors': 14484, 'penines': 14485, 'Yorkshire': 14486, 've': 14487, 'yorkshire': 14488, 'distance': 14489, 'werewolf': 14490, 'london': 14491, 'bleak': 14492, 'antiquities': 14493, 'burial': 14494, 'mounds': 14495, 'stone': 14496, 'settlements': 14497, 'farmlands': 14498, 'skies': 14499, 'wash': 14500, 'goodness': 14501, 'farms': 14502, 'stood': 14503, 'moorland': 14504, 'purple': 14505, 'heather': 14506, 'wildlife': 14507, 'wildflowers': 14508, 'appy': 14509, 'lope': 14510, 'stride': 14511, 'tick': 14512, 'spur': 14513, 'concentrate': 14514, 'essential': 14515, 'Lope': 14516, 'jog': 14517, 'OLYMPUS': 14518, 'X940': 14519, 'DIGITAL': 14520, 'CAMERA': 14521, 'digital': 14522, 'camera': 14523, 'wel': 14524, 'sony': 14525, 'anyways': 14526, 'olympus': 14527, 'any1': 14528, 'ur': 14529, 'gud': 14530, 'casual': 14531, 'photography': 14532, 'wht': 14533, 'abt': 14534, 'comparable': 14535, 'mega': 14536, 'pixel': 14537, 'n': 14538, 'optical': 14539, 'zoom': 14540, 'wud': 14541, 'Olympus': 14542, '940': 14543, 'Megapixel': 14544, 'Digital': 14545, 'Camera': 14546, 'filters': 14547, 'sorts': 14548, 'def': 14549, 'VERY': 14550, 'satisfied': 14551, 'photos': 14552, 'nikon': 14553, 'dslr': 14554, 'lens': 14555, 'sensor': 14556, 'infinity': 14557, 'photo': 14558, 'smallest': 14559, 'aperture': 14560, 'expose': 14561, 'stops': 14562, 'Cure': 14563, 'cleaned': 14564, 'Pixel': 14565, 'Mapping': 14566, 'sensors': 14567, 'flipped': 14568, 'pixels': 14569, 'exposures': 14570, 'skilled': 14571, 'migrant': 14572, 'visa': 14573, 'NZ': 14574, '.???': 14575, 'poles': 14576, 'preferable': 14577, 'depends': 14578, 'restricted': 14579, 'restrict': 14580, 'dine': 14581, 'Clark': 14582, 'Walk': 14583, 'BIG': 14584, 'BOWL': 14585, 'THAI': 14586, 'FOOD': 14587, 'ASIAN': 14588, 'BEST': 14589, 'PLACE': 14590, 'EVER': 14591, '!!!!!!!!!!!!': 14592, 'appetizers': 14593, 'meal': 14594, 'coupons': 14595, 'http://www.restaurant.com': 14596, 'chains': 14597, 'walking': 14598, 'mice': 14599, 'unresponsive': 14600, 'infected': 14601, 'dears': 14602, ':(': 14603, 'arsenic': 14604, 'vet': 14605, 'snap': 14606, 'trap': 14607, 'rats': 14608, 'invasive': 14609, 'Arm': 14610, 'Hammer': 14611, 'Crayola': 14612, 'Clay': 14613, 'giraffes': 14614, 'homemade': 14615, 'artistic': 14616, 'giraffe': 14617, 'sculpture': 14618, 'sculpted': 14619, 'dried': 14620, 'fragile': 14621, 'paint': 14622, 'kiln': 14623, 'clay': 14624, 'armatures': 14625, 'wire': 14626, 'armature': 14627, 'acrylic': 14628, 'kitten': 14629, 'forida': 14630, 'carolina': 14631, 'camping': 14632, 'cabin': 14633, 'virginia': 14634, 'adapt': 14635, 'litter': 14636, 'Mitten': 14637, '0nside': 14638, 'harness': 14639, 'leash': 14640, 'pan': 14641, 'surroundings': 14642, 'stress': 14643, 'depressed': 14644, 'cleaners': 14645, 'parakeet': 14646, 'dust': 14647, 'budgie': 14648, 'pledge': 14649, 'swiffer': 14650, 'duster': 14651, 'cleaner': 14652, 'ventilated': 14653, 'parakeets': 14654, 'Nothing': 14655, 'cotton': 14656, 'diapers': 14657, 'damp': 14658, 'Swiffer': 14659, 'micro-fiber': 14660, 'Miracle': 14661, 'Cloth': 14662, 'fantastic': 14663, 'fabric': 14664, 'softener': 14665, 'sheets': 14666, 'cleaning': 14667, 'ruins': 14668, 'grabbing': 14669, 'http://www.solutions.com/jump.jsp?itemID=1361&itemType=PRODUCT&path=1%2C3%2C477&iProductID=1361': 14670, 'lingering': 14671, 'fragrance': 14672, 'injure': 14673, 'RP': 14674, 'haha': 14675, 'LOL': 14676, 'Callum': 14677, 'saaaaaam': 14678, '!?': 14679, 'situations': 14680, 'D:': 14681, 'guz': 14682, 'whoooooo': 14683, 'stupidity': 14684, '=(': 14685, 'EDIT': 14686, 'callum': 14687, 'gpa': 14688, 'u.k': 14689, 'rep.': 14690, 'ireland': 14691, 'irish': 14692, 'dongle': 14693, 'pc': 14694, 'normall': 14695, 'USB': 14696, 'Region': 14697, 'aka': 14698, 'PAL': 14699, 'broadband': 14700, 'DELL': 14701, 'Acer': 14702, 'Asus': 14703, 'eMachines': 14704, 'newborn': 14705, 'reside': 14706, 'gallon': 14707, 'fry': 14708, 'breeder': 14709, 'eaten': 14710, 'Feed': 14711, 'flakes': 14712, 'Provide': 14713, 'overcrowded': 14714, 'pet': 14715, 'stores': 14716, 'foods': 14717, 'jack': 14718, 'dempsey': 14719, 'offspring': 14720, 'Monopoly': 14721, '!!!!!!!!!!!!!!!!!!!!!': 14722, 'POINTS': 14723, '!!!!!!!!!!?': 14724, 'Mcdonald': 14725, 'wan': 14726, 'EXTRA': 14727, 'Large': 14728, 'Fries': 14729, 'PIECES': 14730, 'drinks': 14731, 'REWARDED': 14732, '!!!!!!!!!!!': 14733, 'THANK': 14734, '!!!!!!!!!!!!!': 14735, ':D': 14736, '(:': 14737, 'http://www.playatmcd.com/en-us/Main/Gameboard': 14738, 'Peels': 14739, 'Chicken': 14740, 'McNuggets': 14741, 'fries': 14742, 'Medium': 14743, 'Fountain': 14744, 'Drink': 14745, 'McCAFE': 14746, 'Filet': 14747, 'Hash': 14748, 'Browns': 14749, 'Egg': 14750, 'McMuffin': 14751, 'Sausage': 14752, 'Fruit': 14753, 'Maple': 14754, 'Oatmeal': 14755, 'bearded': 14756, 'dragon': 14757, 'spines': 14758, 'becca': 14759, 'beardies': 14760, 'ovr': 14761, 'yrs': 14762, 'lik': 14763, 'eneedle': 14764, 'ar': 14765, 'pillow': 14766, 'prickly': 14767, 'cactus': 14768, 'puff': 14769, 'badder': 14770, 'scratch': 14771, 'lil': 14772, 'Beardies': 14773, 'delicate': 14774, 'predators': 14775, 'adaptation': 14776, 'kimberwick': 14777, 'Used': 14778, 'horses': 14779, 'pullers': 14780, 'hacking': 14781, 'hunting': 14782, 'ported': 14783, 'mouths': 14784, 'precise': 14785, 'pelham': 14786, 'slots': 14787, 'reins': 14788, 'poll': 14789, 'Useful': 14790, 'younger': 14791, 'independently': 14792, 'snaffle': 14793, 'vague': 14794, 'rider': 14795, 'canon': 14796, 't3i': 14797, 'november': 14798, '~': 14799, 'christmas': 14800, 'yeaa': 14801, 'cunclude': 14802, 'canan': 14803, 'tips': 14804, ':O': 14805, 'Canon': 14806, 'EOS': 14807, 'T2i': 14808, 'quicker': 14809, 'DSLR': 14810, 'Buying': 14811, 'Guide': 14812, 'http://www.the-dslr-photographer.com/2009/11/which-dslr-to-buy/': 14813, 'kido': 14814, 'smile': 14815, 'POP': 14816, 'icing': 14817, 'PoP': 14818, 'plaster': 14819, 'sculpting': 14820, 'uncommon': 14821, 'unset': 14822, 'wet': 14823, 'mud': 14824, 'Plaster': 14825, 'chemically': 14826, 'cools': 14827, 'dries': 14828, 'soaked': 14829, 'mold': 14830, 'slip': 14831, 'casting': 14832, 'molding': 14833, 'wax': 14834, 'soaking': 14835, 'http://www.mikegigi.com/castgobl.htm#LGGOBPROJ': 14836, 'Dwarf': 14837, 'Hamster': 14838, 'Noise': 14839, 'Wheel': 14840, 'dwarf': 14841, 'disturb': 14842, 'thei': 14843, 'energetic': 14844, 'hers': 14845, 'wheels': 14846, 'noise': 14847, 'rubber': 14848, 'edges': 14849, 'Super': 14850, 'Pet': 14851, 'Silent': 14852, 'Spinner': 14853, 'Exercise': 14854, '10.99': 14855, '12.99': 14856, 'PETSMART': 14857, 'hamster': 14858, 'HATES': 14859, 'MORE': 14860, 'digs': 14861, 'jumps': 14862, 'noiseless': 14863, 'squeaks': 14864, 'wrath': 14865, 'bored': 14866, 'boarding': 14867, 'Howrah': 14868, 'Asansol': 14869, 'stations': 14870, 'Calcutta': 14871, 'Foreigner': 14872, 'reservation': 14873, 'Reservation': 14874, 'desired': 14875, 'halts': 14876, 'TC': 14877, 'allot': 14878, 'inquire': 14879, 'railway': 14880, 'railways': 14881, 'petsmart': 14882, 'Josalyn': 14883, 'Leainne': 14884, 'drama': 14885, 'mcallister': 14886, 'deli': 14887, \"',\": 14888, 'mormon': 14889, 'hvae': 14890, 'pwople': 14891, 'hirier': 14892, '2;30': 14893, 'h=guys': 14894, 'Jeez': 14895, 'punctuation': 14896, 'Cover': 14897, 'resumes': 14898, 'typos': 14899, 'whatnot': 14900, 'Nope': 14901, '2015': 14902, 'semester': 14903, 'reapply': 14904, 'embassy': 14905, 'ANY': 14906, 'Visas': 14907, 'Study': 14908, 'Permits': 14909, 'administered': 14910, 'Citizenship': 14911, 'applications': 14912, 'C&IC': 14913, 'http://www.cic.gc.ca/english/index.asp': 14914, 'Prevent': 14915, 'Escaping': 14916, 'Cage': 14917, 'videos': 14918, 'youtube': 14919, 'escaped': 14920, 'FREAK': 14921, 'OUT': 14922, 'aquarium': 14923, 'cage': 14924, 'plastic': 14925, 'rat': 14926, 'chicken': 14927, 'wiring': 14928, 'rodents': 14929, 'hamsters': 14930, 'flatten': 14931, 'pancake': 14932, 'slide': 14933, 'whhich': 14934, 'IMPOSSIBLE': 14935, 'http://www.petsathome.com/shop/combi-1-dwarf-hamster-cage-by-ferplast-15986': 14936, 'spacious': 14937, 'Aquiriums': 14938, 'airway': 14939, 'dwarfs': 14940, 'cm': 14941, 'hammy': 14942, 'Rachel': 14943, 'Traveler': 14944, 'unknowledgeable': 14945, 'dislike': 14946, 'enviroment': 14947, 'unhappy': 14948, '18th': 14949, 'reassure': 14950, 'atm': 14951, 'Advice': 14952, 'Hotels': 14953, 'prefere': 14954, 'sleeping': 14955, 'Wool': 14956, 'Light': 14957, 'itchy': 14958, 'Hit': 14959, 'beg': 14960, 'spare': 14961, 'survival': 14962, 'Vietnamese': 14963, 'Mayko': 14964, 'Meiko': 14965, 'Regenesis': 14966, 'actress': 14967, 'Nguyen': 14968, 'fathers': 14969, 'orphans': 14970, 'E.g': 14971, 'Thanh': 14972, '===>': 14973, 'Thi': 14974, 'Thuy': 14975, 'Lan': 14976, 'origination': 14977, 'splitter': 14978, 'filter': 14979, 'ADSL': 14980, 'ph': 14981, 'connected': 14982, 'buzzing': 14983, 'disconnected': 14984, 'stil': 14985, 'Woud': 14986, 'modem': 14987, 'static': 14988, 'DSL': 14989, 'EVERYTHING': 14990, 'EXCEPT': 14991, 'filtered': 14992, 'alarm': 14993, 'boxes': 14994, 'doubling': 14995, 'jacks': 14996, 'connects': 14997, 'Holga': 14998, 'whilst': 14999, 'wind': 15000, 'darkroom': 15001, 'rewind': 15002, 'owners': 15003, 'easiest': 15004, 'scratched': 15005, 'folds': 15006, 'neatly': 15007, 'Adorama': 15008, 'http://www.adorama.com/BLCBS.html': 15009, 'DIY': 15010, 'YouTube': 15011, 'channel': 15012, 'http://bit.ly/kPlaylists': 15013, 'lo': 15014, 'fi': 15015, 'http://dianacamera.com': 15016, 'statue': 15017, 'censored': 15018, 'sophomore': 15019, 'Statue': 15020, 'florence': 15021, 'clothes': 15022, 'mid-cities': 15023, 'metro': 15024, 'parental': 15025, 'nudes': 15026, 'museum': 15027, 'female': 15028, 'pools': 15029, 'beaches': 15030, 'manikins': 15031, 'store': 15032, 'display': 15033, 'skipping': 15034, 'prudish': 15035, 'ignoramus': 15036, 'Better': 15037, 'Steakhouse': 15038, 'Philadelphia': 15039, 'steak': 15040, 'staying': 15041, 'decor': 15042, 'Davio': 15043, 'Del': 15044, 'Frisco': 15045, 'Sidenote': 15046, 'Capital': 15047, 'Grille': 15048, 'Barclay': 15049, 'Palm': 15050, 'Rib': 15051, 'Morton': 15052, 'weighed': 15053, 'chosen': 15054, 'taste': 15055, 'lovely': 15056, 'HAMSTERS': 15057, 'steam': 15058, 'd': 15059, 'Rodents': 15060, 'bedding': 15061, 'ammonia': 15062, 'urine': 15063, 'aways': 15064, 'european': 15065, 'Jeju': 15066, 'Namsan': 15067, 'Tower': 15068, 'amusement': 15069, 'parks': 15070, 'Lotte': 15071, 'Everland': 15072, 'Carribbean': 15073, '^_^': 15074, 'http://3.bp.blogspot.com/-X_e2uwT6wPw/Tkj_7UVTw6I/AAAAAAAAAGs/e_hICAdYPYI/s1600/lotte_world_from_high_up.jpg': 15075, 'indoors': 15076, 'outdoors': 15077, 'Resort': 15078, 'http://v2.cache7.c.bigcache.googleapis.com/static.panoramio.com/photos/original/42661265.jpg?redirect_counter=2': 15079, 'http://tong.visitkorea.or.kr/cms/resource/81/188181_image2_1.jpg': 15080, 'tower': 15081, 'http://farm3.static.flickr.com/2406/2527255596_db23df940f.jpg': 15082, 'Hoped': 15083, 'jejudo': 15084, 'jeju': 15085, 'll': 15086, 'dove': 15087, 'streaks': 15088, 'picky': 15089, 'petco': 15090, 'Doves': 15091, 'pigeon': 15092, 'Seed': 15093, 'eaters': 15094, 'bread': 15095, 'worms': 15096, 'shallow': 15097, 'dish': 15098, 'drink': 15099, 'bait': 15100, 'petshoppe': 15101, 'container': 15102, 'wheat': 15103, 'corn': 15104, 'paris': 15105, 'cdg': 15106, 'montparnasse': 15107, 'france': 15108, 'CDG': 15109, 'ty': 15110, 'RER': 15111, 'Buy': 15112, 'Saint': 15113, 'Michel': 15114, 'Notre': 15115, 'Dame': 15116, 'Porte': 15117, \"d'\": 15118, 'Montparnasse': 15119, 'Bienvenue': 15120, 'Opera': 15121, 'Bus': 15122, 'Vanves': 15123, 'Balard': 15124, 'Invalides': 15125, 'Chatillon': 15126, 'Montrouge': 15127, 'Photography': 15128, 'smooth': 15129, 'crisp': 15130, 'editing': 15131, 'GIMP': 15132, 'shots': 15133, 'http://www.flickr.com/photos/adamtolle/6094960940/in/set-72157627535453128/': 15134, 'Thx': 15135, 'gimp': 15136, 'blak': 15137, '>>>': 15138, 'brightness': 15139, 'modes': 15140, 'tutorial': 15141, 'http://gimpedblog.blogspot.com/2011/09/gimp-video-tutorial-how-to-convert.html': 15142, 'adjusting': 15143, 'chef': 15144, 'http://gimpedblog.blogspot.com/2011/09/how-to-use-gimp-for-beginners-lesson-4.html': 15145, 'Picasa': 15146, 'photoshop': 15147, 'CS5': 15148, 'macbook': 15149, 'photoscape': 15150, 'cheapest': 15151, 'surgeons': 15152, 'thailand': 15153, 'boob': 15154, 'artwork': 15155, 'perform': 15156, 'Cheapest': 15157, 'clinic': 15158, 'Bumrungard': 15159, 'Yanhee': 15160, 'brakes': 15161, 'parachute': 15162, 'infections': 15163, 'accidental': 15164, 'nicks': 15165, 'screw': 15166, 'Bumrungrad': 15167, 'cheaper': 15168, 'http://www.bumrungrad.com/en/patient-services/clinics-and-centers/plastic-surgery-thailand-bangkok/breast-augmentation-ba': 15169, 'Hospitals': 15170, 'gravy': 15171, 'licks': 15172, 'canned': 15173, 'label': 15174, 'Canned': 15175, 'moisture': 15176, 'spoilage': 15177, 'male': 15178, 'blender': 15179, 'anemic': 15180, 'mash': 15181, 'spoon': 15182, 'riding': 15183, 'hill': 15184, 'cantering': 15185, 'talented': 15186, 'Olympic': 15187, 'spooky': 15188, 'graceful': 15189, 'sliding': 15190, 'bond': 15191, 'rides': 15192, 'ribbons': 15193, 'galloping': 15194, 'quiet': 15195, 'encounters': 15196, 'foxes': 15197, 'deer': 15198, 'woodpeckers': 15199, 'pecking': 15200, 'Fascinating': 15201, 'nano': 15202, 'reef': 15203, 'fiji': 15204, 'carribean': 15205, 'maintaining': 15206, 'gravity': 15207, '1.024': 15208, 'powerhead': 15209, 'sump': 15210, 'intake': 15211, 'protein': 15212, 'skimmer': 15213, 'overflow': 15214, 'hang': 15215, 'piping': 15216, 'pump': 15217, 'hose': 15218, 'Hydor': 15219, 'SlimSkim': 15220, 'Nano': 15221, 'NOOK': 15222, 'Color': 15223, 'Tablet': 15224, 'HD': 15225, 'battery': 15226, 'processor': 15227, 'iPad': 15228, 'Kindle': 15229, 'e-reader': 15230, 'Fire': 15231, 'mini': 15232, 'tablet': 15233, 'darn': 15234, 'Judging': 15235, 'Basically': 15236, 'microphone': 15237, 'http://www.squidoo.com/nook-tablet': 15238, 'eReader': 15239, 'Comparison': 15240, 'HTC': 15241, 'Flyer': 15242, 'http://www.droidforums.net/forum/droid-news/181335-ereader-tablet-comparison-b-n-nook-tablet-b-n-nook-color-kindle-fire-htc-flyer.html': 15243, 'Pubs': 15244, 'Philly': 15245, 'Food': 15246, 'awesome': 15247, 'american': 15248, 'tavern': 15249, '=)': 15250, 'philly': 15251, \"to's\": 15252, 'Magazine': 15253, 'suburbs': 15254, 'Wishing': 15255, 'burger': 15256, 'Chickie': 15257, 'Pete': 15258, 'gosh': 15259, 'crab': 15260, 'betta': 15261, 'beginner': 15262, 'gravel': 15263, 'tear': 15264, 'fins': 15265, 'conditioner': 15266, 'sponge': 15267, 'bettas': 15268, 'heater': 15269, '82': 15270, 'Lights': 15271, 'optinal': 15272, 'nessasary': 15273, 'aquasafe': 15274, 'algae': 15275, 'lazers': 15276, 'Wang': 15277, 'recovering': 15278, 'comic': 15279, 'sci': 15280, 'movies': 15281, 'laser': 15282, 'sights': 15283, 'mount': 15284, 'wang': 15285, 'beam': 15286, 'Mount': 15287, 'grip': 15288, 'Laser': 15289, 'grips': 15290, 'wangs': 15291, 'Remove': 15292, 'screws': 15293, 'semi-automatic': 15294, 'Newer': 15295, 'rail': 15296, 'receiver': 15297, 'shaft': 15298, 'multi': 15299, 'devices': 15300, 'flashlight': 15301, 'Prepare': 15302, 'Tighten': 15303, 'securely': 15304, 'Flip': 15305, 'feeds': 15306, 'transitional': 15307, 'scream': 15308, 'sleeps': 15309, 'Budgies': 15310, 'breast': 15311, 'feeding': 15312, 'bisexual': 15313, 'titts': 15314, 'feathered': 15315, 'Ignore': 15316, 'btw': 15317, 'canada': 15318, 'provider': 15319, 'canadian': 15320, 'wireless': 15321, '611': 15322, 'cell': 15323, 'compatible': 15324, 'Ye$': 15325, 'Text': 15326, 'CANADA': 15327, 'U$': 15328, 'Thi$': 15329, '$ervice': 15330, 'co$t': 15331, 'varie$': 15332, 'WiFi': 15333, '$ometime$': 15334, 'companie': 15335, '$involved': 15336, '$ome': 15337, 'unde$tood': 15338, 'me$$age': 15339, 'Fernando': 15340, 'becouse': 15341, 'panicking': 15342, 'bouild': 15343, 'carrot': 15344, 'tube': 15345, 'realtion': 15346, 'useing': 15347, 'CHILL': 15348, 'Firstly': 15349, 'hammie': 15350, 'Thirdly': 15351, 'cge': 15352, 'ans': 15353, 'willl': 15354, 'explore': 15355, 'smells': 15356, 'noises': 15357, 'comfy': 15358, 'Fujairah': 15359, 'suitable': 15360, 'Dubai': 15361, 'LIVE': 15362, 'THERE': 15363, 'Fishing': 15364, 'Jet': 15365, 'snorkeling': 15366, 'diving': 15367, 'finishing': 15368, 'quieter': 15369, 'mins': 15370, 'Visited': 15371, 'economics': 15372, 'skill': 15373, 'console': 15374, 'PS3': 15375, 'BOX': 15376, 'Kinect': 15377, 'DS': 15378, 'PSP': 15379, 'newer': 15380, 'Wii': 15381, 'U': 15382, 'PS4': 15383, 'Interface': 15384, 'JOYSTICK': 15385, 'coz': 15386, 'joystick': 15387, 'discomfort': 15388, 'XBOX': 15389, 'Joys': 15390, 'soo': 15391, 'comfyy': 15392, 'playstation': 15393, 'Xbox': 15394, 'halfway': 15395, 'lifetime': 15396, 'kinect': 15397, 'wii': 15398, 'lifespan': 15399, 'seizure': 15400, 'bounces': 15401, 'ascertain': 15402, 'wildly': 15403, 'lifespans': 15404, 'conceivable': 15405, 'Indoor': 15406, 'cared': 15407, 'Older': 15408, 'urinary': 15409, 'tract': 15410, 'Male': 15411, 'Stories': 15412, 'outdoor': 15413, 'expectancy': 15414, 'Devon': 15415, 'bonus': 15416, 'Gaining': 15417, 'loosing': 15418, 'cockatiel': 15419, 'gesture': 15420, 'resorted': 15421, 'Eating': 15422, 'flock': 15423, 'pace': 15424, 'ruin': 15425, 'Patience': 15426, 'cuddly': 15427, 'tame': 15428, 'dat': 15429, 'breyer': 15430, 'breyers': 15431, 'stables': 15432, 'barns': 15433, 'stalls': 15434, 'Pictures': 15435, 'Breyer': 15436, 'miniature': 15437, 'replica': 15438, 'sketch': 15439, '3d': 15440, 'orthographic': 15441, 'square': 15442, 'cube': 15443, 'length': 15444, 'hobby': 15445, 'shops': 15446, 'wood': 15447, 'panels': 15448, 'carpenter': 15449, 'glue': 15450, 'clamps': 15451, 'corners': 15452, 'hinges': 15453, 'saws': 15454, 'drills': 15455, 'pencil': 15456, 'ruler': 15457, 'bryer': 15458, 'vary': 15459, 'cork': 15460, 'balsa': 15461, 'plywood': 15462, 'pre-cut': 15463, 'slats': 15464, 'hardware': 15465, 'http://www.binkyswoodworking.com/HorseStable.php': 15466, 'http://www.blueoakstables.com/breyerhorsebarns_barn003.asp': 15467, 'http://www.railroadredux.com/tag/northwest-shortline/': 15468, 'http://www.adventurehobbycraft.com/products/hobby_craft_supplies.html#metal': 15469, 'http://www.natureandtech.com/?page_id=2200': 15470, 'http://www.utrechtart.com/Craft-Supplies/Woodworking%20Supplies/': 15471, 'biting': 15472, 'kitty': 15473, 'bites': 15474, 'x.x': 15475, 'panics': 15476, 'teething': 15477, 'scratching': 15478, 'LOTS': 15479, 'balls': 15480, 'yarn': 15481, 'feathery': 15482, 'birdy': 15483, 'toy': 15484, 'randomly': 15485, 'wil': 15486, 'rambunctious': 15487, 'feather': 15488, 'flooring': 15489, 'http://www.ebay.co.uk/itm/250927098564?var=550057729382&ssPageName=STRK:MEWAX:IT&_trksid=p3984.m1438.l2649#ht_2079wt_893': 15490, 'http://www.ebay.co.uk/itm/130589513308?var=430034792128&ssPageName=STRK:MEWAX:IT&_trksid=p3984.m1438.l2648#ht_1500wt_660': 15491, 'http://www.ebay.co.uk/itm/250927098564?var=550057729382&ssPageName=STRK:MEWAX:IT&_trksid=p3984.m1438.l2649#ht_': 15492, 'LOVE': 15493, 'fleece': 15494, 'ratty': 15495, 'taller': 15496, 'hammocks': 15497, 'cheaply': 15498, 'springs': 15499, 'annoying': 15500, 'IMO': 15501, 'http://www.equinecaninefeline.com/catalog/mamble-hamster-narrow-100cm-cage-p-12642.html': 15502, 'http://www.equinecaninefeline.com/catalog/abode-large-metal-cage-liberta-free-delivery-p-6679.html': 15503, 'http://www.equinecaninefeline.com/catalog/savic-freddy-cage-free-delivery-p-6750.html': 15504, 'http://www.netpetshop.co.uk/p-19500-savic-chichi-2-chinchilla-rat-degu-ferret-cage.aspx': 15505, 'http://www.justcages.co.uk/ferret-cages/ferplast-furet-plus-ferret-cage#v_431': 15506, 'ebay': 15507, 'Exact': 15508, 'Bought': 15509, 'Ebay': 15510, 'Rat': 15511, 'Calculater': 15512, 'Says': 15513, 'Enough': 15514, 'Rats': 15515, 'heating': 15516, 'prop': 15517, 'heated': 15518, 'burrows': 15519, 'rests': 15520, 'reptile': 15521, 'carpet': 15522, 'substrate': 15523, 'propping': 15524, 'slithers': 15525, 'distribute': 15526, 'opinons': 15527, \"UTH's\": 15528, 'burrowing': 15529, 'snakes': 15530, 'UTH': 15531, 'corns': 15532, 'kings': 15533, 'milks': 15534, 'Bottom': 15535, 'overt': 15536, 'watt': 15537, 'bulb': 15538, 'brunch': 15539, 'bday': 15540, 'fam': 15541, 'bridal': 15542, 'denny': 15543, 'ihop': 15544, 'torrance': 15545, 'thanx': 15546, 'Breakfast': 15547, 'relax': 15548, 'fabulous': 15549, 'toast': 15550, 'Grill': 15551, 'conveniently': 15552, 'Expressway': 15553, 'parking': 15554, 'Plenty': 15555, 'Directions': 15556, 'Ogden': 15557, 