TP12_bert_finetuning.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gguex/ISH_ressources_cours_ML/blob/main/TP12_bert_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TP 12 : BERT fine-tuning\n",
        "\n",
        "Dans ce TP, nous allons construire un modèle d'analyse des sentiments avec comme base un modèle BERT pré-entrainé. Nous allons ajouter une couche entièrement connectée à la sortie (du premier token) d'un modèle BERT, projetant le vecteur de contexte de BERT sur un seul neurone sensé représenter le log-odd de la séquence d'appartenir à la classe \"sentiment positif\".\n",
        "\n",
        "Pour entrainer notre modèle, nous allons utiliser le jeu de données \"Large Movie Review Dataset\" (https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews), qui contient 50'000 évaluations de films faites par des utilisateurs sur IMDB. Ces reviews ont une annotation comme étant positives ou négatives.\n",
        "\n",
        "Notre module d'analyse des sentiments va ensuite servir (même si l'entrainement sur des évaluations de films est discutable pour cette utilisation) à évaluer le sentiment positif/négatif qui se dégage à l'intérieur de chaque livre des Misérables.\n",
        "\n",
        "Le modèle BERT pré-entrainé sera issu de https://huggingface.co/, où l'on peut trouver une grande quantité de modèles pré-entrainés. Ces modèles se téléchargent et s'utilisent généralement en utilisant la librairie `transformers`."
      ],
      "metadata": {
        "id": "4A_v85Z6qNlu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va utiliser les librairies suivantes :"
      ],
      "metadata": {
        "id": "I50_Kxy4180T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.gridspec import GridSpec\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, \\\n",
        "  get_linear_schedule_with_warmup\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "GvOo6CBV-QCQ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Et nous allons utiliser l'accélération graphique, si elle est disponible :"
      ],
      "metadata": {
        "id": "wtACoIOOAF3i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfZFEO-387ud",
        "outputId": "99f53065-8869-4cd6-b99f-fb2463ee3bea"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Construction et finetuning du modèle\n",
        "\n",
        "Avant de construire notre modèle, regardons nos données. On les charge et les affiche. La colonne \"review\" contient la critique, et la colonne \"sentiment\" l'appréciation, qui peut être \"positive\" ou \"negative\". Il s'agira de traduire ces deux colonnes dans des données utilisables par notre modèle. Nous y reviendrons après avoir examiné le modèle que nous allons utiliser."
      ],
      "metadata": {
        "id": "3-g0wc3OAMiR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = \"/content/drive/MyDrive/Colab Notebooks/\" \\\n",
        "              \"ml_data/TP12/IMDB Dataset.csv\"\n",
        "data = pd.read_csv(data_path)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QDguNX-iGFD3",
        "outputId": "9803c531-fa09-4300-f9bf-684b72be2afc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                  review sentiment\n",
              "0      One of the other reviewers has mentioned that ...  positive\n",
              "1      A wonderful little production. <br /><br />The...  positive\n",
              "2      I thought this was a wonderful way to spend ti...  positive\n",
              "3      Basically there's a family where a little boy ...  negative\n",
              "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "...                                                  ...       ...\n",
              "49995  I thought this movie did a down right good job...  positive\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
              "49997  I am a Catholic taught in parochial elementary...  negative\n",
              "49998  I'm going to have to disagree with the previou...  negative\n",
              "49999  No one expects the Star Trek movies to be high...  negative\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49582,\n        \"samples\": [\n          \"\\\"Soul Plane\\\" is a horrible attempt at comedy that only should appeal people with thick skulls, bloodshot eyes and furry pawns. <br /><br />The plot is not only incoherent but also non-existent, acting is mostly sub sub-par with a gang of highly moronic and dreadful characters thrown in for bad measure, jokes are often spotted miles ahead and almost never even a bit amusing. This movie lacks any structure and is full of racial stereotypes that must have seemed old even in the fifties, the only thing it really has going for it is some pretty ladies, but really, if you want that you can rent something from the \\\"Adult\\\" section. OK?<br /><br />I can hardly see anything here to recommend since you'll probably have a lot a better and productive time chasing rats with a sledgehammer or inventing waterproof teabags or whatever.