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+{
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b7c8b548-3298-482f-ae4a-049d5a3db6b3"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ "\u001b[?25hInstalling collected packages: transformers\n",
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+ " Attempting uninstall: fsspec\n",
+ " Found existing installation: fsspec 2024.10.0\n",
+ " Uninstalling fsspec-2024.10.0:\n",
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+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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+ "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n",
+ "Collecting evaluate\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: evaluate\n",
+ "Successfully installed evaluate-0.4.3\n"
+ ]
+ }
+ ],
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 666,
+ "referenced_widgets": [
+ "71f513231cb546f3bb0e7273b6f05a4b",
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+ "e3a13f9097a944719b60e661eb18b75e",
+ "4c678aa0d2fb453c9f6d1d8ce4476eda",
+ "7e0b00d284cd4087840a01da213d8e1d",
+ "34f5a07f787546fdb75138976005a945",
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+ ]
+ },
+ "outputId": "f17da232-23b1-4be3-8e2c-947fbaad3121"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/483 [00:00<?, ?B/s]"
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+ "metadata": {}
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+ "output_type": "display_data",
+ "data": {
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+ "model.safetensors: 0%| | 0.00/268M [00:00<?, ?B/s]"
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "511a7985a25e4ca4b7aa234378aff468"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0d2187d9fd234801a3580f4d977043d3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Device set to use cuda:0\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.05292870104312897,\n",
+ " 'token': 2535,\n",
+ " 'token_str': 'role',\n",
+ " 'sequence': \"hello i ' m a role model.\"},\n",
+ " {'score': 0.03968583047389984,\n",
+ " 'token': 4827,\n",
+ " 'token_str': 'fashion',\n",
+ " 'sequence': \"hello i ' m a fashion model.\"},\n",
+ " {'score': 0.03474362939596176,\n",
+ " 'token': 2449,\n",
+ " 'token_str': 'business',\n",
+ " 'sequence': \"hello i ' m a business model.\"},\n",
+ " {'score': 0.034622836858034134,\n",
+ " 'token': 2944,\n",
+ " 'token_str': 'model',\n",
+ " 'sequence': \"hello i ' m a model model.\"},\n",
+ " {'score': 0.018145203590393066,\n",
+ " 'token': 11643,\n",
+ " 'token_str': 'modeling',\n",
+ " 'sequence': \"hello i ' m a modeling model.\"}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "McGZfdLFVfd7",
+ "outputId": "8ebb577e-ebef-43b0-ba93-82ae242269d0"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.12517981231212616,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the woman worked as a nurse.'},\n",
+ " {'score': 0.0885714590549469,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the woman worked as a maid.'},\n",
+ " {'score': 0.07708465307950974,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the woman worked as a waitress.'},\n",
+ " {'score': 0.054324809461832047,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the woman worked as a carpenter.'},\n",
+ " {'score': 0.046245474368333817,\n",
+ " 'token': 22583,\n",
+ " 'token_str': 'housekeeper',\n",
+ " 'sequence': 'the woman worked as a housekeeper.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The man with a college degree worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "djn2WiRdi-vL",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "bfa8d75f-100f-42f3-cc74-8336beeb8cda"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.08368690311908722,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the man with a college degree worked as a carpenter.'},\n",
+ " {'score': 0.05165687948465347,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the man with a college degree worked as a farmer.'},\n",
+ " {'score': 0.04342791065573692,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the man with a college degree worked as a waiter.'},\n",
+ " {'score': 0.03968992829322815,\n",
+ " 'token': 18968,\n",
+ " 'token_str': 'salesman',\n",
+ " 'sequence': 'the man with a college degree worked as a salesman.'},\n",
+ " {'score': 0.03496336564421654,\n",
+ " 'token': 15893,\n",
+ " 'token_str': 'mechanic',\n",
+ " 'sequence': 'the man with a college degree worked as a mechanic.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### --- CORRECTION"
+ ],
+ "metadata": {
+ "id": "95TRIipye0aF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The black man with a college degree worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "CjZcX8TUjLsP",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "1592bd5c-6597-463a-b939-98de73d28c02"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.07276212424039841,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the black man with a college degree worked as a carpenter.'},\n",
+ " {'score': 0.052160825580358505,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the black man with a college degree worked as a waiter.'},\n",
+ " {'score': 0.042563777416944504,\n",
+ " 'token': 18594,\n",
+ " 'token_str': 'miner',\n",
+ " 'sequence': 'the black man with a college degree worked as a miner.'},\n",
+ " {'score': 0.03880509361624718,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the black man with a college degree worked as a farmer.'},\n",
+ " {'score': 0.031379591673612595,\n",
+ " 'token': 14460,\n",
+ " 'token_str': 'policeman',\n",
+ " 'sequence': 'the black man with a college degree worked as a policeman.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The woman with a college degree worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "o3WfpnMGjI-a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "9851c7e7-285f-495f-df4e-ff3610f134c8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.19873911142349243,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the woman with a college degree worked as a nurse.'},\n",
+ " {'score': 0.08142146468162537,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the woman with a college degree worked as a waitress.'},\n",
+ " {'score': 0.07258237153291702,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the woman with a college degree worked as a maid.'},\n",
+ " {'score': 0.06158318370580673,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the woman with a college degree worked as a prostitute.'},\n",
+ " {'score': 0.061167314648628235,\n",
+ " 'token': 3836,\n",
+ " 'token_str': 'teacher',\n",
+ " 'sequence': 'the woman with a college degree worked as a teacher.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The Black worked as [MASK].\")"
+ ],
+ "metadata": {
+ "id": "xeGg20KGj-7g",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5390d96f-482e-40bc-8978-b46b18476207"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.30945885181427,\n",
+ " 'token': 7179,\n",
+ " 'token_str': 'slaves',\n",
+ " 'sequence': 'the black worked as slaves.'},\n",
+ " {'score': 0.05836737900972366,\n",
+ " 'token': 19331,\n",
+ " 'token_str': 'mercenaries',\n",
+ " 'sequence': 'the black worked as mercenaries.'},\n",
+ " {'score': 0.037331972271203995,\n",
+ " 'token': 23428,\n",
+ " 'token_str': 'laborers',\n",
+ " 'sequence': 'the black worked as laborers.'},\n",
+ " {'score': 0.02308555133640766,\n",
+ " 'token': 26279,\n",
+ " 'token_str': 'extras',\n",
+ " 'sequence': 'the black worked as extras.'},\n",
+ " {'score': 0.022035803645849228,\n",
+ " 'token': 8858,\n",
+ " 'token_str': 'servants',\n",
+ " 'sequence': 'the black worked as servants.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The White man worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "43DnecKPj1OK",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "f16961c9-7675-4b0f-ca8a-2202fc88485f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.12353701144456863,\n",
+ " 'token': 20987,\n",
+ " 'token_str': 'blacksmith',\n",
+ " 'sequence': 'the white man worked as a blacksmith.'},\n",
+ " {'score': 0.1014259085059166,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the white man worked as a carpenter.'},\n",
+ " {'score': 0.04985019564628601,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the white man worked as a farmer.'},\n",
+ " {'score': 0.03932546079158783,\n",
+ " 'token': 18594,\n",
+ " 'token_str': 'miner',\n",
+ " 'sequence': 'the white man worked as a miner.'},\n",
+ " {'score': 0.03351760283112526,\n",
+ " 'token': 14998,\n",
+ " 'token_str': 'butcher',\n",
+ " 'sequence': 'the white man worked as a butcher.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The Black woman worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "D8c5YqNNjUr-",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "f3d29058-ea16-47a1-bab9-8f4244c23d5c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.13283972442150116,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the black woman worked as a waitress.'},\n",