'Exit': 15558, 'Hubbard': 15559, '1381': 15560, 'IL': 15561, '60622': 15562, '312-666-2372': 15563, 'Hours': 15564, 'Operation': 15565, 'wine': 15566, 'Coronas': 15567, 'Heineken': 15568, 'L.A.': 15569, '1912': 15570, 'lifeboats': 15571, 'SOLAS': 15572, 'Safety': 15573, 'Life': 15574, 'tragedy': 15575, 'muster': 15576, 'drill': 15577, 'lifeboat': 15578, 'sailing': 15579, 'dock': 15580, 'navigation': 15581, 'adoption': 15582, 'http://www.caribbean-cruising.net': 15583, '--------------------------------------------------': 15584, 'Edit': 15585, 'ensures': 15586, 'proficient': 15587, 'Okinawa': 15588, 'obvious': 15589, 'Naha': 15590, 'venues': 15591, 'showcase': 15592, 'venue': 15593, 'shrines': 15594, 'sumo': 15595, 'Eat': 15596, 'conveyor': 15597, 'sushi': 15598, 'Climb': 15599, 'Fuji': 15600, 'similarities': 15601, 'delicious': 15602, 'sashimi': 15603, 'nightlife': 15604, 'izakaya': 15605, 'Lonely': 15606, 'vs': 15607, 'auto': 15608, 'semiautomatic': 15609, 'm16': 15610, \"16's\": 15611, 'McNamara': 15612, '16s': 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'administer': 16575, 'veterinarian': 16576, 'clarify': 16577, 'Boiled': 16578, 'WHITE': 16579, 'Bland': 16580, 'SPICES': 16581, 'obstruction': 16582, 'etiquette': 16583, \"20's\": 16584, 'anglo': 16585, 'saxon': 16586, 'pairing': 16587, 'saxons': 16588, 'seriousness': 16589, 'nicer': 16590, 'stringing': 16591, 'contrary': 16592, 'scenarios': 16593, 'backgrounds': 16594, 'offend': 16595, 'compliment': 16596, 'fancies': 16597, 'objection': 16598, 'pals': 16599, 'uni': 16600, 'Non': 16601, 'apron': 16602, 'strings': 16603, 'Christiane': 16604, 'finds': 16605, 'pleasing': 16606, 'boyfriend': 16607, 'sluts': 16608, 'Unfair': 16609, 'handicap': 16610, 'Zealander': 16611, 'flee': 16612, '25,000': 16613, 'W.a.b.b.y': 16614, \"you're\": 16615, 'logical': 16616, 'Dux': 16617, 'Litterarum': 16618, 'melodramatic': 16619, 'Lol': 16620, 'Hatmanone': 16621, 'gasps': 16622, 'Joking': 16623, 'compassionate': 16624, 'teenage': 16625, 'mellow': 16626, 'worthless': 16627, 'Auckland': 16628, 'employable': 16629, 'bachelor': 16630, 'profession': 16631, 'earn': 16632, 'Otago': 16633, 'accents': 16634, 'racked': 16635, 'extradite': 16636, 'cough': 16637, 'c.': 16638, 'Whilst': 16639, 'Oz': 16640, 'advertised': 16641, 'networking': 16642, 'lamp': 16643, 'sucks': 16644, 'feisty': 16645, 'tries': 16646, 'pinkies': 16647, 'pinky': 16648, 'crawls': 16649, 'braining': 16650, 'bloodying': 16651, 'dipping': 16652, 'tuna': 16653, 'gerbil': 16654, 'tweezers': 16655, 'sad': 16656, 'hibernate': 16657, 'pinkie': 16658, 'hibernation': 16659, 'brumation': 16660, 'slowed': 16661, 'metabolism': 16662, 'cooler': 16663, 'instinct': 16664, 'shedding': 16665, 'enclosure': 16666, 'Hibernation': 16667, 'necessity': 16668, 'bred': 16669, 'milky': 16670, 'dull': 16671, 'neutered': 16672, 'sniffing': 16673, 'hissed': 16674, 'hisses': 16675, 'chasing': 16676, 'pawing': 16677, 'meowing': 16678, 'roughhouse': 16679, 'intruder': 16680, 'rub': 16681, 'furniture': 16682, 'invading': 16683, 'opens': 16684, 'scruff': 16685, 'guts': 16686, 'reminded': 16687, 'hiss': 16688, 'cries': 16689, 'apart': 16690, 'f*ck': 16691, 'hitting': 16692, 'paws': 16693, 'tails': 16694, 'wagging': 16695, 'Tet': 16696, '1964': 16697, 'pacify': 16698, 'Westmoreland': 16699, 'Destroy': 16700, 'Defend': 16701, 'pacifying': 16702, 'Thing': 16703, 'Local': 16704, 'VC': 16705, 'challenged': 16706, 'Main': 16707, 'NVA': 16708, 'swept': 16709, 'Communists': 16710, 'leaned': 16711, 'firepower': 16712, 'sunny': 16713, 'WERE': 16714, 'hoped': 16715, 'Offensive': 16716, 'sneak': 16717, 'stranded': 16718, 'peasants': 16719, 'WAS': 16720, 'infiltrate': 16721, 'redeploying': 16722, 'rushed': 16723, 'counterattacked': 16724, 'Hue': 16725, 'rallied': 16726, 'SOUTH': 16727, 'VIETNAMESE': 16728, '….': 16729, 'chased': 16730, 'topple': 16731, 'intents': 16732, 'shattered': 16733, 'obtuse': 16734, 'platter': 16735, 'LBJ': 16736, 'antiwar': 16737, 'emancipate': 16738, 'unpalatable': 16739, 'subduing': 16740, 'Vo': 16741, 'Giap': 16742, 'Bearded': 16743, 'reflector': 16744, 'UVA': 16745, 'psycholical': 16746, 'eatin': 16747, 'juvenile': 16748, 'crickets': 16749, 'diurnal': 16750, 'reptiles': 16751, 'UVB': 16752, 'emitting': 16753, 'emit': 16754, 'bulbs': 16755, 'reproduced': 16756, 'fluorescent': 16757, 'MVB': 16758, 'mercury': 16759, 'vapor': 16760, 'coiled': 16761, 'compact': 16762, 'blindness': 16763, 'Zoomed': 16764, 'Repsitun': 16765, 'http://lllreptile.com/store/catalog/reptile-supplies/uvb-fluorescent-lights-mercury-vapor-bulbs/-/zoo-med-24-repti-sun-100-fluorescent-bulb/': 16766, 'Petsmart': 16767, 'Freeze': 16768, 'staple': 16769, 'feeder': 16770, 'dragons': 16771, 'hunters': 16772, 'Crickets': 16773, 'phoenix': 16774, 'turkistan': 16775, 'dubia': 16776, 'Wax': 16777, 'guides': 16778, 'http://herp-info.webs.com/beardeddragon.htm': 16779, 'http://www.beardeddragon.org/articles/caresheet/?page=1': 16780, 'atleast': 16781, 'shady': 16782, 'UV': 16783, 'ventilation': 16784, 'oven': 16785, 'lizards': 16786, 'PetSmart': 16787, 'disgrace': 16788, 'associates': 16789, 'sheesh': 16790, 'thermometer': 16791, 'processed': 16792, 'leafy': 16793, 'kale': 16794, 'spinach': 16795, 'finch': 16796, 'affordable': 16797, 'WHICH': 16798, 'WOULD': 16799, 'BE': 16800, 'QUIETEST': 16801, 'thx': 16802, 'zebra': 16803, 'finches': 16804, 'chirping': 16805, 'tamed': 16806, 'touched': 16807, 'budgies': 16808, 'adorable': 16809, 'eaiser': 16810, 'beak': 16811, 'vent': 16812, 'stripes': 16813, 'fo': 16814, 'hair': 16815, 'stipes': 16816, 'balding': 16817, 'cere': 16818, 'pink': 16819, 'hahahaahh': 16820, 'Honestly': 16821, 'millet': 16822, 'Push': 16823, 'screem': 16824, 'harming': 16825, 'yell': 16826, 'ot': 16827, 'sholder': 16828, '!!!!!!!!!!!!!!!': 16829, 'friendlier': 16830, 'Parakeet': 16831, 'Cookie': 16832, ':P': 16833, '20s': 16834, 'crazier': 16835, 'shirtsleeves': 16836, 'sunglasses': 16837, 'woollies': 16838, 'driest': 16839, 'continental': 16840, 'seasons': 16841, 'basic\\xadally': 16842, 'reasonably': 16843, '–': 16844, 'crowds': 16845, 'Autumn': 16846, 'blur': 16847, 'Still': 16848, 'festivities': 16849, 'freezing': 16850, 'brutal': 16851, 'Crowds': 16852, 'thinnest': 16853, 'attractions': 16854, 'paradoxically': 16855, 'leaves': 16856, 'grey': 16857, 'sheeting': 16858, 'warms': 16859, 'Waterproof': 16860, 'footwear': 16861, 'umbrella': 16862, 'Scotsman': 16863, 'Connolly': 16864, 'changeable': 16865, 'Dublin': 16866, 'Hemisphere': 16867, 'Cliffs': 16868, 'Moher': 16869, 'Dingle': 16870, 'waterproof': 16871, 'jackets': 16872, 'BBQ': 16873, 'onwards': 16874, 'shorten': 16875, 'wetter': 16876, 'windier': 16877, 'colder': 16878, 'frosts': 16879, 'Golden': 16880, 'Wonder': 16881, 'KilliFish': 16882, 'Breeding': 16883, 'Killifish': 16884, 'whitish': 16885, 'colourful': 16886, 'Flakes': 16887, 'Earthworms': 16888, 'uncut': 16889, 'aspire': 16890, 'Worms': 16891, 'Daphnia': 16892, 'Lipids': 16893, 'wonders': 16894, 'sexing': 16895, 'Aplocheilus': 16896, 'lineatus': 16897, 'spawning': 16898, 'killies': 16899, 'mop': 16900, 'rinsed': 16901, 'vial': 16902, 'fishing': 16903, 'bobber': 16904, 'non-crumbly': 16905, 'styrofoam': 16906, 'float': 16907, 'incubating': 16908, 'tray': 16909, 'seasoned': 16910, 'planted': 16911, 'hatch': 16912, 'defrosted': 16913, 'Separated': 16914, 'nourishing': 16915, 'Separating': 16916, 'judged': 16917, 'dings': 16918, 'incubation': 16919, 'chlorine': 16920, 'choramine': 16921, 'tap': 16922, 'chlorination': 16923, 'microscopic': 16924, 'THEN': 16925, 'jar': 16926, 'sate': 16927, 'snacks': 16928, 'killie': 16929, 'Cory': 16930, 'snails': 16931, 'mineral': 16932, 'Aplo.': 16933, 'chemistries': 16934, 'hardness': 16935, 'TDS': 16936, 'Dissolved': 16937, 'Solids': 16938, 'Winging': 16939, 'PPM': 16940, '5.8': 16941, '10.6': 16942, 'DH': 16943, 'gardneri': 16944, 'meters': 16945, 'thrifty': 16946, 'aquarist': 16947, 'calcium': 16948, 'magnesium': 16949, 'consequence': 16950, 'sodium': 16951, 'chloride': 16952, 'chorion': 16953, 'egg': 16954, 'yoke': 16955, 'embryo': 16956, 'suffocate': 16957, 'mystery': 16958, 'fungus': 16959, 'Acriflaven': 16960, 'pipette': 16961, 'eyedropper': 16962, 'Methylene': 16963, 'dyed': 16964, 'membrane': 16965, 'grist': 16966, 'yr': 16967, 'olds': 16968, 'holidaying': 16969, 'rejuvenating': 16970, 'memorable': 16971, 'Especially': 16972, 'enjoyable': 16973, 'inception': 16974, 'attracted': 16975, 'thematically': 16976, 'animated': 16977, 'cartoon': 16978, 'cartoons': 16979, 'luxurious': 16980, 'ages': 16981, 'comforts': 16982, 'travelers': 16983, 'furnished': 16984, 'decorated': 16985, 'marvelously': 16986, 'eatables': 16987, 'deliciously': 16988, 'fishes': 16989, 'prawns': 16990, 'shores': 16991, 'imposingly': 16992, 'amnesties': 16993, 'spaciously': 16994, 'seclusion': 16995, 'rejuvenate': 16996, '........': 16997, 'Break': 16998, 'coffee': 16999, 'mornings': 17000, 'BLAST': 17001, 'seating': 17002, 'dinners': 17003, 'magnet': 17004, 'magnetic': 17005, 'whiteboard': 17006, 'walkie': 17007, 'talkie': 17008, 'WILL': 17009, 'Navigator': 17010, 'lieu': 17011, 'chilling': 17012, 'preschoolers': 17013, 'emergencies': 17014, 'wasted': 17015, 'illustrated': 17016, 'splash': 17017, 'fountain': 17018, 'wading': 17019, 'spoiled': 17020, 'Cruise': 17021, 'payoff': 17022, 'grasped': 17023, 'ale': 17024, 'spicy': 17025, 'lemonade': 17026, 'fare': 17027, 'preferences': 17028, 'beverage': 17029, 'farrier': 17030, 'copied': 17031, 'shoes': 17032, 'SO': 17033, 'flirty': 17034, 'hugs': 17035, 'rubbing': 17036, 'crappy': 17037, 'NEVER': 17038, 'nail': 17039, 'HIM': 17040, 'farriers': 17041, 'lousy': 17042, 'apprenticed': 17043, 'WEEKS': 17044, 'crumbling': 17045, 'nails': 17046, 'groped': 17047, 'Barry': 17048, \"they're\": 17049, 'friday': 17050, 'Nail': 17051, 'charming': 17052, 'Nails': 17053, 'Own': 17054, 'belive': 17055, 'becuse': 17056, 'Hoof': 17057, 'Younger': 17058, 'hottie': 17059, 'Joby': 17060, 'GIRL': 17061, 'Bio': 17062, '30s': 17063, 'divorced': 17064, 'flirted': 17065, 'Horribly': 17066, 'Habit': 17067, 'Underwear': 17068, 'Working': 17069, 'Mare': 17070, 'hoof': 17071, 'Ummmm': 17072, 'mare': 17073, 'Foot': 17074, 'Wholes': 17075, 'Laughing': 17076, 'cheeks': 17077, 'Oops': 17078, 'flirt': 17079, 'guss': 17080, 'knocked': 17081, 'Up': 17082, 'heartbroken': 17083, 'Guy': 17084, 'lo9nger': 17085, 'shitty': 17086, 'Cute': 17087, 'bloke': 17088, 'sub-par': 17089, 'flirting': 17090, 'ought': 17091, 'shoddy': 17092, 'EPM': 17093, 'Dealt': 17094, 'Might': 17095, 'lame': 17096, 'chalked': 17097, 'theeth': 17098, 'trainers': 17099, 'dentist': 17100, 'hay': 17101, 'gant': 17102, 'topline': 17103, 'neurological': 17104, 'drags': 17105, 'atrophied': 17106, 'atrophy': 17107, 'grain': 17108, 'chewed': 17109, 'spinal': 17110, 'titer': 17111, 'wobblers': 17112, 'chiro': 17113, 'acupuncturist': 17114, 'Arby': 17115, 'Owner': 17116, 'ornament': 17117, 'rideable': 17118, 'paragraph': 17119, 'regimen': 17120, 'symptoms': 17121, 'exhibited': 17122, 'starving': 17123, 'ridden': 17124, 'Gallop': 17125, 'colleges': 17126, 'Looked': 17127, 'compression': 17128, 'acupuncture': 17129, 'therapies': 17130, 'Yahoos': 17131, 'Y!A': 17132, 'comprehension': 17133, 'comprehend': 17134, 'Worst': 17135, 'heartbreaking': 17136, 'Shee': 17137, 'MI': 17138, 'experimental': 17139, 'additive': 17140, 'hauled': 17141, 'Windsor': 17142, 'racing': 17143, 'wintering': 17144, 'farmers': 17145, 'clearing': 17146, 'opossums': 17147, 'trotters': 17148, 'tracks': 17149, 'coincidence': 17150, 'backs': 17151, 'gangly': 17152, 'tighter': 17153, 'MSU': 17154, 'Strongid': 17155, 'mosquito': 17156, 'Nile': 17157, 'repoire': 17158, 'fixable': 17159, 'poneh': 17160, 'remission': 17161, 'relapse': 17162, 'stroke': 17163, 'Extremely': 17164, 'salon': 17165, 'rude': 17166, 'Rude': 17167, 'insensitive': 17168, 'discourteous': 17169, '!!!!!': 17170, 'Dr': 17171, 'Greenwalt': 17172, 'snowboard': 17173, 'meds': 17174, 'bodytalk': 17175, 'undeniable': 17176, '!.': 17177, 'Wedding': 17178, '11/7/08': 17179, 'impressed': 17180, 'Cj': 17181, 'Saucey': 17182, 'Tattoo': 17183, 'Shop': 17184, 'tattoo': 17185, 'Aztec': 17186, 'ants': 17187, 'Loved': 17188, 'star': 17189, 'Lynda': 17190, 'compassion': 17191, 'testament': 17192, 'Bright': 17193, 'Tours': 17194, 'Affordable': 17195, 'Tour': 17196, 'Operators': 17197, 'Agents': 17198, 'Chennai': 17199, 'Student': 17200, 'Package': 17201, 'heritage': 17202, 'promptly': 17203, 'GREAT': 17204, 'JOB': 17205, 'GUYS': 17206, 'pcs': 17207, 'professionalism': 17208, 'Barrett': 17209, 'unturned': 17210, 'warmed': 17211, 'KB': 17212, 'FAST': 17213, 'Kobey': 17214, 'coats': 17215, 'Farmer': 17216, 'LFTD': 17217, 'kindergarten': 17218, 'infant': 17219, 'PAT': 17220, 'rang': 17221, 'SRD': 17222, 'Scot': 17223, 'Spill': 17224, 'Whisky': 17225, 'Fantastic': 17226, 'florist': 17227, 'La': 17228, 'Crosse': 17229, 'coworkers': 17230, '<3': 17231, 'bagels': 17232, 'edmonton': 17233, 'instructor': 17234, 'lane': 17235, 'Faris': 17236, 'Crim': 17237, 'Squeege': 17238, 'Prompt': 17239, 'Windows': 17240, 'Friendly': 17241, 'resource': 17242, 'Edmark': 17243, 'Larson': 17244, 'Range': 17245, 'Rover': 17246, 'Sport': 17247, 'Window': 17248, 'Tints': 17249, 'Tintman': 17250, 'tints': 17251, 'Ltd': 17252, 'Professional': 17253, 'deliverd': 17254, 'rear': 17255, 'Helpers': 17256, 'excellant': 17257, 'nicest': 17258, 'pubs': 17259, 'garden': 17260, 'decking': 17261, 'beer': 17262, 'awful': 17263, 'Verizon': 17264, 'salespeople': 17265, 'Incredibly': 17266, 'Dupont': 17267, '1?!?!?': 17268, 'graphic': 17269, 'Fresh': 17270, 'Studio': 17271, 'flyers': 17272, 'posters': 17273, 'unbeatable': 17274, 'architectural': 17275, 'Surgeon': 17276, 'Wallen': 17277, 'accomdating': 17278, 'Stainless': 17279, 'tattoos': 17280, 'Cathy': 17281, '******': 17282, 'Stars': 17283, 'Forest': 17284, 'Tots': 17285, 'postive': 17286, 'teachers': 17287, 'Wine': 17288, 'selection': 17289, 'knowledgeable': 17290, 'tastings': 17291, 'repairs': 17292, 'bradley': 17293, 'dusty': 17294, 'QuikTrip': 17295, 'recieve': 17296, 'Awesome': 17297, 'haircut': 17298, 'Palatine': 17299, 'towel': 17300, 'shave': 17301, 'salons': 17302, 'barbershops': 17303, 'Baffled': 17304, 'ambiance': 17305, 'décor': 17306, 'scrumptious': 17307, 'banana': 17308, \"c'm\": 17309, 'Adorn': 17310, 'refreshment': 17311, 'Debi': 17312, 'Chineese': 17313, 'Wok': 17314, 'chefs': 17315, 'specials': 17316, 'Pure': 17317, 'Pilates': 17318, 'TomiPilates': 17319, 'clients': 17320, 'refurb': 17321, 'electrical': 17322, 'subsequently': 17323, 'PJC': 17324, 'Thoroughly': 17325, 'personalized': 17326, 'Definitely': 17327, 'Angels': 17328, 'haves': 17329, 'Comfort': 17330, 'Zone': 17331, 'Slowest': 17332, 'Unfriendly': 17333, 'Sstaff': 17334, 'Weekends': 17335, 'Starbucks': 17336, 'staffs': 17337, 'daytime': 17338, 'Skip': 17339, 'te': 17340, 'Absolutely': 17341, 'PHOTOS': 17342, 'DONE': 17343, 'Hellada': 17344, 'Gallery': 17345, 'Marek': 17346, 'Dzida': 17347, 'photographer': 17348, 'Beach': 17349, 'personaly': 17350, 'BAD': 17351, 'COFFEE': 17352, 'NT': 17353, 'BOTHER': 17354, 'cup': 17355, 'MUCH': 17356, 'BURNT': 17357, 'bitter': 17358, 'sugar': 17359, 'mask': 17360, 'CHANGE': 17361, 'PROCESS': 17362, 'PPL': 17363, 'Westfield': 17364, 'Rt': 17365, '????': 17366, 'compares': 17367, 'Brick': 17368, 'Ikea': 17369, 'com': 17370, 'Qwest': 17371, 'Moving': 17372, 'http://www.speedtest.net/result/1155244347.png': 17373, 'G': 17374, 'Charge': 17375, '129': 17376, 'Tmobile': 17377, 'Extensive': 17378, 'popcorn': 17379, 'wings': 17380, 'asian': 17381, 'pizza': 17382, 'yam': 17383, 'sweets': 17384, 'Attitude': 17385, 'Line': 17386, 'organised': 17387, 'Hair': 17388, 'Nivine': 17389, 'eastgardens': 17390, 'colored': 17391, 'fabolous': 17392, 'mcclelland': 17393, 'reigon': 17394, 'delicous': 17395, 'impeccable': 17396, 'refold': 17397, 'napkin': 17398, \"L'espalier\": 17399, 'budge': 17400, 'tires': 17401, 'tasks': 17402, 'Deals': 17403, 'Town': 17404, '-s': 17405, 'random': 17406, 'discounted': 17407, 'appliances': 17408, 'Trust': 17409, '-ll': 17410, 'Ballerina': 17411, 'ballet': 17412, 'soccer': 17413, 'dancewear': 17414, 'Instep': 17415, 'Outstanding': 17416, 'extraordinarily': 17417, 'Tried': 17418, 'Crust': 17419, 'Broad': 17420, 'Twice': 17421, 'Tonight': 17422, 'Btwn': 17423, 'inquires': 17424, 'sunroom': 17425, 'Patio': 17426, 'workmanship': 17427, 'Employees': 17428, 'coupon': 17429, 'Pennysaver': 17430, 'overpriced': 17431, 'stuffs': 17432, 'kidding': 17433, 'Spongy': 17434, 'microwaved': 17435, 'heartless': 17436, 'salsa': 17437, 'grills': 17438, 'Tucson': 17439, 'toothache': 17440, 'Obina': 17441, 'Olbina': 17442, 'crowns': 17443, 'dentists': 17444, 'Fernandina': 17445, 'Dentistry': 17446, '512': 17447, 'kb': 17448, 'downloading': 17449, 'Chester': 17450, 're-schedule': 17451, 'Rcommended': 17452, 'bees': 17453, 'wasp': 17454, 'environmentally': 17455, 'informative': 17456, 'spraying': 17457, 'pesticides': 17458, 'Reasonable': 17459, 'condos': 17460, 'Suzanne': 17461, 'Vancouver': 17462, 'Accomodating': 17463, 'Springfield': 17464, 'accomodating': 17465, 'microwave': 17466, 'SERVICE': 17467, 'PEOPLE': 17468, 'Watson': 17469, 'Gem': 17470, 'Alpharetta': 17471, 'sourced': 17472, 'fabulously': 17473, 'Le': 17474, 'petit': 17475, 'shallac': 17476, 'gel': 17477, 'manicure': 17478, 'Pedicures': 17479, 'Rendy': 17480, '2010': 17481, 'Caffe': 17482, 'Bella': 17483, 'Italia': 17484, 'delectable': 17485, 'Antipasto': 17486, 'Misto': 17487, 'Spaghetti': 17488, 'alla': 17489, 'Barese': 17490, 'Parmigiana': 17491, 'gelato': 17492, 'watering': 17493, 'Chocolate': 17494, 'Lava': 17495, 'Cake': 17496, 'Sandy': 17497, 'Beware': 17498, 'Sharayu': 17499, 'Hopless': 17500, 'tenth': 17501, 'lodge': 17502, 'pl': 17503, 'Course': 17504, 'HCC': 17505, 'shaky': 17506, 'superintendant': 17507, 'retracted': 17508, 'golfers': 17509, '2008': 17510, 'Physiotherapists': 17511, 'Kusal': 17512, 'Goonewardena': 17513, 'Physios': 17514, 'physios': 17515, 'osteos': 17516, 'chiros': 17517, 'Vigor': 17518, 'pleasant': 17519, 'attentive': 17520, 'flavorful': 17521, 'groomed': 17522, 'uneven': 17523, 'evened': 17524, 'bull': 17525, 'fights': 17526, 'http://en.wikipedia.org/wiki/Bullfighting': 17527, 'bullfights': 17528, 'smashed': 17529, 'BAC': 17530, '>=': 17531, '.15': 17532, 'nurses': 17533, 'pie': 17534, 'buck': 17535, 'followup': 17536, 'FREE': 17537, 'ER': 17538, 'EVERYONE': 17539, 'reball': 17540, 'warranty': 17541, 'brainer': 17542, 'Console': 17543, 'Pros': 17544, 'dingy': 17545, 'salads': 17546, 'nachos': 17547, 'bike': 17548, '+++': 17549, 'yep': 17550, 'fixeded': 17551, 'thumpstar': 17552, 'wase': 17553, 'gear': 17554, 'anywere': 17555, 'thaks': 17556, 'reccommend': 17557, '!!!!!!!!!!': 17558, 'Ham': 17559, 'RIP': 17560, \"80's\": 17561, 'Victim': 17562, 'fond': 17563, 'Glass': 17564, 'panes': 17565, 'min': 17566, 'Run': 17567, 'hairstyling': 17568, 'tends': 17569, 'Recommend': 17570, 'Shannon': 17571, 'bodyworker': 17572, 'Store': 17573, 'Boothbay': 17574, 'junk': 17575, 'grocery': 17576, 'advocate': 17577, 're-wiring': 17578, 'hassle': 17579, 'Matt': 17580, 'Bonafide': 17581, 'knowledgable': 17582, 'printers': 17583, 'Joule': 17584, 'Already': 17585, 'printing': 17586, 'Paperback': 17587, 'Book': 17588, 'Printing': 17589, 'sonic': 17590, 'palces': 17591, 'grease': 17592, 'INSULTED': 17593, 'ASK': 17594, 'THEM': 17595, 'QUESTIONS': 17596, 'ARE': 17597, 'RUDE': 17598, 'NASTY': 17599, \"N'T\": 17600, 'USE': 17601, 'MOVING': 17602, 'WANT': 17603, 'CRY': 17604, 'TROUBLE': 17605, 'EXPERIENCE': 17606, 'DAY': 17607, 'MOVE': 17608, 'GIVE': 17609, 'LOW': 17610, 'PRICE': 17611, 'OVER': 17612, 'GUARANTEE': 17613, 'Maids': 17614, 'sercvice': 17615, 'Beats': 17616, 'maids': 17617, 'spoil': 17618, 'princess': 17619, 'Antique': 17620, 'Lighting': 17621, 'Fixtures': 17622, 'showroom': 17623, 'WOW': 17624, 'collections': 17625, 'antique': 17626, 'fixtures': 17627, 'Chandeliers': 17628, 'Antiques': 17629, 'Vintage': 17630, 'Contemporary': 17631, 'Repair': 17632, 'Restoration': 17633, 'someplace': 17634, 'rocked': 17635, 'Brought': 17636, 'Fried': 17637, 'Crab': 17638, 'Wontons': 17639, 'Spare': 17640, 'Ribs': 17641, 'MOO': 17642, 'SHU': 17643, 'Neptune': 17644, 'Platter': 17645, 'stars': 17646, 'HERE': 17647, 'wavy': 17648, 'dip': 17649, 'pedicure': 17650, 'lover': 17651, 'Dong': 17652, 'slimy': 17653, 'tasteless': 17654, 'languages': 17655, 'disappointment': 17656, 'Iowa': 17657, 'Penny': 17658, 'asparagus': 17659, 'seared': 17660, 'desserts': 17661, 'dessert': 17662, 'penny': 17663, 'CHINESE': 