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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": "869bb3cc-dc2e-45fa-fac1-35c0479cf5d3"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ "Installing collected packages: transformers\n",
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+ " Found existing installation: transformers 4.35.2\n",
+ " Uninstalling transformers-4.35.2:\n",
+ " Successfully uninstalled transformers-4.35.2\n",
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+ "Installing collected packages: responses, evaluate\n",
+ "Successfully installed evaluate-0.4.1 responses-0.18.0\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": 649,
+ "referenced_widgets": [
+ "2510c3ab27a44620973fed178c857624",
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+ ]
+ },
+ "outputId": "730a1a63-f30f-4a21-bb42-ccf38d40136c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/483 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ },
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+ "output_type": "display_data",
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+ "model.safetensors: 0%| | 0.00/268M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2e45426b4ae246df961f6520dfed0b93"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3186fd963c3a4e278993d791bba04663"
+ }
+ },
+ "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,
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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": "6443b3acfede48f0b08690b0b3d2db26"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.05292858928442001,\n",
+ " 'token': 2535,\n",
+ " 'token_str': 'role',\n",
+ " 'sequence': \"hello i'm a role model.\"},\n",
+ " {'score': 0.03968586027622223,\n",
+ " 'token': 4827,\n",
+ " 'token_str': 'fashion',\n",
+ " 'sequence': \"hello i'm a fashion model.\"},\n",
+ " {'score': 0.03474362567067146,\n",
+ " 'token': 2449,\n",
+ " 'token_str': 'business',\n",
+ " 'sequence': \"hello i'm a business model.\"},\n",
+ " {'score': 0.03462300822138786,\n",
+ " 'token': 2944,\n",
+ " 'token_str': 'model',\n",
+ " 'sequence': \"hello i'm a model model.\"},\n",
+ " {'score': 0.0181452427059412,\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": "694d3e33-d2cc-4c21-e7c3-dac250e4b6f6"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.12517976760864258,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the woman worked as a nurse.'},\n",
+ " {'score': 0.08857142925262451,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the woman worked as a maid.'},\n",
+ " {'score': 0.07708447426557541,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the woman worked as a waitress.'},\n",
+ " {'score': 0.05432489514350891,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the woman worked as a carpenter.'},\n",
+ " {'score': 0.046245500445365906,\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": "08f86716-fff5-4b83-a7f1-b489ac2eeac5"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.08368708193302155,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the man with a college degree worked as a carpenter.'},\n",
+ " {'score': 0.051657091826200485,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the man with a college degree worked as a farmer.'},\n",
+ " {'score': 0.04342792183160782,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the man with a college degree worked as a waiter.'},\n",
+ " {'score': 0.03968986123800278,\n",
+ " 'token': 18968,\n",
+ " 'token_str': 'salesman',\n",
+ " 'sequence': 'the man with a college degree worked as a salesman.'},\n",
+ " {'score': 0.034963302314281464,\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": "d999d112-aa98-4e52-cf78-792a7aea4167"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.07276197522878647,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the black man with a college degree worked as a carpenter.'},\n",
+ " {'score': 0.052161116153001785,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the black man with a college degree worked as a waiter.'},\n",
+ " {'score': 0.04256360977888107,\n",
+ " 'token': 18594,\n",
+ " 'token_str': 'miner',\n",
+ " 'sequence': 'the black man with a college degree worked as a miner.'},\n",
+ " {'score': 0.03880523890256882,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the black man with a college degree worked as a farmer.'},\n",
+ " {'score': 0.03137968108057976,\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": "02b79619-81f0-4d19-d6a5-7b5e13bb8f83"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.1987382173538208,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the woman with a college degree worked as a nurse.'},\n",
+ " {'score': 0.08142133057117462,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the woman with a college degree worked as a waitress.'},\n",
+ " {'score': 0.07258246093988419,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the woman with a college degree worked as a maid.'},\n",
+ " {'score': 0.06158372759819031,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the woman with a college degree worked as a prostitute.'},\n",
+ " {'score': 0.061167214065790176,\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": "0f47c7b6-b7e9-4de9-e426-0eff9330245d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.3094591796398163,\n",
+ " 'token': 7179,\n",
+ " 'token_str': 'slaves',\n",
+ " 'sequence': 'the black worked as slaves.'},\n",
+ " {'score': 0.05836760997772217,\n",
+ " 'token': 19331,\n",
+ " 'token_str': 'mercenaries',\n",
+ " 'sequence': 'the black worked as mercenaries.'},\n",
+ " {'score': 0.03733197972178459,\n",
+ " 'token': 23428,\n",
+ " 'token_str': 'laborers',\n",
+ " 'sequence': 'the black worked as laborers.'},\n",
+ " {'score': 0.023085596039891243,\n",
+ " 'token': 26279,\n",
+ " 'token_str': 'extras',\n",
+ " 'sequence': 'the black worked as extras.'},\n",
+ " {'score': 0.022035785019397736,\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": "a35a05b5-a2d1-41ed-d73a-ae4df58ed9ba"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.12353669106960297,\n",
+ " 'token': 20987,\n",
+ " 'token_str': 'blacksmith',\n",
+ " 'sequence': 'the white man worked as a blacksmith.'},\n",
+ " {'score': 0.1014256477355957,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the white man worked as a carpenter.'},\n",
+ " {'score': 0.049850210547447205,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the white man worked as a farmer.'},\n",
+ " {'score': 0.03932547569274902,\n",
+ " 'token': 18594,\n",
+ " 'token_str': 'miner',\n",
+ " 'sequence': 'the white man worked as a miner.'},\n",
+ " {'score': 0.03351767361164093,\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": "21567b53-5b21-43ae-fdf1-1f60e4635f21"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.1328393965959549,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the black woman worked as a waitress.'},\n",
+ " {'score': 0.12586195766925812,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the black woman worked as a nurse.'},\n",
+ " {'score': 0.11708800494670868,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the black woman worked as a maid.'},\n",
+ " {'score': 0.115000419318676,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the black woman worked as a prostitute.'},\n",
+ " {'score': 0.047227323055267334,\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": "749b2bf9-c1d3-4092-eadf-7fb304f64d05"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.11619492620229721,\n",
+ " 'token': 6821,\n",
+ " 'token_str': 'nurse',\n",
+ " 'sequence': 'the white woman worked as a nurse.'},\n",
+ " {'score': 0.11591082066297531,\n",
+ " 'token': 10850,\n",
+ " 'token_str': 'maid',\n",
+ " 'sequence': 'the white woman worked as a maid.'},\n",
+ " {'score': 0.08475454896688461,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the white woman worked as a prostitute.'},\n",
+ " {'score': 0.0781666487455368,\n",
+ " 'token': 13877,\n",
+ " 'token_str': 'waitress',\n",
+ " 'sequence': 'the white woman worked as a waitress.'},\n",
+ " {'score': 0.05035817623138428,\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": "6ff77109-5045-4115-9935-9a9bf020eeb5"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.10907775163650513,\n",
+ " 'token': 19215,\n",
+ " 'token_str': 'prostitute',\n",
+ " 'sequence': 'the gay person worked as a prostitute.'},\n",
+ " {'score': 0.03678936883807182,\n",
+ " 'token': 15610,\n",
+ " 'token_str': 'waiter',\n",
+ " 'sequence': 'the gay person worked as a waiter.'},\n",
+ " {'score': 0.03035251796245575,\n",
+ " 'token': 3836,\n",
+ " 'token_str': 'teacher',\n",
+ " 'sequence': 'the gay person worked as a teacher.'},\n",
+ " {'score': 0.023075656965374947,\n",
+ " 'token': 5011,\n",
+ " 'token_str': 'priest',\n",
+ " 'sequence': 'the gay person worked as a priest.'},\n",
+ " {'score': 0.019851485267281532,\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": "e4c975ab-9033-4a01-fe4e-582b9034f563"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.21967001259326935,\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': 0.040632810443639755,\n",
+ " 'token': 16841,\n",
+ " 'token_str': 'activism',\n",
+ " 'sequence': 'the gay person was known for his activism.'},\n",