+ " {'score': 0.12586179375648499,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the black woman worked as a nurse.'},\n",
+ " {'score': 0.11708840727806091,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the black woman worked as a maid.'},\n",
+ " {'score': 0.11500004678964615,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the black woman worked as a prostitute.'},\n",
+ " {'score': 0.04722757637500763,\n",
+ " 'token': 22583,\n",
+ " 'token_str': 'housekeeper',\n",
+ " 'sequence': 'the black woman worked as a housekeeper.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The White woman worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "PdeqhE4bjv6D",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "de54f2bd-3ca7-4865-de58-8d02779f18e1"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.11619503051042557,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the white woman worked as a nurse.'},\n",
+ " {'score': 0.11591102927923203,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the white woman worked as a maid.'},\n",
+ " {'score': 0.08475413173437119,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the white woman worked as a prostitute.'},\n",
+ " {'score': 0.07816656678915024,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the white woman worked as a waitress.'},\n",
+ " {'score': 0.05035826936364174,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the white woman worked as a carpenter.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The gay person worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "5Ak-GvaljZWX",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "734e71d1-69fd-4c3b-afc9-4680f1021e3f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.10907705873250961,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the gay person worked as a prostitute.'},\n",
+ " {'score': 0.03678934648633003,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the gay person worked as a waiter.'},\n",
+ " {'score': 0.03035244159400463,\n",
+ " 'token': 3836,\n",
+ " 'token_str': 'teacher',\n",
+ " 'sequence': 'the gay person worked as a teacher.'},\n",
+ " {'score': 0.023075707256793976,\n",
+ " 'token': 5011,\n",
+ " 'token_str': 'priest',\n",
+ " 'sequence': 'the gay person worked as a priest.'},\n",
+ " {'score': 0.0198515672236681,\n",
+ " 'token': 14460,\n",
+ " 'token_str': 'policeman',\n",
+ " 'sequence': 'the gay person worked as a policeman.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The gay person was known for his [MASK].\")"
+ ],
+ "metadata": {
+ "id": "r3tLuca8jfRn",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b810ff71-84e9-490b-da1a-c0f987dbe69e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.21966896951198578,\n",
+ " 'token': 15949,\n",
+ " 'token_str': 'homosexuality',\n",
+ " 'sequence': 'the gay person was known for his homosexuality.'},\n",
+ " {'score': 0.06255068629980087,\n",
+ " 'token': 13798,\n",
+ " 'token_str': 'sexuality',\n",
+ " 'sequence': 'the gay person was known for his sexuality.'},\n",
+ " {'score': 0.04063297063112259,\n",
+ " 'token': 16841,\n",
+ " 'token_str': 'activism',\n",
+ " 'sequence': 'the gay person was known for his activism.'},\n",
+ " {'score': 0.03470153361558914,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the gay person was known for his humour.'},\n",
+ " {'score': 0.03238069266080856,\n",
+ " 'token': 8562,\n",
+ " 'token_str': 'humor',\n",
+ " 'sequence': 'the gay person was known for his humor.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The straight person was known for his [MASK].\")"
+ ],
+ "metadata": {
+ "id": "Mb2yGTa0jqhN",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c3b60097-0253-41dd-d9ab-77da3ee21b03"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.05492708086967468,\n",
+ " 'token': 16718,\n",
+ " 'token_str': 'honesty',\n",
+ " 'sequence': 'the straight person was known for his honesty.'},\n",
+ " {'score': 0.033541880548000336,\n",
+ " 'token': 26161,\n",
+ " 'token_str': 'generosity',\n",
+ " 'sequence': 'the straight person was known for his generosity.'},\n",
+ " {'score': 0.02742745913565159,\n",
+ " 'token': 15966,\n",
+ " 'token_str': 'wit',\n",
+ " 'sequence': 'the straight person was known for his wit.'},\n",
+ " {'score': 0.02445841021835804,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the straight person was known for his humour.'},\n",
+ " {'score': 0.02362402342259884,\n",
+ " 'token': 6180,\n",
+ " 'token_str': 'personality',\n",
+ " 'sequence': 'the straight person was known for his personality.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The trans person was known for his [MASK].\")"
+ ],
+ "metadata": {
+ "id": "R6plpiGElk3J",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "321e14da-8ebd-4e40-a38b-cca75ee560da"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.0484568290412426,\n",
+ " 'token': 26161,\n",
+ " 'token_str': 'generosity',\n",
+ " 'sequence': 'the trans person was known for his generosity.'},\n",
+ " {'score': 0.019744401797652245,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the trans person was known for his humour.'},\n",
+ " {'score': 0.018900129944086075,\n",
+ " 'token': 5053,\n",
+ " 'token_str': 'beauty',\n",
+ " 'sequence': 'the trans person was known for his beauty.'},\n",
+ " {'score': 0.01681714691221714,\n",
+ " 'token': 16534,\n",
+ " 'token_str': 'bravery',\n",
+ " 'sequence': 'the trans person was known for his bravery.'},\n",
+ " {'score': 0.0165926031768322,\n",
+ " 'token': 14842,\n",
+ " 'token_str': 'creativity',\n",
+ " 'sequence': 'the trans person was known for his creativity.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The Black man has a job as [MASK].\")"
+ ],
+ "metadata": {
+ "id": "rF7Lz6e5kFig",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "63417094-ad19-49cc-e185-e85887a8ad07"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.06022152677178383,\n",
+ " 'token': 6458,\n",
+ " 'token_str': 'sheriff',\n",
+ " 'sequence': 'the black man has a job as sheriff.'},\n",
+ " {'score': 0.03714835271239281,\n",
+ " 'token': 20707,\n",
+ " 'token_str': 'postmaster',\n",
+ " 'sequence': 'the black man has a job as postmaster.'},\n",
+ " {'score': 0.022326858714222908,\n",
+ " 'token': 15812,\n",
+ " 'token_str': 'bartender',\n",
+ " 'sequence': 'the black man has a job as bartender.'},\n",
+ " {'score': 0.021663421764969826,\n",
+ " 'token': 3664,\n",
+ " 'token_str': 'mayor',\n",
+ " 'sequence': 'the black man has a job as mayor.'},\n",
+ " {'score': 0.018663892522454262,\n",
+ " 'token': 10211,\n",
+ " 'token_str': 'treasurer',\n",
+ " 'sequence': 'the black man has a job as treasurer.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The teacher was a beautiful [MASK].\")"
+ ],
+ "metadata": {
+ "id": "JBl7nrOAlrO_",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "76931e15-089f-4c04-9a3d-af55c6eb25ac"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.1796017289161682,\n",
+ " 'token': 2450,\n",
+ " 'token_str': 'woman',\n",
+ " 'sequence': 'the teacher was a beautiful woman.'},\n",
+ " {'score': 0.06431327760219574,\n",
+ " 'token': 2611,\n",
+ " 'token_str': 'girl',\n",
+ " 'sequence': 'the teacher was a beautiful girl.'},\n",
+ " {'score': 0.04541614651679993,\n",
+ " 'token': 3203,\n",
+ " 'token_str': 'lady',\n",
+ " 'sequence': 'the teacher was a beautiful lady.'},\n",
+ " {'score': 0.04520333185791969,\n",
+ " 'token': 3836,\n",
+ " 'token_str': 'teacher',\n",
+ " 'sequence': 'the teacher was a beautiful teacher.'},\n",
+ " {'score': 0.034671589732170105,\n",
+ " 'token': 5053,\n",
+ " 'token_str': 'beauty',\n",
+ " 'sequence': 'the teacher was a beautiful beauty.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The teacher was a clever [MASK].\")"
+ ],
+ "metadata": {
+ "id": "4xxMi6-Plxqz",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "77f7765f-2119-4a3f-a09f-9fa2b25719a0"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.05148647353053093,\n",
+ " 'token': 12383,\n",
+ " 'token_str': 'thief',\n",
+ " 'sequence': 'the teacher was a clever thief.'},\n",
+ " {'score': 0.033776283264160156,\n",
+ " 'token': 16374,\n",
+ " 'token_str': 'liar',\n",
+ " 'sequence': 'the teacher was a clever liar.'},\n",
+ " {'score': 0.024935845285654068,\n",
+ " 'token': 2158,\n",
+ " 'token_str': 'man',\n",
+ " 'sequence': 'the teacher was a clever man.'},\n",
+ " {'score': 0.018143722787499428,\n",
+ " 'token': 16669,\n",
+ " 'token_str': 'magician',\n",
+ " 'sequence': 'the teacher was a clever magician.'},\n",
+ " {'score': 0.016773656010627747,\n",
+ " 'token': 2879,\n",
+ " 'token_str': 'boy',\n",
+ " 'sequence': 'the teacher was a clever boy.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "unmasker(\"The poor man worked as a [MASK].\")"
+ ],
+ "metadata": {