<br /><br />2/10\",\n          \"Guest from the Future tells a fascinating story of time travel, friendship, battle of good and evil -- all with a small budget, child actors, and few special effects. Something for Spielberg and Lucas to learn from. ;) A sixth-grader Kolya \\\"Nick\\\" Gerasimov finds a time machine in the basement of a decrepit building and travels 100 years into the future. He discovers a near-perfect, utopian society where robots play guitars and write poetry, everyone is kind to each other and people enjoy everything technology has to offer. Alice is the daughter of a prominent scientist who invented a device called Mielophone that allows to read minds of humans and animals. The device can be put to both good and bad use, depending on whose hands it falls into. When two evil space pirates from Saturn who want to rule the universe attempt to steal Mielophone, it falls into the hands of 20th century school boy Nick. With the pirates hot on his tracks, he travels back to his time, followed by the pirates, and Alice. Chaos, confusion and funny situations follow as the luckless pirates try to blend in with the earthlings. Alice enrolls in the same school Nick goes to and demonstrates superhuman abilities in PE class. The catch is, Alice doesn't know what Nick looks like, while the pirates do. Also, the pirates are able to change their appearance and turn literally into anyone. (Hmm, I wonder if this is where James Cameron got the idea for Terminator...) Who gets to Nick -- and Mielophone -- first? Excellent plot, non-stop adventures, and great soundtrack. I wish Hollywood made kid movies like this one...\",\n          \"\\\"National Treasure\\\" (2004) is a thoroughly misguided hodge-podge of plot entanglements that borrow from nearly every cloak and dagger government conspiracy clich\\u00e9 that has ever been written. The film stars Nicholas Cage as Benjamin Franklin Gates (how precious is that, I ask you?); a seemingly normal fellow who, for no other reason than being of a lineage of like-minded misguided fortune hunters, decides to steal a 'national treasure' that has been hidden by the United States founding fathers. After a bit of subtext and background that plays laughably (unintentionally) like Indiana Jones meets The Patriot, the film degenerates into one misguided whimsy after another \\u0096 attempting to create a 'Stanley Goodspeed' regurgitation of Nicholas Cage and launch the whole convoluted mess forward with a series of high octane, but disconnected misadventures.<br /><br />The relevancy and logic to having George Washington and his motley crew of patriots burying a king's ransom someplace on native soil, and then, going through the meticulous plan of leaving clues scattered throughout U.S. currency art work, is something that director Jon Turteltaub never quite gets around to explaining. Couldn't Washington found better usage for such wealth during the start up of the country? Hence, we are left with a mystery built on top of an enigma that is already on shaky ground by the time Ben appoints himself the new custodian of this untold wealth. Ben's intentions are noble \\u0096 if confusing. He's set on protecting the treasure. For who and when?\\u0085your guess is as good as mine.<br /><br />But there are a few problems with Ben's crusade. First up, his friend, Ian Holmes (Sean Bean) decides that he can't wait for Ben to make up his mind about stealing the Declaration of Independence from the National Archives (oh, yeah \\u0096 brilliant idea!). Presumably, the back of that famous document holds the secret answer to the ultimate fortune. So Ian tries to kill Ben. The assassination attempt is, of course, unsuccessful, if overly melodramatic. It also affords Ben the opportunity to pick up, and pick on, the very sultry curator of the archives, Abigail Chase (Diane Kruger). She thinks Ben is clearly a nut \\u0096 at least at the beginning. But true to action/romance form, Abby's resolve melts quicker than you can say, \\\"is that the Hope Diamond?\\\" The film moves into full X-File-ish mode, as the FBI, mistakenly believing that Ben is behind the theft, retaliate in various benign ways that lead to a multi-layering of action sequences reminiscent of Mission Impossible meets The Fugitive. Honestly, don't those guys ever get 'intelligence' information that is correct? In the final analysis, \\\"National Treasure\\\" isn't great film making, so much as it's a patchwork rehash of tired old bits from other movies, woven together from scraps, the likes of which would make IL' Betsy Ross blush.<br /><br />The Buena Vista DVD delivers a far more generous treatment than this film is deserving of. The anamorphic widescreen picture exhibits a very smooth and finely detailed image with very rich colors, natural flesh tones, solid blacks and clean whites. The stylized image is also free of blemishes and digital enhancements. The audio is 5.1 and delivers a nice sonic boom to your side and rear speakers with intensity and realism. Extras include a host of promotional junket material that is rather deep and over the top in its explanation of how and why this film was made. If only, as an audience, we had had more clarification as to why Ben and co. were chasing after an illusive treasure, this might have been one good flick. Extras conclude with the theatrical trailer, audio commentary and deleted scenes. Not for the faint-hearted \\u0096 just the thick-headed.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Après avoir trouvé un modèle qui semble convenir sur https://huggingface.co/, nous pouvons sauvegarder son nom dans une variable. Ici deux modèles sont suggérés :\n",
        "\n",
        "* `\"bert-base-uncased\"` : le modèle qui est le plus fréquemment utilisé lorsque l'on utilise BERT, mais qui est relativement volumineux.\n",
        "* `\"prajjwal1/bert-mini\"` : un modèle miniature de BERT, permettant des calculs plus rapides au détriment d'une moins bonne représentation des séquences d'entrée.\n",
        "\n",
        "Vous pouvez décommenter le modèle qui vous convient, ou en trouver encore un autre. Les deux modèles sont \"uncased\", c'est-à-dire qu'ils ne prennent pas en compte les majuscules."