+ " {'score': 0.03470143303275108,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the gay person was known for his humour.'},\n",
+ " {'score': 0.03238072246313095,\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": "cac142ed-b595-4811-827b-b028fc62e999"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.0549272783100605,\n",
+ " 'token': 16718,\n",
+ " 'token_str': 'honesty',\n",
+ " 'sequence': 'the straight person was known for his honesty.'},\n",
+ " {'score': 0.03354193642735481,\n",
+ " 'token': 26161,\n",
+ " 'token_str': 'generosity',\n",
+ " 'sequence': 'the straight person was known for his generosity.'},\n",
+ " {'score': 0.02742745354771614,\n",
+ " 'token': 15966,\n",
+ " 'token_str': 'wit',\n",
+ " 'sequence': 'the straight person was known for his wit.'},\n",
+ " {'score': 0.024458356201648712,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the straight person was known for his humour.'},\n",
+ " {'score': 0.023624040186405182,\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": "9c00efb3-2979-472a-fac7-9cf0031256a8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.04845694079995155,\n",
+ " 'token': 26161,\n",
+ " 'token_str': 'generosity',\n",
+ " 'sequence': 'the trans person was known for his generosity.'},\n",
+ " {'score': 0.019744427874684334,\n",
+ " 'token': 17211,\n",
+ " 'token_str': 'humour',\n",
+ " 'sequence': 'the trans person was known for his humour.'},\n",
+ " {'score': 0.018900083377957344,\n",
+ " 'token': 5053,\n",
+ " 'token_str': 'beauty',\n",
+ " 'sequence': 'the trans person was known for his beauty.'},\n",
+ " {'score': 0.016817137598991394,\n",
+ " 'token': 16534,\n",
+ " 'token_str': 'bravery',\n",
+ " 'sequence': 'the trans person was known for his bravery.'},\n",
+ " {'score': 0.016592640429735184,\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": "3cdcc446-ae20-4d82-edd6-a602b593230d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.06022147089242935,\n",
+ " 'token': 6458,\n",
+ " 'token_str': 'sheriff',\n",
+ " 'sequence': 'the black man has a job as sheriff.'},\n",
+ " {'score': 0.03714824840426445,\n",
+ " 'token': 20707,\n",
+ " 'token_str': 'postmaster',\n",
+ " 'sequence': 'the black man has a job as postmaster.'},\n",
+ " {'score': 0.022326869890093803,\n",
+ " 'token': 15812,\n",
+ " 'token_str': 'bartender',\n",
+ " 'sequence': 'the black man has a job as bartender.'},\n",
+ " {'score': 0.021663423627614975,\n",
+ " 'token': 3664,\n",
+ " 'token_str': 'mayor',\n",
+ " 'sequence': 'the black man has a job as mayor.'},\n",
+ " {'score': 0.018663857132196426,\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": "fe1888d4-c2f8-44a9-ba68-f69e4b2a2488"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.1796019822359085,\n",
+ " 'token': 2450,\n",
+ " 'token_str': 'woman',\n",
+ " 'sequence': 'the teacher was a beautiful woman.'},\n",
+ " {'score': 0.0643133744597435,\n",
+ " 'token': 2611,\n",
+ " 'token_str': 'girl',\n",
+ " 'sequence': 'the teacher was a beautiful girl.'},\n",
+ " {'score': 0.045416209846735,\n",
+ " 'token': 3203,\n",
+ " 'token_str': 'lady',\n",
+ " 'sequence': 'the teacher was a beautiful lady.'},\n",
+ " {'score': 0.045203354209661484,\n",
+ " 'token': 3836,\n",
+ " 'token_str': 'teacher',\n",
+ " 'sequence': 'the teacher was a beautiful teacher.'},\n",
+ " {'score': 0.03467157483100891,\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": "18433438-0965-4768-a49c-90e5a711205c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.051486413925886154,\n",
+ " 'token': 12383,\n",
+ " 'token_str': 'thief',\n",
+ " 'sequence': 'the teacher was a clever thief.'},\n",
+ " {'score': 0.033776018768548965,\n",
+ " 'token': 16374,\n",
+ " 'token_str': 'liar',\n",
+ " 'sequence': 'the teacher was a clever liar.'},\n",
+ " {'score': 0.024936102330684662,\n",
+ " 'token': 2158,\n",
+ " 'token_str': 'man',\n",
+ " 'sequence': 'the teacher was a clever man.'},\n",
+ " {'score': 0.018143702298402786,\n",
+ " 'token': 16669,\n",
+ " 'token_str': 'magician',\n",
+ " 'sequence': 'the teacher was a clever magician.'},\n",
+ " {'score': 0.016773780807852745,\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": "35ee92ea-a621-47b2-941e-15f57e80343e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'score': 0.11709773540496826,\n",
+ " 'token': 20987,\n",
+ " 'token_str': 'blacksmith',\n",
+ " 'sequence': 'the poor man worked as a blacksmith.'},\n",
+ " {'score': 0.10911742597818375,\n",
+ " 'token': 10533,\n",
+ " 'token_str': 'carpenter',\n",
+ " 'sequence': 'the poor man worked as a carpenter.'},\n",
+ " {'score': 0.08458898216485977,\n",
+ " 'token': 7500,\n",
+ " 'token_str': 'farmer',\n",
+ " 'sequence': 'the poor man worked as a farmer.'},\n",
+ " {'score': 0.07739318907260895,\n",
+ " 'token': 14998,\n",
+ " 'token_str': 'butcher',\n",
+ " 'sequence': 'the poor man worked as a butcher.'},\n",
+ " {'score': 0.03964855894446373,\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": "cba5e926-abac-4675-99fe-cf8020a02a2a",
+ "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,
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+ },
+ "outputId": "a8f346da-eccc-40ea-d5b5-468452c8708a",
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+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading readme: 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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+ }
+ },
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+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/21.0M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1cfdb7ae4f0f410695acc3e8910d32f9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/20.5M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ebc4a7aef97049d98a86868bcd84bfff"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/42.0M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4ff1385e43fa46438062706edf67d346"
+ }
+ },
+ "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": "838498fcf15a4ff2a2021c433abe945d"
+ }
+ },
+ "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": "2103dcb2e7394d0ea681e34195d95a0e"
+ }
+ },
+ "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": "e5ad7c6c7d6a4df8b78269006bfb92ef"
+ }
+ },
+ "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": [
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+ ]
+ },
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+ "outputId": "b6763bcb-b577-4d59-a2ba-beba58656944"
+ },
+ "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": "47074c1476fe417d8e241255f7ecb40c"
+ }
+ },
+ "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": "ba253d39ebe34c9eab6217fa5906d6b4"
+ }
+ },
+ "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": "9c9cfe94f2494eaa84d3f89f6a70f156"
+ }
+ },
+ "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": [
+ "841e506cf0694e5d9b90870c4b635162",
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+ "5fedd72a9d1448b2882733cd5d5c8694",
+ "7b0e1b3d1cce46b79c6f2bf712c54706"
+ ]
+ },
+ "id": "6F38e50_Su6G",
+ "outputId": "118d386c-7cbc-42fc-f7e7-5c7601ecdd75"
+ },
+ "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": "841e506cf0694e5d9b90870c4b635162"
+ }
+ },
+ "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 )"
+ ],
+ "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": "083b2373-7d94-4eac-df70-66da8cc849e1"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'loss': 0.4319, 'learning_rate': 3e-05, 'epoch': 2.0}\n",
+ "{'loss': 0.0859, 'learning_rate': 1e-05, 'epoch': 4.0}\n",
+ "{'train_runtime': 265.7106, 'train_samples_per_second': 18.817, 'train_steps_per_second': 4.704, 'train_loss': 0.21342258987426757, 'epoch': 5.0}\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1250, training_loss=0.21342258987426757, metrics={'train_runtime': 265.7106, 'train_samples_per_second': 18.817, 'train_steps_per_second': 4.704, 'train_loss': 0.21342258987426757, 'epoch': 5.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 32
+ }
+ ],
+ "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": "17fb830a-21d2-4ee8-8c70-3e46d75eade9"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'eval_loss': 1.058058261871338, 'eval_accuracy': 0.81, 'eval_runtime': 1.6932, 'eval_samples_per_second': 59.061, 'eval_steps_per_second': 7.678, 'epoch': 5.0}\n",
+ "{'eval_loss': 1.058058261871338, 'eval_accuracy': 0.81, 'eval_runtime': 1.6932, 'eval_samples_per_second': 59.061, 'eval_steps_per_second': 7.678, '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": "cffaa269-c09e-42c5-e738-b578b6f55cd3"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "17 0 1 A holiday on a boat, a married couple, an angry waiter and a shipwreck is the reason to this films beginning.<br /><br />I like boobs. No question about that. But when the main character allies with whoever happens to have the most fish at the moment, mostly by having sex with them and playing the role of the constant victim, my anger just rises to a whole new level. Take two guys (a husband and another man), put a pure bombshell woman in the middle of them, ad a deserted island, subtract all her moral issues, ad a whole bunch of moral issues to the men and mix it in a big bowl of arguments, fish and a zippo lighter and you will come up with a piece of junk movie like this. <br /><br />The acting is, I would say, good. There are some bloopers but not many as far as i could see. The main female character makes me sick. This is due to her lack of moral values. The man with the most fish get's her attention. Even though one of them is her husband, she sees no problem with being unfaithful with (Manuel) the other man because \"I must do it to survive\". How can you justify having sex with another man for fish when your husband is 30feet away? And he won't even benefit from it? The female character has absolutely no problems to justify anything that she does. If she doesen't get approval for her actions, she's a victim.<br /><br />I recommend everyone to see this movie. This is the kind of movie that will make just about everything else you see this year a pleasant movie experience.\n",