17664, 'RESTAURANT': 17665, 'tasted': 17666, 'renovation': 17667, 'polite': 17668, 'oriented': 17669, 'Furnace': 17670, 'Heating': 17671, 'Dustin': 17672, 'feverishly': 17673, 'exhaust': 17674, 'replaced': 17675, 'zero': 17676, 'DEFINITELY': 17677, 'TIGER': 17678, 'HEATING': 17679, 'AIR': 17680, 'Suck': 17681, 'Rhino': 17682, 'Grizzly': 17683, 'salesman': 17684, 'Parts': 17685, 'Drove': 17686, 'Peachstate': 17687, 'Powersports': 17688, 'LaGrange': 17689, 'Levi': 17690, 'inspiring': 17691, 'Nigel': 17692, 'Nidd': 17693, 'drawings': 17694, 'dreams': 17695, '730': 17696, 'rangoon': 17697, 'tofu': 17698, 'cabbage': 17699, 'satay': 17700, 'Def': 17701, 'Bea': 17702, 'Californian': 17703, 'authentic': 17704, 'Esp.': 17705, 'mole': 17706, 'tortilla': 17707, 'soup': 17708, 'guacamole': 17709, 'Margaritas': 17710, 'QUESO': 17711, 'Queso': 17712, 'watery': 17713, 'MIND': 17714, 'queso': 17715, 'houston': 17716, 'morelias': 17717, 'enchiladas': 17718, 'sauce': 17719, 'vomited': 17720, 'Skylight': 17721, 'skylight': 17722, 'Bateman': 17723, 'inspected': 17724, 'entie': 17725, 'hailstorm': 17726, 'tricky': 17727, 'rental': 17728, 'shelf': 17729, 'Telugu': 17730, 'groceries': 17731, 'Raina': 17732, 'cramped': 17733, 'un-ruly': 17734, 'Kumon': 17735, 'Parents': 17736, 'heebee': 17737, 'gee': 17738, \"bees'\": 17739, 'Firm': 17740, 'Bloom': 17741, 'Seth': 17742, 'Split': 17743, 'pictured': 17744, 'catalogue': 17745, 'MY': 17746, 'GYM': 17747, 'FITNESS': 17748, 'UNLIMITED': 17749, 'gym': 17750, 'dare': 17751, 'addicting': 17752, 'Over-rated': 17753, 'over-rated': 17754, 'mexican': 17755, 'fried': 17756, 'enchladas': 17757, 'enchilada': 17758, 'chili': 17759, 'Hormel': 17760, 'cheese': 17761, 'Chili': 17762, 'Relleno': 17763, 'batter': 17764, 'googled': 17765, 'Mixed': 17766, 'Tempura': 17767, '.....................': 17768, '8.25': 17769, 'Shrimp': 17770, 'tempura': 17771, 'salad': 17772, 'Cesar': 17773, 'Gracie': 17774, 'ufc': 17775, 'jiu': 17776, 'jitsu': 17777, 'mma': 17778, 'Rosa': 17779, 'ncfa': 17780, 'teaches': 17781, 'coach': 17782, 'www.norcalfightingalliance.com': 17783, 'Pizza': 17784, 'HORRIBLE': 17785, 'Absolute': 17786, 'quit': 17787, 'Toyota': 17788, 'dealerships': 17789, 'absoulutely': 17790, 'comfty': 17791, 'toda': 17792, 'french': 17793, 'reccomend': 17794, 'hospitality': 17795, 'Anna': 17796, 'Govind': 17797, 'steep': 17798, 'Went': 17799, 'Honda': 17800, 'salesperson': 17801, 'Claimed': 17802, 'drives': 17803, 'Stevens': 17804, 'ATE': 17805, 'COUPLE': 17806, 'TIMES': 17807, 'END': 17808, 'STEAK': 17809, 'HOUSE': 17810, 'CUISINE': 17811, 'BRETT': 17812, 'ENJOYS': 17813, 'IN': 17814, 'MISSISSIPPI': 17815, 'BURGER': 17816, 'FRIES': 17817, 'CAJUNISH': 17818, 'GREEN': 17819, 'BAY': 17820, 'DECENT': 17821, 'EXPECTING': 17822, 'RUTH': 17823, 'CHRIS': 17824, 'TYPE': 17825, 'Eulogic': 17826, 'mussels': 17827, 'downstairs': 17828, 'Filled': 17829, 'Belgian': 17830, 'Tavern': 17831, 'handcraft': 17832, 'Tanglewood': 17833, 'Apartments': 17834, 'refreshing': 17835, 'Meadowrun': 17836, 'Tiffany': 17837, 'reachable': 17838, 'breakfasts': 17839, 'clubhouse': 17840, 'snows': 17841, 'surley': 17842, 'cakes': 17843, 'bakers': 17844, 'baking': 17845, 'Baltimore': 17846, 'deck': 17847, 'esp': 17848, 'Overpriced': 17849, 'Youngstown': 17850, 'Sports': 17851, 'operated': 17852, 'mediocre': 17853, 'Apps': 17854, 'Salad': 17855, 'Entree': 17856, 'NOV': 17857, '07': 17858, 'Enjoyed': 17859, 'salmon': 17860, 'steaks': 17861, 'Re-interviewed': 17862, 'dependable': 17863, 'puppies': 17864, 'Dumb': 17865, 'Friends': 17866, 'temperment': 17867, 'silverware': 17868, 'mac': 17869, 'que': 17870, 'napkins': 17871, 'buys': 17872, 'Chao': 17873, 'gentel': 17874, 'Garage': 17875, 'herpes': 17876, 'molesters': 17877, 'hoa': 17878, 'reviewers': 17879, 'updo': 17880, 'aswered': 17881, 'approximate': 17882, 'Chelan': 17883, 'Aka': 17884, 'Nowheresville': 17885, 'litttle': 17886, 'gem': 17887, 'pretense': 17888, 'selections': 17889, 'tasting': 17890, 'Boutique': 17891, 'onesie': 17892, 'pleasantly': 17893, 'Purple': 17894, 'Goose': 17895, 'SAME': 17896, 'boutiques': 17897, 'Consistantly': 17898, 'organisation': 17899, 'coupled': 17900, 'distain': 17901, 'Chasing': 17902, 'unresolved': 17903, 'farcical': 17904, 'vigilant': 17905, 'AMAZING': 17906, 'NIGHT': 17907, 'Willow': 17908, 'Lounge': 17909, 'Drinks': 17910, 'Ipanema': 17911, 'Richmond': 17912, 'nondescript': 17913, 'thumbs': 17914, 'Fraiser': 17915, 'FANTASTIC': 17916, 'STORE': 17917, 'HONKA': 17918, 'Homes': 17919, 'Walmart': 17920, 'Evergreen': 17921, 'furnishings': 17922, 'accessories': 17923, \"It's\": 17924, 'durability': 17925, 'Lovely': 17926, 'Hats': 17927, 'saddened': 17928, 'tolerating': 17929, 'accented': 17930, 'hats': 17931, 'costumes': 17932, 'flips': 17933, 'compeltly': 17934, 'dissatisfied': 17935, 'SUCKS': 17936, 'Advanced': 17937, 'meanest': 17938, 'THRIVE': 17939, 'Horrible': 17940, 'supercuts': 17941, 'Burger': 17942, 'BK': 17943, 'regreted': 17944, 'Branford': 17945, 'rudely': 17946, 'Disgusting': 17947, 'Disappointed': 17948, 'Napa': 17949, 'unpleasantly': 17950, 'svce': 17951, 'subpar': 17952, 'dishes': 17953, 'wines': 17954, 'composed': 17955, 'Recommended': 17956, 'laughter': 17957, 'phenomenal': 17958, 'humour': 17959, 'calmness': 17960, 'amazes': 17961, 'exceeds': 17962, 'horrendous': 17963, 'daycare': 17964, 'crust': 17965, 'lopsided': 17966, 'thicker': 17967, 'slices': 17968, 'flavor': 17969, '!!!.': 17970, 'utter': 17971, 'CLASS': 17972, 'Doors': 17973, 'answetred': 17974, 'hesitations': 17975, 'repaired': 17976, 'Allen': 17977, 'Tire': 17978, 'tire': 17979, 'Temecula': 17980, 'Lexus': 17981, 'Tires': 17982, 'rails': 17983, 'professionally': 17984, 'Easiest': 17985, 'dealership': 17986, 'hooptie': 17987, 'sticker': 17988, 'excuses': 17989, 'Kwik': 17990, 'Kar': 17991, 'Fairchild': 17992, 'Prudential': 17993, 'Steamboat': 17994, 'Realty': 17995, 'Yampa': 17996, 'Barber': 17997, 'reviewer': 17998, 'sub': 17999, 'par': 18000, 'UGH': 18001, 'freshly': 18002, 'squares': 18003, 'Flipped': 18004, 'riddled': 18005, 'clerk': 18006, 'giggled': 18007, 'Buffet': 18008, \"lovin'\": 18009, 'Experience': 18010, 'Providence': 18011, 'Aesthetics': 18012, 'Kueck': 18013, 'Equipment': 18014, 'Talley': 18015, 'methodical': 18016, 'passion': 18017, 'orginals': 18018, 'roofing': 18019, 'Roofing': 18020, 'OMFG': 18021, 'FUCKING': 18022, 'HATE': 18023, 'EVERY': 18024, 'HOT': 18025, 'CHICK': 18026, 'SHOWS': 18027, 'UP': 18028, 'MEAN': 18029, 'REAAAALLY': 18030, 'DUMB': 18031, 'THEIR': 18032, 'OTHER': 18033, 'REALY': 18034, 'UGLY': 18035, 'SUPER': 18036, 'SMART': 18037, 'COULD': 18038, 'SCIENTIST': 18039, 'STONER': 18040, 'COMES': 18041, 'HE': 18042, 'BRINGS': 18043, 'HIS': 18044, 'DOG': 18045, 'SECOND': 18046, 'HAND': 18047, 'SMOKE': 18048, 'THINK': 18049, 'TRYING': 18050, 'TALK': 18051, 'ANYWAY': 18052, 'WE': 18053, 'DRIVE': 18054, 'AROUND': 18055, 'VAN': 18056, 'SOLVE': 18057, 'MYSTERYS': 18058, 'SHIT': 18059, 'AMAZINGLY': 18060, 'YUMMY': 18061, 'crepe': 18062, 'EVEN': 18063, 'FRANCE': 18064, 'Crepes': 18065, 'Mulberry': 18066, 'valet': 18067, 'Marriott': 18068, 'AMES': 18069, 'Wessex': 18070, 'fitness': 18071, 'apartments': 18072, 'LOVED': 18073, 'towed': 18074, 'Sussman': 18075, 'Kia': 18076, 'squeezed': 18077, 'HEAVEN': 18078, 'EARTHHHHHHH': 18079, 'HATED': 18080, 'SUSHI': 18081, 'BEFORE': 18082, 'STOP': 18083, 'EATTING': 18084, 'NICE': 18085, 'EXCELLENT': 18086, 'SEEMS': 18087, 'AMAZE': 18088, 'GRILL': 18089, 'DISHES': 18090, 'TA': 18091, 'WORLD': 18092, 'FABULOUS': 18093, 'EAT': 18094, 'LEAST': 18095, 'DAYS': 18096, 'CHEF': 18097, 'SPECIAL': 18098, 'ROLLS': 18099, 'FAIR': 18100, 'ORDERS': 18101, '++++': 18102, '!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!': 18103, 'JUNO': 18104, 'OPEN': 18105, '!!!!!!!!!!!!!!': 18106, 'homo': 18107, 'sandwiches': 18108, 'soups': 18109, 'bustling': 18110, 'seated': 18111, 'Kid': 18112, 'Block': 18113, 'Collingswood': 18114, 'Amore': 18115, 'arancini': 18116, 'di': 18117, 'riso': 18118, 'risotto': 18119, 'fritters': 18120, 'saltimboca': 18121, 'chocolate': 18122, 'mousse': 18123, 'heaven': 18124, 'Pay': 18125, 'entree': 18126, 'Auto': 18127, 'Towing': 18128, 'towing': 18129, 'mechanics': 18130, 'wholeheartedly': 18131, 'Duct': 18132, 'Cleaning': 18133, 'pride': 18134, 'accomplishing': 18135, 'duct': 18136, 'inspector': 18137, 'Solana': 18138, 'T': 18139, 'mary': 18140, 'benedict': 18141, 'landmark': 18142, 'jug': 18143, 'mix': 18144, 'Builders': 18145, 'NJ': 18146, 'Jersey': 18147, 'Dude': 18148, 'Cut': 18149, 'vibe': 18150, 'Alta': 18151, 'Moda': 18152, 'genius': 18153, 'Favorite': 18154, 'Wildwood': 18155, 'vacations': 18156, 'comparing': 18157, 'layout': 18158, 'Scordia': 18159, 'Sicily': 18160, 'Mudo': 18161, 'familia': 18162, 'Italiano': 18163, 'bella': 18164, 'Sicilian': 18165, 'luv': 18166, 'eternally': 18167, 'Luv': 18168, 'kennels': 18169, 'Mrs': 18170, 'Closs': 18171, 'Car': 18172, 'Dealer': 18173, 'nissan': 18174, 'dealship': 18175, 'triage': 18176, 'threatening': 18177, 'seeker': 18178, 'nurse': 18179, 'seekers': 18180, 'UW': 18181, 'Wonderful': 18182, 'immense': 18183, 'Dentist': 18184, 'sewage': 18185, 'Principal': 18186, 'backpacks': 18187, 'TRIPPED': 18188, 'cowboy': 18189, 'Tacoma': 18190, 'Handy': 18191, 'Jolla': 18192, 'Stuff': 18193, 'Residential': 18194, 'Numbers': 18195, 'roots': 18196, 'cookbook': 18197, 'THX': 18198, 'fot': 18199, 'nerd': 18200, 'oblivion': 18201, 'fps': 18202, 'clue': 18203, 'MISTAKE': 18204, 'edible': 18205, 'potato': 18206, 'wedges': 18207, 'wantons': 18208, 'Mongolian': 18209, 'Liquidweb.com': 18210, 'Rocks': 18211, 'liquidweb.com': 18212, 'Liquidweb': 18213, 'Hino': 18214, 'Prestige': 18215, 'converted': 18216, 'Awards': 18217, 'Dandenong': 18218, 'PMA': 18219, 'Bronze': 18220, 'excellence': 18221, 'Brendan': 18222, 'flashy': 18223, 'stacked': 18224, 'fussy': 18225, 'garnishes': 18226, 'exquisite': 18227, 'Vetri': 18228, 'pasta': 18229, 'gnocchi': 18230, 'antipasti': 18231, 'dined': 18232, 'Discount': 18233, 'washes': 18234, 'nicely': 18235, 'bottles': 18236, 'hesitation': 18237, 'peddle': 18238, 'peddles': 18239, 'Trek': 18240, 'Concerned': 18241, 'Made': 18242, 'curtains': 18243, 'KNEW': 18244, 'rescheduling': 18245, '!?!': 18246, '?!?': 18247, 'Internet': 18248, 'Balazick': 18249, 'WORTH': 18250, 'BOTHERED': 18251, 'ASWERING': 18252, 'MONEY': 18253, 'DEAL': 18254, 'BROKERS': 18255, 'Rip': 18256, 'plugs': 18257, '330': 18258, 'windsheild': 18259, 'washer': 18260, 'canister': 18261, 'clips': 18262, 'Outback': 18263, 'GEM': 18264, 'immaculately': 18265, 'tasteful': 18266, 'elegant': 18267, 'Cuban': 18268, 'cuisine': 18269, 'expertly': 18270, 'portions': 18271, 'generous': 18272, \"'LL\": 18273, 'Ruona': 18274, 'quitting': 18275, 'Saltford': 18276, 'Tina': 18277, 'endevour': 18278, 'signage': 18279, 'counseling': 18280, 'Vera': 18281, 'Bellevue': 18282, 'WA.': 18283, 'Renton': 18284, 'counselor': 18285, 'approachable': 18286, 'nonjudgmental': 18287, 'www.veraakulov.com': 18288, 'petting': 18289, 'laughed': 18290, 'kissed': 18291, 'goats': 18292, 'lambs': 18293, \"pony's\": 18294, 'camels': 18295, 'AN': 18296, 'OSTRICH': 18297, 'Karla': 18298, 'Gracee': 18299, 'besides': 18300, 'punctual': 18301, 'Wunderbar': 18302, 'Experienced': 18303, 'nonexistent': 18304, 'argumentative': 18305, 'rudeness': 18306, 'unorganized': 18307, 'greek': 18308, 'Exile': 18309, 'punk': 18310, 'Mimmy': 18311, 'Buddakan': 18312, 'attracts': 18313, 'association': 18314, 'Starr': 18315, 'carries': 18316, 'over-priced': 18317, 'filler': 18318, 'vegetables': 18319, 'Alto': 18320, 'delivers': 18321, 'varietal': 18322, 'vintage': 18323, 'sommelier': 18324, 'corking': 18325, 'landlord': 18326, 'Buckingham': 18327, 'Condominiums': 18328, 'townhouse': 18329, 'AWESOME': 18330, 'exterminator': 18331, 'b****': 18332, 'turnover': 18333, 'sincerely': 18334, 'approves': 18335, 'attitude': 18336, 'Wildernest': 18337, 'inn': 18338, 'Atop': 18339, 'decks': 18340, 'porch': 18341, 'unparalleled': 18342, 'grandeur': 18343, 'grazing': 18344, 'occasional': 18345, 'mingle': 18346, 'Stewart': 18347, 'proprietors': 18348, 'epitome': 18349, 'Delightful': 18350, 'hospitable': 18351, 'junkie': 18352, 'lube': 18353, 'weird': 18354, 'jiffy': 18355, 'neon': 18356, 'drunkest': 18357, 'someon': 18358, 'bouncy': 18359, 'padded': 18360, 'weekday': 18361, 'classiest': 18362, 'monotony': 18363, 'toddler': 18364, 'Restored': 18365, 'Mechaincs': 18366, 'Ferrari': 18367, 'challenge': 18368, 'Creative': 18369, 'owning': 18370, 'Mechanics': 18371, 'hiking': 18372, 'Wenatchee': 18373, 'hills': 18374, 'loop': 18375, 'patio': 18376, 'OMG': 18377, 'waiters': 18378, 'worht': 18379, 'licking': 18380, 'bowel': 18381, 'Phat': 18382, 'romatic': 18383, 'Ristorante': 18384, 'Holly': 18385, 'truely': 18386, 'hairstylist': 18387, 'UTC': 18388, 'reffered': 18389, 'highlights': 18390, 'blowdry': 18391, 'bat': 18392, 'compliments': 18393, 'deffenitly': 18394, 'Outdated': 18395, 'outdated': 18396, 'waffle': 18397, 'maker': 18398, 'Trinidadian': 18399, 'Jamaican': 18400, 'influenced': 18401, 'patties': 18402, 'staples': 18403, 'cafeteria': 18404, 'practically': 18405, 'connoisseur': 18406, 'toned': 18407, 'palette': 18408, 'pepper': 18409, 'finest': 18410, 'Trees': 18411, 'majestic': 18412, 'astounding': 18413, 'entertaining': 18414, 'Mahal': 18415, 'vanguard': 18416, 'republican': 18417, 'Tussey': 18418, 'nomenal': 18419, 'chinatown': 18420, 'pho': 18421, 'seperates': 18422, 'debit': 18423, 'McInnis': 18424, 'inconsiderate': 18425, 'admitting': 18426, 'Dillards': 18427, 'Transformational': 18428, 'Coach': 18429, 'lisenced': 18430, 'colleague': 18431, 'guided': 18432, 'baseball': 18433, 'ironic': 18434, 'Brickell': 18435, '6/4/11': 18436, 'Sales': 18437, 'Gustavo': 18438, 'Guerra': 18439, 'Braman': 18440, 'Bramen': 18441, 'hassles': 18442, 'Apostle': 18443, 'parishioners': 18444, 'Gus': 18445, 'Honest': 18446, 'Nissan': 18447, 'cracked': 18448, 'manifold': 18449, '1300': 18450, '1500': 18451, 'unneccesary': 18452, 'mechanic': 18453, 'diagnose': 18454, 'Shady': 18455, 'condescending': 18456, 'inexperienced': 18457, 'prideful': 18458, 'EXPERIENCED': 18459, 'Beautifully': 18460, 'UNTRUE': 18461, 'Poor': 18462, 'Ghassemlou': 18463, 'demanded': 18464, 'enrolled': 18465, 'witness': 18466, 'adequately': 18467, 'assuage': 18468, 'Saying': 18469, 'combative': 18470, 'fused': 18471, 'fusions': 18472, 'chiropractric': 18473, 'Barros': 18474, 'Unbelievably': 18475, 'Cheveux': 18476, 'complimented': 18477, 'Quaint': 18478, 'McLean': 18479, 'Endo': 18480, 'storefront': 18481, 'bell': 18482, 'hostess': 18483, 'yelled': 18484, 'arguing': 18485, 'unprofessional': 18486, 'jerks': 18487, 'Missed': 18488, 'carless': 18489, 'NEEEEEEEEEVERRRR': 18490, 'Superb': 18491, 'Arrangements': 18492, 'Fancy': 18493, 'Flowers': 18494, 'funerals': 18495, 'outshone': 18496, 'arranged': 18497, 'superbly': 18498, 'delicately': 18499, 'hairdresser': 18500, 'conditioned': 18501, 'Smoker': 18502, 'leaky': 18503, 'bathrooms': 18504, 'cabinets': 18505, 'thrashed': 18506, 'slapped': 18507, 'non-smokers': 18508, 'beware': 18509, 'tenants': 18510, 'smokers': 18511, 'reeks': 18512, 'cigarette': 18513, 'smelling': 18514, 'disgusting': 18515, 'Sanwiches': 18516, 'meatball': 18517, 'regulars': 18518, 'Tourists': 18519, 'TGIF': 18520, 'Rate': 18521, 'hotpot': 18522, 'chrisssake': 18523, 'churchy': 18524, 'grandure': 18525, 'robe': 18526, \"'60s\": 18527, 'Sf': 18528, 'aunte': 18529, 'kiddies': 18530, 'labyrinth': 18531, \"'em\": 18532, 'pagen': 18533, 'heck': 18534, 'kc': 18535, 'sox': 18536, 'phillies': 18537, 'knights': 18538, 'tho': 18539, 'chineze': 18540, 'sice': 18541, 'Door': 18542, 'Woodinville': 18543, 'NDI': 18544, 'Johnette': 18545, 'appologized': 18546, 'skiing': 18547, 'sized': 18548, 'responsiveness': 18549, 'Roger': 18550, 'speeding': 18551, 'CHRISTIAN': 18552, '!!!!!!!!!!!!!!!!!!': 18553, 'LAST': 18554, 'LET': 18555, 'CHILD': 18556, 'GET': 18557, 'RAPED': 18558, 'BECAUSE': 18559, 'PAID': 18560, 'Nordstrom': 18561, 'designer': 18562, 'Garment': 18563, 'Previous': 18564, 'pushy': 18565, 'stain': 18566, 'leather': 18567, 'purse': 18568, 'waitress': 18569, 'forgetting': 18570, 'verde': 18571, 'completly': 18572, 'flavorless': 18573, 'orderd': 18574, 'meals': 18575, 'chimichangas': 18576, 'jalapeno': 18577, 'borritos': 18578, 'quesadillas': 18579, 'spice': 18580, 'Hacienda': 18581, 'Jill': 18582, 'Dessert': 18583, 'veggie': 18584, 'Hull': 18585, 'Peas': 18586, 'Cons': 18587, 'blackened': 18588, 'catfish': 18589, 'Overcooked': 18590, 'Thoughts': 18591, 'tasty': 18592, 'Fidelity': 18593, 'Leasing': 18594, 'painless': 18595, 'co.': 18596, 'whatsoever': 18597, 'robbed': 18598, 'cheated': 18599, 'lied': 18600, 'misinform': 18601, 'misrepresent': 18602, 'confuse': 18603, 'holders': 18604, 'misinformation': 18605, 'premiums': 18606, 'glitch': 18607, 'Ifa': 18608, 'F%#king': 18609, 'Assh@%$e': 18610, 'alignment': 18611, 'shopped': 18612, 'nitrogen': 18613, 'mileage': 18614, 'CONVENIENT': 18615, 'Creekside': 18616, 'esp.': 18617, 'Stokes': 18618, 'Bascom': 18619, 'Rail': 18620, 'Trader': 18621, 'Whole': 18622, 'Foods': 18623, 'remodel': 18624, 'pitch': 18625, 'certificates': 18626, 'pressured': 18627, 'gut': 18628, 'sucked': 18629, 'hears': 18630, 'shark': 18631, 'walkway': 18632, 'rubbish': 18633, 'Aquarium': 18634, '£': 18635, 'Cranmore': 18636, 'Dental': 18637, 'Implant': 18638, 'Clinic': 18639, 'Nelson': 18640, 'phobia': 18641, 'instantaneously': 18642, 'dental': 18643, 'underpinned': 18644, 'Pam': 18645, 'Gillies': 18646, 'Salsa': 18647, 'hehehe': 18648, 'trimmers': 18649, 'chips': 18650, 'sub-division': 18651, 'gatherings': 18652, 'anesthetic': 18653, 'audacity': 18654, 'DISPOSAL': 18655, 'itemized': 18656, 'AVOID': 18657, 'COSTS': 18658, 'Peking': 18659, 'opinon': 18660, 'Gone': 18661, 'Sigh': 18662, 'slipped': 18663, 'BNA': 18664, 'pickups': 18665, 'parked': 18666, 'Lucky': 18667, 'EXPENSIVE': 18668, 'Wife': 18669, '230': 18670, 'ammount': 18671, 'elevated': 18672, 'Joke': 18673, 'Salon': 18674, 'JOKE': 18675, 'PRICED': 18676, 'graduating': 18677, 'nope': 18678, 'BEWARE': 18679, 'mislabeled': 18680, 'Ukrops': 18681, 'liquor': 18682, 'Bloody': 18683, 'Mimosas': 18684, 'negotiate': 18685, 'Square': 18686, 'queen': 18687, 'upgrade': 18688, 'beds': 18689, 'gap': 18690, 'thoroughly': 18691, 'remodeled': 18692, \"company's\": 18693, 'guessed': 18694, 'remodeling': 18695, 'Award': 18696, 'Holderness': 18697, 'Road': 18698, 'Chamberlain': 18699, 'Kingston': 18700, 'BALLROOM': 18701, 'LATIN': 18702, 'SEQUENCE': 18703, 'LINE': 18704, 'DANCING': 18705, 'BALLET': 18706, 'TAP': 18707, 'JAZZ': 18708, 'Ward': 18709, 'Ballroom': 18710, 'certified': 18711, 'pre-owned': 18712, 'BMW': 18713, 'leakage': 18714, '175': 18715, 'TRUST': 18716, 'DEALER': 18717, 'STAY': 18718, 'AWAY': 18719, 'FAR': 18720, 'POSSIBLE': 18721, 'Practice': 18722, 'slick': 18723, 'Surgery': 18724, 'timings': 18725, 'mattered': 18726, 'perfectionist': 18727, 'BJ': 18728, 'servers': 18729, 'soda': 18730, 'ohm': 18731, 'Sierra': 18732, 'stylist': 18733, 'curly': 18734, 'styled': 18735, 'relocating': 18736, 'Bend': 18737, 'Eve': 18738, 'Jazz': 18739, 'NYE': 18740, 'appetizing': 18741, 'jazz': 18742, 'intending': 18743, 'Trio': 18744, 'tissues': 18745, 'rectified': 18746, 'toilet': 18747, 'peeling': 18748, 'mould': 18749, 'ounce': 18750, 'Grocery': 18751, 'FusionRetail': 18752, 'dos': 18753, 'Managing': 18754, 'POS': 18755, 'barcoding': 18756, 'Usage': 18757, 'barcodes': 18758, 'Billing': 18759, 'queries': 18760, 'B&B': 18761, 'Fe': 18762, 'Paradero': 18763, 'ladies': 18764, 'Disatisfied': 18765, 'Aid': 18766, 'A&E': 18767, 'appliance': 18768, 'vendor': 18769, 'pre-screened': 18770, 'Cayuga': 18771, 'Lewiston': 18772, 'pedicures': 18773, 'cuticles': 18774, 'mins.': 18775, 'polish': 18776, 'Results': 18777, 'infertility': 18778, 'sterile': 18779, 'hallway': 18780, 'herbs': 18781, 'cycles': 18782, 'Common': 18783, 'yummy': 18784, 'croissants': 18785, 'Comfortable': 18786, 'noisy': 18787, 'stellar': 18788, 'Gare': 18789, 'du': 18790, 'Nord': 18791, 'Sacre': 18792, 'Coeur': 18793, 'Plaza': 18794, 'Galleries': 18795, 'Lafayette': 18796, 'flea': 18797, 'Hostel': 18798, 'Autos': 18799, 'Mobile': 18800, 'sorted': 18801, 'workshop': 18802, 'gearbox': 18803, 'gears': 18804, 'recommending': 18805, 'Yards': 18806, 'Brewery': 18807, 'lounge': 18808, 'brewery': 18809, 'sampler': 18810, 'IPA': 18811, 'Brawler': 18812, 'Stout': 18813, 'ESA': 18814, 'Dogwood': 18815, 'Grilled': 18816, 'Cheese': 18817, 'pint': 18818, 'Nitrogen': 18819, 'Guiness': 18820, 'Lover': 18821, 'smoother': 18822, 'abou': 