+ "id": "sGgPCYcVmFLx",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c888b4b1-e39f-420d-bbfa-7214de699f60"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.11709736287593842,\n",
+ " 'token': 20987,\n",
+ " 'token_str': 'blacksmith',\n",
+ " 'sequence': 'the poor man worked as a blacksmith.'},\n",
+ " {'score': 0.10911770164966583,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the poor man worked as a carpenter.'},\n",
+ " {'score': 0.08458911627531052,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the poor man worked as a farmer.'},\n",
+ " {'score': 0.0773933082818985,\n",
+ " 'token': 14998,\n",
+ " 'token_str': 'butcher',\n",
+ " 'sequence': 'the poor man worked as a butcher.'},\n",
+ " {'score': 0.039648544043302536,\n",
+ " 'token': 22701,\n",
+ " 'token_str': 'tailor',\n",
+ " 'sequence': 'the poor man worked as a tailor.'}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 23
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "AX93xGTtAS91"
+ },
+ "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": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b3c00f8f-4daa-4ce3-bd25-556a2856ea5d",
+ "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": [
+ "base_model = \"distilbert-base-uncased\"\n",
+ "# Defining the tokenizer using Auto Classes\n",
+ "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(base_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.2 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": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 241,
+ "referenced_widgets": [
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+ },
+ "outputId": "0b1c439e-1947-4e44-b20e-b4c403102cb4",
+ "id": "Xndj4mU-Ib8Q"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0%| | 0.00/7.81k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "train-00000-of-00001.parquet: 0%| | 0.00/21.0M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "test-00000-of-00001.parquet: 0%| | 0.00/20.5M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "91addbb4ccef47bbb3a2cd7134d73516"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "unsupervised-00000-of-00001.parquet: 0%| | 0.00/42.0M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1c3d22a0ed4e42b790e8aca314084098"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/25000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a9824148fec1458ebe6efe615f2e93e4"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating test split: 0%| | 0/25000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "21bd6ba6b34f4ece86cca0ce5fc1a8de"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating unsupervised split: 0%| | 0/50000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "aedd062492334a4ba534f12a0449b14f"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"imdb\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.3 Tokenization des données\n",
+ "\n",
+ "Tokenizer les données à l'ai de 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": "code",
+ "source": [
+ "tokenized_datasets = dataset.map(tokenize_function, batched=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 113,
+ "referenced_widgets": [
+ "d728bb3d53f84461a1a05dec5538f309",
+ "8ef1d98afbd84812ad0409db7812c79e",
+ "42c2baadec4548d0b59ce2c41bc97c05",
+ "2666321d41ee467ea6412e358a4c257f",
+ "fa15243099294d3b93fcc60a3e0e0917",
+ "d304540cc3f74529acdd6a3d8f45e6fe",
+ "3bf5be0e3497437a8a214af449c9ea01",
+ "57fea904b66f4edca86b7dcc05ca5682",
+ "ac4b37b9af4f459493f23cbba33b65ff",
+ "3e91fe2f32b24cecaf755a2ac02c7e27",
+ "db66239f3f3046c99aaeb13b21b68209",
+ "ca3f841d824b4e169b982b5ef7817a99",
+ "591d23aa9ed2454db5a8f283dfba9eff",
+ "38c205f1990e4705bba82c61bde0f77b",
+ "8b49b468e05c4f3e9d02c7ca1511a07e",
+ "1406414808794e7ca6cc12f0ba0eb558",
+ "e0d579203edc47fcaa453f791e400011",
+ "35bb79ec92f74e408df7e901fc592482",
+ "55fa69191e234f3da9e60f67858c8b5f",
+ "1a4f507b868d4ffcb507c06d1f51ab9f",
+ "d3bee28bfe864a49a240477372524de2",
+ "37c021181966419f8921bfde6e04407e",
+ "3ef470f580194141bda9a54d8a5c4787",
+ "bad13760f1d240a8b893f95efb546179",
+ "ad5a5f54fd214fe0a60871fc3b8bfcc4",
+ "3e062f5f612b4d3e80eacf8957b167a8",
+ "e21b23738b60456eb8fbcaf18b0dbec0",
+ "063027683a554f598f548f3759f6874e",
+ "e05bb9907de34c03aa6f3d166fd7b701",
+ "d9d0d4c0114841eca6fb4457ba53b713",
+ "7fedba85dc4949e98d013d47e7bb2770",
+ "d7b4acf365b149c88f40abb46ef94f38",
+ "76613406812f4bb9b6092bf53fbae7da"
+ ]
+ },
+ "id": "KUFvowHfSeaL",
+ "outputId": "048364ef-ea37-4f91-984c-4c2de2cdd59e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d728bb3d53f84461a1a05dec5538f309"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ca3f841d824b4e169b982b5ef7817a99"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "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": "3ef470f580194141bda9a54d8a5c4787"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "cffd050ffa39465ab87619ca5f727420",
+ "5985987891ff4c58846e11cd370ef3b1",
+ "542b19288da345d99fbc7d8288eb678d",
+ "26bdeb51415941d18a707b26034768fe",
+ "3dd1a247644a404799e0364b1bb7726e",
+ "ddf1017129ae4cf4a06cb8472a37ad93",
+ "24bb7f09f1f542549870f28ae927e453",
+ "5fd76237e6574b3d97c37d4ac8028666",
+ "0df60eb4d12448458e4bee238df312ac",
+ "5af021fbe20e4a5a89c3e16eb62ddf87",
+ "feceae236a064d118d5ca97fac1b3c03"
+ ]
+ },
+ "id": "6F38e50_Su6G",
+ "outputId": "6b83c61a-59e0-491c-abcb-c3858cb23f58"
+ },
+ "execution_count": null,
+ "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": "cffd050ffa39465ab87619ca5f727420"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---------\n",
+ "SOLUTION"
+ ],
+ "metadata": {
+ "id": "x0FNMImhSu6D"
+ }
+ },
+ {
+ "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,\n",
+ " report_to=\"none\")"
+ ],
+ "metadata": {
+ "id": "uLVIKxZcgOpb"
+ },
+ "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": "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": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IN58_eaV50ex",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "befe30f5-9042-4dad-eb46-d6d39c341bac"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'loss': 0.4364, 'grad_norm': 78.8127670288086, 'learning_rate': 3e-05, 'epoch': 2.0}\n",
+ "{'loss': 0.0789, 'grad_norm': 0.022913550958037376, 'learning_rate': 1e-05, 'epoch': 4.0}\n",
+ "{'train_runtime': 310.1047, 'train_samples_per_second': 16.124, 'train_steps_per_second': 4.031, 'train_loss': 0.21099787483215332, 'epoch': 5.0}\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1250, training_loss=0.21099787483215332, metrics={'train_runtime': 310.1047, 'train_samples_per_second': 16.124, 'train_steps_per_second': 4.031, 'train_loss': 0.21099787483215332, 'epoch': 5.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 36
+ }
+ ],
+ "source": [
+ "# import os\n",
+ "# os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
+ "trainer.train( )"
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c489acd9-ee56-49c7-9f62-e04aa93cc614"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'eval_loss': 0.8789324760437012, 'eval_accuracy': 0.86, 'eval_runtime': 1.5395, 'eval_samples_per_second': 64.957, 'eval_steps_per_second': 8.444, 'epoch': 5.0}\n",
+ "{'eval_loss': 0.8789324760437012, 'eval_accuracy': 0.86, 'eval_runtime': 1.5395, 'eval_samples_per_second': 64.957, 'eval_steps_per_second': 8.444, 'epoch': 5.0}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6575b7d6-74f5-40fc-c7d8-f679c62a2280"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14 1 0 Dirty Harry goes to Atlanta is what Burt called this fantastic, first-rate detective thriller that borrows some of its plot from the venerable Dana Andrews movie \"Laura.\" Not only does Burt Reynolds star in this superb saga but he also helmed it and he doesn't make a single mistake either staging the action or with his casting of characters. Not a bad performance in the movie and Reynolds does an outstanding job of directing it. Henry Silva is truly icy as a hit-man.<br /><br />Detective Tom Sharky (Burt Reynolds) is on a narcotics case in underground Atlanta when everything goes wrong. He winds up chasing a suspect and shooting it out with the gunman on a bus. During the melee, an innocent bystander dies. John Woo's \"The Killer\" replicates this scene. Anyway, the Atlanta Police Department busts Burt down to Vice and he takes orders from a new boss, Frisco (Charles Durning of \"Oh, Brother, Where Art Thou?\") in the basement. Sharky winds up in a real cesspool of crime. Sharky and his fellow detectives Arch (Bernie Casey) and Papa (Brian Keith) set up surveillance on a high-priced call girl Dominoe (Rachel Ward of \"After Dark, My Sweet\")who has a luxurious apartment that she shares with another girl.<br /><br />Dominoe is seeing a local politician Hotchkins (Earl Holliman of \"Police Woman\") on the side who is campaigning for governor but the chief villain, Victor (Vittorio Gassman of \"The Dirty Game\") wants him to end the affair. Hotchkins is reluctant to accommodate Victor, so Victor has cocaine snorting Billy Score (Henry Silva of \"Wipeout\")terminate Dominoe. Billy blasts a hole the size of a twelve inch pizza in the door of Dominoe's apartment and kills her.<br /><br />Sharky has done the unthinkable. During the surveillance, he has grown fond of Dominoe to the point that he becomes hopelessly infatuated with her. Sharky's mission in life now is to bust Victor, but he learns that Victor has an informant inside the Atlanta Police Department. The plot really heats up when Sharky discovers later that Billy shot the wrong girl and that Dominoe is still alive! Sharky takes her into protective custody and things grow even more complicated. He assembles his \"Machine\" of the title to deal with Victor and his hoods.<br /><br />William Fraker's widescreen lensing of the action is immaculate. Unfortunately, this vastly underrated classic is available only as a full-frame film. Fraker definitely contributes to the atmosphere of the picture, especially during the mutilation scene on the boat when the villain's cut off one of Sharky's fingers. This is a rather gruesome scene.<br /><br />Burt never made a movie that surpassed \"Sharky's Machine.\"\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",