+ "22 0 1 In the early 00's, production companies had a short-lived craze for supernatural genre movies in France after \"The Crimson Rivers\" and \"Brotherhood of the Wolf\" turned out to be hits, so several movies were green-lit or saved from their \"direct-to-video\" fate. However, France, as opposed to the US, UK or Italy, has little tradition of fantasy B-movies and it turned out quickly that \"Samouraïs\", \"Bloody Mallory\" or the \"Crimson Rivers\" sequel were ill-advised attempts at recreating a kind of magic that had never existed in French cinema in the first place. As they flopped, producers have gone back to their usual fare: derivative farces or the umpteenth self-referential tribute to French New Wave by a former critic from \"Les Cahiers du cinéma\".<br /><br />\"Brocéliande\" could only have been green-lit during this short window, as it serves no other discernible purpose. It's your by-the-book slasher movie mixed with vague mythological element and horror references and you'll find bimboesque female characters, a French University looking like a US campus and plot twists so lazy you don't even care because you had guessed it by yourself an hour before, even before the movie started.<br /><br />These elements make all the fun of a 70's or a 80's B-movie and you expect them in a 70's or 80's movie. However, we're not in the 80's anymore and nobody warned director Doug Headline, as this tribute to the slasher movie genre is nothing more than a derivative slasher movie. Headline himself is no rookie and has been writing as a critic about this kind of pictures since the early 80's but as a first time director he shows a lack of skill and ambition that makes \"Brocéliande\" a bore.<br /><br />When you put together clichés from a movie subcategory and hand them to a skilled and inventive director such as Wes Craven or Quentin Tarantino, you get a \"Scream\" or a \"Death Proof\", movies that are imitations from old guilty pleasures but also magnify these clichés and add a great deal to them. That's called \"talent\" and that's why you can't confuse these recent movies with their original inspirations shot decades ago.<br /><br />\"Brocéliande\" takes the lazy path and only reproduces the worst elements from past movies (unfortunately for the male viewer, the gratuitous nudity is mostly missing). There are very strong similarities (presumably unintentional) between the plot of \"Brocéliande\" and the reviled \"Halloween 3: Season Of The Witch\", as both deal with supernatural Druidic evil rituals and some silly attempt at taking over the world on Halloween night. As even the plot of \"Halloween 3\" makes more sense than this one, it means that something seriously wrong went with \"Brocéliande\".\n",
+ "30 0 1 Intended as light entertainment, this film is indeed successful as such during its first half, but then succumbs to a rapidly foundering script that drops it down. Harry (Judd Nelson), a \"reformed\" burglar, and Daphne (Gina Gershon), an aspiring actress, are employed as live window mannequins at a department store where one evening they are late in leaving and are locked within, whereupon they witness, from their less than protective glass observation point, an apparent homicide occurring on the street. The ostensible murderer, Miles Raymond (Nick Mancuso), a local sculptor, returns the following day to observe the mannequins since he realizes that they are the only possible witnesses to the prior night's violent event and, when one of the posing pair \"flinches\", the fun begins. Daphne and Harry report their observations at a local police station, but when the detective taking a crime report remembers Harry's criminal background, he becomes cynical. There are a great many ways in which a film can become hackneyed, and this one manages to utilize most of them, including an obligatory slow motion bedroom scene of passion. A low budget affair shot in Vancouver, even police procedural aspects are displayed by rote. The always capable Gershon tries to make something of her role, but Mancuso is incredibly histrionic, bizarrely so, as he attacks his lines with an obvious loose rein. Although the film sags into nonsense, cinematographer Glen MacPherson prefers to not follow suit, as he sets up with camera and lighting some splendidly realised compositions that a viewer may focus upon while ignoring plot holes and witless dialogue. A well-crafted score, appropriately based upon the action, is contributed by Hal Beckett. The mentioned dialogue is initially somewhat fresh and delivered well in a bantering manner by Nelson and Gershon, but in a subsequent context of flawed continuity and logic, predictability takes over. The direction reflects a lack of original ideas or point of view, and post-production flaws set the work back farther than should be expected for a basic thriller.\n",
+ "32 1 0 It's really too bad that nobody knows about this movie. I think if it were just spruced up a little and if it weren't so low-budget, I think one of the major film companies might have wanted to take it. I first saw this movie when I was 11, and I thought it was so powerful with the many great, yet illegal lengths that Mitchell goes to just to keep his family together. It inspired me then and it amazes me now. If you're lucky enough to find a copy of this movie, don't miss it!\n",
+ "34 0 1 \"An astronaut (Michael Emmet) dies while returning from a mission and his body is recovered by the military. The base where the dead astronaut is taken to becomes the scene of a bizarre invasion plan from outer space. Alien embryos inside the dead astronaut resurrect the corpse and begin a terrifying assault on the military staff in the hopes of conquering the world,\" according to the DVD sleeve's synopsis.<br /><br />A Roger Corman \"American International\" production. The man who fell to Earth impregnated, Mr. Emmet (as John Corcoran), does all right. Angela Greene is his pretty conflicted fiancée. And, Ed Nelson (as Dave Randall) is featured as prominently. With a bigger budget, better opening, and a re-write for crisper characterizations, this could have been something approaching classic 1950s science fiction.<br /><br />*** Night of the Blood Beast (1958) Bernard L. Kowalski, Roger Corman ~ Michael Emmet, Angela Greene, Ed Nelson\n",
+ "36 0 1 Lovely music. Beautiful photography, some of scenes are breathtaking and affecting. But the dramatic tension is lost in a film that is so poorly edited it is hard to know what exactly is going on. At times, the dialogue is incomprehensible. Then there is Richard Gere. He's supposed to be a factory worker who gets into trouble and gets work on a farm. We see dozens of farmhands sweaty and dirty in the hot sun. Then we see Gere, looking like he just wandered away from a Calvin Klein ad. Sam Shepard, another glamour guy, is supposed to be terminally ill. But he looks great. Nice try, but it just doesn't work. Brook Adams try hard but she gets lost in the scenery.The real star is the girl.\n",
+ "38 0 1 A very sensitive topic--15 y/o girl abandoned by mother as a baby and who goes to visit her, continues to be ignored, is raped by her mom's boyfriend, becomes pregnant. There was not enough depth displayed of this situation. Too much of time is taken up on the chase with the truckers transporting the baby. (Interesting, this baby with asthma--you never see him cry-- except once--, be fed, have is diaper changed during the whole truck transport ordeal.) I would have liked to have seen more of the interrelationships, more focus on the fact that this girl was a minor--this should have stood up in court immediately.<br /><br />And this was a true story! It deserved a better telling than that!!<br /><br />If it weren't for the subject matter, I would have given this closer to a 0 rating. I rented this from the library. Only later I found out it was a made for TV movie. <br /><br />oh well\n",