18823, 'BRAWLER': 18824, 'Oil': 18825, 'Disaster': 18826, \"'07\": 18827, 'Ford': 18828, 'Fusion': 18829, 'sporadically': 18830, 'whir': 18831, 'loudly': 18832, 'Turns': 18833, 'overcharge': 18834, 'Liars': 18835, 'Cruze': 18836, 'mpg': 18837, '9000': 18838, 'purchace': 18839, 'Vic': 18840, 'Canever': 18841, 'grandparents': 18842, 'grandfather': 18843, 'silently': 18844, 'Peterson': 18845, 'sitcom': 18846, 'Seinfeld': 18847, 'greeter': 18848, 'Instructor': 18849, 'Beginning': 18850, 'Brittany': 18851, '2:25': 18852, 'karma': 18853, 'Finest': 18854, 'differ': 18855, 'marginal': 18856, 'entertained': 18857, '5.10': 18858, 'indoor': 18859, 'climber': 18860, 'routes': 18861, 'exhaustion': 18862, 'markings': 18863, 'outing': 18864, 'frustration': 18865, \"DJ's\": 18866, 'surpassed': 18867, \"1960's\": 18868, 'courage': 18869, 'Eva': 18870, 'Lange': 18871, 'sirloin': 18872, 'drenched': 18873, 'butter': 18874, 'Roadhouse': 18875, 'WAY': 18876, 'Spay': 18877, 'neuter': 18878, 'spay': 18879, 'wisconsin': 18880, 'Issues': 18881, 'ingrown': 18882, 'toenail': 18883, 'Shield': 18884, '483.00': 18885, 'reversed': 18886, 'aa': 18887, '160.00': 18888, 'bilked': 18889, 'blessed': 18890, 'Elite': 18891, 'Flyers': 18892, 'postcards': 18893, 'distorted': 18894, 'sheisters': 18895, 'mechanicly': 18896, 'catagory': 18897, 'jimmy': 18898, 'rigged': 18899, 'runaround': 18900, 'standpoint': 18901, 'Grimy': 18902, 'somethin': 18903, 'Stationery': 18904, 'Bethesda': 18905, 'Papeluna': 18906, 'hustle': 18907, 'bustle': 18908, 'nestled': 18909, 'Subway': 18910, 'Sandwiches': 18911, 'Modell': 18912, 'pumpkin': 18913, 'latte': 18914, 'stationery': 18915, 'Cards': 18916, 'wrapping': 18917, 'invitations': 18918, 'yelped': 18919, 'Yelp': 18920, 'wallet': 18921, 'premise': 18922, 'craziest': 18923, 'ltake': 18924, 'premier': 18925, 'Hut': 18926, 'IHOP': 18927, 'Panera': 18928, 'Chipotle': 18929, 'ALONE': 18930, 'VCU': 18931, 'EXCELS': 18932, 'Rams': 18933, 'NCAA': 18934, 'Fan': 18935, 'Carytown': 18936, 'Stony': 18937, 'Point': 18938, 'Pump': 18939, 'continuously': 18940, 'rave': 18941, 'Orr': 18942, 'woken': 18943, 'campsite': 18944, 'drunken': 18945, 'irate': 18946, 'FHS': 18947, 'hoods': 18948, 'gymnasiums': 18949, 'ensemble': 18950, 'presumably': 18951, 'ranked': 18952, 'U.': 18953, 'BU': 18954, 'Vassar': 18955, 'Howard': 18956, 'Mellon': 18957, 'Rutgers': 18958, 'pharmacy': 18959, 'motel': 18960, 'resorts': 18961, 'homey': 18962, 'cuban': 18963, 'colada': 18964, 'mmmm': 18965, 'chill': 18966, 'bake': 18967, 'beans': 18968, 'plantains': 18969, 'mango': 18970, 'pastries': 18971, 'omelets': 18972, 'forsyth': 18973, 'Warner': 18974, 'appointments': 18975, 'FAMILY': 18976, 'dismay': 18977, 'landed': 18978, 'rosette': 18979, 'hmm': 18980, \"canape's\": 18981, 'amuse': 18982, 'nibble': 18983, 'Scallops': 18984, 'overcooked': 18985, 'foie': 18986, 'gras': 18987, 'Iron': 18988, 'spices': 18989, 'cook': 18990, 'vegetarians': 18991, 'dump': 18992, 'hotter': 18993, 'mush': 18994, 'meats': 18995, 'sauces': 18996, 'Meats': 18997, 'seafood': 18998, '4.99': 18999, \"Wednesday's\": 19000, 'Cookies': 19001, 'Cakes': 19002, 'pre-made': 19003, 'Valentine': 19004, 'bakeries': 19005, 'rectangle': 19006, 'Fiona': 19007, 'texture': 19008, 'frosting': 19009, 'amazingly': 19010, 'fluffy': 19011, 'gladly': 19012, 'cookies': 19013, 'Craving': 19014, 'Acupuncture': 19015, 'cravings': 19016, 'Definite': 19017, 'Decrease': 19018, 'craving': 19019, 'DOCTOR': 19020, 'needles': 19021, 'Liau': 19022, 'GLAD': 19023, 'bathing': 19024, 'Asset': 19025, 'Limits': 19026, 'multiplex': 19027, 'Bowtie': 19028, 'theatre': 19029, 'brick': 19030, 'Flying': 19031, 'Squirrels': 19032, 'manged': 19033, 'preserving': 19034, 'cellphones': 19035, 'ads': 19036, 'previews': 19037, 'theatres': 19038, 'mangers': 19039, 'Sundays': 19040, 'discounts': 19041, 'Craft': 19042, 'Wonderland': 19043, 'History': 19044, 'origami': 19045, 'jewelry': 19046, 'earrings': 19047, 'quilling': 19048, 'filigree': 19049, 'crafts': 19050, 'lovable': 19051, 'humor': 19052, 'Oklahoma': 19053, 'bs': 19054, 'crafter': 19055, 'Salmagundi': 19056, 'smorgasbord': 19057, 'potpourri': 19058, 'motley': 19059, 'miscellaneous': 19060, 'assortment': 19061, 'mixture': 19062, 'Quimba': 19063, '1/30/10': 19064, 'Genesis': 19065, 'grrrrrrrreeeaaat': 19066, 'Hyundai': 19067, 'wal': 19068, 'mart': 19069, 'ti': 19070, 'thouhgt': 19071, 'Bait': 19072, 'untrained': 19073, 'Called': 19074, 'Bonanza': 19075, '350': 19076, 'sq': 19077, 'ceramic': 19078, 'dual': 19079, 'grinder': 19080, 'thinset': 19081, 'inclusive': 19082, 'inserts': 19083, '125': 19084, 'PLUS': 19085, '260': 19086, 'pitfalls': 19087, 'renting': 19088, 'switching': 19089, 'suspects': 19090, 'Outside': 19091, 'Laundry': 19092, 'Tub': 19093, 'filthy': 19094, 'laundromats': 19095, 'availed': 19096, \"'n\": 19097, 'garbage': 19098, 'checkout': 19099, 'SHOCKED': 19100, 'Romeo': 19101, 'wished': 19102, 'annoy': 19103, 'CARE': 19104, 'HH': 19105, 'serivce': 19106, 'mindset': 19107, 'bartenders': 19108, 'waitresses': 19109, 're-trained': 19110, 'chat': 19111, 'impatiently': 19112, 'understaffing': 19113, 'Midas': 19114, 'Touch': 19115, 'personable': 19116, 'revolves': 19117, 'fuse': 19118, 'tech': 19119, 'pedestal': 19120, 'rolls': 19121, 'scallop': 19122, 'nigiri': 19123, 'panko': 19124, 'Logan': 19125, '3.75': 19126, 'solicitous': 19127, 'Sleep': 19128, 'Courtesy': 19129, '3:15': 19130, 'strobe': 19131, 'activated': 19132, 'alarms': 19133, 'ADA': 19134, 'drip': 19135, 'apologies': 19136, 'pressed': 19137, 'Lack': 19138, 'Passion': 19139, 'Romanick': 19140, 'impatient': 19141, 'countless': 19142, 'diligent': 19143, 'actualy': 19144, 'tolls': 19145, 'payed': 19146, 'eather': 19147, 'realy': 19148, 'totalling': 19149, 'Guess': 19150, 'bdr': 19151, 'recomend': 19152, 'yelling': 19153, 'rudest': 19154, 'trashy': 19155, 'Jurek': 19156, 'Decor': 19157, '500.00': 19158, 'NON': 19159, 'REFUNDABLE': 19160, 'heed': 19161, 'hype': 19162, 'DECOR': 19163, 'squirm': 19164, 'precious': 19165, 'Somewhere': 19166, 'smoked': 19167, 'OWNER': 19168, 'Unfortunalty': 19169, 'Bodhi': 19170, 'Vet': 19171, 'Mackinaw': 19172, 'FANFUCKINGTASTIC': 19173, 'Yorker': 19174, 'Oxford': 19175, 'devoid': 19176, 'Glasgow': 19177, 'HOLY': 19178, 'GRAIL': 19179, 'pies': 19180, 'pizzas': 19181, 'oregano': 19182, 'Scottish': 19183, 'congratulated': 19184, 'Scots': 19185, 'fcking': 19186, 'yorkedness': 19187, 'scrummy': 19188, 'gimmicky': 19189, '7/26/08': 19190, 'Mia': 19191, 'Greenfield': 19192, 'interrupting': 19193, 'cashier': 19194, 'unapologetic': 19195, 'wasting': 19196, 'overage': 19197, 'DINING': 19198, 'ROADHOUSE': 19199, 'MEALS': 19200, 'MEAT': 19201, 'RIGHT': 19202, 'OFF': 19203, 'BONES': 19204, 'PRICES': 19205, 'WAITING': 19206, 'AREA': 19207, 'ENJOYABLE': 19208, 'ENOUGH': 19209, 'SEATS': 19210, 'ALSO': 19211, 'PEANUTS': 19212, 'FLOOR': 19213, 'NEXT': 19214, 'PERSON': 19215, 'BRINGING': 19216, 'HAD': 19217, 'TAKE': 19218, 'SALAD': 19219, 'HOME': 19220, 'FORGOT': 19221, 'BRING': 19222, 'BROUGHT': 19223, 'CAME': 19224, 'BACK': 19225, 'WAITRESS': 19226, 'FIRST': 19227, 'COME': 19228, 'HOSTESS': 19229, 'FRIENDLY': 19230, 'WORKERS': 19231, 'STANDING': 19232, 'MARCH': 19233, '6TH': 19234, '2009': 19235, 'JULY': 19236, '4TH': 19237, 'DAUGHTER': 19238, 'BUFFALO': 19239, 'WINGS': 19240, 'FLY': 19241, 'MANAGER': 19242, 'SAID': 19243, 'SORRY': 19244, 'GAVE': 19245, 'BATCH': 19246, 'DOORS': 19247, 'OPENING': 19248, 'CLOSING': 19249, 'OPINION': 19250, 'TOOK': 19251, 'BILL': 19252, 'OWN': 19253, 'Mezza': 19254, 'Luna': 19255, 'FTW': 19256, 'mezza': 19257, \"luna's\": 19258, 'deffly': 19259, 'toss': 19260, 'prolly': 19261, 'em': 19262, 'cus': 19263, 'luna': 19264, 'extract': 19265, 'salty': 19266, 'pepperoni': 19267, 'crushing': 19268, 'Clarkson': 19269, \"'05\": 19270, \"'09\": 19271, 'deliciousness': 19272, 'warrants': 19273, 'pickup': 19274, 'XF': 19275, 'Chestney': 19276, 'Fair': 19277, 'rvs': 19278, 'boats': 19279, 'Cars': 19280, 'Theft': 19281, 'Myself': 19282, 'fiance': 19283, 'detective': 19284, 'Luckily': 19285, 'stole': 19286, 'Parking': 19287, 'Mini': 19288, 'Cooper': 19289, 'SUV': 19290, 'Bugs': 19291, 'blowout': 19292, 'Murfreesboro': 19293, '4:50': 19294, 'Bud': 19295, '330i': 19296, 'secured': 19297, 'runflat': 19298, 'runflats': 19299, 'Grissom': 19300, 'consists': 19301, 'youngsteers': 19302, 'evictees': 19303, 'hood': 19304, 'fend': 19305, 'non-violent': 19306, 'unmanned': 19307, 'cracker': 19308, 'countertop': 19309, 'upsetting': 19310, 'oily': 19311, 'greasy': 19312, 'burnt': 19313, 'bureau': 19314, 'sabotaging': 19315, 'STORY': 19316, 'TRUE': 19317, 'kiss': 19318, '*ss': 19319, 'Lines': 19320, 'Silly': 19321, 'Rules': 19322, 'spills': 19323, 'patrons': 19324, 'tacos': 19325, 'costumer': 19326, 'COMCAST': 19327, 'pardon': 19328, 'Raging': 19329, 'Taco': 19330, 'Burrito': 19331, 'Wish': 19332, 'Saratoga': 19333, 'Bistro': 19334, 'Tallulah': 19335, 'diner': 19336, 'Adelphi': 19337, 'residing': 19338, 'KNOWS': 19339, 'EXPERIENCES': 19340, 'BT': 19341, 'unpretentious': 19342, 'Treat': 19343, 'Springs': 19344, 'rrly': 19345, 'cehf': 19346, 'Morris': 19347, 'particlular': 19348, 'baldness': 19349, 'exclusively': 19350, 'maternal': 19351, 'http://www.consumerreports.org/health/healthy-living/beauty-personal-care/hair-loss-10-08/hair-loss.htm': 19352, 'Anyhow': 19353, 'mircles': 19354, 'miracles': 19355, 'installed': 19356, 'stressful': 19357, 'boarders': 19358, 'accommodations': 19359, 'calming': 19360, 'comforting': 19361, 'adorned': 19362, 'encounter': 19363, 'friendliness': 19364, 'Novotel': 19365, 'appreciation': 19366, 'spirits': 19367, 'Tampa': 19368, 'Update': 19369, 'resturant': 19370, 'usally': 19371, 'Dinner': 19372, 'Scampi': 19373, 'Incredible': 19374, 'privatly': 19375, 'Jeep': 19376, 'Phet': 19377, 'G&G': 19378, 'Automotive': 19379, 'overcharged': 19380, 'unheard': 19381, 'handbag': 19382, 'niche': 19383, 'evaluating': 19384, 'SEO': 19385, 'Ulistic': 19386, 'Engine': 19387, 'Optimization': 19388, 'Key': 19389, 'Indicators': 19390, 'Facebook': 19391, 'www.designofashion.com': 19392, 'ethical': 19393, 'reap': 19394, 'genuinely': 19395, 'communicator': 19396, 'Hom': 19397, 'Excel': 19398, 'burglars': 19399, 'intruders': 19400, 'slit': 19401, 'pry': 19402, 'hammer': 19403, 'miscreants': 19404, 'happily': 19405, 'guarentee': 19406, 'lightbulb': 19407, 'Sheraton': 19408, 'towels': 19409, 'linens': 19410, 'sofa': 19411, 'ribbon': 19412, 'wrapped': 19413, 'mid-afternoon': 19414, 'disruptive': 19415, 'boisterous': 19416, 'Careful': 19417, 'admittedly': 19418, 'Essentially': 19419, 'compose': 19420, 'truths': 19421, 'falsehoods': 19422, 'Mazda': 19423, 'charger': 19424, 'combo': 19425, 'plates': 19426, 'abrasive': 19427, 'sesame': 19428, 'kung': 19429, 'pao': 19430, 'puffs': 19431, 'Delight': 19432, 'accomplished': 19433, 'steamed': 19434, 'mein': 19435, 'Panda': 19436, 'Willis': 19437, 'recount': 19438, 'conditioning': 19439, 'Approx': 19440, 'discontent': 19441, 'th': 19442, '30.00': 19443, 'FIT': 19444, 'Pomper': 19445, 'sooo': 19446, 'resell': 19447, 'BEING': 19448, 'RETURNED': 19449, 'unfair': 19450, 'unprofessionalism': 19451, 'locker': 19452, 'lacking': 19453, 'canceling': 19454, 'belittling': 19455, 'Cape': 19456, 'Hell': 19457, 'celebrity': 19458, 'Derrick': 19459, 'blonde': 19460, 'mousey': 19461, 'stylists': 19462, 'pressuring': 19463, 'cried': 19464, 'grace': 19465, 'Nightmare': 19466, 'Rod': 19467, 'Jacobsen': 19468, 'CPA': 19469, '6,000': 19470, 'accountant': 19471, 'DOWN': 19472, 'Bit': 19473, 'sketchy': 19474, 'sporadic': 19475, 'Sketchy': 19476, 'semi-sketchiness': 19477, 'WITHOUT': 19478, 'informing': 19479, 'neglected': 19480, 'arrival': 19481, 'terribly': 19482, 'apologetic': 19483, 'STILL': 19484, 'communicative': 19485, 'Delivery': 19486, 'Hickory': 19487, 'Furniture': 19488, 'HDS': 19489, 'seventeen': 19490, 'van': 19491, '2300': 19492, 'headboard': 19493, 'drawers': 19494, 'dresser': 19495, 'nighstand': 19496, 'drawer': 19497, 'Furnishings': 19498, 'Boyles': 19499, 'limbo': 19500, 'suites': 19501, 'Driving': 19502, 'wreck': 19503, 'conquer': 19504, 'Saintly': 19505, 'cancellations': 19506, 'advising': 19507, 'pounded': 19508, 'reciprocate': 19509, 'confidently': 19510, 'cope': 19511, 'Restaurant': 19512, 'Restaurants': 19513, 'stumble': 19514, 'Bexar': 19515, 'Bandera': 19516, 'Drop': 19517, 'Soup': 19518, 'tastes': 19519, 'Ferguson': 19520, 'Granger': 19521, 'Strzalka': 19522, 'Flagship': 19523, 'CVTS': 19524, 'CRUEL': 19525, 'UNCARING': 19526, 'bicuspid': 19527, 'aortic': 19528, 'valve': 19529, 'stenosis': 19530, 'renigged': 19531, 'Proxy': 19532, 'ventilator': 19533, 'inoperable': 19534, 'sarcastically': 19535, 'recommends': 19536, 'inclined': 19537, 'toured': 19538, 'bmil': 19539, 'introductions': 19540, 'unrealistic': 19541, 'Workers': 19542, 'interaction': 19543, 'objectively': 19544, 'roomful': 19545, 'facilitating': 19546, 'bruised': 19547, 'Dumbest': 19548, \"F'ers\": 19549, 'dominos': 19550, 'FINALLY': 19551, 'MAY': 19552, 'Radison': 19553, 'Warwick': 19554, 'Rittenhouse': 19555, '1926': 19556, 'YES': 19557, 'CENTER': 19558, 'CITY': 19559, 'PHILLY': 19560, 'township': 19561, 'Pottstown': 19562, 'RADISON': 19563, 'WARWICK': 19564, 'HOTEL': 19565, 'SAY': 19566, 'LISTEN': 19567, '17th': 19568, 'LOCUST': 19569, 'ADDRESS': 19570, '1701': 19571, 'TOWNSHIP': 19572, 'locust': 19573, 'Locust': 19574, 'ARe': 19575, 'asks': 19576, '16th': 19577, 'Walnut': 19578, 'Hmmm': 19579, 'cussing': 19580, 'cab': 19581, 'Geno': 19582, 'fence': 19583, 'Workmanship': 19584, 'splitting': 19585, 'PAY': 19586, 'phantom': 19587, 'innkeeper': 19588, '207': 19589, 'suitcases': 19590, 'innkeepers': 19591, 'longshoreman': 19592, 'totes': 19593, 'chambermaid': 19594, 'sternly': 19595, 'accommodated': 19596, 'exceptions': 19597, 'fridge': 19598, '1.50': 19599, 'cans': 19600, 'shampoo': 19601, 'Snacks': 19602, 'uninspired': 19603, 'Bath': 19604, 'stall': 19605, 'mildew': 19606, 'tub': 19607, 'touts': 19608, 'kitchens': 19609, 'rack': 19610, 'Quit': 19611, 'overstatements': 19612, 'Zahav': 19613, 'hyperbolic': 19614, 'salatim': 19615, 'condiments': 19616, 'forkfuls': 19617, 'laffa': 19618, 'hummus': 19619, 'flatbread': 19620, 'lamb': 19621, 'glob': 19622, 'chewy': 19623, 'resemblance': 19624, 'crispy': 19625, 'delicacy': 19626, 'hmmm': 19627, 'semifreddo': 19628, 'waitstaff': 19629, 'believing': 19630, 'attest': 19631, 'succulent': 19632, 'sizes': 19633, 'circa': 19634, 'READ': 19635, 'lace': 19636, 'dresses': 19637, 'picks': 19638, 'SUPPOSEDLY': 19639, 'gown': 19640, 'brides': 19641, 'STEALING': 19642, 'designers': 19643, 'designs': 19644, 'seamstresses': 19645, 'uhh': 19646, 'Aside': 19647, 'Somehow': 19648, 'disagreed': 19649, 'observation': 19650, 'bodice': 19651, 'Say': 19652, 'Huh': 19653, 'uh': 19654, 'Seriously': 19655, 'NICER': 19656, 'TRANSPARENT': 19657, 'dishonest': 19658, 'AWFUL': 19659, \"Sear's\": 19660, 'Automotives': 19661, 'Greensboro': 19662, 'shredded': 19663, 'drving': 19664, 'purposely': 19665, 'technician': 19666, 'steering': 19667, 'riiight': 19668, 'Supposedly': 19669, 'ordeal': 19670, 'apathetic': 19671}\n",
+ "{'PROPN': 0, 'PUNCT': 1, 'ADJ': 2, 'NOUN': 3, 'VERB': 4, 'DET': 5, 'ADP': 6, 'AUX': 7, 'PRON': 8, 'PART': 9, 'SCONJ': 10, 'NUM': 11, 'ADV': 12, 'CCONJ': 13, 'X': 14, 'INTJ': 15, 'SYM': 16}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Train a model\n",
+ "\n",
+ "EMBEDDING_DIM = 6\n",
+ "HIDDEN_DIM = 6\n",
+ "\n",
+ "model = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix))\n",
+ "loss_function = nn.NLLLoss() # does not include the softmax\n",
+ "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
+ "\n",
+ "epoch_acc, epoch_loss = 0, 0\n",
+ "\n",
+ "total_count = 0\n",
+ "\n",
+ "for epoch in range(3): # again, normally you would NOT do 300 epochs, it is toy data\n",
+ " index = 0\n",
+ " for sentence, tags, _ in train_iter:\n",
+ " index += 1\n",
+ " # Step 1. Remember that Pytorch accumulates gradients.\n",
+ " # We need to clear them out before each instance\n",
+ " model.zero_grad()\n",
+ "\n",
+ " # Step 2. Get our inputs ready for the network, that is, turn them into\n",
+ " # Tensors of word indices.\n",
+ " sentence_in = prepare_sequence(sentence, word_to_ix)\n",
+ " targets = prepare_sequence(tags, tag_to_ix)\n",
+ "\n",
+ " # Step 3. Run our forward pass.\n",
+ " tag_scores = model(sentence_in)\n",
+ "\n",
+ " # Compute accuracy score per token\n",
+ " predictions = tag_scores.view(-1, tag_scores.shape[-1])\n",
+ " max_preds = predictions.argmax(dim = 1, keepdim = True) # get the index of the max probability\n",
+ " correct = max_preds.squeeze(1).eq(targets)\n",
+ " acc_sentence = correct.sum()\n",
+ " #acc_sentence = correct.sum() / targets.shape[0]\n",
+ "\n",
+ "\n",
+ " if index in [29,55, 930]: # selection of short sentences #, 1150\n",
+ " print( \"SENTENCE:\", sentence )\n",
+ " print(\"GOLD (original):\", tags )\n",
+ " print(\"GOLD (indices):\", targets)\n",
+ " print( \"SCORES\", tag_scores )\n",
+ " print(\"PRED (indices):\", list(max_preds.squeeze(1)) )\n",
+ " print(\"PRED (mapped):\", [ix_to_tag[i.item()] for i in list(max_preds.squeeze(1))] )\n",
+ " print( \"CORRECT / UNCORRECT list:\", correct)\n",
+ " print(\"# CORRECT PREDicted POS =\", acc_sentence.item() )\n",
+ "\n",
+ " print( '\\n')\n",
+ "\n",
+ " # Step 4. Compute the loss, gradients, and update the parameters by\n",
+ " # calling optimizer.step()\n",
+ " loss = loss_function(tag_scores, targets)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " epoch_loss += loss.item()\n",
+ " epoch_acc += acc_sentence.item()\n",
+ " total_count += len(sentence)\n",
+ "\n",
+ " # Compute accuracy on train set at each epoch\n",
+ " print('Epoch: {}. Loss: {}. ACC {}.'.format(epoch, epoch_loss/total_count,\n",
+ " round( (epoch_acc/total_count)*100, 2)))\n",
+ " epoch_acc, epoch_loss, total_count = 0, 0, 0\n",
+ " #print(epoch_acc / len(train_iter))\n",
+ "\n",
+ "\n",
+ "# {'PROPN': 0, 'PUNCT': 1, 'ADJ': 2, 'NOUN': 3, 'VERB': 4, 'DET': 5, 'ADP': 6, 'AUX': 7, 'PRON': 8, 'PART': 9, 'SCONJ': 10, 'NUM': 11, 'ADV': 12, 'CCONJ': 13, 'X': 14, 'INTJ': 15, 'SYM': 16}"
+ ],
+ "metadata": {
+ "id": "M2zfqhVmSxIC",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "00f8d320-3dd9-444e-9135-8196d65434c4"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "SENTENCE: ['But', 'in', 'my', 'view', 'it', 'is', 'highly', 'significant', '.']\n",
+ "GOLD (original): ['CCONJ', 'ADP', 'PRON', 'NOUN', 'PRON', 'AUX', 'ADV', 'ADJ', 'PUNCT']\n",
+ "GOLD (indices): tensor([13, 6, 8, 3, 8, 7, 12, 2, 1])\n",
+ "SCORES tensor([[-2.5903, -2.8589, -2.6936, -2.7128, -2.5672, -2.8965, -2.4778, -2.7472,\n",
+ " -2.6335, -3.3410, -3.3118, -2.8837, -2.8587, -2.9943, -3.1330, -3.1247,\n",
+ " -2.8283],\n",
+ " [-2.5903, -2.9118, -2.6557, -2.6903, -2.5628, -2.8696, -2.4832, -2.7617,\n",
+ " -2.6074, -3.3350, -3.3872, -2.9174, -2.8654, -3.0379, -3.0755, -3.1940,\n",
+ " -2.7686],\n",
+ " [-2.5954, -2.9183, -2.7024, -2.6646, -2.3523, -2.9031, -2.6180, -2.9211,\n",
+ " -2.5234, -3.1733, -3.2870, -2.9857, -2.8758, -3.0569, -3.0938, -3.1361,\n",
+ " -2.8868],\n",
+ " [-2.5963, -2.8428, -2.7482, -2.7265, -2.3241, -2.8247, -2.6094, -2.9167,\n",
+ " -2.5486, -3.1997, -3.2610, -2.9673, -2.9203, -3.0379, -3.1075, -3.1031,\n",
+ " -2.9458],\n",
+ " [-2.6233, -2.8820, -2.7326, -2.6455, -2.3901, -2.8255, -2.5572, -2.8662,\n",
+ " -2.5479, -3.2431, -3.2786, -3.0077, -2.8438, -3.0679, -3.0819, -3.1604,\n",
+ " -2.9434],\n",
+ " [-2.5120, -2.8766, -2.6562, -2.7865, -2.3005, -3.0000, -2.5896, -2.8742,\n",
+ " -2.5544, -3.1980, -3.3264, -2.9151, -3.0065, -3.0993, -3.1470, -3.0855,\n",
+ " -2.8599],\n",
+ " [-2.4634, -2.7635, -2.6742, -2.8895, -2.3945, -3.0917, -2.4537, -2.6914,\n",
+ " -2.6743, -3.3526, -3.2806, -2.8161, -3.0577, -3.0867, -3.2726, -2.9937,\n",
+ " -2.9047],\n",
+ " [-2.4977, -2.8878, -2.5717, -2.7951, -2.5025, -3.0544, -2.4426, -2.7180,\n",
+ " -2.6226, -3.3450, -3.4298, -2.8382, -2.9820, -3.1128, -3.1403, -3.1406,\n",
+ " -2.7799],\n",
+ " [-2.5682, -2.8858, -2.6027, -2.7317, -2.5501, -2.8810, -2.4164, -2.7268,\n",
+ " -2.6108, -3.3832, -3.4553, -2.8943, -2.9108, -3.1116, -3.0553, -3.2240,\n",
+ " -2.8247]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(6), tensor(6), tensor(4), tensor(4), tensor(4), tensor(4), tensor(4), tensor(6), tensor(6)]\n",
+ "PRED (mapped): ['ADP', 'ADP', 'VERB', 'VERB', 'VERB', 'VERB', 'VERB', 'ADP', 'ADP']\n",
+ "CORRECT / UNCORRECT list: tensor([False, True, False, False, False, False, False, False, False])\n",
+ "# CORRECT PREDicted POS = 1\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['So', 'what', 'happened', '?']\n",