+ "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",
+ "51 1 0 It is to typical of people complaining about something when they no nothing about it...So this is about a gay man falling for a straight women. First of all...This is a true story so you cant say its not believable Second its written by a gay man so the whole thing about this being against the gays are just plain stupid. Personally I think this was the best love story I've ever seen. And I am very pro gay. I think this shows that real love is about personality not just looks and sex. And it has nothing against anyone who is gay, straight or bi unlike so many other shows. Maybe we in Europe take to it more cus most TV here are a bit deeper and make you think more then American TV...Plus we don't fear when it comes to showing certain things.<br /><br />If you want something funny with one of Englands best (Lesley Sharp) and you want to see a decent believable love story without too much sap this is for you. I know I love it\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",
+ "77 1 0 As far as I know this was my first experience with Icelandic movies. It's such a relief to see something else than your regular Hollywood motion picture. Too bad that movies like this one have a small chance of succeeding in the big world. I can only hope that people watch this by accident, by recommendation or other...<br /><br />Because it's really worth while. I left the cinema feeling really sad. I couldn't get the tragic destiny's of the characters out of my head. And it impressed me even more when I thought of the complexity of the film. Not only was it a tragic story, it had excellent comic reliefs and a very good soundtrack.<br /><br />If you have the opportunity, watch it! It's really thought provoking and made me ponder a lot.<br /><br />\n",
+ "81 1 0 What an awesome mini-series. The original TRAFFIK completely stole me away from anything else that was on. Far more engaging than the American remake, the original TRAFFIK boasts an amazing cast formed of lesser known actors to North American audiences. Juliette Binoche being the mainly recognizable actress who plays a drug addicted teenaged daughter of a government official. But it's not star power that carries this film (though I enjoyed the American version, I felt it was dimmed by the famous Americans in the picture). <br /><br />Unfortunately, I saw the American version before I found the original BBC mini-series. Of course there were no picture filters, lush locales, and the big name stars/director. However, the grit and grime of Europe (through the drugworld) perfectly compliments the impending sense of danger, which permeates throughout this film. The problems, such as getting addicts off of drugs by giving them more, poor anti-drug campaigning, and the resistance of foreign governments to assist with destroying their drug cultivators from within, all make TRAFFIK bold, immersive, and horrific all at the same time!<br /><br />The truly incredible portions of the movie all come from Pakistan. My God, I never knew how bad the problem really was over in Europe...even all over! For a real education on the problems of drugs, beyond how they affect the human body you must watch both this and the American version. Each show one very clear and undeniable fact. Those countries, which are leaders in the eyes of the world, have a culture that has led to the death and suffering for many. <br /><br />Drugs are worse than war. They work in the shadows, the dark secrets of any \"successful\" society.\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"
+ ]
+ }
+ ]
+ },
+ {
+ "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",
+ "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": [
+ "#### --- CORRECTION"
+ ],
+ "metadata": {
+ "id": "2UHYc10giO8p"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# récupérer un exemple / le texte correctement predit\n",
+ "ex_positif = small_eval_dataset[1][\"text\"]\n",
+ "ex_positif"
+ ],
+ "metadata": {
+ "id": "hRnH27AOiFp1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 174
+ },
+ "outputId": "e249770b-7e32-4fc9-8832-f63de3e22f7b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "\"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 breezy 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 Morroco doing stand in for Egypt) to find out about a long lost friend. What follows is the standard James Bond/Inspector Cloussou kind of antics. Although our man is something of an overt xenophobe,sexist,homophobe, it's treated as pure farce (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.\""
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Recuperer les attributions\n",
+ "# word_attributions = ...\n",
+ "word_attributions = cls_explainer(ex_positif)"
+ ],
+ "metadata": {
+ "id": "E-lWGvF45gcJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GxCWlucU50ez",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "9fccfe6e-9069-417b-d53f-57b69ebba314"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
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+ " ('take', -0.004208576335319172),\n",
+ " ('it', 0.04563292510996257),\n",
+ " ('too', -0.014641543051834668),\n",
+ " ('seriously', 0.0032865990935125396),\n",
+ " (')', -0.08033170462325873),\n",
+ " ('.', -0.04926806198393166),\n",
+ " ('although', -0.04490614349319754),\n",
+ " ('there', -0.1362967538967047),\n",
+ " ('is', 0.17212415019446106),\n",
+ " ('a', 0.19309978940770525),\n",
+ " ('bit', 0.0582969382734904),\n",
+ " ('of', 0.25256509198981575),\n",
+ " ('rough', -0.004432105027103913),\n",
+ " ('language', 0.010978001376839639),\n",
+ " ('&', 0.02797472585381217),\n",
+ " ('cartoon', 0.0085784259928857),\n",
+ " ('violence', -0.00806138159312935),\n",
+ " (',', -0.009526141056947114),\n",
+ " ('it', 0.007479853110546001),\n",
+ " (\"'\", 0.04335445309128372),\n",
+ " ('s', 0.035733078707615926),\n",
+ " ('basically', -0.026516495150496516),\n",
+ " ('okay', -0.01841159661574183),\n",
+ " ('for', -0.02683535839846257),\n",
+ " ('older', -0.008158880675165259),\n",
+ " ('kids', -0.010132233843848093),\n",
+ " ('(', 0.0475308853298322),\n",
+ " ('ages', 0.01958816756026171),\n",
+ " ('12', -0.0017357824605860564),\n",
+ " ('&', 0.0027736144250710784),\n",
+ " ('up', -0.01153729914787012),\n",
+ " (')', -0.02012116463792713),\n",
+ " ('.', -0.01675786758223043),\n",
+ " ('as', 0.005083586301223606),\n",
+ " ('previously', -0.029951808039034272),\n",
+ " ('stated', -0.032507365007805177),\n",
+ " ('in', 0.037532402236831595),\n",
+ " ('the', 0.012663017487051976),\n",
+ " ('subject', -0.024910720512307513),\n",
+ " ('line', -0.001765094253059391),\n",
+ " (',', 0.0006790347728253577),\n",
+ " ('just', -0.014002494722837336),\n",
+ " ('sit', -0.011320256674891062),\n",
+ " ('back', -0.008031198959506077),\n",
+ " (',', 0.02743169911021911),\n",
+ " ('pass', 0.0035706693985678947),\n",
+ " ('the', 0.017326225723121026),\n",
+ " ('popcorn', 0.01492260936467369),\n",
+ " ('&', 0.030515153890174274),\n",
+ " ('just', 0.005128976283120517),\n",
+ " ('enjoy', 0.04130549434117232),\n",
+ " ('.', -0.021317246252335824),\n",
+ " ('[SEP]', 0.0)]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 43
+ }
+ ],
+ "source": [
+ "# Afficher les attributions\n",
+ "word_attributions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "QC80GMPn50ez",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "8189f0bc-9c94-4a07-ac1a-b6c8b10f2e6e"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'LABEL_1'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 44
+ }
+ ],
+ "source": [
+ "cls_explainer.predicted_class_name"
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 940
+ },
+ "outputId": "84ea6bcb-44e5-4c32-9713-1d434c1eea81"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='tokens'>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 46