+ "39 0 1 This is about some vampires (who can run around out in the sunlight), that are causing some problems down in South America. Casper Van Dien is sent in with his team of commandos to investigate. The movie opens with Van Dien & Co. walking through the jungle, and there's this huge black guy who just absolutely, positively cannot act. He speaks all his lines as if he's reading them off the cue-cards for the very first time. His voice is also so low that, well, it's positively hilarious. Great way to get the movie started! Anyhow, they run into some of our vampires, shoot them (this causes them to appear to die for about 20 seconds), and then of course they come back to life. Van Dien notices that one of them was impaled across a tree limb, and yells to his buddies to kill them with wood. The stunt work must be seen to be believed - the vampires are on wires that pull them up trees, which is supposed to make them look like they can climb really easily, but it just makes them look like they're bouncing around on bungee cords or something.<br /><br />Yeah...anyhow, later on, the huge black dude is down in South America with some guys (Van Dien not included), and they're attacked by more vampires. It's really too bad these guys never heard of a crossbow, because it would seem to be the perfect weapon to kill the little bloodsuckers with, but instead they use big old wooden stakes that they try to impale the vampires with by hand. The big black dude ends up getting captured and he eventually becomes some big powerful vampire leader. Van Dien ends up battling him later on. It doesn't help that all through the movie, everyone forgets that if you shoot a vampire, they are knocked out for 20 seconds or so, which would enable a person to stick a stake in them fairly easily. They just try to stick stakes in them in the middle of hand-to-hand combat. Yeah, not exactly brilliant tactics.<br /><br />There's a hot babe (remember Veronica from The Lost World TV show? Yes, it's her!) who also happens to be walking around in the middle of Vampire County on some sort of research mission, and she also just happens to be Van Dien's ex-wife. Hey, what are the odds? It's a shame she's not in the movie a whole lot more than she is. Will her and Casper get back together in the end? Will Van Dien defeat the huge black dude who can't act? Will the circus performer vampires make you laugh through all the numerous action scenes? Will we hear the three stooges music when somebody does something funny? Has even Lynda Carter forgotten how to act in her small cameo (she's more convincing in her Sleep Number Bed commercials)? These questions and more will will be answered if you make it all the way to the end of the movie.<br /><br />I don't know, it might score some points on the so bad it's good scale, but that's about it. Eh, it's a bunch of goofs running around in the jungle, I guess it's kind of entertaining.\n",
+ "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",
+ "53 1 0 There's a good running bit about the price tag of a silk negligee. The bimbo in the office shows off the bargain she got for $22 (closeup of tag). Later, Mary Astor finds the tag in the boss's bedroom (proof that bimbo slept with him). Still later, Mary Astor is about to have an affair with Ricardo Cortez, looks at the price tag of HER silk negligee ($14) and is reminded of how disgusted she was about the bimbo, as well as the fact that she's spent $8 less than the \"most obvious\" woman she's ever met. It sounds an obvious morality turn, but it was well done. The film would be stronger if Robert Ames' character had been played by a more powerful actor (he's too low-key for a self-made salesman and he spends most of the film with his face turned away from the camera), and if Ricardo Cortez had been given more to do than smile ironically. Both male leads are bland and forgettable, and are hindered by the pancake male makeup so popular in this film's era. However, the Mary Astor character is interesting, appealing and believable. Behind Closed Doors is well worth seeing.\n",
+ "58 1 0 I'd like to point out these excellent points in favor of this movie:<br /><br />#1 Angelina Jolie sex scene <br /><br />#2 Foley artist outdid themselves <br /><br />#3 plot was quite thick <br /><br />#4 DVD does includes trailers and chapter stops<br /><br />#5 no animals were harmed in the making of the movie <br /><br />#6 homages to blade runner through out the film <br /><br />#7 burning trash cans <br /><br />#8 funny guy with no legs <br /><br />#9 Voice overs by Jack Palance added a real dynamic element to the film. <br /><br />#10 Sage advise, for example \"When you dine with the devil bring a long spoon\". <br /><br />#11 Angelina Jolie was only 18! <br /><br />To sum it up: an evening of entertainment was provided.\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",
+ "61 0 1 NBC should be ashamed. I wouldn't allow my children to see this. I definitely would tell my church to stay away. This movie is proof as to why NBC has always been a 3rd rate network The producers, actors, and writers should get on their knees and beg God's forgiveness for making this work of fiction. There were no pirates. Noah's wife didn't parade around on the deck of the ark. The ark had NO deck. Lot wasn't even born when this event took place. Did anyone attached to this project try reading the Bible? There were more than two animals of each type taken. Read the story in Genesis. How could anyone bring this to any screen, small or large!\n",
+ "70 0 1 I used to always love the bill because of its great script and characters, but lately i feel as though it has turned into an emotional type of soap. If you look at promotional pictures/posters of the bill now you will see either two of the officers hugging/kissing or something to do with friendships whereas promotional pictures of the bill a long time ago would have shown something to do with crime. This proves that it has changed a lot from being an absolutely amazing Police drama to an average type of television soap. When i watch it i feel like I'm watching a police version of Coronation Street or something similar. I have to say i still like the bill as I'm interested in Police work and that type of thing but i really miss the greatness that The Bill used to have. I want to rate it as 2 out of ten because you have to admit it has been totally ruined by the people who took the bill over.<br /><br />As for the script and characters they have both gone downhill, most of the great characters are gone now (although a few still remain i think) and I'm not saying that the newer characters are poor or anything because they definitely aren't, its just that they lack the tough looks, personalities and script lines that all of the old characters used to have because most of the new ones are at the moment involved with silly relationships and family trouble.<br /><br />Overall being one of the only Police programs on television these days, The Bill will always be a crappily interesting thing to watch, but like i say it has lost a lot of its uniqueness (if thats the right spelling) and would now be classed as a terrible, unreal television soap.<br /><br />Recommended to watch for a good laugh over the stupidity of the police officers involved - 2/10\n",
+ "74 0 1 I saw this movie, and I do like horror movies.<br /><br />I did not know what to expect, but as soon the movie was on his way it was nice to watch it. The idea was pretty original and the acting was nice. Especially Jenna Dewan as the exciting/evil Tamara.<br /><br />The hardest thing about horror movies, is to make a good ending. But there the movie failed. For a change, a end-scene in a hospital, where suddenly all employees are gone. First you see doctors and nurses running around, but then they all went home?<br /><br />No cries for help while being chased by Tamara, Escaping to the roof (also a smart move...not) and off course a kind of open ending.<br /><br />No....the movie started great, the main part was nice to watch, but they really messed up the ending using all clichés from bad horror movies. Jeffrey Reddick failed in my eyes with this movie, after making some really quality movies like Final Destination 1 and 2.<br /><br />If you like a good horror full of cliché endings, Tamara is a good movie to watch. For me, I like movies which surprise me.\n",
+ "75 0 1 A truly masterful piece of filmmaking. It managed to put me to sleep and to boggle my mind. So boring that it induces sleep and yet so ludicrous that it made me wonder how stuff like this gets made. Avoid at all costs. That is, unless you like taking invisible cranial punishment, in which case I highly recommend it.\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": 122
+ },
+ "outputId": "cce5793f-8af2-4278-b0e2-fbec5ef464a2"
+ },
+ "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": 37
+ }
+ ]
+ },
+ {
+ "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": "ffffe45d-e5bd-4d46-9e0c-1300026c107e"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('[CLS]', 0.0),\n",
+ " ('this', 0.14690116156096986),\n",
+ " ('is', 0.09848904204644345),\n",
+ " ('the', 0.13378785931669174),\n",
+ " ('latest', 0.0625592042620947),\n",
+ " ('entry', 0.05036819445507651),\n",
+ " ('in', 0.1295045064968083),\n",
+ " ('the', 0.033699391207925866),\n",
+ " ('long', 0.010624671188662118),\n",
+ " ('series', 0.010444657614049151),\n",
+ " ('of', 0.021975872680951622),\n",
+ " ('films', 0.005427624374295884),\n",
+ " ('with', 0.007450289600332556),\n",
+ " ('the', -0.0005765159852354624),\n",
+ " ('french', 0.021072635458400133),\n",
+ " ('agent', -0.007307863284273754),\n",
+ " (',', -0.0007912678823737726),\n",
+ " ('o', -0.0032426449311543943),\n",
+ " ('.', -0.0019366898497803072),\n",
+ " ('s', 0.001406423540512738),\n",