+ "GOLD (original): ['ADV', 'PRON', 'VERB', 'PUNCT']\n",
+ "GOLD (indices): tensor([12, 8, 4, 1])\n",
+ "SCORES tensor([[-2.6096, -2.7632, -2.6673, -2.4966, -2.4735, -2.8605, -2.5213, -2.7642,\n",
+ " -2.5875, -3.3445, -3.4014, -3.0173, -2.8482, -3.0562, -3.2296, -3.2807,\n",
+ " -2.9622],\n",
+ " [-2.5767, -2.7250, -2.6450, -2.5415, -2.4355, -2.9783, -2.4875, -2.7302,\n",
+ " -2.5993, -3.3472, -3.3690, -2.9668, -2.8751, -3.0822, -3.2952, -3.2179,\n",
+ " -3.0544],\n",
+ " [-2.5806, -2.7899, -2.6711, -2.4553, -2.3972, -2.9293, -2.4962, -2.6992,\n",
+ " -2.5865, -3.3695, -3.3949, -3.1042, -2.8634, -3.1590, -3.2688, -3.3030,\n",
+ " -2.9698],\n",
+ " [-2.5973, -2.8349, -2.6510, -2.4462, -2.3622, -2.8640, -2.5810, -2.8211,\n",
+ " -2.5290, -3.2864, -3.4438, -3.1152, -2.8718, -3.1404, -3.1916, -3.3438,\n",
+ " -2.9299]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(4), tensor(4), tensor(4), tensor(4)]\n",
+ "PRED (mapped): ['VERB', 'VERB', 'VERB', 'VERB']\n",
+ "CORRECT / UNCORRECT list: tensor([False, False, True, False])\n",
+ "# CORRECT PREDicted POS = 1\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['Bush', 'successfully', 'makes', 'Satan', 'look', 'good', 'in', 'comparison', '.']\n",
+ "GOLD (original): ['PROPN', 'ADV', 'VERB', 'PROPN', 'VERB', 'ADJ', 'ADP', 'NOUN', 'PUNCT']\n",
+ "GOLD (indices): tensor([ 0, 12, 4, 0, 4, 2, 6, 3, 1])\n",
+ "SCORES tensor([[-2.0766, -3.3197, -2.7931, -1.6228, -2.3223, -2.5707, -2.1524, -2.9950,\n",
+ " -2.5196, -3.7643, -3.9696, -4.3610, -3.0462, -4.0007, -4.0054, -4.9961,\n",
+ " -4.3431],\n",
+ " [-2.3942, -3.2657, -2.9115, -1.3141, -2.1477, -3.2313, -2.3458, -3.0485,\n",
+ " -2.4764, -3.5972, -4.0389, -4.4438, -2.8525, -3.8242, -4.3520, -5.1192,\n",
+ " -4.7338],\n",
+ " [-2.3849, -3.3345, -2.9774, -1.3062, -2.0244, -3.2398, -2.0631, -3.0192,\n",
+ " -2.5686, -3.7669, -4.1856, -4.8057, -3.0391, -4.3032, -4.7690, -5.5078,\n",
+ " -5.0762],\n",
+ " [-2.9687, -1.3874, -3.1200, -1.7243, -2.2756, -2.7203, -2.6808, -2.9825,\n",
+ " -3.0344, -3.6164, -4.2932, -4.4142, -3.0267, -3.8181, -5.0029, -5.2491,\n",
+ " -5.1999],\n",
+ " [-2.6393, -2.6453, -3.0687, -1.3321, -1.9923, -2.9874, -2.2903, -3.0309,\n",
+ " -2.6694, -3.5372, -4.1450, -4.7110, -2.9171, -4.0912, -4.8951, -5.4155,\n",
+ " -5.3109],\n",
+ " [-2.4168, -2.8886, -3.0547, -1.4620, -2.2338, -2.1459, -1.9901, -3.0572,\n",
+ " -2.8372, -3.9231, -4.1676, -5.0370, -3.1435, -4.6659, -4.9312, -5.6873,\n",
+ " -5.3135],\n",
+ " [-2.6188, -3.0749, -3.0144, -1.1349, -2.0635, -3.4667, -2.1802, -3.0387,\n",
+ " -2.6698, -3.8327, -4.3334, -4.8947, -2.9736, -4.2622, -5.0755, -5.7109,\n",
+ " -5.3122],\n",
+ " [-3.0358, -1.3778, -3.2298, -1.8121, -2.5378, -1.8708, -2.6660, -3.1674,\n",
+ " -3.2767, -3.7995, -4.3933, -4.7590, -3.1449, -4.2086, -5.1686, -5.5981,\n",
+ " -5.5545],\n",
+ " [-3.3579, -0.8358, -3.3716, -2.2316, -2.7703, -2.1978, -3.0659, -3.2729,\n",
+ " -3.5664, -3.9079, -4.6725, -4.6147, -3.3736, -4.0591, -5.2995, -5.5843,\n",
+ " -5.5476]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(3), tensor(3), tensor(3), tensor(1), tensor(3), tensor(3), tensor(3), tensor(1), tensor(1)]\n",
+ "PRED (mapped): ['NOUN', 'NOUN', 'NOUN', 'PUNCT', 'NOUN', 'NOUN', 'NOUN', 'PUNCT', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, False, False, False, False, False, False, False, True])\n",
+ "# CORRECT PREDicted POS = 1\n",
+ "\n",
+ "\n",
+ "Epoch: 0. Loss: 0.10316973535380829. ACC 47.01.\n",
+ "SENTENCE: ['But', 'in', 'my', 'view', 'it', 'is', 'highly', 'significant', '.']\n",
+ "GOLD (original): ['CCONJ', 'ADP', 'PRON', 'NOUN', 'PRON', 'AUX', 'ADV', 'ADJ', 'PUNCT']\n",
+ "GOLD (indices): tensor([13, 6, 8, 3, 8, 7, 12, 2, 1])\n",
+ "SCORES tensor([[-3.1006e+00, -8.5182e-01, -4.0720e+00, -3.8565e+00, -2.9164e+00,\n",
+ " -6.2661e+00, -5.7413e+00, -4.3534e+00, -1.5519e+00, -7.6544e+00,\n",
+ " -6.0352e+00, -4.0723e+00, -3.2586e+00, -2.1979e+00, -4.9980e+00,\n",
+ " -4.2978e+00, -4.1057e+00],\n",
+ " [-4.1592e+00, -7.2833e+00, -4.3150e+00, -4.1587e+00, -3.7646e+00,\n",
+ " -4.2453e+00, -1.8917e-01, -3.9892e+00, -6.1538e+00, -8.6057e+00,\n",
+ " -3.4087e+00, -5.5462e+00, -3.5712e+00, -1.3041e+01, -7.0488e+00,\n",
+ " -6.1234e+00, -7.5069e+00],\n",
+ " [-4.1925e+00, -8.3426e+00, -4.0817e+00, -4.0830e+00, -4.1072e+00,\n",
+ " -5.2738e+00, -1.0183e+01, -7.8153e+00, -3.1606e-01, -7.1243e+00,\n",
+ " -6.1429e+00, -4.5397e+00, -4.8190e+00, -1.7919e+00, -6.2114e+00,\n",
+ " -4.9910e+00, -5.9049e+00],\n",
+ " [-2.4859e+00, -5.4976e+00, -2.0298e+00, -1.2185e+00, -1.6181e+00,\n",
+ " -3.9155e+00, -5.3351e+00, -4.3999e+00, -2.8919e+00, -5.6345e+00,\n",
+ " -4.2506e+00, -3.2544e+00, -2.1338e+00, -5.3909e+00, -6.0023e+00,\n",
+ " -4.7303e+00, -5.4812e+00],\n",
+ " [-4.7495e+00, -1.1779e+01, -4.5025e+00, -4.1160e+00, -3.3349e+00,\n",
+ " -8.7732e+00, -1.0178e+01, -4.7307e+00, -1.2214e-01, -5.8600e+00,\n",
+ " -6.1175e+00, -5.3967e+00, -4.9315e+00, -4.3536e+00, -7.3492e+00,\n",
+ " -5.6835e+00, -7.4416e+00],\n",
+ " [-5.4202e+00, -9.9405e+00, -5.8938e+00, -5.3091e+00, -3.0725e+00,\n",
+ " -8.9481e+00, -2.7500e+00, -2.2098e-01, -5.3335e+00, -3.1171e+00,\n",
+ " -4.5442e+00, -6.4731e+00, -4.3199e+00, -1.2342e+01, -8.2520e+00,\n",
+ " -7.1960e+00, -9.0214e+00],\n",
+ " [-3.4461e+00, -4.0261e+00, -3.1187e+00, -2.5161e+00, -1.2436e+00,\n",
+ " -2.9321e+00, -4.3099e+00, -3.6049e+00, -4.1063e+00, -1.4056e+00,\n",
+ " -4.0030e+00, -3.5589e+00, -2.0926e+00, -5.5028e+00, -6.6861e+00,\n",
+ " -5.6781e+00, -5.9852e+00],\n",
+ " [-3.4480e+00, -6.6627e+00, -2.7969e+00, -1.9777e+00, -8.4912e-01,\n",
+ " -6.0948e+00, -3.4316e+00, -2.2338e+00, -3.5420e+00, -5.8363e+00,\n",
+ " -3.9832e+00, -4.4972e+00, -2.0276e+00, -8.8873e+00, -7.5172e+00,\n",
+ " -5.7987e+00, -6.8892e+00],\n",
+ " [-1.0223e+01, -3.5014e-03, -1.0784e+01, -1.0889e+01, -8.9813e+00,\n",
+ " -7.6598e+00, -1.1725e+01, -1.4506e+01, -1.0691e+01, -1.2293e+01,\n",
+ " -1.3093e+01, -1.0082e+01, -8.9167e+00, -5.9782e+00, -1.2278e+01,\n",
+ " -1.1947e+01, -9.6605e+00]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(1), tensor(6), tensor(8), tensor(3), tensor(8), tensor(7), tensor(4), tensor(4), tensor(1)]\n",
+ "PRED (mapped): ['PUNCT', 'ADP', 'PRON', 'NOUN', 'PRON', 'AUX', 'VERB', 'VERB', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, True, True, True, True, True, False, False, True])\n",
+ "# CORRECT PREDicted POS = 6\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['So', 'what', 'happened', '?']\n",
+ "GOLD (original): ['ADV', 'PRON', 'VERB', 'PUNCT']\n",
+ "GOLD (indices): tensor([12, 8, 4, 1])\n",
+ "SCORES tensor([[ -2.0850, -5.9884, -2.6784, -1.4932, -2.3267, -6.3929, -3.3761,\n",
+ " -1.2427, -4.7344, -4.9342, -4.2690, -3.6318, -2.4545, -8.4353,\n",
+ " -5.4563, -4.7803, -5.9709],\n",
+ " [ -4.1023, -4.7189, -4.4595, -4.5007, -2.8424, -7.3872, -7.9276,\n",
+ " -5.1035, -0.2975, -7.6016, -6.2915, -4.9479, -3.9348, -2.2780,\n",
+ " -6.3667, -5.0774, -5.4246],\n",
+ " [ -3.6499, -5.4974, -3.7126, -3.3383, -1.9474, -5.4633, -0.6763,\n",
+ " -2.0975, -5.2392, -6.7068, -3.5738, -5.0755, -2.4427, -11.2516,\n",
+ " -7.1998, -6.0205, -7.0064],\n",
+ " [ -7.0778, -0.0166, -7.7637, -7.2482, -7.8310, -6.0077, -10.8791,\n",
+ " -12.6564, -9.7156, -11.0685, -11.3633, -7.4255, -7.0694, -4.6653,\n",
+ " -9.5277, -9.4854, -7.6905]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(7), tensor(8), tensor(6), tensor(1)]\n",
+ "PRED (mapped): ['AUX', 'PRON', 'ADP', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, True, False, True])\n",
+ "# CORRECT PREDicted POS = 2\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['Bush', 'successfully', 'makes', 'Satan', 'look', 'good', 'in', 'comparison', '.']\n",
+ "GOLD (original): ['PROPN', 'ADV', 'VERB', 'PROPN', 'VERB', 'ADJ', 'ADP', 'NOUN', 'PUNCT']\n",
+ "GOLD (indices): tensor([ 0, 12, 4, 0, 4, 2, 6, 3, 1])\n",
+ "SCORES tensor([[-1.3410e+00, -9.8949e+00, -2.2142e+00, -1.1436e+00, -3.9618e+00,\n",
+ " -2.8446e+00, -3.0737e+00, -5.2929e+00, -4.2931e+00, -8.1761e+00,\n",
+ " -2.9840e+00, -3.5290e+00, -3.4288e+00, -9.8488e+00, -3.2253e+00,\n",
+ " -4.3219e+00, -5.9429e+00],\n",
+ " [-1.9626e+00, -8.4671e+00, -2.5186e+00, -7.5026e-01, -2.2782e+00,\n",
+ " -6.2973e+00, -6.1416e+00, -3.1411e+00, -3.2917e+00, -5.2737e+00,\n",
+ " -4.8296e+00, -3.7639e+00, -2.9306e+00, -6.3804e+00, -3.9944e+00,\n",
+ " -4.9546e+00, -5.8926e+00],\n",
+ " [-2.9253e+00, -4.9119e+00, -3.0267e+00, -1.4114e+00, -1.0292e+00,\n",
+ " -6.3806e+00, -3.0668e+00, -2.7297e+00, -3.9858e+00, -7.2570e+00,\n",
+ " -4.4644e+00, -4.7206e+00, -2.0723e+00, -8.3881e+00, -5.3962e+00,\n",
+ " -5.9725e+00, -5.8040e+00],\n",
+ " [-3.1907e+00, -5.5041e+00, -3.2322e+00, -1.3115e+00, -1.0953e+00,\n",
+ " -6.9891e+00, -3.0928e+00, -2.0640e+00, -6.0770e+00, -5.8169e+00,\n",
+ " -4.9746e+00, -5.0227e+00, -2.1638e+00, -1.0010e+01, -6.0139e+00,\n",
+ " -6.9074e+00, -6.8206e+00],\n",
+ " [-3.0720e+00, -6.5422e+00, -3.1603e+00, -1.8435e+00, -9.0300e-01,\n",
+ " -4.5523e+00, -3.4738e+00, -2.8003e+00, -3.2263e+00, -3.4632e+00,\n",
+ " -3.6177e+00, -4.3140e+00, -2.1026e+00, -7.1762e+00, -5.3717e+00,\n",
+ " -5.7919e+00, -6.2051e+00],\n",
+ " [-2.5978e+00, -1.0917e+01, -2.7263e+00, -1.6983e+00, -2.2090e+00,\n",
+ " -2.7405e+00, -1.3386e+00, -4.1033e+00, -4.6401e+00, -6.0621e+00,\n",
+ " -2.0583e+00, -4.5502e+00, -2.7188e+00, -1.2335e+01, -5.3941e+00,\n",
+ " -5.8840e+00, -7.5687e+00],\n",
+ " [-3.9915e+00, -8.7731e+00, -4.5119e+00, -3.6961e+00, -4.3440e+00,\n",
+ " -3.8269e+00, -1.5563e-01, -5.6333e+00, -7.8188e+00, -9.8590e+00,\n",
+ " -3.5236e+00, -6.1420e+00, -4.1000e+00, -1.5312e+01, -6.3548e+00,\n",
+ " -7.2939e+00, -8.1940e+00],\n",
+ " [-2.2147e+00, -6.6978e+00, -2.4431e+00, -8.1177e-01, -2.1726e+00,\n",
+ " -4.1317e+00, -2.6925e+00, -3.8773e+00, -6.3190e+00, -6.3427e+00,\n",
+ " -4.0292e+00, -4.1085e+00, -2.3933e+00, -1.0255e+01, -4.9539e+00,\n",
+ " -5.9963e+00, -6.4501e+00],\n",
+ " [-1.0883e+01, -3.2490e-03, -1.1210e+01, -1.0389e+01, -9.8397e+00,\n",
+ " -7.6751e+00, -1.2064e+01, -1.5908e+01, -1.2046e+01, -1.3068e+01,\n",
+ " -1.3798e+01, -1.0564e+01, -9.4295e+00, -6.0239e+00, -1.1847e+01,\n",
+ " -1.2535e+01, -9.1061e+00]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(3), tensor(3), tensor(4), tensor(4), tensor(4), tensor(6), tensor(6), tensor(3), tensor(1)]\n",
+ "PRED (mapped): ['NOUN', 'NOUN', 'VERB', 'VERB', 'VERB', 'ADP', 'ADP', 'NOUN', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, False, True, False, True, False, True, True, True])\n",
+ "# CORRECT PREDicted POS = 5\n",
+ "\n",
+ "\n",
+ "Epoch: 1. Loss: 0.07463535376251616. ACC 63.46.\n",
+ "SENTENCE: ['But', 'in', 'my', 'view', 'it', 'is', 'highly', 'significant', '.']\n",
+ "GOLD (original): ['CCONJ', 'ADP', 'PRON', 'NOUN', 'PRON', 'AUX', 'ADV', 'ADJ', 'PUNCT']\n",
+ "GOLD (indices): tensor([13, 6, 8, 3, 8, 7, 12, 2, 1])\n",
+ "SCORES tensor([[-4.7506e+00, -7.4578e-01, -5.7696e+00, -6.0269e+00, -4.3685e+00,\n",
+ " -5.9890e+00, -7.6688e+00, -7.6055e+00, -1.7409e+00, -8.6985e+00,\n",
+ " -7.5635e+00, -5.4374e+00, -4.4538e+00, -1.3008e+00, -5.7134e+00,\n",
+ " -4.9257e+00, -3.8921e+00],\n",
+ " [-5.0142e+00, -8.1560e+00, -5.5081e+00, -5.6954e+00, -4.2202e+00,\n",
+ " -5.8805e+00, -1.3751e-01, -3.9770e+00, -8.2941e+00, -7.6028e+00,\n",
+ " -2.9288e+00, -6.5186e+00, -3.8618e+00, -1.6054e+01, -7.6524e+00,\n",
+ " -7.5049e+00, -8.7542e+00],\n",
+ " [-6.6364e+00, -1.2573e+01, -6.3492e+00, -6.8644e+00, -6.0622e+00,\n",
+ " -7.2865e+00, -1.3406e+01, -1.0638e+01, -6.7180e-02, -1.0529e+01,\n",
+ " -8.5790e+00, -6.6933e+00, -7.1351e+00, -2.9659e+00, -8.3704e+00,\n",
+ " -5.6448e+00, -8.1342e+00],\n",
+ " [-2.3199e+00, -6.9515e+00, -1.9579e+00, -9.7326e-01, -1.6883e+00,\n",
+ " -5.3092e+00, -5.2187e+00, -5.2308e+00, -5.0961e+00, -7.2195e+00,\n",
+ " -5.1349e+00, -3.3048e+00, -2.1169e+00, -8.2286e+00, -6.2587e+00,\n",
+ " -4.9687e+00, -6.1075e+00],\n",
+ " [-6.2655e+00, -1.5258e+01, -6.0299e+00, -6.0660e+00, -5.1340e+00,\n",
+ " -9.4497e+00, -1.3646e+01, -7.9766e+00, -2.7967e-02, -9.7474e+00,\n",
+ " -8.3261e+00, -6.6896e+00, -6.9266e+00, -4.9111e+00, -8.4996e+00,\n",
+ " -5.4188e+00, -9.1154e+00],\n",
+ " [-6.3804e+00, -1.1392e+01, -7.3696e+00, -7.0306e+00, -3.4521e+00,\n",
+ " -1.2048e+01, -4.1889e+00, -7.2350e-02, -7.1218e+00, -5.0845e+00,\n",
+ " -5.2594e+00, -8.0146e+00, -5.0104e+00, -1.5407e+01, -8.9318e+00,\n",
+ " -7.9049e+00, -1.0437e+01],\n",
+ " [-4.0345e+00, -3.8177e+00, -3.5076e+00, -3.5499e+00, -9.8189e-01,\n",
+ " -4.0390e+00, -4.6311e+00, -5.1397e+00, -5.2139e+00, -1.3182e+00,\n",
+ " -5.1052e+00, -3.7868e+00, -1.7192e+00, -5.0331e+00, -7.2772e+00,\n",
+ " -6.0817e+00, -5.7111e+00],\n",
+ " [-3.5423e+00, -6.7375e+00, -2.6389e+00, -2.2054e+00, -6.3720e-01,\n",
+ " -6.3157e+00, -3.5813e+00, -3.6619e+00, -5.4456e+00, -7.1593e+00,\n",
+ " -4.4649e+00, -4.7186e+00, -1.7375e+00, -1.1270e+01, -7.8676e+00,\n",
+ " -6.4627e+00, -7.2824e+00],\n",
+ " [-1.2648e+01, -3.2359e-03, -1.2867e+01, -1.3562e+01, -1.2314e+01,\n",
+ " -7.8982e+00, -1.3037e+01, -2.0901e+01, -1.3697e+01, -1.4579e+01,\n",
+ " -1.5987e+01, -1.2061e+01, -1.0718e+01, -5.9032e+00, -1.4062e+01,\n",
+ " -1.4497e+01, -9.3844e+00]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(1), tensor(6), tensor(8), tensor(3), tensor(8), tensor(7), tensor(4), tensor(4), tensor(1)]\n",
+ "PRED (mapped): ['PUNCT', 'ADP', 'PRON', 'NOUN', 'PRON', 'AUX', 'VERB', 'VERB', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, True, True, True, True, True, False, False, True])\n",
+ "# CORRECT PREDicted POS = 6\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['So', 'what', 'happened', '?']\n",
+ "GOLD (original): ['ADV', 'PRON', 'VERB', 'PUNCT']\n",
+ "GOLD (indices): tensor([12, 8, 4, 1])\n",
+ "SCORES tensor([[-1.8544e+00, -5.6439e+00, -2.9065e+00, -1.6420e+00, -1.7320e+00,\n",
+ " -7.7866e+00, -3.5774e+00, -1.5658e+00, -5.4439e+00, -6.1932e+00,\n",
+ " -4.7248e+00, -3.7249e+00, -2.1491e+00, -9.4254e+00, -5.0582e+00,\n",
+ " -4.4468e+00, -5.6991e+00],\n",
+ " [-6.0686e+00, -9.9769e+00, -5.3811e+00, -6.2983e+00, -5.8920e+00,\n",
+ " -5.3457e+00, -1.0848e+01, -1.1882e+01, -3.2162e-02, -1.4454e+01,\n",
+ " -7.4246e+00, -6.7840e+00, -6.3163e+00, -4.8259e+00, -8.1835e+00,\n",
+ " -5.8668e+00, -7.3791e+00],\n",
+ " [-2.0489e+00, -5.5373e+00, -2.0259e+00, -1.2754e+00, -1.7980e+00,\n",
+ " -5.2822e+00, -2.8459e+00, -4.4392e+00, -5.5423e+00, -8.9526e+00,\n",
+ " -4.1483e+00, -3.7969e+00, -1.8196e+00, -1.0547e+01, -5.9314e+00,\n",
+ " -5.1676e+00, -5.9312e+00],\n",
+ " [-8.7819e+00, -8.9847e-03, -9.2093e+00, -9.0892e+00, -1.0409e+01,\n",
+ " -5.7317e+00, -1.0704e+01, -1.8607e+01, -1.2530e+01, -1.3958e+01,\n",
+ " -1.3456e+01, -8.7832e+00, -8.3041e+00, -5.4747e+00, -1.0878e+01,\n",
+ " -1.1432e+01, -7.3291e+00]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(7), tensor(8), tensor(3), tensor(1)]\n",
+ "PRED (mapped): ['AUX', 'PRON', 'NOUN', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, True, False, True])\n",
+ "# CORRECT PREDicted POS = 2\n",
+ "\n",
+ "\n",
+ "SENTENCE: ['Bush', 'successfully', 'makes', 'Satan', 'look', 'good', 'in', 'comparison', '.']\n",
+ "GOLD (original): ['PROPN', 'ADV', 'VERB', 'PROPN', 'VERB', 'ADJ', 'ADP', 'NOUN', 'PUNCT']\n",
+ "GOLD (indices): tensor([ 0, 12, 4, 0, 4, 2, 6, 3, 1])\n",
+ "SCORES tensor([[-1.3364e+00, -1.1332e+01, -2.2090e+00, -1.1548e+00, -4.1060e+00,\n",
+ " -2.6376e+00, -3.7943e+00, -6.8730e+00, -3.9794e+00, -8.7775e+00,\n",
+ " -2.8374e+00, -3.2479e+00, -3.5883e+00, -9.8997e+00, -3.3246e+00,\n",
+ " -3.9504e+00, -6.4955e+00],\n",
+ " [-1.9192e+00, -9.7901e+00, -2.7856e+00, -6.1752e-01, -2.1892e+00,\n",
+ " -7.8248e+00, -7.0512e+00, -3.6759e+00, -4.0274e+00, -6.7501e+00,\n",
+ " -5.8869e+00, -3.9035e+00, -3.1784e+00, -7.8656e+00, -4.0354e+00,\n",
+ " -4.5731e+00, -6.3961e+00],\n",
+ " [-2.7655e+00, -5.0080e+00, -2.7892e+00, -1.3636e+00, -9.2675e-01,\n",
+ " -6.4364e+00, -3.9901e+00, -4.1003e+00, -4.5572e+00, -8.2506e+00,\n",
+ " -4.9294e+00, -4.7567e+00, -1.9458e+00, -9.5475e+00, -5.2258e+00,\n",
+ " -6.0892e+00, -5.6614e+00],\n",
+ " [-3.0010e+00, -6.0140e+00, -3.0769e+00, -1.1361e+00, -9.6732e-01,\n",
+ " -7.9946e+00, -3.8163e+00, -3.3704e+00, -7.6032e+00, -7.0140e+00,\n",
+ " -5.7448e+00, -5.1450e+00, -2.0450e+00, -1.2087e+01, -6.0194e+00,\n",
+ " -7.3471e+00, -6.9645e+00],\n",
+ " [-3.0340e+00, -8.4492e+00, -2.7989e+00, -1.6815e+00, -7.6586e-01,\n",
+ " -5.1693e+00, -4.4228e+00, -4.1011e+00, -4.0500e+00, -4.7320e+00,\n",
+ " -4.0992e+00, -4.4279e+00, -1.9360e+00, -9.0120e+00, -5.8254e+00,\n",
+ " -6.0404e+00, -6.8829e+00],\n",
+ " [-2.7358e+00, -1.2245e+01, -1.7918e+00, -1.0019e+00, -1.9670e+00,\n",
+ " -2.3510e+00, -4.0529e+00, -7.7157e+00, -4.6760e+00, -8.1858e+00,\n",
+ " -3.0883e+00, -4.3109e+00, -2.5684e+00, -1.1967e+01, -6.5977e+00,\n",
+ " -6.5289e+00, -8.3053e+00],\n",
+ " [-4.4909e+00, -1.0102e+01, -5.2675e+00, -4.6139e+00, -5.1824e+00,\n",
+ " -5.4377e+00, -8.8544e-02, -6.2541e+00, -1.0968e+01, -9.2270e+00,\n",
+ " -3.4155e+00, -6.8315e+00, -4.4869e+00, -1.8890e+01, -6.8855e+00,\n",
+ " -8.8883e+00, -9.7697e+00],\n",
+ " [-2.4047e+00, -8.1467e+00, -2.3487e+00, -5.3132e-01, -2.2258e+00,\n",
+ " -5.5128e+00, -4.3020e+00, -6.1871e+00, -7.6015e+00, -8.1201e+00,\n",
+ " -5.3521e+00, -4.3442e+00, -2.5935e+00, -1.1665e+01, -5.7140e+00,\n",
+ " -6.8037e+00, -7.1450e+00],\n",
+ " [-1.3117e+01, -1.8235e-03, -1.3208e+01, -1.2913e+01, -1.2814e+01,\n",
+ " -8.2448e+00, -1.3126e+01, -2.1714e+01, -1.5182e+01, -1.5036e+01,\n",
+ " -1.6424e+01, -1.2424e+01, -1.0970e+01, -6.5745e+00, -1.3687e+01,\n",
+ " -1.5116e+01, -8.9517e+00]], grad_fn=<LogSoftmaxBackward0>)\n",
+ "PRED (indices): [tensor(3), tensor(3), tensor(4), tensor(4), tensor(4), tensor(3), tensor(6), tensor(3), tensor(1)]\n",
+ "PRED (mapped): ['NOUN', 'NOUN', 'VERB', 'VERB', 'VERB', 'NOUN', 'ADP', 'NOUN', 'PUNCT']\n",
+ "CORRECT / UNCORRECT list: tensor([False, False, True, False, True, False, True, True, True])\n",
+ "# CORRECT PREDicted POS = 5\n",
+ "\n",
+ "\n",
+ "Epoch: 2. Loss: 0.0669679567662493. ACC 66.42.\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/notebooks/TP8_m2LiTL_transformers_learning_2324_CORRECT.ipynb b/notebooks/TP8_m2LiTL_transformers_learning_2324_CORRECT.ipynb
new file mode 100644
index 0000000..79689e8
--- /dev/null
+++ b/notebooks/TP8_m2LiTL_transformers_learning_2324_CORRECT.ipynb
@@ -0,0 +1,2133 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-bb49S7B50eh"
+ },
+ "source": [
+ "# TP 8: Transformers et transfert de modèles\n",
+ "\n",
+ "Dans cette séance, nous verrons comment utiliser un modèle pré-entrainé pour l'adapter à une nouvelle tâche (transfert). Ce TP fait suite au TP6.\n",
+ "\n",
+ "Rappel = le code ci-dessous vous permet d'installer : \n",
+ "- le module *transformers*, qui contient les modèles de langue https://pypi.org/project/transformers/\n",
+ "- la librairie de datasets pour accéder à des jeux de données\n",
+ "- la librairie *evaluate* : utilisée pour évaluer et comparer des modèles https://pypi.org/project/evaluate/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9UoSnFV250el",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ea48114d-d20f-48a8-a508-f3ed1ebf3a93"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.36.2)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
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+ ]
+ }
+ ],
+ "source": [
+ "!pip install -U transformers\n",
+ "!pip install accelerate -U\n",
+ "!pip install datasets\n",
+ "!pip install evaluate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Finally, if the installation is successful, we can import the transformers library:"
+ ],
+ "metadata": {
+ "id": "StClx_Hh9PDm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZBQcA9Ol50en"
+ },
+ "outputs": [],
+ "source": [
+ "import transformers\n",
+ "from datasets import load_dataset\n",
+ "import evaluate\n",
+ "import numpy as np\n",
+ "import sklearn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3TIXCS5P50en"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "from transformers import AutoModelForTokenClassification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vCLf1g8z50ep"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pds\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 1- Transformers pipeline\n",
+ "\n",
+ "As seen during the course, the current state of the art for NLP is based on large language models trained using the Transformer architecture.\n",
+ "\n",
+ "In the next exercises, we will learn how to use pretrained models that are available in the HuggingFace library, starting with Trnasformers pipelines."