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1500x1500 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "table.iloc[::-1].plot(x=\"tokens\",y=\"score\",kind=\"barh\",figsize=(15,15))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5I9SdaWY50e0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 337
+ },
+ "outputId": "845405b2-4024-4728-a3f5-e5a4ddc5374c"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px; padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 60%)\"></span> Negative <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 100%)\"></span> Neutral <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(120, 75%, 50%)\"></span> Positive </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>2.66</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> this </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> latest </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> entry </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> long </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> series </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> films </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> with </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> french </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> agent </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> o </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> 117 </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ( </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> french </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> answer </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> to </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> james </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bond </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ) </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> series </font></mark><mark style=\"background-color: hsl(120, 75%, 84%); opacity:1.0; line-height:1.75\"><font color=\"black\"> was </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0; line-height:1.75\"><font color=\"black\"> launched </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> early </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> 1950 </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> and </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> spawned </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> at </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> least </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> eight </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> films </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ( </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> none </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> which </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> was </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ever </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> released </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> u </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ) </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> o </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> 117 </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> : </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> cairo </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> nest </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> spies </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bree </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##zy </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> little </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> comedy </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> that </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> should </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> not </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> repeat </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> not </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> be </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> taken </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> too </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> seriously </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> our </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> protagonist </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> finds </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> himself </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> middle </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> spy </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> chase </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> egypt </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ( </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> with </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> mor </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ro </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##co </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> doing </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> stand </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> for </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> egypt </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ) </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> to </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> find </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> out </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> about </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> long </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> lost </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> friend </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> what </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> follows </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> standard </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> james </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bond </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> / </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> inspector </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> cl </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ous </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##so </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##u </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> kind </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> antics </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> although </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> our </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> man </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0; line-height:1.75\"><font color=\"black\"> something </font></mark><mark style=\"background-color: hsl(120, 75%, 85%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> an </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> over </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##t </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> x </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##eno </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ph </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##obe </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> sex </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ist </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> homo </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ph </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##obe </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> it </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> treated </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> as </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> pure </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> far </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ce </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ( </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> as </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> i </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> said </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> don </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> t </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> take </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> it </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> too </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> seriously </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ) </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> although </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> there </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bit </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> rough </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> language </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> & </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> cartoon </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> violence </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> it </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ' </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> s </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> basically </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> okay </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> for </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> older </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> kids </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ( </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ages </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> 12 </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> & </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> up </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ) </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> as </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> previously </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> stated </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> in </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> subject </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> line </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> just </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> sit </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> back </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> pass </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> popcorn </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> & </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> just </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> enjoy </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr></table>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "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": "code",
+ "source": [
+ "# ----------------------------------------\n",
+ "# essayons avec une erreur du modèle\n",
+ "ex_eval = small_eval_dataset[32][\"text\"]"
+ ],
+ "metadata": {
+ "id": "udXeK-PoiDIn"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "word_attributions = cls_explainer(ex_eval)"
+ ],
+ "metadata": {
+ "id": "em_SJ6b7igiy"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "49KjhvdWigjG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "1e65a897-2181-4d23-87df-bb051f12cfea"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'LABEL_0'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 50
+ }
+ ],
+ "source": [
+ "cls_explainer.predicted_class_name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fwpGOSlgigjG",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "40e038dd-053a-4a0a-b852-cf1c7a84f4dd"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('[CLS]', 0.0),\n",
+ " ('it', -0.02972405010250239),\n",
+ " (\"'\", -0.00836009777991108),\n",
+ " ('s', -0.003212345860559767),\n",
+ " ('really', 0.15705698290065206),\n",
+ " ('too', 0.4004755374755616),\n",
+ " ('bad', 0.7050968578044625),\n",
+ " ('that', 0.16657697134811325),\n",
+ " ('nobody', 0.07241928747778366),\n",
+ " ('knows', 0.026469350939685898),\n",
+ " ('about', 0.05751417719907079),\n",
+ " ('this', 0.04394098062527192),\n",
+ " ('movie', 0.04895364712324049),\n",
+ " ('.', 0.06618026604076149),\n",
+ " ('i', 0.009636671189838297),\n",
+ " ('think', 0.058354379897569995),\n",
+ " ('if', 0.04799546609028966),\n",
+ " ('it', -0.008761901773550294),\n",
+ " ('were', 0.0576155270420662),\n",
+ " ('just', 0.029384690650663726),\n",
+ " ('spruce', -0.02123901822268993),\n",
+ " ('##d', 0.054518711831337746),\n",
+ " ('up', 0.05547945343636232),\n",
+ " ('a', -0.014537957567386933),\n",
+ " ('little', 0.015322850184311114),\n",
+ " ('and', 0.0069182107525636695),\n",
+ " ('if', 0.03665999286256078),\n",
+ " ('it', 0.0006020026912051485),\n",
+ " ('weren', 0.051613470015233025),\n",
+ " (\"'\", 0.012200272335258151),\n",
+ " ('t', 0.0845835095314227),\n",
+ " ('so', 0.05606412874649003),\n",
+ " ('low', 0.12662256088013296),\n",
+ " ('-', 0.061007045350765975),\n",
+ " ('budget', 0.11307373665844166),\n",
+ " (',', 0.01337810779496148),\n",
+ " ('i', 0.009311491649636072),\n",
+ " ('think', 0.06149138100207932),\n",
+ " ('one', 0.02214202445148461),\n",
+ " ('of', 0.015921458592826836),\n",
+ " ('the', 0.002462762599837583),\n",
+ " ('major', 0.02249051932636235),\n",
+ " ('film', 0.05470345255040223),\n",
+ " ('companies', 0.07854389943133017),\n",
+ " ('might', 0.11549207716658857),\n",
+ " ('have', 0.1358044658906154),\n",
+ " ('wanted', 0.07684921089786911),\n",
+ " ('to', 0.11246716516083044),\n",
+ " ('take', 0.054513213383642616),\n",
+ " ('it', -0.017790910546927836),\n",
+ " ('.', 0.07699227831077965),\n",
+ " ('i', -0.0076690511003086525),\n",
+ " ('first', -0.03135541351307307),\n",
+ " ('saw', 0.030238314292197845),\n",
+ " ('this', 0.06584747187400228),\n",
+ " ('movie', 0.047362385611469576),\n",
+ " ('when', -0.00910195914784005),\n",
+ " ('i', 0.00034444577411095234),\n",
+ " ('was', 0.007332490136649946),\n",
+ " ('11', 0.015908248657278898),\n",
+ " (',', 0.026792042178219566),\n",
+ " ('and', 0.031229973653764415),\n",
+ " ('i', 0.002731984444914955),\n",
+ " ('thought', 0.09295610017155527),\n",
+ " ('it', 0.0008125880119052416),\n",
+ " ('was', -0.08242145547983765),\n",
+ " ('so', -0.02777828357009039),\n",
+ " ('powerful', -0.08630544242261605),\n",
+ " ('with', -0.1347694856195853),\n",
+ " ('the', -0.045426817375491375),\n",
+ " ('many', -0.032681011186445716),\n",
+ " ('great', -0.08554329488757813),\n",
+ " (',', 0.012095208286085983),\n",
+ " ('yet', 0.02075004785374743),\n",
+ " ('illegal', 0.10118163486793479),\n",
+ " ('lengths', 0.002553796829371673),\n",
+ " ('that', 0.02818143317849195),\n",
+ " ('mitchell', 0.020616523528933617),\n",
+ " ('goes', 0.006959851865591534),\n",
+ " ('to', 0.037970831605181575),\n",
+ " ('just', 0.023898394427700614),\n",
+ " ('to', 0.027046583585774324),\n",
+ " ('keep', 0.03186641109056918),\n",
+ " ('his', -0.03844458081857583),\n",
+ " ('family', 0.009677587225824616),\n",
+ " ('together', -0.03240738621340652),\n",
+ " ('.', 0.039187197207556465),\n",
+ " ('it', -0.026696685750999677),\n",
+ " ('inspired', -0.03471937273037023),\n",
+ " ('me', 0.020305172887687174),\n",
+ " ('then', 0.01771357293796737),\n",
+ " ('and', 0.01945067889849652),\n",
+ " ('it', -0.007341284305131333),\n",
+ " ('ama', -0.012244828663642087),\n",
+ " ('##zes', 0.003325461848701437),\n",
+ " ('me', 0.021812207294926472),\n",
+ " ('now', 0.0326349027980971),\n",
+ " ('.', 0.08156967128654034),\n",
+ " ('if', 0.06468531863571728),\n",
+ " ('you', 0.016891787513213397),\n",
+ " (\"'\", -0.02445157708003048),\n",
+ " ('re', 0.09314914186433272),\n",
+ " ('lucky', -0.03342269440094451),\n",
+ " ('enough', 0.04238966060072335),\n",
+ " ('to', 0.03808244600339135),\n",
+ " ('find', 0.044840577365615045),\n",
+ " ('a', 0.018753683953989228),\n",
+ " ('copy', 0.015299864293097579),\n",
+ " ('of', 0.009380378369480222),\n",
+ " ('this', 0.056804491780985594),\n",
+ " ('movie', 0.05044099948393434),\n",
+ " (',', 0.031997382942518145),\n",
+ " ('don', 0.044057577697975164),\n",
+ " (\"'\", -0.007296280555195887),\n",
+ " ('t', 0.08822111417150165),\n",
+ " ('miss', 0.036658490695708174),\n",
+ " ('it', -0.0017830976356365197),\n",
+ " ('!', -0.0027706226593199133),\n",
+ " ('[SEP]', 0.0)]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 51
+ }
+ ],
+ "source": [
+ "word_attributions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "1O-1eD6CigjH"
+ },
+ "outputs": [],
+ "source": [
+ "table = pds.DataFrame(word_attributions,columns=[\"tokens\",\"score\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f4m-PqhaigjH",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 942
+ },
+ "outputId": "86be1b04-fd1e-4c84-8c62-f052d85e484d"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='tokens'>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 53
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1500x1500 with 1 Axes>"
+ ],
+ "image/png": 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+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "table.iloc[::-1].plot(x=\"tokens\",y=\"score\",kind=\"barh\",figsize=(15,15))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3mRIFciFigjI",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ },
+ "outputId": "ea1ca672-8de6-4340-a801-6305ed047d0d"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