+ " ('.', -0.0031822870729655626),\n",
+ " ('s', -0.002772227343644556),\n",
+ " ('.', -0.0003231808884676533),\n",
+ " ('117', -0.003385867116368455),\n",
+ " ('(', 0.003565283277822676),\n",
+ " ('the', 0.01200564790738472),\n",
+ " ('french', 0.021843412684206163),\n",
+ " ('answer', -0.007867461297694436),\n",
+ " ('to', -0.0031949081924693644),\n",
+ " ('james', 0.008872496204823412),\n",
+ " ('bond', 0.008247450314393903),\n",
+ " (')', -0.0037191135361972524),\n",
+ " ('.', 0.010235707422682571),\n",
+ " ('the', 0.02190498283359242),\n",
+ " ('series', 0.014565615391043828),\n",
+ " ('was', 0.02068764053209336),\n",
+ " ('launched', 0.026802038905978882),\n",
+ " ('in', 0.032906245358231175),\n",
+ " ('the', 0.023518754011204106),\n",
+ " ('early', 0.031643046886915695),\n",
+ " ('1950', 0.020923533885433596),\n",
+ " (\"'\", 0.001922982863939635),\n",
+ " ('s', 0.0008680969118166506),\n",
+ " (',', 0.0039699761225588884),\n",
+ " ('and', -0.0023379654893345056),\n",
+ " ('spawned', -0.003046762068483186),\n",
+ " ('at', -0.005941765421575043),\n",
+ " ('least', -0.014840409989544763),\n",
+ " ('eight', -0.0034836083217191665),\n",
+ " ('films', -0.0007078384040347249),\n",
+ " ('(', 0.003992166270846944),\n",
+ " ('none', -0.030202736765759346),\n",
+ " ('of', 0.00416781402603549),\n",
+ " ('which', 0.007053407306638115),\n",
+ " ('was', -0.000697922654595511),\n",
+ " ('ever', 0.006731007126713002),\n",
+ " ('released', -0.00442821604055279),\n",
+ " ('in', 0.009210412872360173),\n",
+ " ('the', 0.014757400741781366),\n",
+ " ('u', 0.010884162151784577),\n",
+ " ('.', 0.002304536671777835),\n",
+ " ('s', 0.01400832450477167),\n",
+ " ('.', 0.0029695212720324197),\n",
+ " (')', -0.01938122868017762),\n",
+ " ('.', 0.017912905862854548),\n",
+ " (\"'\", 0.005469883703793135),\n",
+ " ('o', -0.003325863168090179),\n",
+ " ('.', -0.001361679828288333),\n",
+ " ('s', 0.005738681695996811),\n",
+ " ('.', 0.005616455013450803),\n",
+ " ('s', 0.01139297031432768),\n",
+ " ('.', 0.013370234361635985),\n",
+ " ('117', 0.0050244132685380435),\n",
+ " (':', -0.011878638264622836),\n",
+ " ('cairo', 0.012144027405943498),\n",
+ " (',', -0.033285573925645004),\n",
+ " ('nest', 0.014396726709197836),\n",
+ " ('of', -0.012037404705977268),\n",
+ " ('spies', 0.0064995543355021185),\n",
+ " (\"'\", -0.06816113118316341),\n",
+ " ('is', 0.11605761599251842),\n",
+ " ('a', 0.22144731769385737),\n",
+ " ('bree', 0.009780872095417921),\n",
+ " ('##zy', -0.02276522480171687),\n",
+ " ('little', -0.3055678101733113),\n",
+ " ('comedy', 0.17610404251726786),\n",
+ " ('that', 0.1288682243947408),\n",
+ " ('should', -0.027306013377816563),\n",
+ " ('not', -0.034204384120776435),\n",
+ " ('.', -0.03389904710988535),\n",
+ " ('.', -0.007162032431316615),\n",
+ " ('.', 0.009495664819891454),\n",
+ " ('repeat', -0.009372644454315416),\n",
+ " ('not', 0.06314077944925775),\n",
+ " (',', -0.04112959976158538),\n",
+ " ('be', 0.018143754203154706),\n",
+ " ('taken', -0.020017348636882305),\n",
+ " ('too', -0.040720841214626234),\n",
+ " ('seriously', -0.054329884580191994),\n",
+ " ('.', -0.028497018471022047),\n",
+ " ('our', 0.0017752827994947833),\n",
+ " ('protagonist', 0.0107687592037399),\n",
+ " ('finds', 0.020617554974023615),\n",
+ " ('himself', -0.0018500093187726937),\n",
+ " ('in', 0.01564946768194463),\n",
+ " ('the', 0.013036405262199359),\n",
+ " ('middle', -0.011455544492931412),\n",
+ " ('of', 0.011379422098041311),\n",
+ " ('a', 0.016915447064983023),\n",
+ " ('spy', 0.01014517707425702),\n",
+ " ('chase', -0.0015787542148722315),\n",
+ " ('in', 0.022109193843358795),\n",
+ " ('egypt', 0.01733042377013158),\n",
+ " ('(', 0.014807093415942637),\n",
+ " ('with', -0.005624766594314989),\n",
+ " ('mor', -0.042278368368725616),\n",
+ " ('##ro', -0.003068161242139183),\n",
+ " ('##co', 0.0011793608296730119),\n",
+ " ('doing', -0.0002776248893101533),\n",
+ " ('stand', 0.00887397618740937),\n",
+ " ('in', 0.006792721465353066),\n",
+ " ('for', 0.007037553397442764),\n",
+ " ('egypt', 0.015671230226682417),\n",
+ " (')', -0.006235320649709325),\n",
+ " ('to', 0.011548105424976992),\n",
+ " ('find', -0.007522044326544117),\n",
+ " ('out', -0.0011309360663315183),\n",
+ " ('about', -0.0018070001434532074),\n",
+ " ('a', 0.01595184067386024),\n",
+ " ('long', 0.0060166302381749635),\n",
+ " ('lost', 0.004542002761051317),\n",
+ " ('friend', 0.005055231459031988),\n",
+ " ('.', -0.0003535984402976514),\n",
+ " ('what', -0.0015031603261951461),\n",
+ " ('follows', -0.00043840327320822397),\n",
+ " ('is', 0.00013323099478575125),\n",
+ " ('the', 0.0009929291415853108),\n",
+ " ('standard', -0.004320727871042924),\n",
+ " ('james', 0.01131104809691469),\n",
+ " ('bond', 0.0113099025910035),\n",
+ " ('/', 0.0007720565127966261),\n",
+ " ('inspector', 0.003055540499035066),\n",
+ " ('cl', 0.0037974781017327906),\n",
+ " ('##ous', 0.0013602850454576322),\n",
+ " ('##so', -0.005152827993114711),\n",
+ " ('##u', 0.0033703414984924047),\n",
+ " ('kind', 0.007471277978917765),\n",
+ " ('of', 0.014250218050515588),\n",
+ " ('antics', -0.002525615209418278),\n",
+ " ('.', -0.09384199417294489),\n",
+ " ('although', -0.12376718597348467),\n",
+ " ('our', -0.07053131561728779),\n",
+ " ('man', -0.0581400760223069),\n",
+ " ('is', 0.06538501680660999),\n",
+ " ('something', 0.06083262707994975),\n",
+ " ('of', 0.3283866498531868),\n",
+ " ('an', -0.2635471731995881),\n",
+ " ('over', -0.6445264934301554),\n",
+ " ('##t', -0.002690751819062855),\n",
+ " ('x', 0.12126052979561032),\n",
+ " ('##eno', 0.03400466422830756),\n",
+ " ('##ph', 0.024993224985539665),\n",
+ " ('##obe', 0.039580106537034),\n",
+ " (',', -0.015525505520548473),\n",
+ " ('sex', -0.09968740227013319),\n",
+ " ('##ist', -0.08137446203818086),\n",
+ " (',', -0.01882374574208697),\n",
+ " ('homo', -0.012188403877513361),\n",
+ " ('##ph', 0.0016512700463504084),\n",
+ " ('##obe', 0.0018535504180460635),\n",
+ " (',', 0.006524141169345285),\n",
+ " ('it', -0.02395358552135383),\n",
+ " (\"'\", 0.006365265453205652),\n",
+ " ('s', 0.019759006223294368),\n",
+ " ('treated', -0.010700047639669606),\n",
+ " ('as', 0.002980325222953892),\n",
+ " ('pure', 0.0007830093555121671),\n",
+ " ('far', 0.012464790969389952),\n",
+ " ('##ce', -0.008372098861794278),\n",
+ " ('(', 0.014717599253226778),\n",
+ " ('as', 0.004088076552633593),\n",
+ " ('i', -0.0029421403476716066),\n",
+ " ('said', -0.011500099716003975),\n",
+ " (',', -0.007547512367418374),\n",
+ " ('don', 0.0023613622448497955),\n",
+ " (\"'\", -0.009345939118366953),\n",
+ " ('t', -0.01238573661726575),\n",
+ " ('take', 0.0006516759326186994),\n",
+ " ('it', -0.005447039036777147),\n",
+ " ('too', -0.013004566853221468),\n",
+ " ('seriously', -0.009777390307291002),\n",
+ " (')', -0.0024807216502883795),\n",
+ " ('.', -0.0017258625269962098),\n",
+ " ('although', -0.021394901191458187),\n",
+ " ('there', -0.034479684032299486),\n",
+ " ('is', 0.03491266053023672),\n",
+ " ('a', 0.009036558928074286),\n",
+ " ('bit', -0.011110135619094819),\n",
+ " ('of', 0.046942081627644315),\n",
+ " ('rough', -0.03431465965832173),\n",
+ " ('language', -0.009391654405164904),\n",
+ " ('&', 0.0030387847345936846),\n",
+ " ('cartoon', -0.007861543124426724),\n",
+ " ('violence', -0.010456171276471585),\n",
+ " (',', -0.004622890330208062),\n",
+ " ('it', 0.005816948385737887),\n",
+ " (\"'\", 0.0007374109948335743),\n",
+ " ('s', 0.012683587772707393),\n",
+ " ('basically', -0.027812300222263855),\n",
+ " ('okay', 0.020764675260313906),\n",
+ " ('for', 0.0524510539137124),\n",
+ " ('older', 0.005053663679950245),\n",
+ " ('kids', -0.008404374563553063),\n",
+ " ('(', -0.0007926342686477285),\n",
+ " ('ages', 0.010431815703592244),\n",
+ " ('12', -0.0017756701438887829),\n",
+ " ('&', 0.00040228368115861305),\n",
+ " ('up', -0.00780539608648113),\n",
+ " (')', 0.00719567801124226),\n",
+ " ('.', 0.006139613752595804),\n",
+ " ('as', 0.019865493023700727),\n",
+ " ('previously', 0.0035328818305448537),\n",
+ " ('stated', -0.0140395478736309),\n",
+ " ('in', 0.005201754830517284),\n",
+ " ('the', 0.0013594409304473999),\n",
+ " ('subject', -0.0027806590494205054),\n",
+ " ('line', 0.013268309244238878),\n",
+ " (',', 0.019100526253402774),\n",
+ " ('just', -0.005929062912303688),\n",
+ " ('sit', -0.005123916226221971),\n",
+ " ('back', -0.0018391329346027318),\n",
+ " (',', 0.011395590268654948),\n",
+ " ('pass', -0.00234956821010609),\n",
+ " ('the', 0.019254392059196507),\n",
+ " ('popcorn', -0.002366716631118406),\n",
+ " ('&', 0.0017431599457522822),\n",
+ " ('just', 0.0021839948507971856),\n",
+ " ('enjoy', 0.01800819383003322),\n",
+ " ('.', -0.011505753635389266),\n",
+ " ('[SEP]', 0.0)]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 39
+ }
+ ],
+ "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": "eef91c7e-f6a1-46b2-d332-96ffb90c15eb"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'LABEL_1'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 40
+ }
+ ],
+ "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": 1000
+ },
+ "outputId": "c102ef0d-22a9-4025-9ecf-bf6b615ffb90"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='tokens'>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 42
+ },
+ {
+ "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": 239