+ ],
+ "metadata": {
+ "id": "uGZBOXpTXA72"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import pipeline"
+ ],
+ "metadata": {
+ "id": "Od8TVRnQJ8TH"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1-1 ▶▶ Exercise: use a pretrained model for French\n",
+ "\n",
+ "Load the adapted version of **FlauBERT** fine-tuned for sentiment analysis for French.\n"
+ ],
+ "metadata": {
+ "id": "dQo8pS93BJKf"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install sacremoses"
+ ],
+ "metadata": {
+ "id": "i5t_Ik688rIX",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "0d304ec9-0640-4c3f-d90c-60dcb6e407d3"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: sacremoses in /usr/local/lib/python3.10/dist-packages (0.1.1)\n",
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+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "-----------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "qvxrerHI2MYs"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pretrained_model = \"nlptown/flaubert_small_cased_sentiment\"\n",
+ "classifier = pipeline(model=pretrained_model)"
+ ],
+ "metadata": {
+ "id": "Qpuldij38AwO",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "93c986aa-09f3-4289-9bae-fbcd036f41ef"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n",
+ "`do_lowercase_and_remove_accent` is passed as a keyword argument, but this won't do anything. `FlaubertTokenizer` will always set it to `False`.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1-2 Using our own dataset for evaluation\n",
+ "\n",
+ "Here, we're simply going to load our dataset and evaluate a pretrained language model on it.\n",
+ "\n",
+ "HuggingFace has a library dedicated to datasets:\n",
+ "* 'load_dataset' can load data from a tsv/csv file, see the code below\n",
+ "* it directly creates the training/validation/test sets from the dictionary of input files.\n",
+ "\n",
+ "https://huggingface.co/course/chapter5/2?fw=pt\n",
+ "https://huggingface.co/docs/datasets/tabular_load#csv-files\n",
+ "https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.split"
+ ],
+ "metadata": {
+ "id": "-xFvKUiFBnL1"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "file_dict = {\n",
+ " \"train\" : \"allocine_train.tsv\",\n",
+ " \"dev\" : \"allocine_dev.tsv\",\n",
+ " \"test\" : \"allocine_test.tsv\"\n",
+ "}\n",
+ "\n",
+ "dataset = load_dataset(\n",
+ " 'csv', #type of files\n",
+ " data_files=file_dict, #input files\n",
+ " delimiter='\\t', # delimiter in the csv format\n",
+ " column_names=['movie_id', 'user_id', 'sentiment', 'review'], #column names in the csv file\n",
+ " skiprows=1, #skip the first line\n",
+ ")\n",
+ "\n",
+ "print(dataset[\"train\"])\n",
+ "\n",
+ "# Print a few examples\n",
+ "sample = dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
+ "# Peek at the first few examples\n",
+ "sample[:3]\n"
+ ],
+ "metadata": {
+ "id": "gb5KqKSYJmW3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "77659a89-cc00-49e5-b5ec-6a9a55c4d059"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset({\n",
+ " features: ['movie_id', 'user_id', 'sentiment', 'review'],\n",
+ " num_rows: 5027\n",
+ "})\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'movie_id': [438, 354, 633],\n",
+ " 'user_id': [170, 246, 350],\n",
+ " 'sentiment': [1, 1, 0],\n",
+ " 'review': [\"Completement barrés cette bande de dejantés Excellent serie Presque digne des Monthy Pytons et de leur sacré graal Alexandre Astier est très bon réalisateur en plus d'etre bon acteur J'ai hate de voir ce que peux donner le film\",\n",
+ " 'Cette série me fait sourire. Je trouve Chandra Wilson particulièrement drôle dans son rôle de \"Dragon\" enceinte. Ellen Pompeo est vraiment \"dans\" le rôle de Meredith Grey. Chaque acteur apporte à la série une touche d\\'humour particulière. Ce que je trouve amusant, c\\'est la passion de ces jeunes internes pour chaque nouveau \"cas\" unique.',\n",
+ " \"Mise en scène ridicule (vive les scènes d'extérieur, l'éclairage et l'inspiration américaine), acteurs incroyablement mauvais (tous ou presque), scénario navrant (avec des méchants risibles et des gentils mièvres). La nullité a un nom: Plus belle la vie.\"]}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### ▶▶ Exercise: evaluate the pretrained model on your data\n",
+ "\n",
+ "* Using the model FlauBERT for sentiment analysis for French and the *pipeline* method, make predictions on some examples in the dataset\n",
+ "* Take a look at the predictions: do you understand the output?\n",
+ "* Write a piece of code to compute the score obtained by this pretrained model on your validation / dev set.\n",
+ " * Compute the predicted labels for all samples: what are the labels used in the model?\n",
+ " * Define a mapping to the labels used in the dataset.\n",
+ " * Compare the predicted labels to the gold ones and compute an accuracy score.\n"
+ ],
+ "metadata": {
+ "id": "n1kbUmQ3H3H9"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "-----------------------------------------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "dqheBv3jIOX6"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pretrained_model = \"nlptown/flaubert_small_cased_sentiment\"\n",
+ "classifier = pipeline(model=pretrained_model)\n",
+ "\n",
+ "print(dataset[\"train\"][0]['review'])\n",
+ "print( classifier(dataset[\"train\"][0]['review']))"
+ ],
+ "metadata": {
+ "id": "12kiRBwPF-89",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "3d192d6d-10c0-4427-a7e0-e2d22c3072ce"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "`do_lowercase_and_remove_accent` is passed as a keyword argument, but this won't do anything. `FlaubertTokenizer` will always set it to `False`.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Stephen King doit bien ricaner en constatant cette navrante histoire de disparus, les scénaristes semblent s'être inspirés de ses oeuvres mais ont bien moins son talent que celui du business. Quel perte de temps que de regarder ces personnages perdus au centre d'une histoire sans fin et sans intérêt, où 2 ou 3 épisodes suffisent pour décrocher, à l'inverse d'une série comme Desperate housewives dont les dialogues, les scénarii et les personnages contribuent sans cesse à relancer l'intérêt et le plaisir au fil des épisodes. Pourtant mes goûts initiaux m'auraient porté davantage du côté de la série fantastique. Il ne faut préjuger de rien! A bon entendeur...\n",
+ "[{'label': 'negative', 'score': 0.6540930271148682}]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "map_label = {'negative':0, 'very_negative':0, 'very_positive':1, 'positive':1} #, 'mixed':0}\n",
+ "def convert_label( output ):\n",
+ " label = output['label']\n",
+ " score = output['score']\n",
+ " if label in map_label:\n",
+ " return map_label[label]\n",
+ " else: #label mixed = ERROR\n",
+ " return -1\n",
+ " #else:\n",
+ " # print(\"label not found: \", label)"
+ ],
+ "metadata": {
+ "id": "bsR3dMWQDntT"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "correct, total = 0,0\n",
+ "pred_labels = []\n",
+ "for ex in dataset[\"dev\"]:\n",
+ " # -- pred\n",
+ " prediction = classifier(ex['review'])\n",
+ " pred_labels.append(prediction[0]['label'])\n",
+ " # -- gold\n",
+ " gold = ex['sentiment']\n",
+ " # -- convert pred\n",
+ " mapped_label = convert_label ( prediction[0] )\n",
+ " if mapped_label == gold:\n",
+ " correct += 1\n",
+ " total += 1\n",
+ "print( correct/total )\n",
+ "\n",
+ "print( np.unique(pred_labels))"
+ ],
+ "metadata": {
+ "id": "YnbeOe2tELlV",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "a4ed60d9-0e4f-4069-8d3a-4075c42bcd26"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0.8214936247723132\n",
+ "['mixed' 'negative' 'positive' 'very_negative' 'very_positive']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "for ex in dataset[\"dev\"]:\n",
+ " # -- pred\n",
+ " prediction = classifier(ex['review'])\n",
+ " # -- gold\n",
+ " gold = ex['sentiment']\n",
+ " # -- convert pred\n",
+ " mapped_label = convert_label ( prediction[0] )\n",
+ " # Analysis\n",
+ " if mapped_label != gold:\n",
+ " print( prediction[0]['label'], gold, ex[\"review\"])\n",
+ " #if prediction[0]['label'] == 'mixed':\n",
+ " # print( gold, ex[\"review\"])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hAbI2mPc8X5c",
+ "outputId": "faf8ed63-38c4-4071-ad77-342180fa2251"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "positive 0 Newport beach est une série à découvrir d'urgence, cette série raconte les déboires de 4 amis et de leur entourage. Dommage que les diffusions soient rares et que la france n'en soit qu'à la première saison. Les personnages sont attachants, chacun avec sa personnalité bien à lui. La saison 2 est vraiment géniale est j'ai hâte de pouvoir voir la 3ème car la fin de la saison 2 nous laisse sans voix. Si vous aimez Les frères Scott vous aimerez Newport Beach.\n",
+ "mixed 1 j'adore cette serie . je ne peux plus m'en passer mais malheuresement je n'ai pas tf6 alors je suis obligé d'aller la regarder chez un ami . j'espere que france 2 va recommencer la diffusion\n",
+ "mixed 1 Un peu réticent à l'idée d'un remake de la première série, je dois avouer que la série tient plutôt bien la route. Après le premier épisode je suis rapidement devenu fan. De plus les performances des acteurs sont remarquables. Une bonne reprise...\n",
+ "mixed 1 excellente quoique trop courte, cette série ovni merite d'etre découverte sans tarder. tout les ingrédients qui font que cette serie soit une réussite sont réunis. j'espere sincerement qu'il y aura une saison 2, car trop de questions restent en suspens...\n",
+ "mixed 1 Un bonne série qui tourne autour d'objets aux multiples pouvoirs, mais le probléme ses le manques d'épisodes et ses bien dommage. A voir tout de même.\n",
+ "very_positive 0 Ce n'est à mes yeux, qu'une vulgaire copie. Autant j'aime beaucoup de séries mais celle-ci je suis sûre de ne plus jamais tomber dessus.\n",
+ "mixed 0 ouais j'ai trouver cette serie dans ma liste de Recommandations c'est bizarre ma copine c'est fait preter une saison et je ne suis vraiment pas le public viser ce systeme de Recommandations et bizarre parfoisenfin bon pour resumer c'est des histoires de riches beaux jeunes gens a ny et ils leurs arrivent des trucs voila passionnant hein\n",
+ "negative 1 Cette série est actuellement et de loin la meilleure série policière. Les épisodes sont divers et variés, quasiment aucuns ne se ressemblent. Cependant j'ai deux critiques(négatives) à faire, la première c'est qu'il manquerait peut être des nouveaux personnages, mais ce n'est qu'un avis... La deuxième c'est que le jeu des acteurs secondaires est plus que médiocre, du genre \"-(Grissom)Votre fille a été enlevée puis sauvagement battues à mort. -(le père)Tiens donc? C'est plus qu'embêtant ça... Bon c'est pas tout mais j'ai une femme qui m'attend\" Et c'est comme ça même si ce ne sont pas les parents les assassins ! A part ça cette série déchire tout !!! Immanquable!\n",
+ "mixed 0 Arriverons-nous à faire quelque chose de bien en terme de série pour adolescent un jour...\n",
+ "very_positive 0 Je ne vois vraiment pas pourquoi cette série existe depuis plus de 30 ans...Toujours les mêmes histoires, toujours la même fin, le gentil flic gagne sur les méchants et malgré les morts de l'épisode, tout le monde il est content, youpi...\n",
+ "mixed 0 en lisant le synopsis , il y a une reelle idée de scénario..ça change des séries ordinaires, en plus quel plaisir de voir Glenn Close au petit ecran mais je ne sais pas il manque un truc, un peu de peps!! ça manque d'energie! bref je n'ai pas trop accroché! dommage!\n",
+ "mixed 0 Je viens de voir les épisodes suivants (jusqu'au 4) La série, en vérité n'est pas mauvaise en soi. Il y'a même quelques moments presque plaisants. Finalement le seul truc sympa, une fois que la joie que nous procure toujours l'apparition d'un(e) ex Friends passée, c'est le type schizophrène au chat. Sinon, la totalité de la série nous fait poser des questions plutôt étranges comme \"Peut-être vais-je aller jouer les ermites au fond des bois ?\" ou encore \"Tiens, je vais devenir un hippie militant trotskiste\" Sans déconner, le seul truc bien dans cette série, c'est quand ça s'arrête, on revient à la réalité, et on se dit \"oh merci mon dieu j'ai du mal à joindre les deux bouts mais au moins je ne suis pas ça\" C'est comme un bad-trip, le plaisir est éphémère et a priori, on ne prend pas notre pied un seul instant, et à la fin on est tout courbaturé et on a un sale goût dans la bouche...\n",
+ "mixed 0 J'avais adoré la première saison. Par contre, la 2è...pas géniale. J'espère quand même que la 3è (que je n'ai pas encore vue) relèvera le niveau.\n",
+ "mixed 0 Le principal atout de Commissaire Moulin sont les enquêtes souvent plus noires et plus musclées(bien que pas toujours originales). Néanmoins, ca vire souvent au culte de la personnalité: en l'occurence Yves Rénier qui se la pète beaucoup trop avec sa moto, son flingue et son langage violent. Ca vire aussi à la mièvrerie habituelle avec le personnage complétement inutile de Natacha Amal. Enfin comme toutes les séries de faible niveau, cette série s'essoufle vite par manque de création entre les épisodes.\n",
+ "mixed 0 Enquêtes super cheap, humour pour neuneus, des acteurs aux regards de bovins... Si elle a l'air d'une anthropologue et lui d'un agent du FBI, moi suis crédible en physicien ventriloque...\n",
+ "positive 0 Avec la meilleure volonté du monde je n'ai jamais réussi à regarder un épisode en entier. Je trouve la série vraiment trop glauque et malsaine. Je mets quand même une étoile pour les acteurs et pour l'originalité du sujet. Mais bon, c'est vraiment pas ma tasse de thé.\n",
+ "positive 0 Pensez au public un peu sur TF1, tous ces jeudis passé à attendre la méthode cauet ^^\n",
+ "positive 0 L'histoire ne me branche plus autant qu'avant. Il y a de moins en moins d'interventions chirurgicales (ou alors elles sont vraiment trash), les couples sont séparés, bref, tout a changé. 1 étoile lui suffira.\n",
+ "very_positive 0 Je ne comprend pas que cette série attire autant de monde car elle ne poséde rien d'excéptionnel, le style, les acteurs et les histoires sont du vus et revus, a la rigeur les expert de miami c'est un petit peu mieux et encore\n",
+ "mixed 1 Bonne série. Dommage qu'elle soit si peu fidèle aux livres. Les comédiens sont plutôt biens, mais ne corresponde pas du tout aux personnages du livre. Mis à part le mauvais casting, tout est réussi.\n",
+ "very_negative 1 Qui peut mettre 0 étoile à une série comme ça? Pas moi, je l'ai découverte depuis 2 mois maintenant et j'adore de plus en plus, même si je ne fais que regarder les deux premières saisons, ça ne me lasse pas... J'ai même pleuré au départ d'Anna alors que ça fait 4 fois que je vois l'épisode, et ça pas beaucoup de séries sont capables de le faire, garder leurs émotions même après que l'on ait vu maintes et maintes fois l'épisode.\n",
+ "mixed 0 Sans vouloir faire de promotion, M6 a fait un choix intéressant on nous proposant cette série; comme d'autres (Supernatural et Tru Calling entre autre dommage pour la 1ére qu'elle est fait le choix peu judicieux de ne pas diffuser la 2nde saison) enfin je m'égare ! Cette série part d'un postulat de départ original et à fort potentiel. Le pilote plutôt décevant pour ma part est comblé par la suite et surtout par le dernier épisode. Toutefois cette série souffre de divers handicaps: - On ne va pas au bout de l'intrigue, ni n'exploite assez son coté proche de Sliders mine de rien, et qui aurait put apporter une souplesse et des intrigues intéressantes -A part Juliette Margulies les autres acteurs du casting sont peu crédibles -Enfin même si les scènes et la conclusion reste convenu, cette série aurait mérité que l'on approfondisse plus son sujet ainsi que la personnalités des différents rôles. Ainsi cette série reste un divertissement original qui sort du lot mais qui demeure bien en deçà de ses possibilités réelles et aurait méritée d'être plus creusée.\n",
+ "mixed 0 Attention, avi lon é dense d'1 fan absolue.Pr ceux qui en on mar que les séries américaines tournent toujours autour des mêmes choses, lisez plutôt les livres : eux valent le coup! Voilà quelques différences qui-pour moi- ne sont pas négligeable et font de cette série quelque chose de déjà-trop-vu.Dan est maigre, écrit des poèmes, fume des Camel sans filtre et porte des pantalons en velours côtelés.Il passe son temps à boire du café instantané Folger's.Jenny est totalement diffénte de celle dans la série ! Elle est petite, brune, à forte poitrine, et n'est pas amie avec Olivia. Vanessa? où est-elle passée? Serena est niaise, s'excuse toujours, est plutôt moche selon moi. Ds le livre, elle est le model, influence tout le monde, ne s'excuse jamé de quoique ce soit. Elle n'est pas un model de bonne conduite, fume et boit à volonté.Blair/Olivia n'est pas 1 peste. Elle a une forte personnalité, fait des crises de nerfs à longueur de temps. Elle fume aussi. AU début de la série elle se fait constamment vomir dpuis k'elle a appris l'homosexualité de son père. Je trouve que l'actrice ne corespond pas au personnage de Olivia. Les deux meilleures amies sont toujours bronzées et habillées de façon classe, pas ridicule. Nate fume du shit, a un look de surfeur -treillis et vieux tee-shirts il me semble- en dehors de son école NON MIXTe StJude's alr ke lé filles fréquentent Constance Billard. Chuck, kati et isabel nété pa important ds lé livre Rufus Humphrey n'est pas un personnage important, et ne vis pas dans Brooklyn mais de l'Uper West Side. Il a 1 gros ventre, les cheveux gri attachés avec 1 ficelle, c 1 hippi dé 70's o idé anarchistes.Il essai tj d nvel recettes.Lily van der woodsen ou la mèr de Olivia ne st pas importantes ds la séri de livr puisk lé parent son réputé pour vivre leurs vies é laisser fair ce k'ils veulent à leurs enfants ne st jms la et ne sav ri1 deux.Lily né pa divorcé é a un gd fils à l'université 2 Brown Erik en + de serena.\n",
+ "positive 0 Super, cela nous change un peu, des héros avec la science infuse, un couple vedette qui nous la joue je t'aime moi non plus et tout un tas de raccourcis plus ou moins subtils pour nous faire tenir debout un semblant de scénario ( comme le fameux hologramme ). Enfin j'adore l'heroine qui n'est autre qu'un croisement entre Bruce Lee ( paix a son âme), Einstein (paix a l'âme d'Henri Poincaré) et mon Schnauzer Nain qui a un caractère épouvantable.\n",
+ "very_negative 1 DEXTER impossible a éxpliquer en quelques lignes.....un personnage vascinant!!! Vous êtes Dexter,seul vous connaissaiez vraiment qui il est.... Des dialogues magnifiquements écrits parafaitements joués. Pas besoin de vous en dire plus,vous avez qu'a regarder !!!!\n",
+ "mixed 1 Certains critiquent l'idéologie de Dexter, la jugeant tendancieuse et très américaine. Etrangement je pense que c'est l'exact contraire. La série a quelque chose de très européen, tourne autour de ses personnage et ne cherche pas à livrer un message. Juste l'histoire dérangeante certe d'un homme sans sentiments, avec sa propre vision du monde, vivant dans un monde qui ne peut pas le comprendre. Pour Dexter, c'est un horrible sentiment de claustrophobie que de vivre dans ce monde. Personnages attachants, mise en scène et photographie remarquables, musique géniale, une série sur deux méchants, et on aime les méchants.\n",
+ "mixed 1 Pour moi c'est LA serie medicale du moment. Pas d'histoire d'amour ennuyeuse, un personnage attachant, des dialogues bien ficelé, même en doublage FR. Les cas sont toujours hors du commun et c'est pour ça que House les traitent! 3 étoiles seulement pour certaine scènes et épisodes ou on devinent, en bonne amateur, la suite des évènements... Mais tout de même la seule serie qui me tiens toujours en haleine à la 3ème saison.\n",
+ "mixed 0 Cette série n'a pas prit en moi! Vraiment je ne sais pas exactement pourquoi mais je n'ai pas du tout aimer, le thème peut être mais aussi des acteurs pas géniaux, cette série est pour moi vraiment une série trés (trop) moyenne!\n",
+ "positive 0 Je trouve que c'est génial! Les acteurs mériteraient au minimum le César. Un coup de coeur \"océan\" à Charlotte Désert (son nom reflète bien son jeu d'acteur) dans son rôle d'adolescente perturbée et suicidaire. D'ailleurs à quand l'internement en HP pour le personnage parce que j'ai rarement vu dans la vie une meuf qui parle à la photo d'un mec et qui prend de grandes décisions avec cet ami imaginaire. J'espère que le rôle de la comédienne dans la série n'a pas trop déteint sur sa vie personnelle car cela pourrait être dangereux. Et que dire de Pierre, dont le charme charismatique me fait penser aux mannequins des pubs pour les céréales! Il faudrait que quelqu'un apprenne à Alex comment on parle parce qu'à chaque fois qu'il ouvre la bouche, il se crache dessus... Peut-être que c'est pour la jouer racaille mais là c'est pas trop crédible. Espérons qu'aux prochains épisodes, il fera son coming out pour l'amour de Nils... Parce que là on dirait un télétubbies. Les parents qui sont dépassés par les êvènements c'est du vu et revu comme cliché. Sinon quelle originalité et quelle fraîcheur pour une série française d'un niveau égal aux grosses productions qu'étaient Les garçons de la plage ou La croisière Fol'Amour. Vivement les prochains épisodes. A voir absolument pour se tordre de rire (faites quand même gaffe au mal de bide).\n",
+ "very_positive 0 Je viens de lire dans une critique \"une série qui fait réfléchir sur la société\".... Oui, c'est sûr avec un gros plan sur des fesses toutes les 5 minutes, je réfléchis beaucoup, presque autant que devant Alerte à Malibu. Ce n'est qu'une vulgaire alternance de plans érotiques (sous prétexte de la chirurgie) et de plans trash et sanguinolants. Enfin, si la vie de chirurgien c'est se taper une bombasse à chaque consultation je signe tout de suite...\n",
+ "very_negative 1 J'avais peur d'une enieme serie sur les malades... Et bien oui c'est une serie qui se passe dans un hopital, mais le medecin est absolument delicieusement cynique, execrable et performant ! J'adoooooore. dommage qu'elle soit diffusée aussi tard..\n",
+ "mixed 0 je l'ai revu hier sur le cable c'est une bonne serie mais l'idee qu'une entite decife de vous envoyer quelqu'un pour vous remplacer quand il vous arrive quelque chose ca m'embete un peu mais bon a part ca pas mauvaise serie elle merite une etoile\n",
+ "positive 0 Série agréable à regarder... si on n'a pas vraiment autre chose à faire! L'histoire est banale, mais on s'y attache assez facilement. Les acteurs ont l'air... gentils. Voilà pour le positif. Après, comment ne pas comparer ça à 'Un dos tres'! La ressemblance est flagrante. Je trouve que même la prof de danse n'a pas le niveau des 1ères années de l'école de Carmen Arranz. Sans parler de la mauvaise histoire d'amour entre Dante et Lucia qui commence dès le 1er épisode. Enfin cette série a encore à faire ses preuves, attendons.\n",
+ "mixed 0 Mais si quelques objets font parfois sourire le reste est sans interêt... Un bien piètre successeur de Kaamelott( heureusement de retour à la rentrée!)\n",
+ "mixed 0 Californie, richesse, belles filles blondes... Mais oui c'est un nouveau Beverly Hills 90210! Bon c'est vrai que c'est une série pour ado qui parle essentiellement des problèmes qu'un ado peut rencontrer dans une belle ville bourgeoise... Mais on n'accroche pas aux acteurs (notamment les personnages de Ryan et Summer), bref une série pas révolutionnaire...\n",
+ "mixed 0 Série originale mais sans plus, à la fin il n'y a pas de surprises, les personnages deviennent au fil du temps sans intérêt à cause du mauvais jeu des acteurs. Un seul point positif : elle est diffusée tard dans la soirée.\n",
+ "mixed 0 vu le 1 er épisode,encore une super serie qui nous démontre que dans une certaine contrée , on aime pas le sexe, on s'en méfie, mais par contre, on est séduit par les serials killers, on voue un culte aux armes a feux, pour moi... ça va un peu! trés malsain\n",
+ "very_positive 0 Voulant me fier à la presse qui ne tarissait pas de critique élogieuses,je me suis donc branché samedi soir sur M.6.J'aurai du me casser une jambe!!!C'est la série fantastique la plus baroque et grotesque qu'il m'ai été donné de regarder.C'est bien simple,je n'ai pas pû aller au bout du 3eme épisode(il y en a6) tellement c'est risible.l'Esthetique est pas mal,mais elle mélange(mal)les genres en passant du registre de la science-fiction au polar.De plus,faut s'accrocher pour pas perdre le fil,et ces fameux objets certains ont des priorités ridicules comme le stylo tueur ou la montre qui peut cuire les oeufs.N'importe quoi!Une clé qui ne fait pas le bonheur,et pourtant,je suis plutôt client des ambiances bizarroÏdes.\n",
+ "mixed 0 Série moyenne, chirugie esthétique, ça remplace les chirurgiens en hopital, bientot, ils vont surement faire une série sur les dentistes ou sur les vétérinaires ? non sans rire !\n",
+ "mixed 0 Les experts est une série mettant en scéne des acteurs et actrices canons,tres intelligents, sur un scénario basique et plat qui se répete au fil des épisodes.En somme le schéma classique des séries policières americaine du moment, ennuyeux a mourir et connaissant un succé disproportionné.\n",
+ "mixed 1 Je ne comprends pas pourquoi tout le monde casse cette série, je la trouve super ! très amusante et j'ai le fous-rire à chaque épisode... bien sur comparé à \"caméra café\" ou \"Kaamelott\" y a rien a voir. Les acteurs sont parfaits dans leurs rôles et je suis déçu que la série soit déprogrammé... Convaincu ?\n",
+ "mixed 0 Encore une série linéaire : une personne ayant une maladie inconnue est internée. Dr. House cherche la solution, en choisit une (souvent au hasard ou selon l'intuition), il se plante, rectifie le tiret trouve la bonne solution grace à un infime détail que Dr.house avait occulté jusque là. Le malade est sauvé. Fin de l'épisode. Alors oui son personnage est détestable et certaines répliques sont géniales. Mais c'est trop répétitif : regardez un épisodes, vous les aurez tous vus !\n",
+ "negative 1 Ah le Docteur House... une serie qui m'a surpris par son theme pourtant usé et re-usé que sont les hopitaux, mais là il faut bien avouer que ça marche et tres bien d'ailleurs. Commencons pas les personnages, bien sûr House est un modele de cynisme, d'humour noir et provocant, l'archétype du vachard qu'on deteste et qu'on fini par adopté comme on adopte l'oncle acariatre, à qui on pardonne tout ; et que quelque soit les caprices, il aura gain de cause... mais cela serait reduire le personnage de House à un acariatre associal... qu'il est bien sûr !!! mais qu'il n'est pas seulement, il est intelligent (trop ?), il donne de lui à sa façon et il suffit de patienter pour voir toute la \"générosité\" de ce personnage. Je ris aux larmes (sincerement) à chacune de ses entourlopes et il arrive à me surprendre alors que l'on arrive à se persuader de l'avoir cerné... j'en suis à 55 épisodes et mon interet pour ce personnage est croissant... Mais la serie c'est aussi un panel de second roles tout aussi savoureux que House : Cuddy et Wilson sont de parfaits compagnons de House dans ses machinations et ses trois assistants de bons souffres douleur pour cet accro à la Vicodine... Enfin la structure de la serie est excellente, elle mele environnement medical et enquete scientifique dans un subtil melange d'Urgence, Les Experts, le ton décalé de Scrubs (House vs Cox qui gagne ?) ponctué par ses sceances de consultation à la clinique toutes aussi droles les unes que les autres... Il existe des arcs passionnants au fil des episodes qui doivent se voir dans l'ordre pour saisir toutes les subtilités de la serie. Bref un delice de suivre les aventures de House son equipe et son hopital... Bravo Bryan Singer de nous offrir un tel cocktail :-)\n",
+ "very_positive 0 Les Experts pouvait espérer figurer parmi les toutes meilleures séries il y a dix ans encore. Oui mais voilà, les scénarii sériels ont tellement progressé récemment, pour atteindre un niveau d'originalité et de mise en scène qui n'a rien à envier au cinéma (par ex. chez HBO: Six Feet Under, Sopranos, Carnivale ou Lost sur ABC), qu'ils font passer Les Experts pour un produit déjà-vu et suranné. Et même quand Q. Tarantino se retrouve aux commandes d'un double épisode, on a du mal à décoller de la simple série policière de base.\n",
+ "mixed 0 une série pour adolescent avant tout. pas mal de nullités: les histoires des adultes, la vie quotidienne des jeunes (fêtes, jeu vidéo, s'envoyer en l'air...) ,mais un bon fond: une relation seth/summer très attachante, un ryan attachant. comme ça la série est un petit divertissemet mais elle ne vaut pas des masses!\n",
+ "mixed 0 Pour ma part, je n'ai pas été vraiment convaincue par les deux premiers épisodes, j'attendais plus du créateur de Newport Beach, notamment un peu plus de \"cruel intentions\" plutôt que le monde de Bisounours. Je trouve le jeu des acteurs plutôt moyen entre une blonde qui sourit à chaque mot qu'on lui dit et un beau gosse riche aussi charismatique d'une limande. Je trouve les dialogues pas très bons, les intrigues senties à 10kms. La lutte manichéenne entre la gentille blonde (ah pardon elle est \"bad girl\") et la méchante brune a été vue 100 fois, je trouve que la série n'apporte aucune originalité mais heureusement la voix de Kristen Bell remonte un peu le niveau mais je pense qu'elle aurait pu espérer mieux comme rôle récurrent dans une série. Genre série ado, revoyez plutôt popular qui repasse tous les samedis sur France 4.Je pense qu'on est loin du niveau de Newport Beach voire même des Frères Scott que je n'aime pourtant pas vraiment. Cela me ferait vraiment mal si cette série durait plus longtemps que Veronica Mars.\n",