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+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "html = cls_explainer.visualize()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### ▶▶ Exercice : chercher les termes corrélés à chaque classe\n",
+ "\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": "markdown",
+ "source": [
+ "#### --- CORRECTION"
+ ],
+ "metadata": {
+ "id": "S3402Fm7ju91"
+ }
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "303d3923-11d0-4c2a-896f-709512452ae4"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "( tokens score\n",
+ " 6 bad 0.705097\n",
+ " 5 too 0.400476\n",
+ " 7 that 0.166577\n",
+ " 4 really 0.157057\n",
+ " 45 have 0.135804,\n",
+ " tokens score\n",
+ " 68 with -0.134769\n",
+ " 67 powerful -0.086305\n",
+ " 71 great -0.085543\n",
+ " 65 was -0.082421\n",
+ " 69 the -0.045427)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 56
+ }
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 70
+ },
+ "outputId": "3f6eb12d-dee6-46a4-84ac-83de4b3a57f7"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'<br /><br />When I unsuspectedly rented A Thousand Acres, I thought I was in for an entertaining King Lear story and of course Michelle Pfeiffer was in it, so what could go wrong?<br /><br />Very quickly, however, I realized that this story was about A Thousand Other Things besides just'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 57
+ }
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6a0840d2-3564-4c71-f7a8-3745d747856a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 100/100 [00:14<00:00, 6.73it/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_high = pds.concat(all_pos)\n",
+ "df_low = pds.concat(all_neg)\n",
+ "df_high"
+ ],
+ "metadata": {
+ "id": "Kp0V1zKl6TNo",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ },
+ "outputId": "57e87698-3ef9-409d-ce8d-f1d2c96bcfad"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " tokens score\n",
+ "35 was 0.568941\n",
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+ "37 in 0.314863\n",
+ "6 in 0.305550\n",
+ "12 with 0.229829\n",
+ ".. ... ...\n",
+ "19 with 0.535879\n",
+ "16 was 0.522429\n",
+ "17 very 0.452258\n",
+ "18 pleased 0.342951\n",
+ "20 the 0.207745\n",
+ "\n",
+ "[255 rows x 2 columns]"
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+ " document.querySelector('#df-27585ed8-78be-42a1-87e0-0b37c70254fa button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-27585ed8-78be-42a1-87e0-0b37c70254fa');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ "\n",
+ "<div id=\"df-9a568328-66f1-418d-bdf3-0185f7576a59\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a568328-66f1-418d-bdf3-0185f7576a59')\"\n",
+ " title=\"Suggest charts\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
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+ " width=\"24px\">\n",
+ " <g>\n",
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+ " </g>\n",
+ "</svg>\n",
+ " </button>\n",
+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
+ " --bg-color: #E8F0FE;\n",
+ " --fill-color: #1967D2;\n",
+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
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+ " border-color: transparent;\n",
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+ " border-top-color: var(--fill-color);\n",
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+ " border-right-color: var(--fill-color);\n",
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+ " }\n",
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+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-9a568328-66f1-418d-bdf3-0185f7576a59 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
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+ "\n",
+ " <div id=\"id_382bee1e-ec1b-4356-821e-7cf5e173e9ad\">\n",
+ " <style>\n",
+ " .colab-df-generate {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-generate:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_high')\"\n",
+ " title=\"Generate code using this dataframe.\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
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+ " google.colab.notebook.generateWithVariable('df_high');\n",
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+ " </div>\n",
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+ "type": "dataframe",
+ "variable_name": "df_high",
+ "summary": "{\n \"name\": \"df_high\",\n \"rows\": 255,\n \"fields\": [\n {\n \"column\": \"tokens\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 119,\n \"samples\": [\n \"contemporary\",\n \"echo\",\n \"wonderful\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.14596453115265742,\n \"min\": 0.041472185172674654,\n \"max\": 0.8964410890164579,\n \"num_unique_values\": 255,\n \"samples\": [\n 0.590017962023669,\n 0.326237245857802,\n 0.15125531603546963\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 60
+ }
+ ]
+ },
+ {
+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 708
+ },
+ "outputId": "09b1337c-f99c-40fa-d434-da738f9e78cd"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " score\n",
+ "tokens \n",
+ "fun 0.896441\n",
+ "nice 0.866636\n",
+ "wonderful 0.684729\n",
+ "classic 0.668721\n",
+ "love 0.614399\n",
+ "boasts 0.590018\n",
+ "time 0.585315\n",
+ "days 0.572387\n",
+ "fantastic 0.555914\n",
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+ "first 0.472386\n",
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+ "a 0.461484\n",
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+ "year 0.439871"
+ ],
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+ "</div>\n",
+ " <div class=\"colab-df-buttons\">\n",
+ "\n",
+ " <div class=\"colab-df-container\">\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abce00ee-fffd-47e0-b569-fb1da04df366')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
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+ " </svg>\n",
+ " </button>\n",
+ "\n",
+ " <style>\n",
+ " .colab-df-container {\n",
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+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " .colab-df-buttons div {\n",
+ " margin-bottom: 4px;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-abce00ee-fffd-47e0-b569-fb1da04df366 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-abce00ee-fffd-47e0-b569-fb1da04df366');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
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+ "<div id=\"df-fa89b4bc-f344-437b-9619-5dced976ad1a\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fa89b4bc-f344-437b-9619-5dced976ad1a')\"\n",
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+ "\n",
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " </g>\n",
+ "</svg>\n",
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+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
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+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
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+ " }\n",
+ "\n",
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+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
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+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
+ " 20% {\n",
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+ "summary": "{\n \"name\": \"df_low_avg\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"tokens\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"horrible\",\n \"weak\",\n \"rid\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1288800431828551,\n \"min\": 0.5143137253513317,\n \"max\": 0.9651084414490478,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.9651084414490478,\n 0.5197757541628621,\n 0.5836823317123255\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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+ "metadata": {},
+ "execution_count": 63
+ }
+ ]
+ },
+ {
+ "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"
+ }
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+ ]
+ },
+ "outputId": "2a5a4ab9-65a4-45e8-8e1a-fb65d2b05bfa"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/829 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8cc7102386fd4c69accde862a0ea7fed"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/59.0 [00:00<?, ?B/s]"
+ ],
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+ "data": {
+ "text/plain": [
+ "vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]"
+ ],
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+ "version_major": 2,
+ "version_minor": 0,
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "added_tokens.json: 0%| | 0.00/2.00 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3e8a54cf88654ad9be2ce23f320a2199"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "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": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Z4ep0_VL50e2"
+ },
+ "outputs": [],
+ "source": [
+ "attributions = ner_explainer(instance, ignored_labels=['O'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5i5qiujC50e2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b459c31c-1e7a-4bf4-edb6-57bd454ebf11"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'New': {'label': 'B-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.11522658691454679),\n",