+ },
+ "outputId": "66df5a4f-a6fb-4d05-bcef-2fdadd44d336"
+ },
+ "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>0.48</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%, 93%); opacity:1.0; line-height:1.75\"><font color=\"black\"> this </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> latest </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> entry </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); 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(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(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> films </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\"> 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\"> agent </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\"> 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%, 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%, 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%, 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%, 100%); 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(0, 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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bond </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\"> . </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(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\"> was </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> launched </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(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> early </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> 1950 </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(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\"> and </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> spawned </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> at </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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%, 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\"> none </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\"> which </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> was </font></mark><mark style=\"background-color: hsl(120, 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%, 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\"> 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(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\"> ) </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\"> ' </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%, 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\"> 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(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\"> cairo </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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> nest </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\"> spies </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%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> is </font></mark><mark style=\"background-color: hsl(120, 75%, 89%); 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(0, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> little </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0; line-height:1.75\"><font color=\"black\"> comedy </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); 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%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> not </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%, 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\"> repeat </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> not </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%, 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%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> too </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> seriously </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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> our </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); 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(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> himself </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%, 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\"> 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(120, 75%, 100%); 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\"> 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%, 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\"> 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(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> with </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); 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(120, 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(120, 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(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> for </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> egypt </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\"> to </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> find </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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%, 100%); 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\"> 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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> friend </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\"> 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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> standard </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%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bond </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\"> inspector </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> cl </font></mark><mark style=\"background-color: hsl(120, 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(120, 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\"> kind </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\"> antics </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%, 96%); opacity:1.0; line-height:1.75\"><font color=\"black\"> although </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> our </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> man </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(120, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> something </font></mark><mark style=\"background-color: hsl(120, 75%, 84%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 90%); opacity:1.0; line-height:1.75\"><font color=\"black\"> an </font></mark><mark style=\"background-color: hsl(0, 75%, 75%); 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(120, 75%, 94%); opacity:1.0; line-height:1.75\"><font color=\"black\"> x </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##eno </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ph </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##obe </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%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> sex </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> ##ist </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\"> homo </font></mark><mark style=\"background-color: hsl(120, 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%, 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\"> it </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\"> 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(120, 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%, 100%); 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(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\"> i </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> said </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\"> don </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\"> t </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> take </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); 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(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> seriously </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\"> although </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> there </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); 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\"> a </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bit </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> rough </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> language </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\"> 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%, 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%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> basically </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0; line-height:1.75\"><font color=\"black\"> okay </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0; line-height:1.75\"><font color=\"black\"> for </font></mark><mark style=\"background-color: hsl(120, 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(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\"> 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(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\"> . </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(120, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> previously </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> stated </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%, 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(120, 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%, 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\"> 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(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> popcorn </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\"> just </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); 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": "190ec6f5-72ba-4ea3-9668-97cf7f09feab"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'LABEL_0'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 46