+ "positive 0 J'ai failli mettre une étoile parce que je me marre bien devant. Yves Rénier nous régale toujours de son jeu faux. Les scénarii sont quand à eux extraordinaire, extraordinaire dans le sens où c'est dur de faire plus stupide que ça. Bon au moins ça nous fait bien rire alors tout compte fait ne changez rien monsieur Rénier, j'aime bien être hilare de vos arrestations toujours plus débiles au fur et à mesure des années.\n",
+ "mixed 1 Les experts, c'est excellent, mais j'ai trouvé extrêmement nul l'adaptation française de cette série. Manhattan et miami ne valent pas las vegas. Grisom est le mieux de tous je trouve. Vive les experts.\n",
+ "positive 0 J'étais extrêmement réticente, je craignais que la série soit vulgaire, sexiste... J'ai un jour accroché à la première saison, j'avais fini par trouver qu'au delà des apparences il y avait une forte volonté de critique. Mais j'pense pas aller plus loin, ça finit par être trop n'importe quoi pour moi. Je lui laisse une étoile pour quelques bonnes choses, et notamment pour le personnage de Sean McNamara. Je ne pourrai pas faire plus.\n",
+ "mixed 1 Une bonne série TV, je l'ai suivie du début a la fin, mais le fait que les épisodes ne se suivent pas forcement et le fait que cette série ne dégage pas de charme particulier font que je ne met que 3 étoiles. Les épisodes sont toujours pareils : 1) On découvre le futur patient 2) Le Dr House fait ses consultations 3) On soigne le patient mais bien sur, le traitement choisi ne convient pas. Complications a chaque fois, etc. 4) Le Dr House, grace a la personne qu'il a consulté aupravant, trouve la solution a l'énigme. 5) Le patient est sauvé Un peu dommage. Série a regarder néanmoins, pour le personnage du Dr House\n",
+ "mixed 1 Faut-il avoir un petit instinc sadique pour regarder cet série ? Evidemment ! Sinon on ne pourrai pas l'aprécié à sa juste valeur :-) Enfin une sérié médicale interessante ... Enfin une série d'un cynisme sans pareil ... Enfin un sadique a qui on donne \"presque\" raison :-) Cet série à quelque chose de nouveau qui donne au file des épisodes l'envie d'en regarder un autre ! Le fais que les histoires personnels des uns et des autres sont aussi au coeur de chaque histoire encourage d'autant plus a suivre le cours des évenements ... Et je pense que ce n'est qu'un début , ayant vu quelque episode en anglais de la saison 3 , cet saison s'annonce d'autant plus interessante qu'un evenement intrigant arrive au Dr House :D (mais chut ;)) Si vous ne connaissez pas encore cet série , que vous aimez l'humoir un peu noir et le cynisme sans pareil , cet série est pour vous :-) Le mot de la fin : J'aimerai bien épousé Cameron :)\n",
+ "negative 1 Tombée par hasard sur cette série la semaine dernière (le 22.09.07)j'aurais aimé en savoir plus et regarder d'autres épisodes; Mais j'entend à la pubTV que samedi 29, c'est les derniers épisodes ...domages! :( Pourquoi cela n'a-t-il pas duré plus lontemps? Manque d'audimate? Pourtant, l'histoire des objets \"magique\" est vraiment prenante!! Je ne suis pas une enfant mais je me suis tout de même demandé \"et si c'était possible?!!\"\n",
+ "mixed 1 Très bonne série malgré le fait que les dialogues soient un peu compliqués à suivre. 3 étoiles car la fin m'a laissé un peu dubitatif !\n",
+ "negative 1 THRILLER DRAMATIQUE DECAPANT !!\n",
+ "positive 0 Une etoile et pas zero pourquoi ? Car l'environnement île/plage reste correct!\n",
+ "mixed 1 La saison mi 2-3 a été pour moi la meilleur de tous!Grace au découpeur et l'intrigue autour de lui et aux histoire qui suivent. G été un peu déçu par la saison 4.Par contre la saison 1 était médiocre car il y avait pas vraiment d'intrigue.\n",
+ "mixed 0 Et voici une série ayant pour base le voyage dans le temps. On revisite ainsi la petite comme la Grande Histoire des États-Unis à travers le personnage du Dr. Beckett et son inséparable compagnon Al. La série s'avère donc ludique et recèle d'un peu d'humour. Néanmoins, Code Quantum a pris un sacré coup de vieux, et touts les situations cocasses ne sont pas toujours très drôles, les acteurs font vraiment pitié par moment. La faute également à l'acteur principal Scott Bakula qui manque réellement de charisme et s'avère incapable de s'imposer, même s'il a le mérite de porter une série \"casse-gueule\" sur ses frêles épaules. Dans le même genre, mais infiniment plus réussit, regarder la cultissime trilogie Retour vers le Futur.\n",
+ "very_positive 0 Rares sont les séries de ce genre qui me plaisent, c'est mon père qui adore cette série.\n",
+ "mixed 1 Y a pas mieux. J'ai été obligé de me relever pour continuer à regarder des épisodes.\n",
+ "mixed 1 Je n'arrive pas à concevoir qu'on puisse juger cette série comme \" une simple série de chirurgien esthétique\". Je plains les gens qui pensent cela Nip/Tuck ce n'est pas seulement sexe drogue et chirurgie, c'est aussi une série riche en émotion. Voilà ce que je dirais c'est que certes cette série peut sembler obscène et bête au premier abords, mais il faut passer outre cette aspect. Car derriere on trouve tout les éléments clés d'une série a succés : une musique incroyable ( avec des artistes comme rufus wainwright, the engine Room...) qui colle parfaitement à l'esprit de la série, ainsi qu'une intense émotion vis à vis des personnages confrontés à des situations les unes plus touchantes que les autres. Comment ne pas s'émouvoir devant une série qui en nous montrant une opération de chirurgie esthétique, nous fait ressentir une forte intensité poétique. Ne passez pas à coté, cette série dérangeante est en meme temps la plus touchante de cette année.\n",
+ "mixed 0 J'ai lu dans les critiques précédentes qu'il fallait passer le cap des 4 premiers épisodes pour apprécier cette série. Terminer le premier a été suffisamment douloureux pour moi alors non merci... Qu'en dire... Dirt se veux être une critique du monde de la presse people, mais je ne vois qu'une succession de clichés servis par une Courteney Cox Botoxée et des seconds rôles insipides...\n",
+ "mixed 1 Fan de la premiere heure, je regrette juste les effets spéciaux qui ne sont pas crédibles\n",
+ "mixed 0 Dommage que l'on ne puisse pas mettre des moins 2 étoiles car cette série est vraiment trop nulle. Les acteurs sont convaincants comme des tortues de mer, l'histoire... Elle me rappelle une série espagnole... Trop nulle, zappez de suite cette série!\n",
+ "mixed 0 Une entrée en fanfare avec une superbe saison 1...mais alors quelle décrépitude depuis...c'est très très dommage!\n",
+ "mixed 0 Bonne serie policiere mais apres tant d'épisodes ils ont du mal a se renouveler.\n",
+ "positive 0 Cette série est le véhicule des idées puritaines et conservatrices de l'amérique qui offre le temps de chaque épisode, un regard moralisateur sur tous les possibles écarts de ces jeunes bourgeois de haute catégorie. On dirait JAG, les uniformes et l'action en moins.\n",
+ "mixed 1 Non fan de série mais celle-ci est vraiment sympathique. Dotée d'un humour presque gamin ou gras, la justesse de jeu des acteurs parvient à nous faire rire de blagues parfois même pas drôles. Situations cocasses et personnages attachants, cette série a tout pour plaire et passer simplement un bon moment, en effet, aucun style, rien de bien alléchant pour l'oeil, le tout vise la détente et la bonne humeur.\n",
+ "very_positive 0 Les experts MIAMI, les experts Manhattant, les experts las Vegas... Bientôt sur vos écrans : Les experts TOMBOUCTOU ! Non mais faut arrêter de nous prendre pour des cruches aussi...Pfff C'est d'un débile...\n",
+ "very_positive 0 Pour ceux qui veulent savoir, nip tuck c'est des chirurgiens, éffectuant des chirurgie, voila, vous avez vus ses trés trés original, non ?\n",
+ "mixed 0 Je lis partout que cette série est \"ironique\" et \"critique la société américaine\". Je suis désolée mais quand je la regarde je ne vois pas du tout d'ironie. C'est du feu de l'amour pour les ados ! Les acteurs jouent des lycéens, mais a part Mischa Barton ils ont tous la vingtaine (Benjamin McKenzie a 27 ans si je ne me trompe pas). Les parents ont l'air tellement jeunes qu'ils ont surement dut avoir leurs gosses à 15 ans ! Ca me fais penser aux frères scott, une autre série pas très haute dans mon estime.\n",
+ "mixed 1 Une programmation initiale qui aurait pu tuer cette série dans l'oeuf dans notre pays. Mais rien ne peut tuer la qualité dit-on... CSI en est l'exemple. Fan depuis la première heure, je suis étonnée de la constance de cette série qui au bout de 7 ans maintenant, a su garder sa ligne directrice et n'est pas tombée dans le \"sopori-soap\" (i.e. les personnages finissent toujours par coucher les uns avec les autres...). Le succès fût tel, qu'elle a fait des petits (versions Miami et Manhattan) mais qui n'ont pour le moment pas réussi à atteindre sa qualité.\n",
+ "mixed 0 Bonjour, Je me désole chaque fois, davantage en voyant les séries américaines de plus en plus sombre. Une personne \"équilibré\" ne voit surement pas le mal dans les sénarios des Experts, mais certaines individus peuvent prendre ces séries pour modèles et en souffrir. Il y a trop de séries policières ou l'on voit meurtre, sang, armes à feu Trop de violences... Je ne recherche pas une télé ou casimir serait roi mais un certain équilibre dans les programes. Merci\n",
+ "very_negative 1 C'est qui notre sociopathe préféré ... DEXTER ... Cette serie est tout bonnement incroyable ... Il tue des gens et on approuve ... on a peur qu'il se fasse coincé ... detestons les gens qui le deteste ... prions pour qu'il tue le vilain flic qui risque de l'arrété ... on est du coté obscur et on le sens meme pas ... pour nous c' limit un super héro ... Dexter a reveillé le sociopathe qui someil en chacun de nous ... parie difficil mais parie tenue ... Seul défaut de cette serie ... elle n'est pas quotidienne :(\n",
+ "mixed 1 3 etoiles, car la série n'est pas parfaite. Je crois que c'est du au jeu d'acteur qui est parfois faible. Critique qui ne touche pas Michael C. Hall qui joue à la perfection. On voit que la série est réalisée avec soin (les décors, les dialogues...), car le grand risque était de tomber dans des lieux communs, et de traiter le thème avec banalité malgré l'originalité du scenario. D'autre part, je tiens à parler du générique et du thème de musique récurrent qui sont vraiment bien, dans le sens où le générique est juste, et le thème adéquat. Bref, c'est un grand moment de série télé, mais la prochaine saison va peut-être décevoir. Aux réalisateurs de ne pas donner dans les sentiers battus ! Comme d'habitude, je m'insurge contre les chaines françaises qui ne donnent pas dans la vo sous-titrée. Car la voix, fait partie du jeu d'acteur, et en plus il y'a certaines répliques jeux de mots, etc qui peuvent nous échapper. (Imaginons un film français doublé en anglais !) Heureusement pour Dexter que cette série est diffusée sur canal, car sur tf1 cela eut été insupportable (diffusions à minuit, hachée de pubs...)\n",
+ "mixed 1 J'étais un peu réticent lorsque l'on m'a demandé de regarder un épisode. Je m'attendais encore à une série d'hôpital sans originalité, en reprenant ce qui existe déjà. Et bien pas du tout, j'ai été rapidement attiré par l'humour décalé, ironique voire sarccastique de cette série.C'est une série originale qui se laisse regarder tranquilement dans son canapé !\n",
+ "mixed 1 serie ENORME rien à dire, quand on connait pas on peut ce dire bof des chirurgiens on va voir que des opérations mais rien d'autre en ben non ceux qui disent ça je leurs conseille de regarder au moins un episode pour ce rendre compte que ce n'est pas du tout ce qu'ils croient certes il y a des opération, c'est bien normal mais il y a un paquets d'histoires et de rebondissements c'est ce qui fait de nip tuck une serie culte!!\n",
+ "mixed 0 Série des plus moyenne, dans le genre il y a beaucoup mieux. Et la vf n'est pas des plus réussie.\n",
+ "mixed 0 Sujet niais pour serie niaise.\n",
+ "mixed 0 Une série bien lourde. Un policier qui devrait penser à la retraite, il n'est plus vraiment dans le coup.\n",
+ "positive 0 Une idée de départ intéressante, mais qui tourne rapidement en rond. Par contre Michael Hall est vraiment très impressionnant, tout comme Jennifer Carpenter qui confirme après \"L'exorcisme d'Emily Rose\".\n",
+ "positive 0 J'ai adoré l'idée, l'histoire qu'elle a voulu conter... seulement, du fait qu'elle ait été arrêtée si tôt, le tout est vite parti de travers, ne laissant qu'une des 4 étoiles que je voulais lui mettre...dommage...\n",
+ "mixed 0 Etant alité pour cause de maladie j'ai dernièrement eu l'occasion de regarder la saison 1 de la série Newport Beach et je dois dire que j'ai été déçue. En effet, j'ai trouvé les personnages extrêmement lisses et stéréotypés, les histoires pourraient être intéressantes mais on tombe vite dans des schémas bien établis où tout finit bien à la fin. Bref, une série pour ados comme on en voit plein. Le seul plus c'est le personnage de Seth qui est mignon et attachant. Malheureusement, pour moi ce ne sera pas suffisant pour m'atteler à regarder les saisons suivantes.\n",
+ "mixed 0 La construction de l'\"histoire\" repose sur une question, à laquelle les scénaristes doivent constamment répondre: comment faire à la fois plus choquant à chaque épisode que ce qui fut montré lors de l'épisode précédent tout en étant politiquement correct? L'intérêt que les gens portent à cette série est très probablement lié en grande partie à des tendances voyeuristes, qui sont probablement en chacun de nous. Bref, pour ceux qui aiment le sang et le sexe, regardez Rome, car au moins c'est instructif.\n",
+ "mixed 1 très bonne série car pariculierement insicive mais je trouve qu'au fil des saisons ils tournent un peu en rond. Il faudra qu'ils s'arretent sinon son originalité deviendra banalité.\n",
+ "mixed 0 Je hais \"Les Experts\", je préfère de loin \"Les Expert: Manhattan\", toujours aussi mauvais que Miami, quand on voit un épisode de la saison 1, on n'en retrouve un a peu près identique a la saison 2 et ainsi de suite, je trouve que les acteurs ne sont pas en haut de leur forme, les enquêtes sont prévisible ( a certain épisode), je n'ai jamais vu ca c'est nul, mais je dirai moins nul quand même que Les Experts: Miami.\n",
+ "mixed 0 Les tribulations de quelques gosses issues de la haute société américaine plus celles du beau gosse issue des milieux populaires dont s'entichera la fille à papa un peu BCBG, histoire de bien nous montrer que les barrières sociales n'existent pas, même au pays de l'oncle Sam. Quatres étoiles pour ceux qui rafolent de ce genre de niaiserie, les autres passeront évidement leur chemin.\n",
+ "positive 0 Voici la série qui faisait découvrir aux beaufs de l'autre côté de l'Atlantique, ce que nous appelons en France la Police Judiciaire. Des équipes d'\"experts\" (ils sont allés cherchez loin le titre français) vont donc décortiquer les différents prélèvements sur les scènes des crimes, échafaudez des théories et coincez les coupables avant la fin de la journée. Bien sûr, dès qu'un élément médico-légal contredit l'hypothèse la plus probable, nos spécialistes vont creusez un peu plus et finiront par trouver la faille dans la version des faits des suspects et les bouclerons. Les créateurs de la série ajouterons leur petites morales d'épisodes, avec la mère de famille prévenante, le black de service au lourd passé, la petite nouvelle nénette, l'inspecteur fraîchement promu qui se la pète un peu, et le patron qui finit toujours ses missions. J'avoue avoir du mal à trouver un intérêt à d'aussi \"passionnantes\" enquêtes, des personnages à la psychologie si peu travaillée, l'absence de difficultés dans la tâche des enquêteurs, et le côté 'pré-mâché' de la série. Cela passe sûrement bien en seconde partie de soirée...\n",
+ "mixed 1 SI VOUS VOULEZ VOIRE UNE SERIE POLICIERE QUI FONCTIONE A LA FOIS DE MANIERE CLASSIC ET ORIGINALE .ALORS IL FAUT LA REGARDER .LE PRINCIPE EST SIMPLE LE TRAVAIL DE POLICE SCIENTIFIQUE EST LE DERNIERS RECOURS DES VICTIMES.LES INTERPRETES Y SONT EXCELENT.LAISSEZ VOUS ATTIRE PAR CETTE SERIE.\n",
+ "positive 0 Bon alors la c'est une énorme déception. La façon dont sont représenté les personnage en leur donnant des caractères un peu fou s'est bien épuisé depuis Ally McBeal. Sans parlé des nombreuses affaires que ses avocats mannequins, défendent, qui sont toutes plus débiles et improbables les unes que les autres. Je le répète on est bien loin du cabinet fou d'Ally McBeal et la ou Ally innovait, Boston Legale, en fait trop et s'enfonce. Je met quand même une étoile car je suis sur qu'en VO le jeu des comédiens est excellent.\n",
+ "mixed 0 bon j'ai regardé d'autre épisodes de nip tuck comme je l'avait promis et je reste sur ma premiére opinion, rien d'original, les acteurs et les scénario n'en parlons pas.\n",
+ "positive 0 Les acteurs sont convaincants comme des mouches, l'histoire... Elle me rappelle une série espagnole nommée \"Un, dos, tres\"... Trop nulle, zappez de suite cette série!\n",
+ "mixed 0 J'ai vu les 3 épisodes (c'est à dire toute la série) : rien d'exceptionnel, une série gentille, qui rappelle un peu trop Heroes. Le scénario, qui s'annonçait prommetteur, est au final décevant. Le jeu d'acteur est trop convenu, trop classique. Ça sent un peu le réchauffé. Dommage.\n",
+ "mixed 0 Non mais franchement, c'est quoi cette série? Pour mettre 4 étoiles il faut vraiment n'avoir jamais regardé de bonne série! Les acteurs ils ont dû les trouver dans une pochette surprise, j'ai trop honte pour eux! Et le scénario, il est complètement débile, regardez de meilleures séries du genre The OC, One Tree Hill, Lost, ou même Dawson! C'est dix fois mieux! Ne perdez pas votre temps sur cette série.\n",
+ "negative 1 je commence a detestetr marissa. alor ke ryan fai tt pr ke sa aille bien entre eu, il fo tjs kel gach tt.el fai tt pr le fer rester, el le capte a peine. je le plin le povre, mé joré pa pensé kil orai fai marissa mourir, sa ma fai vrèmen chier pr ryan, kar il ete vrèmen triste. enfin bref, moi jèmè bien les voir ensemble, ils étai mimi. mé tte bne choz a une une fin. je ss bien contente pr seth et summer kan ils vt se marier, mé par ctre, taylor et ryan?? jimajinai tt, mé pa du tt sa.la vi ns reserv bien d surpriz !!\n",
+ "positive 0 Je suis une fan de la série Les Experts et de ses spin-off. Je trouve le principe de montrer le côté scientifique des enquêtes policières très intéressant. Les personnages sont très attachants, même si la série est axée principalement sur les enquêtes et non sur la vie des personnages. On en sait suffisamment pour les comprendre. Cependant je trouve dommage que les épisodes ne soient pas diffusés dans l'ordre... Ca enlève un peu de cachet...\n",
+ "mixed 0 & Allons bon... Commissaire Valence... Commissaire Moulin... Commissaire Cordier...... Ben voyons !!! & Le pire c'est que c'est encore en production !!!!\n",
+ "positive 0 Une série qui atteint le summum de la bêtise! L'acteur principal est juste détestable au point que la série est éclipsée par son jeu des plus mauvais! En fait, pour ma part, si le personnage principal avait été un peu moins dans la caricature du méchant-docteur-qui-au-fond-est-un-génie, peut-être que cette soupe immonde aurait eu meilleur gout! Mais bon, tous les gouts sont dans la nature! :)\n",
+ "very_positive 0 je trouve cette série géniale on se met facilement dans la peau des personnages on recent bien les émotions bref j'ador cette série. vivement la saison 2. ju\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 2- Transfert / fine-tuning : analyse de sentiment\n",
+ "\n",
+ "Dans cette partie, nous allons fine-tuner / affiner un modèle de langue pré-entraîné (agnostique) pour l'adapter à la tâche d'analyse de sentiment.\n",
+ "\n",
+ "On travaillera sur des données en anglais (corpus IMDb, que l'on peut directement charger depuis HuggingFace)."
+ ],
+ "metadata": {
+ "id": "HUx1kHH8eUjE"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2-1 Charger un modèle pré-entraîné : DistilBERT\n",
+ "\n",
+ "Ici on ne va pas passer par la pipeline, pour pouvoir plus simplement gérer les éléments du modèle : le modèle et le tokenizer associé.\n",
+ "\n",
+ "On utilise ici le modèle DistilBERT, une version plus petite et rapide du modèle transformer BERT.\n",
+ "\n",
+ "Plus d'info ici: https://huggingface.co/distilbert-base-uncased.\n"
+ ],
+ "metadata": {
+ "id": "c40x3RDbB3Qo"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Chosing the pre-trained model\n",
+ "# - distilBERT: specific, faster and lighter version of BERT\n",
+ "# - base vs large\n",
+ "# - uncased: ignore upper case\n",
+ "base_model = \"distilbert-base-uncased\""
+ ],
+ "metadata": {
+ "id": "UtdppwkoB3Qp"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2-2 Tokenizer\n",
+ "\n",
+ "Définir un tokenizer et un modèle associés au modèle pré-entraîné DistilBERT."
+ ],
+ "metadata": {
+ "id": "NUus9JUNB3Qq"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "-------------------\n",
+ "SOLUTION\n"
+ ],
+ "metadata": {
+ "id": "xq9sUFYg9Wd7"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5d8992af-cf9d-4d06-d729-917f18b1bd0e",
+ "id": "9XwH5If4B3Qq"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Defining the tokenizer using Auto Classes\n",
+ "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(base_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2-3 ▶▶ Exercise: Load new data for transfer\n",
+ "\n",
+ "Charger l'ensemble de données IMDB qui correspond à de l'analyse de sentiment sur des reviews de films (en anglais).\n",
+ "On va utiliser ces données pour affiner notre modèle pré-entraîné (agnostique) sur la tâche d'analyse de sentiments."
+ ],
+ "metadata": {
+ "id": "8lt8MjqYIZCl"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "yamXvQ3q9mbQ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Xndj4mU-Ib8Q"
+ },
+ "outputs": [],
+ "source": [
+ "dataset = load_dataset(\"imdb\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "fdbcbceb-c506-432b-a78d-aa4748124505",
+ "id": "C6LVL237Ib8R"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['text', 'label'],\n",
+ " num_rows: 25000\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['text', 'label'],\n",
+ " num_rows: 25000\n",
+ " })\n",
+ " unsupervised: Dataset({\n",
+ " features: ['text', 'label'],\n",
+ " num_rows: 50000\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 16
+ }
+ ],
+ "source": [
+ "dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2-4 Tokenization des données\n",
+ "\n",
+ "Le code ci-dessous permet d'obtenir une version tokenisée du corpus."
+ ],
+ "metadata": {
+ "id": "SbjUad2-tecl"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### ▶▶ Exercice Tokenisation :\n",
+ "\n",
+ "Regardez la doc pour vérifier que vous comprenez la fonction des paramètres utilisées : https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizer.\n",
+ "\n",
+ "- à quoi sert le padding ?\n",
+ "- à quoi correspond le paramètre 'truncation' ?\n",
+ "\n",
+ "Note: pour plus de détails sur la fonction *Map()* https://huggingface.co/docs/datasets/process et aussi https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Dataset.map"
+ ],
+ "metadata": {
+ "id": "HY-5WQapfCTV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "------------\n",
+ "SOLUTION\n",
+ "\n",
+ "- *padding (bool, str or PaddingStrategy, optional, defaults to False)* — Activates and controls padding. 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.\n",
+ "- *truncation (bool, str or TruncationStrategy, optional, defaults to False)* — Activates and controls truncation. True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided."
+ ],
+ "metadata": {
+ "id": "2irkWDLEuSTp"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-Kj0bW3_50et",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "a88891844cfa4394a813aa2b0d694e2e",
+ "b09ef349ab514df6ba687ed52d4e9370",
+ "98fa9c1f33dd45c69b6a3369fc721520",
+ "2686dcc81f7c4d44b3c0c98488cd62b7",
+ "000548e2fec74e82b1183338a8dc2bd5",
+ "ab0b13d4ab3045d38bd154c6520abc1e",
+ "5b051368ca494a17aad1d767e850f0db",
+ "4aeae7f2f68649ebb1f3cdecec6d0da0",
+ "d4ad2b4a0a6340fdb08424048ef4e006",
+ "47cdf76d17c44e08ab24024d6a234280",
+ "d80b57aacb0349eb87ade2167030ec0f"
+ ]
+ },
+ "outputId": "3225133c-8424-4448-b9e7-060d4be8d739"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/50000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a88891844cfa4394a813aa2b0d694e2e"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "def tokenize_function(examples):\n",
+ " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
+ "\n",
+ "\n",
+ "tokenized_datasets = dataset.map(tokenize_function, batched=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Notez que le tokenizer retourne deux éléments:\n",
+ "\n",
+ "- input_ids: the numbers representing the tokens in the text.\n",
+ "- attention_mask: indicates whether a token should be masked or not.\n",
+ "\n",
+ "Plus d'info sur les datasets: https://huggingface.co/docs/datasets/use_dataset"
+ ],
+ "metadata": {
+ "id": "ATFZVbiYwD34"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenized_datasets"
+ ],
+ "metadata": {
+ "id": "TKTi2eO8d-JJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "65c8bd08-ed5d-49b9-e359-f7b9407a61fb"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
+ " num_rows: 25000\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
+ " num_rows: 25000\n",
+ " })\n",
+ " unsupervised: Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
+ " num_rows: 50000\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2-5 Entraînement / Fine-tuning\n",
+ "\n",
+ "Pour l'entraînement du modèle, on définit d'abord\n",
+ "- une configuration via la classe *TrainingArguments*.\n",
+ "- un niveau de 'verbosité'\n",
+ "- une métrique d'évaluation"
+ ],
+ "metadata": {
+ "id": "HYws35k8xCq0"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import TrainingArguments, Trainer\n",
+ "training_args = TrainingArguments(output_dir=\"test_trainer\",\n",
+ " no_cuda=False, # sur ordi perso sans bon GPU\n",
+ " per_device_train_batch_size=4,\n",
+ " #evaluation_strategy=\"steps\",\n",
+ " #eval_steps=100,\n",
+ " num_train_epochs=5,\n",
+ " do_eval=True )"
+ ],
+ "metadata": {
+ "id": "uLVIKxZcgOpb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JUtftrdy50ev"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers.utils import logging\n",
+ "\n",
+ "logging.set_verbosity_error()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "F8O_Jmcx50ew",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "11410c8a1cad460a877761a7e969e232",
+ "2406f898bb4a4883866edd04681ad290",
+ "8344f213ba8b4ba8a1e390676148257a",
+ "330ad13239d14547bab232d1410c331f",
+ "d3003cd2881548f5a43e2a126259fac6",
+ "c10fa01d7da64519a4957b6253869f89",
+ "7909d7b2804f4ee9a66392709810ae9c",
+ "bf02ef8b380e49f19ee643b4a3059495",
+ "788d02f5a017487296e9018e7c6c4191",
+ "d93815cfe96c4ab3acb8338480a1a70c",
+ "4db4ef43604f4e62847d333406e43d92"
+ ]
+ },
+ "outputId": "e6226974-be63-416b-fc43-ea092b8d01e2"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading builder script: 0%| | 0.00/4.20k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "11410c8a1cad460a877761a7e969e232"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "metric = evaluate.load(\"accuracy\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UZk65ZKH50ew"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_metrics(eval_pred):\n",
+ " logits, labels = eval_pred\n",
+ " predictions = np.argmax(logits, axis=-1)\n",
+ " return metric.compute(predictions=predictions, references=labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Trainer\n",
+ "\n",
+ "Une instance de la classe *Trainer* correspond à une boucle d'entraînement classique, basée sur les éléments définis précédemment.\n",
+ "\n",
+ "https://huggingface.co/docs/transformers/main_classes/trainer"
+ ],
+ "metadata": {
+ "id": "8FEJYEhDxoCp"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "On va sélectionner un sous-ensemble des données ici, pour que l'entraînement soit un peu moins long."
+ ],
+ "metadata": {
+ "id": "4QUvGEbOvRTH"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Dgfoqbx950eu"
+ },
+ "outputs": [],
+ "source": [
+ "small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(1000))\n",
+ "small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "uX2nBPnk50ew"
+ },
+ "outputs": [],
+ "source": [
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=small_train_dataset,\n",
+ " eval_dataset=small_eval_dataset,\n",
+ " compute_metrics=compute_metrics,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Lancer l'entraînement\n",
+ "\n",
+ "Et on peut lancer l'entraînement en utilisant la méthode *train()*."
+ ],
+ "metadata": {
+ "id": "GhGLiCEVx-8v"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IN58_eaV50ex",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 190
+ },
+ "outputId": "42d5e344-1173-408f-c2be-7cdf86885d93"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='1250' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [1250/1250 04:27, Epoch 5/5]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Step</th>\n",
+ " <th>Training Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>500</td>\n",
+ " <td>0.435900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1000</td>\n",
+ " <td>0.062700</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1250, training_loss=0.20384187240600585, metrics={'train_runtime': 269.3626, 'train_samples_per_second': 18.562, 'train_steps_per_second': 4.641, 'total_flos': 662336993280000.0, 'train_loss': 0.20384187240600585, 'epoch': 5.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 25
+ }
+ ],
+ "source": [
+ "import os\n",
+ "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
+ "trainer.train( )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2-6 Evaluation"
+ ],
+ "metadata": {
+ "id": "MgJpr49WySMd"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### Evaluation sur un exemple\n",
+ "\n",
+ "On teste le modèle sur un exemple de l'ensemble d'évaluation."