      ],
      "metadata": {
        "id": "dtXnhxpMH1cQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model_name = \"bert-base-uncased\"\n",
        "model_name = \"prajjwal1/bert-mini\""
      ],
      "metadata": {
        "id": "h-0xPwbQI4Kl"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Les modèles de la librairie `transformers` proposent des tokenisateurs associés, permettant ainsi de pré-traiter les données textuelles de manière à les rendre compatible avec les entrées attendues. Nous allons charger le tokenisateur de type BERT basé sur le nom de notre modèle."
      ],
      "metadata": {
        "id": "5bcdRUnYJNzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "qEGN4bzs-pxn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "cf9f393ec60f46119152c18ab4f349e0",
            "4601db9f1e2d442da7632bf44b84a708",
            "3eca68dc21bd4e7c833438f1f76ee7dd",
            "bf73d3b4b7d14896931d5f74fb136094",
            "669587deb0204781a6620f3c9b65968b",
            "da0c74e1bec3448aa7a23be1f0441f43",
            "d9efc57e44304879b10e2730e31a1056",
            "3ce35531fcce4833a50ae6b92b1fb4ee",
            "f0ecba1d7e77466da25127bb36c5e848",
            "c5bb5f7d53dd440cbc7dc59db2341ca6",
            "9350037293164bf0ab130c1e354acdd3",
            "ce8f5ec0b7b141638fc533145c98aab7",
            "e9a5719d72dc4cb99a6e45c29656e69a",
            "e852e061e0bb4fc389bcf55014f74486",
            "6fee9ffb86b74fe7aa2b8cefb03dbe00",
            "34ec771cc7e94f29bad6f52c62bed289",
            "9d233eb9ecc142478a96ba888293061e",
            "20d9e11808c84d36b3654942eb1f915a",
            "f4164aec434b47f7a80143f9a0da3fd4",
            "fc3a44209fb0444dbed4b4fb20047e1f",
            "5aaf19835bfd4184a3b4f578d1aa2444",
            "e62ef0465a7048f68b88e8d98818dd4d"
          ]
        },
        "outputId": "af202cb9-f0fe-4138-c149-0460bb6f9536"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
            "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
            "You are not authenticated with the Hugging Face Hub in this notebook.\n",
            "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf9f393ec60f46119152c18ab4f349e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce8f5ec0b7b141638fc533145c98aab7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regardons quelques opérations qu'il est possible d'effectuer avec cet objet, tel que la séparation et la transformation en identifiants numériques d'une chaine de caractères."
      ],
      "metadata": {
        "id": "VusbiSXbJ7Re"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example_text = \"We are going to train a model, for sentiment classification!\"\n",
        "tokens = tokenizer.tokenize(example_text)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(example_text)\n",
        "print(tokens)\n",
        "print(token_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxmP2aWk_8Mh",
        "outputId": "275fbead-2d22-4b2f-a0b7-3ec75967fb0f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We are going to train a model, for sentiment classification!\n",
            "['we', 'are', 'going', 'to', 'train', 'a', 'model', ',', 'for', 'sentiment', 'classification', '!']\n",
            "[2057, 2024, 2183, 2000, 3345, 1037, 2944, 1010, 2005, 15792, 5579, 999]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La méthode `encode_plus()` permet un traitement plus complet d'une chaine de caractères, en donnant non seulement la traduction en identifiants numériques des différents tokens, mais aussi le masque pour le méchanisme d'attention. Différentes options sont possibles, telles que la longueur maximale des séquences (`max_length=32`) et le type de tenseurs à donner (tenseurs de Pytorch avec `return_tensors=\"pt\"`)."
      ],
      "metadata": {
        "id": "t_rBI_HgKJgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "    example_text,\n",
        "    truncation=True,\n",
        "    max_length=32,\n",
        "    add_special_tokens=True,\n",
        "    return_token_type_ids=False,\n",
        "    padding=\"max_length\",\n",
        "    return_attention_mask=True,\n",
        "    return_tensors=\"pt\"\n",
        ")\n",
        "print(encoding[\"input_ids\"])\n",
        "print(encoding[\"attention_mask\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVDaxRMuEwZ3",
        "outputId": "471c1cc5-3f16-4ef2-fa50-db4daef4ae49"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[  101,  2057,  2024,  2183,  2000,  3345,  1037,  2944,  1010,  2005,\n",
            "         15792,  5579,   999,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cette méthode nous permet donc de construire une transformation que nous allons utiliser sur notre dataset, où l'on précise la longueur maximale des séquences et le tokenisateur à utiliser."