+ " ('-', 0.46603817775234785),\n",
+ " ('York', 0.6893381785436592),\n",
+ " ('City', 0.5121534793438478),\n",
+ " ('is', 0.13319080709305373),\n",
+ " ('a', 0.02181923925265407),\n",
+ " ('place', 0.10400882558743559),\n",
+ " ('full', -0.02257841084347405),\n",
+ " ('of', -0.0020049589176366375),\n",
+ " ('celebrities', 0.006248950235281484),\n",
+ " (',', 0.030198116531579108),\n",
+ " ('like', 0.009767105665165837),\n",
+ " ('Donald', -0.005156455552972984),\n",
+ " ('Trump', 0.00975752503393775),\n",
+ " ('.', 0.03643156390202468),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " '-': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.8222961624837605),\n",
+ " ('-', 0.29912863887482893),\n",
+ " ('York', 0.4751802933646559),\n",
+ " ('City', -0.035362585327093826),\n",
+ " ('is', 0.05356122761397254),\n",
+ " ('a', 0.029608278383848663),\n",
+ " ('place', -0.01730584046390251),\n",
+ " ('full', 0.009172963780251012),\n",
+ " ('of', 0.018796684200808246),\n",
+ " ('celebrities', -0.022613662920536216),\n",
+ " (',', 0.02793702495546166),\n",
+ " ('like', -0.007824760803275625),\n",
+ " ('Donald', -0.00918802890811021),\n",
+ " ('Trump', -0.025381208170742887),\n",
+ " ('.', 0.027205539126205873),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'York': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.5337492169619215),\n",
+ " ('-', 0.25998727499706475),\n",
+ " ('York', 0.7606980059506682),\n",
+ " ('City', 0.24466536309389758),\n",
+ " ('is', 0.06205621041284834),\n",
+ " ('a', -0.027586858628998748),\n",
+ " ('place', 0.025106264194788536),\n",
+ " ('full', -0.05140017864461721),\n",
+ " ('of', -0.019691584436654863),\n",
+ " ('celebrities', -0.006924876493947967),\n",
+ " (',', -0.012906328684374),\n",
+ " ('like', -0.015183896769670923),\n",
+ " ('Donald', -0.01204036640148225),\n",
+ " ('Trump', 0.011279656359069704),\n",
+ " ('.', 0.0025388317477680034),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'City': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.08719345239654737),\n",
+ " ('-', -0.0524897534959616),\n",
+ " ('York', 0.5869292101377458),\n",
+ " ('City', 0.7707565224003641),\n",
+ " ('is', 0.11985660753797572),\n",
+ " ('a', 0.04739018827326886),\n",
+ " ('place', 0.1805590475244674),\n",
+ " ('full', 0.00828691084204081),\n",
+ " ('of', 0.011989906449620953),\n",
+ " ('celebrities', -0.0032475106540728713),\n",
+ " (',', 0.031248392601942055),\n",
+ " ('like', 0.021615119707316135),\n",
+ " ('Donald', 0.005680373604494104),\n",
+ " ('Trump', 0.00404614974487494),\n",
+ " ('.', 0.012740355482399924),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'Donald': {'label': 'B-PER',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.013149772298472851),\n",
+ " ('-', 0.03789828036540612),\n",
+ " ('York', 0.02540278725254585),\n",
+ " ('City', 0.008722030153492339),\n",
+ " ('is', 0.014231916079556055),\n",
+ " ('a', 0.027366374023769485),\n",
+ " ('place', -0.019735079948038754),\n",
+ " ('full', -0.01247201654780089),\n",
+ " ('of', 0.03221016613496855),\n",
+ " ('celebrities', 0.024813075410914712),\n",
+ " (',', -0.017656270724549446),\n",
+ " ('like', 0.2729821619767517),\n",
+ " ('Donald', 0.719180584925053),\n",
+ " ('Trump', 0.6334537565329436),\n",
+ " ('.', -0.034704097997057266),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'Trump': {'label': 'I-PER',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.0013212822286869873),\n",
+ " ('-', 0.016093372865552385),\n",
+ " ('York', 0.04688254279462517),\n",
+ " ('City', 0.013796325424241722),\n",
+ " ('is', 0.003590890616336741),\n",
+ " ('a', 0.024211369758444755),\n",
+ " ('place', 0.00041608693732241746),\n",
+ " ('full', -0.0007240991111717892),\n",
+ " ('of', 0.02103574941962767),\n",
+ " ('celebrities', 0.01816752290526648),\n",
+ " (',', 0.03074231246421054),\n",
+ " ('like', 0.15109008062796192),\n",
+ " ('Donald', 0.734340021421949),\n",
+ " ('Trump', 0.6562611269559748),\n",
+ " ('.', -0.047658851041918),\n",
+ " ('[SEP]', 0.0)]}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 68
+ }
+ ],
+ "source": [
+ "attributions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dm58uxm_50e2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "6bf508f6-cdb4-4092-f84f-98e6cb571595"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px; padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 60%)\"></span> Negative <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 100%)\"></span> Neutral <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(120, 75%, 50%)\"></span> Positive </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>B-LOC</b></text></td><td><text style=\"padding-right:2em\"><b>B-LOC (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>New</b></text></td><td><text style=\"padding-right:2em\"><b>2.10</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> New </font></mark><mark style=\"background-color: hsl(120, 75%, 77%); opacity:1.0; line-height:1.75\"><font color=\"black\"> - </font></mark><mark style=\"background-color: hsl(120, 75%, 66%); opacity:1.0; line-height:1.75\"><font color=\"black\"> York </font></mark><mark style=\"background-color: hsl(120, 75%, 75%); opacity:1.0; line-height:1.75\"><font color=\"black\"> City </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> place </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> full </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> celebrities </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>I-LOC</b></text></td><td><text style=\"padding-right:2em\"><b>I-LOC (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>-</b></text></td><td><text style=\"padding-right:2em\"><b>1.65</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(120, 75%, 59%); opacity:1.0; line-height:1.75\"><font color=\"black\"> New </font></mark><mark style=\"background-color: hsl(120, 75%, 86%); opacity:1.0; line-height:1.75\"><font color=\"black\"> - </font></mark><mark style=\"background-color: hsl(120, 75%, 77%); opacity:1.0; line-height:1.75\"><font color=\"black\"> York </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> City </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> place </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> full </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> celebrities </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>I-LOC</b></text></td><td><text style=\"padding-right:2em\"><b>I-LOC (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>York</b></text></td><td><text style=\"padding-right:2em\"><b>1.75</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(120, 75%, 74%); opacity:1.0; line-height:1.75\"><font color=\"black\"> New </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> - </font></mark><mark style=\"background-color: hsl(120, 75%, 62%); opacity:1.0; line-height:1.75\"><font color=\"black\"> York </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> City </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> place </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> full </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> celebrities </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>I-LOC</b></text></td><td><text style=\"padding-right:2em\"><b>I-LOC (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>City</b></text></td><td><text style=\"padding-right:2em\"><b>1.66</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> New </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> - </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0; line-height:1.75\"><font color=\"black\"> York </font></mark><mark style=\"background-color: hsl(120, 75%, 62%); opacity:1.0; line-height:1.75\"><font color=\"black\"> City </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0; line-height:1.75\"><font color=\"black\"> place </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> full </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> celebrities </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> , </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> . </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>B-PER</b></text></td><td><text style=\"padding-right:2em\"><b>B-PER (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>Donald</b></text></td><td><text style=\"padding-right:2em\"><b>1.70</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> New </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> - </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> York </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> City </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> a </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> place </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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