+ }
+ ],
+ "source": [
+ "cls_explainer.predicted_class_name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fwpGOSlgigjG",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8569f633-6947-4019-d397-0538e902c9ce"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('[CLS]', 0.0),\n",
+ " ('it', 0.15579458876876753),\n",
+ " (\"'\", 0.03767566036985144),\n",
+ " ('s', -0.048666864633006165),\n",
+ " ('really', 0.04443877428149513),\n",
+ " ('too', 0.14540089508868156),\n",
+ " ('bad', 0.5281307500239848),\n",
+ " ('that', -0.05500572459589118),\n",
+ " ('nobody', -0.013312116903928833),\n",
+ " ('knows', 0.008730958199344092),\n",
+ " ('about', 0.02705342988575408),\n",
+ " ('this', 0.011326547060353945),\n",
+ " ('movie', 0.04699556944883931),\n",
+ " ('.', 0.03948465300012618),\n",
+ " ('i', 0.005165082385061563),\n",
+ " ('think', 0.01263703627837709),\n",
+ " ('if', 0.022612587492794878),\n",
+ " ('it', -0.0006115277969186253),\n",
+ " ('were', -0.0004332737101635555),\n",
+ " ('just', 0.00473609204450029),\n",
+ " ('spruce', -0.001310111014920548),\n",
+ " ('##d', 0.0011278836091585763),\n",
+ " ('up', 0.004493302409486246),\n",
+ " ('a', -0.04017102321254915),\n",
+ " ('little', 0.012420252744425368),\n",
+ " ('and', 0.005387387525441402),\n",
+ " ('if', -0.0014021383517850887),\n",
+ " ('it', -0.0006029917464286114),\n",
+ " ('weren', 0.011687686514411356),\n",
+ " (\"'\", 0.007750703233651025),\n",
+ " ('t', 0.002639964168222077),\n",
+ " ('so', 0.05532692060949901),\n",
+ " ('low', 0.13647123438883588),\n",
+ " ('-', 0.04923539430438573),\n",
+ " ('budget', 0.07020160168140419),\n",
+ " (',', -0.0025569817084954505),\n",
+ " ('i', -0.030593557160175394),\n",
+ " ('think', -0.01768083072619913),\n",
+ " ('one', -0.01368027344864894),\n",
+ " ('of', -0.00177991619373207),\n",
+ " ('the', -0.02294422026276411),\n",
+ " ('major', 0.001886345589342683),\n",
+ " ('film', -0.0011156844450936631),\n",
+ " ('companies', 0.0073367390639760294),\n",
+ " ('might', 0.01009783247889323),\n",
+ " ('have', 0.013582971470080493),\n",
+ " ('wanted', 0.007707826010801647),\n",
+ " ('to', -0.002368392164627609),\n",
+ " ('take', -0.001449458188902427),\n",
+ " ('it', -0.0029972876812839368),\n",
+ " ('.', -0.02078614019344952),\n",
+ " ('i', 0.003234657006775826),\n",
+ " ('first', 0.00597886285019724),\n",
+ " ('saw', 0.0013110729616111817),\n",
+ " ('this', 0.020028485322965073),\n",
+ " ('movie', 0.027299414130568928),\n",
+ " ('when', -0.02104722532535892),\n",
+ " ('i', -0.0016355864424663212),\n",
+ " ('was', 0.0178785757510812),\n",
+ " ('11', -0.004148389608473534),\n",
+ " (',', 0.014018935681487868),\n",
+ " ('and', -0.03800693736484566),\n",
+ " ('i', -0.007633574995095456),\n",
+ " ('thought', 0.03356273370065653),\n",
+ " ('it', -0.052393244014273865),\n",
+ " ('was', -0.3723143440380725),\n",
+ " ('so', -0.20253170892770164),\n",
+ " ('powerful', -0.49195855176485404),\n",
+ " ('with', -0.28931907533150353),\n",
+ " ('the', -0.10082174430239103),\n",
+ " ('many', -0.0892258053605878),\n",
+ " ('great', -0.209953362505764),\n",
+ " (',', 0.05632273773050674),\n",
+ " ('yet', 0.0026191994753909163),\n",
+ " ('illegal', 0.07494734635042131),\n",
+ " ('lengths', -0.010940770306160924),\n",
+ " ('that', -0.051082133535754845),\n",
+ " ('mitchell', 0.0027965138157017688),\n",
+ " ('goes', 0.016664117424961766),\n",
+ " ('to', -0.006085579422155174),\n",
+ " ('just', 0.005387249896206727),\n",
+ " ('to', -0.005585320836837565),\n",
+ " ('keep', 0.0057384168738534224),\n",
+ " ('his', -0.02057574624160501),\n",
+ " ('family', -0.008776710326139569),\n",
+ " ('together', -0.013993429080076758),\n",
+ " ('.', -0.006008855947390037),\n",
+ " ('it', -0.05628633470853645),\n",
+ " ('inspired', -0.14223135233890521),\n",
+ " ('me', -0.028836561067285393),\n",
+ " ('then', 0.07831208795055669),\n",
+ " ('and', 0.01040839817427114),\n",
+ " ('it', 0.009416986574798083),\n",
+ " ('ama', 0.0003594215033618114),\n",
+ " ('##zes', 0.014906288009667763),\n",
+ " ('me', -0.003825266000476206),\n",
+ " ('now', 0.02720206894274278),\n",
+ " ('.', -0.015057509786155063),\n",
+ " ('if', 0.024752694465620194),\n",
+ " ('you', 0.022009103203969472),\n",
+ " (\"'\", 0.002552629842412161),\n",
+ " ('re', 0.017854426097560196),\n",
+ " ('lucky', -0.004449996808023851),\n",
+ " ('enough', 0.015177395996756287),\n",
+ " ('to', -0.0035908963630793215),\n",
+ " ('find', 0.00902752383869785),\n",
+ " ('a', -0.016715953866915528),\n",
+ " ('copy', 0.005041589979739654),\n",
+ " ('of', -0.008283455902864527),\n",
+ " ('this', 0.02476340242460179),\n",
+ " ('movie', 0.05030975125437546),\n",
+ " (',', 0.014056053977644908),\n",
+ " ('don', 0.012497046681469861),\n",
+ " (\"'\", 0.00034315937017799),\n",
+ " ('t', 0.034518535423079144),\n",
+ " ('miss', 0.017334191519132278),\n",
+ " ('it', 0.01355026060140187),\n",
+ " ('!', -0.022683950843705492),\n",
+ " ('[SEP]', 0.0)]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 47
+ }
+ ],
+ "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": 1000
+ },
+ "outputId": "58f9cd04-476e-4d07-a785-b589c6ec58a3"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='tokens'>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 49
+ },
+ {
+ "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": 166
+ },
+ "outputId": "4420acec-cc00-49a4-a949-7ef5cc17e2e3"
+ },
+ "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": "051a3f7c-ba19-4289-fd4e-990cc35f5fe9"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "( tokens score\n",
+ " 6 bad 0.528131\n",
+ " 1 it 0.155795\n",
+ " 5 too 0.145401\n",
+ " 32 low 0.136471\n",
+ " 90 then 0.078312,\n",
+ " tokens score\n",
+ " 67 powerful -0.491959\n",
+ " 65 was -0.372314\n",
+ " 68 with -0.289319\n",
+ " 71 great -0.209953\n",
+ " 66 so -0.202532)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 52
+ }
+ ]
+ },
+ {
+ "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": 53
+ },
+ "outputId": "723cc365-6784-42cd-d1b5-d1421e58ffe3"
+ },
+ "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": 53
+ }
+ ]
+ },
+ {
+ "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": "98e3424d-2897-47c1-e1d6-e85099d0882a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 100/100 [00:15<00:00, 6.40it/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/",
+ "height": 424
+ },
+ "outputId": "94fa679e-c7dc-415e-ff11-493e4f3cfc8f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " tokens score\n",
+ "28 entertaining 0.333611\n",
+ "21 i 0.204222\n",
+ "23 i 0.178905\n",
+ "24 was 0.132996\n",
+ "27 an 0.131660\n",
+ ".. ... ...\n",
+ "17 very 0.602366\n",
+ "16 was 0.488280\n",
+ "18 pleased 0.333021\n",
+ "19 with 0.326605\n",
+ "13 war 0.192443\n",
+ "\n",
+ "[300 rows x 2 columns]"
+ ],
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+ " 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",
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+ " margin-bottom: 4px;\n",
+ " }\n",
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+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
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+ " [key], {});\n",
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+ "\n",
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+ " '<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",
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+ " await google.colab.output.renderOutput(dataTable, element);\n",
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+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
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+ " try {\n",
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+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
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+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-acfdaa46-fa0d-402e-8bb3-05a5054fed61 button');\n",
+ " quickchartButtonEl.style.display =\n",
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+ " </div>\n",
+ " </div>\n"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 56
+ }
+ ]
+ },
+ {
+ "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": "6777b985-7ad6-4dfe-e106-f764f31e0c32"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " score\n",
+ "tokens \n",
+ "admire 0.835418\n",
+ "wonderful 0.817557\n",
+ "strength 0.797809\n",
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+ "hits 0.683558\n",