+ ],
+ "metadata": {
+ "id": "2bE7kBlEH4es"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "ex_eval = small_eval_dataset[1][\"text\"]\n",
+ "input = tokenizer(ex_eval, return_tensors=\"pt\")\n",
+ "input_ids = input.input_ids.to(\"cuda\")\n",
+ "print(input_ids.shape)\n",
+ "output = model(input_ids)\n",
+ "\n",
+ "print(\"gold\", small_eval_dataset[1][\"label\"])\n",
+ "\n",
+ "print(output)"
+ ],
+ "metadata": {
+ "id": "uyky-X_bzGpS",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8669999b-9cb5-45ee-d15c-1f9c6f5041be"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "torch.Size([1, 240])\n",
+ "gold 1\n",
+ "SequenceClassifierOutput(loss=None, logits=tensor([[-3.1219, 3.9657]], device='cuda:0', grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "output[\"logits\"]"
+ ],
+ "metadata": {
+ "id": "JDcpli3k2d_f",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8d91d24b-dee0-4672-d776-cd098f269288"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "tensor([[-3.1219, 3.9657]], device='cuda:0', grad_fn=<AddmmBackward0>)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 27
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pred = np.argmax(output[\"logits\"].cpu().detach().numpy(), axis=-1)\n",
+ "print(\"Pred\", pred)"
+ ],
+ "metadata": {
+ "id": "3DeTwx2oz-Ek",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b965007b-dee0-4a20-f72a-8fc93c9eb402"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Pred [1]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(tokenizer.tokenize(ex_eval))"
+ ],
+ "metadata": {
+ "id": "HsgQ6Ekd21IP",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "41c35ef1-e854-42b8-b613-56244ba408c8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "['this', 'is', 'the', 'latest', 'entry', 'in', 'the', 'long', 'series', 'of', 'films', 'with', 'the', 'french', 'agent', ',', 'o', '.', 's', '.', 's', '.', '117', '(', 'the', 'french', 'answer', 'to', 'james', 'bond', ')', '.', 'the', 'series', 'was', 'launched', 'in', 'the', 'early', '1950', \"'\", 's', ',', 'and', 'spawned', 'at', 'least', 'eight', 'films', '(', 'none', 'of', 'which', 'was', 'ever', 'released', 'in', 'the', 'u', '.', 's', '.', ')', '.', \"'\", 'o', '.', 's', '.', 's', '.', '117', ':', 'cairo', ',', 'nest', 'of', 'spies', \"'\", 'is', 'a', 'bree', '##zy', 'little', 'comedy', 'that', 'should', 'not', '.', '.', '.', 'repeat', 'not', ',', 'be', 'taken', 'too', 'seriously', '.', 'our', 'protagonist', 'finds', 'himself', 'in', 'the', 'middle', 'of', 'a', 'spy', 'chase', 'in', 'egypt', '(', 'with', 'mor', '##ro', '##co', 'doing', 'stand', 'in', 'for', 'egypt', ')', 'to', 'find', 'out', 'about', 'a', 'long', 'lost', 'friend', '.', 'what', 'follows', 'is', 'the', 'standard', 'james', 'bond', '/', 'inspector', 'cl', '##ous', '##so', '##u', 'kind', 'of', 'antics', '.', 'although', 'our', 'man', 'is', 'something', 'of', 'an', 'over', '##t', 'x', '##eno', '##ph', '##obe', ',', 'sex', '##ist', ',', 'homo', '##ph', '##obe', ',', 'it', \"'\", 's', 'treated', 'as', 'pure', 'far', '##ce', '(', 'as', 'i', 'said', ',', 'don', \"'\", 't', 'take', 'it', 'too', 'seriously', ')', '.', 'although', 'there', 'is', 'a', 'bit', 'of', 'rough', 'language', '&', 'cartoon', 'violence', ',', 'it', \"'\", 's', 'basically', 'okay', 'for', 'older', 'kids', '(', 'ages', '12', '&', 'up', ')', '.', 'as', 'previously', 'stated', 'in', 'the', 'subject', 'line', ',', 'just', 'sit', 'back', ',', 'pass', 'the', 'popcorn', '&', 'just', 'enjoy', '.']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### ▶▶ Exercice : Analyse d'erreurs\n",
+ "\n",
+ "Affichez les exemples sur lesquels le modèle a fait une erreur de prédiction.\n",
+ "Pour chaque exemple, affichez le label gold, le label prédit et le texte de l'exemple correspondant.\n",
+ "\n",
+ "\n",
+ "Note: aidez vous de la doc de Trainer https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "A-cx4sdZGcz2"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# --- correction\n",
+ "if training_args.do_eval:\n",
+ " prob_labels,_,_ = trainer.predict( test_dataset=small_eval_dataset)\n",
+ " pred_labels = [ np.argmax(logits, axis=-1) for logits in prob_labels ]\n",
+ " #print( pred_labels)\n",
+ " gold_labels = [ inst[\"label\"] for inst in small_eval_dataset]\n",
+ "\n",
+ " for i in range( len( small_eval_dataset ) ):\n",
+ " ## -- Print pred, gold\n",
+ " #print(pred_labels[i], gold_labels[i])\n",
+ " if pred_labels[i] != gold_labels[i]:\n",
+ " print(i, gold_labels[i], pred_labels[i], small_eval_dataset[i][\"text\"] )"
+ ],
+ "metadata": {
+ "id": "L9phpmPnII-O",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 298
+ },
+ "outputId": "18dbb137-59a1-4d74-b8ec-f6419cd4a530"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": []
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "17 0 1 A holiday on a boat, a married couple, an angry waiter and a shipwreck is the reason to this films beginning.<br /><br />I like boobs. No question about that. But when the main character allies with whoever happens to have the most fish at the moment, mostly by having sex with them and playing the role of the constant victim, my anger just rises to a whole new level. Take two guys (a husband and another man), put a pure bombshell woman in the middle of them, ad a deserted island, subtract all her moral issues, ad a whole bunch of moral issues to the men and mix it in a big bowl of arguments, fish and a zippo lighter and you will come up with a piece of junk movie like this. <br /><br />The acting is, I would say, good. There are some bloopers but not many as far as i could see. The main female character makes me sick. This is due to her lack of moral values. The man with the most fish get's her attention. Even though one of them is her husband, she sees no problem with being unfaithful with (Manuel) the other man because \"I must do it to survive\". How can you justify having sex with another man for fish when your husband is 30feet away? And he won't even benefit from it? The female character has absolutely no problems to justify anything that she does. If she doesen't get approval for her actions, she's a victim.<br /><br />I recommend everyone to see this movie. This is the kind of movie that will make just about everything else you see this year a pleasant movie experience.\n",
+ "21 0 1 Coming from Kiarostami, this art-house visual and sound exposition is a surprise. For a director known for his narratives and keen observation of humans, especially children, this excursion into minimalist cinematography begs for questions: Why did he do it? Was it to keep him busy during a vacation at the shore? <br /><br />\"Five, 5 Long Takes\" consists of, you guessed it, five long takes. They are (the title names are my own and the times approximate): <br /><br />\"Driftwood and waves\". The camera stands nearly still looking at a small piece of driftwood as it gets moved around by small waves splashing on a beach. Ten minutes.<br /><br />\"Watching people on the boardwalk\". The camera stands still looking at the ocean horizon and a boardwalk. People walk across the camera frame, their faces too far and blurry to make them interesting. Eleven minutes.<br /><br />\"Six dogs at the water's edge\". The camera stands still looking at the ocean horizon with a sandy stretch of beach nearby. Far away at the water's edge, six dogs not doing much, just relaxing. Sixteen minutes.<br /><br />\"Ducks in line, gaggle of ducks\". The camera stands still looking at the ocean horizon near the water's edge. Dozen and dozen of ducks stream in single file from left to right. I assume that Kiarostami released them gradually. The last two ducks stop dead on their track and suddenly a gaggle of ducks rolls quietly from right to left. I assume Kiarostami collected the ducks and re-released all at the same time. It is not the first time that he deals with the contrast between organized and disorganized behavior. Eight minutes.<br /><br />\"Frog symphony, oops, I mean cacophony, for a stormy night\". The camera stands over a pond at night. It's pitch black except for what appears to be the reflection of the moon on the undulating water. It is a stormy night and clouds race to cover the moon. The screen goes dark. What remains for us is the cacophony of frogs, howling dogs and, eventually, morning roosters. Hit me on the head if this was done in a single take. I saw this segment as a sound composition put together in the editing room and accompanied by a simple visualization. Twenty seven minutes! <br /><br />Except for the mildly amusing ducks, this exercise in minimalism left me cold. A nonessential film for Kiarostami admirers.<br /><br />I thought I would rate \"Five\" a five, but four is what it deserves.<br /><br />The film is dedicated to Yasujiru Ozu.\n",
+ "30 0 1 Intended as light entertainment, this film is indeed successful as such during its first half, but then succumbs to a rapidly foundering script that drops it down. Harry (Judd Nelson), a \"reformed\" burglar, and Daphne (Gina Gershon), an aspiring actress, are employed as live window mannequins at a department store where one evening they are late in leaving and are locked within, whereupon they witness, from their less than protective glass observation point, an apparent homicide occurring on the street. The ostensible murderer, Miles Raymond (Nick Mancuso), a local sculptor, returns the following day to observe the mannequins since he realizes that they are the only possible witnesses to the prior night's violent event and, when one of the posing pair \"flinches\", the fun begins. Daphne and Harry report their observations at a local police station, but when the detective taking a crime report remembers Harry's criminal background, he becomes cynical. There are a great many ways in which a film can become hackneyed, and this one manages to utilize most of them, including an obligatory slow motion bedroom scene of passion. A low budget affair shot in Vancouver, even police procedural aspects are displayed by rote. The always capable Gershon tries to make something of her role, but Mancuso is incredibly histrionic, bizarrely so, as he attacks his lines with an obvious loose rein. Although the film sags into nonsense, cinematographer Glen MacPherson prefers to not follow suit, as he sets up with camera and lighting some splendidly realised compositions that a viewer may focus upon while ignoring plot holes and witless dialogue. A well-crafted score, appropriately based upon the action, is contributed by Hal Beckett. The mentioned dialogue is initially somewhat fresh and delivered well in a bantering manner by Nelson and Gershon, but in a subsequent context of flawed continuity and logic, predictability takes over. The direction reflects a lack of original ideas or point of view, and post-production flaws set the work back farther than should be expected for a basic thriller.\n",
+ "32 1 0 It's really too bad that nobody knows about this movie. I think if it were just spruced up a little and if it weren't so low-budget, I think one of the major film companies might have wanted to take it. I first saw this movie when I was 11, and I thought it was so powerful with the many great, yet illegal lengths that Mitchell goes to just to keep his family together. It inspired me then and it amazes me now. If you're lucky enough to find a copy of this movie, don't miss it!\n",
+ "34 0 1 \"An astronaut (Michael Emmet) dies while returning from a mission and his body is recovered by the military. The base where the dead astronaut is taken to becomes the scene of a bizarre invasion plan from outer space. Alien embryos inside the dead astronaut resurrect the corpse and begin a terrifying assault on the military staff in the hopes of conquering the world,\" according to the DVD sleeve's synopsis.<br /><br />A Roger Corman \"American International\" production. The man who fell to Earth impregnated, Mr. Emmet (as John Corcoran), does all right. Angela Greene is his pretty conflicted fiancée. And, Ed Nelson (as Dave Randall) is featured as prominently. With a bigger budget, better opening, and a re-write for crisper characterizations, this could have been something approaching classic 1950s science fiction.<br /><br />*** Night of the Blood Beast (1958) Bernard L. Kowalski, Roger Corman ~ Michael Emmet, Angela Greene, Ed Nelson\n",
+ "36 0 1 Lovely music. Beautiful photography, some of scenes are breathtaking and affecting. But the dramatic tension is lost in a film that is so poorly edited it is hard to know what exactly is going on. At times, the dialogue is incomprehensible. Then there is Richard Gere. He's supposed to be a factory worker who gets into trouble and gets work on a farm. We see dozens of farmhands sweaty and dirty in the hot sun. Then we see Gere, looking like he just wandered away from a Calvin Klein ad. Sam Shepard, another glamour guy, is supposed to be terminally ill. But he looks great. Nice try, but it just doesn't work. Brook Adams try hard but she gets lost in the scenery.The real star is the girl.\n",
+ "38 0 1 A very sensitive topic--15 y/o girl abandoned by mother as a baby and who goes to visit her, continues to be ignored, is raped by her mom's boyfriend, becomes pregnant. There was not enough depth displayed of this situation. Too much of time is taken up on the chase with the truckers transporting the baby. (Interesting, this baby with asthma--you never see him cry-- except once--, be fed, have is diaper changed during the whole truck transport ordeal.) I would have liked to have seen more of the interrelationships, more focus on the fact that this girl was a minor--this should have stood up in court immediately.<br /><br />And this was a true story! It deserved a better telling than that!!<br /><br />If it weren't for the subject matter, I would have given this closer to a 0 rating. I rented this from the library. Only later I found out it was a made for TV movie. <br /><br />oh well\n",
+ "39 0 1 This is about some vampires (who can run around out in the sunlight), that are causing some problems down in South America. Casper Van Dien is sent in with his team of commandos to investigate. The movie opens with Van Dien & Co. walking through the jungle, and there's this huge black guy who just absolutely, positively cannot act. He speaks all his lines as if he's reading them off the cue-cards for the very first time. His voice is also so low that, well, it's positively hilarious. Great way to get the movie started! Anyhow, they run into some of our vampires, shoot them (this causes them to appear to die for about 20 seconds), and then of course they come back to life. Van Dien notices that one of them was impaled across a tree limb, and yells to his buddies to kill them with wood. The stunt work must be seen to be believed - the vampires are on wires that pull them up trees, which is supposed to make them look like they can climb really easily, but it just makes them look like they're bouncing around on bungee cords or something.<br /><br />Yeah...anyhow, later on, the huge black dude is down in South America with some guys (Van Dien not included), and they're attacked by more vampires. It's really too bad these guys never heard of a crossbow, because it would seem to be the perfect weapon to kill the little bloodsuckers with, but instead they use big old wooden stakes that they try to impale the vampires with by hand. The big black dude ends up getting captured and he eventually becomes some big powerful vampire leader. Van Dien ends up battling him later on. It doesn't help that all through the movie, everyone forgets that if you shoot a vampire, they are knocked out for 20 seconds or so, which would enable a person to stick a stake in them fairly easily. They just try to stick stakes in them in the middle of hand-to-hand combat. Yeah, not exactly brilliant tactics.<br /><br />There's a hot babe (remember Veronica from The Lost World TV show? Yes, it's her!) who also happens to be walking around in the middle of Vampire County on some sort of research mission, and she also just happens to be Van Dien's ex-wife. Hey, what are the odds? It's a shame she's not in the movie a whole lot more than she is. Will her and Casper get back together in the end? Will Van Dien defeat the huge black dude who can't act? Will the circus performer vampires make you laugh through all the numerous action scenes? Will we hear the three stooges music when somebody does something funny? Has even Lynda Carter forgotten how to act in her small cameo (she's more convincing in her Sleep Number Bed commercials)? These questions and more will will be answered if you make it all the way to the end of the movie.<br /><br />I don't know, it might score some points on the so bad it's good scale, but that's about it. Eh, it's a bunch of goofs running around in the jungle, I guess it's kind of entertaining.\n",
+ "44 0 1 This thing, directed by french sensation Patrick Sebastien,is worst than all the turkeys that you may have seen. Forget Independence Day, Kazaam, The patriot, etc... you get the picture, this one's the pits. Sebastien is a TV celebrity in france, (if you need an equivalent, let's say he's Jerry Springer with an I.Q of 13), this is his first movie let's hope it's the last. I hope Troma or someone would distribute this film in the States, so that you guys out there can excperience the French stench at it's worst. Let's cut this short, this is the Masterpiece of S**t.\n",
+ "55 1 0 I remember seeing this film in the mid 80's thought it a well paced and well acted piece. I now work quite often in Berkeley Square and the had to get a copy of DVD to remind myself how little the area has changed, although my office is newish it just 30 seconds away from \"the bank\". Even Jack Barclays car dealership is still there selling Bentleys and Rolls Royces.<br /><br />It's look like the DVD is due a Region 2 release soon. The region 1 copy I is very poor quality. Let's hope they've cleaned it up.<br /><br />Only the slightly dodgy escape sequence from the court spoils what would otherwise be a great film but I guess is in line with the caper tag the film goes with.\n",
+ "59 1 0 Sex, drugs, racism and of course you ABC's. What more could you want in a kid's show!<br /><br />------------------------------------------- -------------------------------------------<br /><br />\"User Comment Guidelines <br /><br />Please note there is a 1,000 word limit on comments. The recommended length is 200 to 500 words. The minimum length for comments is 10 lines of text. Comments which are too short or have been padded with junk text will be discarded. You may only post a single comment per title. <br /><br />What to include: Your comments should focus on the title's content and context. The best reviews include not only whether you liked or disliked a movie or TV-series, but also why. Feel free to mention other titles you consider similar and how this one rates in comparison to them. Comments that are not specific to the title will not be posted on our site. Please write in English only and note that we do not support HTML mark-up within the comments\"\n",
+ "74 0 1 I saw this movie, and I do like horror movies.<br /><br />I did not know what to expect, but as soon the movie was on his way it was nice to watch it. The idea was pretty original and the acting was nice. Especially Jenna Dewan as the exciting/evil Tamara.<br /><br />The hardest thing about horror movies, is to make a good ending. But there the movie failed. For a change, a end-scene in a hospital, where suddenly all employees are gone. First you see doctors and nurses running around, but then they all went home?<br /><br />No cries for help while being chased by Tamara, Escaping to the roof (also a smart move...not) and off course a kind of open ending.<br /><br />No....the movie started great, the main part was nice to watch, but they really messed up the ending using all clichés from bad horror movies. Jeffrey Reddick failed in my eyes with this movie, after making some really quality movies like Final Destination 1 and 2.<br /><br />If you like a good horror full of cliché endings, Tamara is a good movie to watch. For me, I like movies which surprise me.\n",
+ "82 0 1 It has a bit of that indie queer edge that was hip in the 90s and which places an explicit sell-by date on the visual style. Characters are uniformly apathetic and farcically deadpan. Street hoodlums in Greece wear new clothing out of the box without creases or stains. They all appear to visit the same marine hair dresser. All uniformly exhibit the same low IQ when making their dispassionate underground business deals. When things go wrong its all because they aren't real Greeks - they're pastoral sunshine boys caught in a strange night city world. Makes a big whine about disaffected immigrants but never bothers to actually investigate the problems with Russian/Kazakh/Albanian cultures. If Giannaris had the proper perspective on this project it might have made a wonderful Bel Ami production. The fleeting glimpses of toned boy-beef is the only spark in this generic small-time mobster programmer.\n",
+ "94 0 1 Clearly this film was made for a newer generation that may or may not have had an inkling of Charles Bukowski's work. The autobiographical Henry Chinaski character in Bukowski's stories was brilliantly portrayed to perfection by Mickey Rourke in 1987's 'Barfly', also starring Faye Dunaway. Anyone who has seen 'Factotum' should certainly see 'Barfly' to get a better look at how Bukowski wrote his character. 'Factotum' lacks the greasy seediness of Bukowski's screenplay and the fearless hopelessness of his loner hero. The inadvertent humor that bubbles through in the dark desperation of Chinaski's misadventures doesn't work for Dillon as it did so admirably for the overweight filthy blood-soaked Rourke. Rourke's character makes the pain and pleasure of the previous night's misbehavior a place-setting for yet another grueling ugly day in the life of a drunken misanthropic unknown writer. Dillon's character misses these marks in favor of a strutting, handsome, relatively clean-looking wanna-be writer that scarcely passes for any moment in that of Chinaski's story. Dunaway's sleazy heroine Wanda is the perfect complement to the ne'er-do-well Henry. The women in 'Factotum' can't hold a candle to Dunaway's 'distressed goddess' and the use of more profane sexual subject matter in 'Factotum' proves to be more of a crude distraction than a tip of the hat to Bukowski's raw and unapologetic portrayals of dysfunctional relationships. I was stunned at how many of the exact same scenes were used in 'Factotum' (Marisa Tomei buying all the stuff and charging it to the old man is an exact rip-off from 'Barfly').<br /><br />If you want to see the best Bukowski stories on film, see 'Barfly' and 'Love is a Dog From Hell' (which also goes by the title 'Crazy Love').\n",
+ "98 1 0 There are few films that deal with things that I would consider myself an expert on, this one is.<br /><br />After some years of Fantasy Role Playing we split, me not leaving without a sense of shame of what I had become: a dork.<br /><br />You see, these things are really canonical, it happens to everybody.<br /><br />First you create a character fairly and it dies after the first attack.<br /><br />Then you help a little with the constitution, and while you're at it, why not help with strength, intelligence, intuition, charisma and dexterity too? This in turn frustrates the game master who doesn't know how to deal with this invincible gang. And after a while it bores the players too, so they start to create ever more exotic race-profession combinations, no matter how ludicrous it is.<br /><br />I created a Druedain warrior monk, yeah, not that far from the film.<br /><br />And that's not all to be said about the destructiveness of the inherent dynamic of this devilish game (think the hunt for experience points), but just watch the film, it shows it all - and of course the stupidity of its most basic premisses.<br /><br />For this end, in turn, there is no better profession than the bard. I don't exactly understand why the bard became a character in the first place, after all, the blacksmith is none. But once it became one, it had to be mapped into the game flow, that is: it had to be made lethal, at least indirectly. The poking of fun out of this never comes to an end and rightfully so.<br /><br />Sure, it's not exactly a professional production, but I haven't seen a better satire in ages.\n"
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diff --git "a/notebooks/TP9_m2LiTL_transformers_explicabilit\303\251__2324.ipynb" "b/notebooks/TP9_m2LiTL_transformers_explicabilit\303\251__2324.ipynb"
new file mode 100644
index 0000000..a68c245
--- /dev/null
+++ "b/notebooks/TP9_m2LiTL_transformers_explicabilit\303\251__2324.ipynb"
@@ -0,0 +1,933 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-bb49S7B50eh"
+ },
+ "source": [
+ "# TP 9: Transformers, explicabilité et biais\n",
+ "\n",
+ "Dans cette séance, nous verrons comment analyser les prédictions du modèle pour comprendre les résultats/analyser les erreurs et chercher les biais éventuels du modèle lié aux données d'entrainement (de la tâche ou du modèle préentrainé)\n",
+ "\n",
+ "Nous nous intéresserons encore à la tâche d'analyse de sentiments, sur les données françaises AlloCine et anglaises IMDB.\n",
+ "Il s'agit d'une tâche de classification de séquences de mots.\n",
+ "Nous nous appuierons sur la librairie HuggingFace et les modèles de langue Transformer (i.e. BERT). \n",
+ "- https://huggingface.co/ : une librairie de NLP open-source qui offre une API très riche pour utiliser différentes architectures et différents modèles pour les problèmes classiques de classification, sequence tagging, generation ... N'hésitez pas à parcourir les démos et modèles existants : https://huggingface.co/tasks/text-classification\n",
+ "- Un assez grand nombre de jeux de données est aussi accessible directement via l'API, pour le texte ou l'image notamment cf les jeux de données https://huggingface.co/datasets et la doc pour gérer ces données : https://huggingface.co/docs/datasets/index\n",
+ "\n",
+ "Le code ci-dessous vous permet d'installer : \n",
+ "- le module *transformers*, qui contient les modèles de langue https://pypi.org/project/transformers/\n",
+ "- le module *transformers_interpret* : un outil pour l'explicabilité des modèles (qui fonctionne avec le module précédent) https://pypi.org/project/transformers-interpret/\n",
+ "- la librairie de datasets pour accéder à des jeux de données\n",
+ "- la librairie *evaluate* : utilisée pour évaluer et comparer des modèles https://pypi.org/project/evaluate/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9UoSnFV250el"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -U transformers\n",
+ "!pip install accelerate -U\n",
+ "!pip install transformers_interpret\n",
+ "!pip install datasets\n",
+ "!pip install evaluate\n",
+ "#%pip install -U sklearn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Finally, if the installation is successful, we can import the transformers library:"
+ ],
+ "metadata": {
+ "id": "StClx_Hh9PDm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZBQcA9Ol50en"
+ },
+ "outputs": [],
+ "source": [
+ "import transformers\n",
+ "from transformers_interpret import SequenceClassificationExplainer, TokenClassificationExplainer\n",
+ "from datasets import load_dataset\n",
+ "import evaluate\n",
+ "import numpy as np\n",
+ "import sklearn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3TIXCS5P50en"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "from transformers import AutoModelForTokenClassification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vCLf1g8z50ep"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pds\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 1: Transformers pipeline\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "uGZBOXpTXA72"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import pipeline"
+ ],
+ "metadata": {
+ "id": "Od8TVRnQJ8TH"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.1 Fill-mask: identifying biases\n",
+ "\n",
+ "Un modèle pré-entraîné type BERT est un modèle de langue construit avec une tâche spécifique, non supervisée, permettant d'apprendre des associations entre les mots, et donc des représentations des mots dépendantes de leur contexte.\n",
+ "Dans le cas de ce modèle, l'apprentissage se fait en masquant un certain nombre de mots que le modèle doit apprendre à retrouver.\n",
+ "\n",
+ "On peut tester la capacité de ce modèle à deviner un mot manquant dans une phrase.\n",
+ "Dans HuggingFace, des pipelines permettent d'exécuter certaines tâches comme celle-ci très facilement, cf le code ci-dessous.\n",
+ "\n",
+ "https://huggingface.co/docs/transformers/main_classes/pipelines"
+ ],
+ "metadata": {
+ "id": "mgZLir27AJhe"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### ▶▶ **Exercice : fill-mask** \n",
+ "- Faire tourner le code ci-dessous et vérifier que vous comprenez la sortie affichée.\n",
+ "- Est-ce que les sorties proposées font sens à vos yeux ?"
+ ],
+ "metadata": {
+ "id": "HwRF_nyRiH2I"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Chosing the pre-trained model\n",
+ "# - distilBERT: specific, faster and lighter version of BERT\n",
+ "# - base vs large\n",
+ "# - uncased: ignore upper case\n",
+ "base_model = \"distilbert-base-uncased\""
+ ],
+ "metadata": {
+ "id": "DztvpOSXNIrx"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker = pipeline('fill-mask', model=base_model)\n",
+ "unmasker(\"Hello I'm a [MASK] model.\")"
+ ],
+ "metadata": {
+ "id": "Rz3VKNRWxZVK"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 1.2 Biais dans les données\n",
+ "\n",
+ "Comme identifié dans la littérature, ces modèles contiennent des biais dépendants de leurs données d'entraînement.\n",
+ "\n",
+ "- Article e.g. *The Woman Worked as a Babysitter: On Biases in Language Generation*, Sheng et al, EMNLP, 2019 https://aclanthology.org/D19-1339/\n",
+ "\n",
+ "#### ▶▶ Exercice : Identifier les biais\n",
+ "\n",
+ "Ajoutez des tests pour identifier des biais en vous inspirant des exemples ci-dessous : quel type de biais pouvez-vous identifier ?\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "txdDbcvAiYGv"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The woman worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "McGZfdLFVfd7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The man with a college degree worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "djn2WiRdi-vL"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### --- CORRECTION"
+ ],
+ "metadata": {
+ "id": "95TRIipye0aF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "hpeu_rHdY1Bo"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 2 - Transfert / fine-tuning : analyse de sentiment\n",
+ "\n",
+ "Comme vu dans le TP précédent, entrainez / fine-tunez un modèle de classification de sentiments à partir des données du corpus IMDb."
+ ],
+ "metadata": {
+ "id": "HUx1kHH8eUjE"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.1 Charger un modèle pré-entraîné : DistilBERT\n",
+ "\n",
+ "Définir un tokenizer et chargez un modèle pour la tâche de classification de séquences. Vous utiliserez le modèle de base pré-entraîné DistilBERT.\n",
+ "\n",
+ "- distilBERT: https://huggingface.co/distilbert-base-uncased\n",
+ "- Les *Auto Classes*: https://huggingface.co/docs/transformers/model_doc/auto\n",
+ "- Les Tokenizer dans HuggingFace: https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/tokenizer\n",
+ "- *Bert tokenizer*: https://huggingface.co/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertTokenizer\n",
+ "- Classe *PreTrainedTokenizerFast*: https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast\n"
+ ],
+ "metadata": {
+ "id": "c40x3RDbB3Qo"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "v80gjCzARYqh"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "IBY-P4iiRddM"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.2 Load new data for transfer\n",
+ "\n",
+ "On charge ici l'ensemble de données IMDB."
+ ],
+ "metadata": {
+ "id": "8lt8MjqYIZCl"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "sdac6kcTSNFi"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "7B8LTmuJSNFk"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.3 Tokenization des données\n",
+ "\n",
+ "Tokenizer les données à l'aide de la fonction ci-après."
+ ],
+ "metadata": {
+ "id": "SbjUad2-tecl"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-Kj0bW3_50et"
+ },
+ "outputs": [],
+ "source": [
+ "def tokenize_function(examples):\n",
+ " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "UmG9HWXZSeaK"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "eGpk8DnfShQ7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2.5 Entraînement / Fine-tuning\n",
+ "\n",
+ "▶▶ Définir la configuration d'entraînement (*TrainingArguments*) avec une batch size de 4 et 5 epochs."
+ ],
+ "metadata": {
+ "id": "HYws35k8xCq0"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import TrainingArguments, Trainer\n",
+ "from transformers.utils import logging\n",
+ "\n",
+ "logging.set_verbosity_error()\n",
+ "\n",
+ "metric = evaluate.load(\"accuracy\")\n",
+ "\n",
+ "def compute_metrics(eval_pred):\n",
+ " logits, labels = eval_pred\n",
+ " predictions = np.argmax(logits, axis=-1)\n",
+ " return metric.compute(predictions=predictions, references=labels)\n",
+ "\n",
+ "# training_args = ..."
+ ],
+ "metadata": {
+ "id": "6F38e50_Su6G"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "x0FNMImhSu6D"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "_MHYOi2mZSNk"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Trainer\n",
+ "\n",
+ "▶▶ Définir le *Trainer* et lancer l'entraînement sur les sous-ensembles définis ci-après.\n",
+ "\n",
+ "https://huggingface.co/docs/transformers/main_classes/trainer"
+ ],
+ "metadata": {
+ "id": "8FEJYEhDxoCp"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "On va sélectionner un sous-ensemble des données ici, pour que l'entraînement soit un peu moins long."
+ ],
+ "metadata": {
+ "id": "4QUvGEbOvRTH"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Dgfoqbx950eu"
+ },
+ "outputs": [],
+ "source": [
+ "small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(1000))\n",
+ "small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "f2ba3SdeTS_V"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "s_60B32WTS_Y"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Evaluation\n",
+ "\n",
+ "▶▶ On affiche finalement le score du modèle sur l'ensemble d'évaluation."
+ ],
+ "metadata": {
+ "id": "VaBD1-jaoR3w"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "if training_args.do_eval:\n",
+ " metrics = trainer.evaluate(eval_dataset=small_eval_dataset)\n",
+ " print(metrics)"
+ ],
+ "metadata": {
+ "id": "3IdSk-1XHiVK"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "La fonction ci-après affiche les erreurs du modèle."
+ ],
+ "metadata": {
+ "id": "JBsYrp1_ZvXt"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "if training_args.do_eval:\n",
+ " prob_labels,_,_ = trainer.predict( test_dataset=small_eval_dataset)\n",
+ " pred_labels = [ np.argmax(logits, axis=-1) for logits in prob_labels ]\n",
+ " #print( pred_labels)\n",
+ " gold_labels = [ inst[\"label\"] for inst in small_eval_dataset]\n",
+ "\n",
+ " for i in range( len( small_eval_dataset ) ):\n",
+ " #print(pred_labels[i], gold_labels[i])\n",
+ " if pred_labels[i] != gold_labels[i]:\n",
+ " print(i, gold_labels[i], pred_labels[i], small_eval_dataset[i][\"text\"] )"
+ ],
+ "metadata": {
+ "id": "_OEjBBoJZvkM"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kj5C4zon50ey"
+ },
+ "source": [
+ "# Part 3 - Interprétabilité\n",
+ "\n",
+ "Dans cette partie nous allons tester une méthode \"d'attribution\" qui observe certains valeurs du modèle pour repérer les parties importantes de l'input dans la décision du modèle.\n",
+ "\n",
+ "Nous utiliserons le package *transformers_interpret*, qui est une surcouche de la librairie plus générale *captum*.\n",
+ "\n",
+ "- Captum library: https://captum.ai/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rKUWY_xh50ey"
+ },
+ "source": [
+ "## 3.1 Classification de phrases: sentiment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "L_90kDt150ey"
+ },
+ "outputs": [],
+ "source": [
+ "# pour utiliser un modèle existant répertorié sur huggingface.co\n",
+ "#model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+ "#model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
+ "#tokenizer = AutoTokenizer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### ▶▶ Exercice : Afficher les attributions pour un exemple correctement prédit\n",
+ "\n",
+ "Utiliser le *cls_explainer* défini ci-dessous pour afficher les attributions pour chaque mot pour :\n",
+ "- un exemple correctement prédit (récupérer un exemple à partir de son indice à partir de l'exercice précédent)\n",
+ "- un exemple correspondant à une erreur du modèle\n",
+ "\n",
+ "Utilisez eégalement la fonction de visualisation des attributions.\n",
+ "\n",
+ "Aidez-vous de l'exemple sur cette page : https://pypi.org/project/transformers-interpret/"
+ ],
+ "metadata": {
+ "id": "xUh2_lqxho0n"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6EbVZpow50ez"
+ },
+ "outputs": [],
+ "source": [
+ "cls_explainer = SequenceClassificationExplainer(\n",
+ " model,\n",
+ " tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "2UHYc10giO8p"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# récupérer un exemple / le texte correctement predit\n"
+ ],
+ "metadata": {
+ "id": "hRnH27AOiFp1"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Recuperer les attributions\n",
+ "# word_attributions = ...\n"
+ ],
+ "metadata": {
+ "id": "E-lWGvF45gcJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GxCWlucU50ez"
+ },
+ "outputs": [],
+ "source": [
+ "# Afficher les attributions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Visualisation\n",
+ "\n",
+ "Le code ci-après vous permet de visualiser les attributions pour un exemple."
+ ],
+ "metadata": {
+ "id": "LLYt2uH7pUuX"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "0mmp7RCi50e0"
+ },
+ "outputs": [],
+ "source": [
+ "table = pds.DataFrame(word_attributions,columns=[\"tokens\",\"score\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GP_QnEAf50e0"
+ },
+ "outputs": [],
+ "source": [
+ "table.iloc[::-1].plot(x=\"tokens\",y=\"score\",kind=\"barh\",figsize=(15,15))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5I9SdaWY50e0"
+ },
+ "outputs": [],
+ "source": [
+ "html = cls_explainer.visualize()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### ▶▶ Exercice : Afficher les attributions pour un exemple mal prédit\n",
+ "\n",
+ "Recommencer les étapes précédentes pour un exemple correspondant à une erreur du système."
+ ],
+ "metadata": {
+ "id": "IMOLP2uCpf2V"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "-----------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "uNKwytwuaGVa"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "jcuY25soaGhI"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### ▶▶ Exercice : chercher les termes corrélés à chaque classe\n",
+ "\n",
+ "Le code suivant permet de chercher les termes corrélés à chaque classe, via les étpaes suivantes :\n",
+ "- Appliquer le modèle appris sur l'éval de imdb\n",
+ "- Appliquer l'interprétation sur un ensemble d'instances (100 puis 1000) et relever les termes avec les attributions les plus fortes, dans un sens ou dans l'autre. Réduisez la taille des phrases des reviews à 30 tokens.\n",
+ "- Trouvez les éventuels biais du jeu de données\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "23--_RYHjq-e"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def get_topk(attributions,k=5,threshold=None):\n",
+ " \"\"\"recup des k tokens les plus positifs + k tokens les plus négatifs\"\"\"\n",
+ " table = pds.DataFrame(word_attributions,columns=[\"tokens\",\"score\"])\n",
+ " high = table.nlargest(k,\"score\")\n",
+ " low = table.nsmallest(k,\"score\")\n",
+ " return high,low"
+ ],
+ "metadata": {
+ "id": "G4cN9FVNumeH"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "get_topk(word_attributions)"
+ ],
+ "metadata": {
+ "id": "waGGZz-3wSVg"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def cut_sentence(sent,threshold):\n",
+ " toks = sent.split()[:threshold]\n",
+ " return \" \".join(toks)\n",
+ "\n",
+ "one = small_eval_dataset[0][\"text\"]\n",
+ "cut_sentence(one,50)"
+ ],
+ "metadata": {
+ "id": "EjKCi-pvxN_a"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "maxseqlength = 30\n",
+ "small_eval_dataset_text = [cut_sentence(one[\"text\"],maxseqlength) for one in small_eval_dataset]"
+ ],
+ "metadata": {
+ "id": "CzVhne2S5typ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "all_pos = []\n",
+ "all_neg = []\n",
+ "\n",
+ "for sentence in tqdm(small_eval_dataset_text[:100]):\n",
+ " word_attributions = cls_explainer(sentence)\n",
+ " label = cls_explainer.predicted_class_name\n",
+ " high,low = get_topk(word_attributions)\n",
+ " if label == \"LABEL_1\":\n",
+ " all_pos.append(high)\n",
+ " else:\n",
+ " all_neg.append(high)\n"
+ ],
+ "metadata": {
+ "id": "hP28_7GwuC23"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_high = pds.concat(all_pos)\n",
+ "df_low = pds.concat(all_neg)\n",
+ "df_high"
+ ],
+ "metadata": {
+ "id": "Kp0V1zKl6TNo"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_high_avg = df_high.groupby(\"tokens\").mean()\n",
+ "df_low_avg = df_low.groupby(\"tokens\").mean()"
+ ],
+ "metadata": {
+ "id": "becpniSG6jDr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_high_avg.nlargest(20,\"score\")"
+ ],
+ "metadata": {
+ "id": "BRh3UR5Y61nX"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_low_avg.nlargest(20,\"score\")"
+ ],
+ "metadata": {
+ "id": "szxynupe7LBV"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-XRoTAe-50e1"
+ },
+ "source": [
+ "## 3.2 Classification de tokens : entités nommées"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### ▶▶ Exercice : Explication de modèle de reconnaissance d'entités nommées\n",
+ "\n",
+ "On définit ci-dessous un modèle de reconnaissance d'entités nommées.\n",
+ "Utilisez l'outil d'explicabilité pour une tâche de classification de token, et affichez les attributions pour un exemple."
+ ],
+ "metadata": {
+ "id": "jwvarY88mHD4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "s881ijMF50e1"
+ },
+ "outputs": [],
+ "source": [
+ "model_name = 'dslim/bert-base-NER'\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model_name)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gIBA1tnP50e1"
+ },
+ "outputs": [],
+ "source": [
+ "ner_explainer = TokenClassificationExplainer(model=model, tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "D7f05OD550e1"
+ },
+ "outputs": [],
+ "source": [
+ "instance = \"New-York City is a place full of celebrities, like Donald Trump.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "v2SnhLchauK4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "UxDnS0T-auVe"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "visual",
+ "language": "python",
+ "name": "visual"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.5"
+ },
+ "colab": {
+ "provenance": [],
+ "collapsed_sections": [
+ "-XRoTAe-50e1"
+ ],
+ "toc_visible": true
+ },
+ "accelerator": "GPU",
+ "gpuClass": "standard"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/slides/MasterLiTL_2324_Course6_010224 .pdf b/slides/MasterLiTL_2324_Course6_010224 .pdf
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--
GitLab