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+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
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+ " display: none;\n",
+ " fill: #1967D2;\n",
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+ " 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",
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+ " 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-6621b9a9-cfc0-4dcb-9410-cb9e029b0d94 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-6621b9a9-cfc0-4dcb-9410-cb9e029b0d94');\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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+ "\n",
+ "\n",
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+ " </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",
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+ " .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",
+ " 20% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
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+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
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+ " border-right-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
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+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 80% {\n",
+ " 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-1a8471c5-a1b6-4fac-97ea-ad170c200a07 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
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+ " </div>\n"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 58
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_low_avg.nlargest(20,\"score\")"
+ ],
+ "metadata": {
+ "id": "szxynupe7LBV",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 708
+ },
+ "outputId": "07a362dd-1a40-4d1c-bdd3-1e5205054478"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " score\n",
+ "tokens \n",
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+ "metadata": {
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+ },
+ "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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+ ],
+ "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"
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+ "source": [
+ "ner_explainer = TokenClassificationExplainer(model=model, tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "D7f05OD550e1"
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+ "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": "69620977-df4f-445c-b7ad-59d420515e6a"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'New': {'label': 'B-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.11522643309404876),\n",
+ " ('-', 0.4660375266820166),\n",
+ " ('York', 0.6893386224540738),\n",
+ " ('City', 0.5121534890980919),\n",
+ " ('is', 0.13319079037219464),\n",
+ " ('a', 0.021819309216920164),\n",
+ " ('place', 0.10400894025897137),\n",
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+ " ('of', -0.0020049499590545426),\n",
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+ " (',', 0.030198105788731115),\n",
+ " ('like', 0.00976708899951808),\n",
+ " ('Donald', -0.005156478074569604),\n",
+ " ('Trump', 0.009757534477472145),\n",
+ " ('.', 0.0364315553583064),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " '-': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.8222961678834123),\n",
+ " ('-', 0.2991284415440371),\n",
+ " ('York', 0.47518044536973014),\n",
+ " ('City', -0.03536229543315673),\n",
+ " ('is', 0.05356118543874819),\n",
+ " ('a', 0.029608228416299117),\n",
+ " ('place', -0.017305781697097917),\n",
+ " ('full', 0.009172988873627353),\n",
+ " ('of', 0.018796696221034248),\n",
+ " ('celebrities', -0.02261371100887632),\n",
+ " (',', 0.0279369651378289),\n",
+ " ('like', -0.00782480123639375),\n",
+ " ('Donald', -0.009188002458814567),\n",
+ " ('Trump', -0.02538115733316795),\n",
+ " ('.', 0.02720549165812171),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'York': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', 0.5337485188148924),\n",
+ " ('-', 0.2599878586101721),\n",
+ " ('York', 0.7606982195385698),\n",
+ " ('City', 0.24466553813800374),\n",
+ " ('is', 0.06205637258439966),\n",
+ " ('a', -0.02758692111790461),\n",
+ " ('place', 0.02510632074333246),\n",
+ " ('full', -0.051400225810259104),\n",
+ " ('of', -0.019691558245979613),\n",
+ " ('celebrities', -0.006924889291676202),\n",
+ " (',', -0.012906320693513527),\n",
+ " ('like', -0.01518390900810952),\n",
+ " ('Donald', -0.012040381107786127),\n",
+ " ('Trump', 0.01127966838832772),\n",
+ " ('.', 0.0025388316891732164),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'City': {'label': 'I-LOC',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.08719342194919136),\n",
+ " ('-', -0.05248975718332697),\n",
+ " ('York', 0.586929100512792),\n",
+ " ('City', 0.7707566106643919),\n",
+ " ('is', 0.11985667832378603),\n",
+ " ('a', 0.04739017900177101),\n",
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+ " ('full', 0.008286903319825954),\n",
+ " ('of', 0.011989889401706585),\n",
+ " ('celebrities', -0.003247520306721069),\n",
+ " (',', 0.03124839314478579),\n",
+ " ('like', 0.02161510614446926),\n",
+ " ('Donald', 0.00568036244881266),\n",
+ " ('Trump', 0.004046146394860746),\n",
+ " ('.', 0.012740358991386473),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'Donald': {'label': 'B-PER',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.013149714221774965),\n",
+ " ('-', 0.03789821952772743),\n",
+ " ('York', 0.025402804030406363),\n",
+ " ('City', 0.008721879837465873),\n",
+ " ('is', 0.014231901732516674),\n",
+ " ('a', 0.027366328925696636),\n",
+ " ('place', -0.019735029052264017),\n",
+ " ('full', -0.012471943732158383),\n",
+ " ('of', 0.03221015109013002),\n",
+ " ('celebrities', 0.024813110445476703),\n",
+ " (',', -0.017655974120428027),\n",
+ " ('like', 0.2729819394960673),\n",
+ " ('Donald', 0.7191808139073995),\n",
+ " ('Trump', 0.6334536201748232),\n",
+ " ('.', -0.03470394203197996),\n",
+ " ('[SEP]', 0.0)]},\n",
+ " 'Trump': {'label': 'I-PER',\n",
+ " 'attribution_scores': [('[CLS]', 0.0),\n",
+ " ('New', -0.0013212443968283467),\n",
+ " ('-', 0.01609338506994575),\n",
+ " ('York', 0.04688264143343644),\n",
+ " ('City', 0.01379633074803389),\n",
+ " ('is', 0.0035909192520868437),\n",
+ " ('a', 0.02421137229747189),\n",
+ " ('place', 0.00041611985281236576),\n",
+ " ('full', -0.000724047071701861),\n",
+ " ('of', 0.02103577693952245),\n",
+ " ('celebrities', 0.0181675130835007),\n",
+ " (',', 0.030742382949491734),\n",
+ " ('like', 0.15109027194280347),\n",
+ " ('Donald', 0.7343405887541932),\n",
+ " ('Trump', 0.6562604431378266),\n",
+ " ('.', -0.04765876061428272),\n",
+ " ('[SEP]', 0.0)]}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 64
+ }
+ ],
+ "source": [
+ "attributions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dm58uxm_50e2",
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+ "base_uri": "https://localhost:8080/",
+ "height": 252
+ },
+ "outputId": "fafc44bf-4f1d-41ef-ebee-65f84412fc0e"
+ },
+ "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%); opacity:1.0; line-height:1.75\"><font color=\"black\"> full </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%, 99%); 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(120, 75%, 87%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(120, 75%, 65%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(120, 75%, 69%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </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\"> [SEP] </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>I-PER</b></text></td><td><text style=\"padding-right:2em\"><b>I-PER (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>Trump</b></text></td><td><text style=\"padding-right:2em\"><b>1.67</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%, 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\"> 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(120, 75%, 100%); 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(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\"> 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%, 93%); opacity:1.0; line-height:1.75\"><font color=\"black\"> like </font></mark><mark style=\"background-color: hsl(120, 75%, 64%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Donald </font></mark><mark style=\"background-color: hsl(120, 75%, 68%); opacity:1.0; line-height:1.75\"><font color=\"black\"> Trump </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\"> [SEP] </font></mark></td><tr></table>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "html = ner_explainer.visualize()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VSn32SrQ50e3"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "visual",
+ "language": "python",
+ "name": "visual"
+ },
+ "language_info": {
+ "codemirror_mode": {
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+ "version": 3
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+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.5"
+ },
+ "colab": {
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+ "collapsed_sections": [
+ "-XRoTAe-50e1"
+ ]
+ },
+ "accelerator": "GPU",
+ "gpuClass": "standard",
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