      ],
      "metadata": {
        "id": "SqIKBE2gLKEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def my_transform(text, tokenizer, max_length):\n",
        "\n",
        "  # L'encodage\n",
        "  encoding = tokenizer.encode_plus(\n",
        "      text,\n",
        "      truncation=True,\n",
        "      max_length=max_length,\n",
        "      add_special_tokens=True,\n",
        "      return_token_type_ids=False,\n",
        "      padding=\"max_length\",\n",
        "      return_attention_mask=True,\n",
        "      return_tensors=\"pt\",\n",
        "  )\n",
        "  # Retour de l'encodage et du masque\n",
        "  return encoding[\"input_ids\"].flatten(), encoding[\"attention_mask\"].flatten()"
      ],
      "metadata": {
        "id": "SQb7Sh3eMVSG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il est temps de créer notre classe héritée de `torch.utils.data.Dataset`. Nous transformons les sorties en valeurs numériques (avec 1 = \"positive\", 0 = \"negative\"), puis en array numpy. Lorsqu'un élément du dataset est demandé, nous appliquons la transformation (donnée en argument) et transformons la sortie en tenseur."
      ],
      "metadata": {
        "id": "e3VjUYJMLfNb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDataset(Dataset):\n",
        "\n",
        "  # Constructeur\n",
        "  def __init__(self, data_path, transform, tokenizer, sent_len):\n",
        "    # On charge les données\n",
        "    df = pd.read_csv(data_path)\n",
        "    self.inputs = df.review.to_numpy()\n",
        "    self.probs = (df.sentiment == \"positive\").to_numpy()\n",
        "    self.transform = transform\n",
        "    self.tokenizer = tokenizer\n",
        "    self.sent_len = sent_len\n",
        "\n",
        "  # Longueur\n",
        "  def __len__(self):\n",
        "    return len(self.probs)\n",
        "\n",
        "  # Un élément\n",
        "  def __getitem__(self, index):\n",
        "    input = self.transform(str(self.inputs[index]),\n",
        "                           self.tokenizer,\n",
        "                           self.sent_len)\n",
        "    prob = torch.tensor(self.probs[index], dtype=torch.float32)\n",
        "    return input, prob"
      ],
      "metadata": {
        "id": "bU7iZmOdFw7M"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Créons une instance de notre classe pour notre dataset, et regardons un élément. Une séquence d'entiers et le masque sont donnés comme éléments d'entrée."
      ],
      "metadata": {
        "id": "qoVlFWkjOpKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 512\n",
        "data_path = \"/content/drive/MyDrive/Colab Notebooks/\" \\\n",
        "              \"ml_data/TP12/IMDB Dataset.csv\"\n",
        "my_data = MyDataset(data_path, my_transform, tokenizer, max_length)\n",
        "my_data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ-gbiBJOxei",
        "outputId": "03c92f47-a6ca-4a30-8baa-fbdb8bb668a3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((tensor([  101,  2028,  1997,  1996,  2060, 15814,  2038,  3855,  2008,  2044,\n",
              "           3666,  2074,  1015, 11472,  2792,  2017,  1005,  2222,  2022, 13322,\n",
              "           1012,  2027,  2024,  2157,  1010,  2004,  2023,  2003,  3599,  2054,\n",
              "           3047,  2007,  2033,  1012,  1026,  7987,  1013,  1028,  1026,  7987,\n",
              "           1013,  1028,  1996,  2034,  2518,  2008,  4930,  2033,  2055, 11472,\n",
              "           2001,  2049, 24083,  1998,  4895, 10258,  2378,  8450,  5019,  1997,\n",
              "           4808,  1010,  2029,  2275,  1999,  2157,  2013,  1996,  2773,  2175,\n",
              "           1012,  3404,  2033,  1010,  2023,  2003,  2025,  1037,  2265,  2005,\n",
              "           1996,  8143, 18627,  2030,  5199,  3593,  1012,  2023,  2265,  8005,\n",
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              "           1012,  2049,  2003, 13076,  1010,  1999,  1996,  4438,  2224,  1997,\n",
              "           1996,  2773,  1012,  1026,  7987,  1013,  1028,  1026,  7987,  1013,\n",
              "           1028,  2009,  2003,  2170, 11472,  2004,  2008,  2003,  1996,  8367,\n",
              "           2445,  2000,  1996, 17411,  4555,  3036,  2110,  7279,  4221, 12380,\n",
              "           2854,  1012,  2009,  7679,  3701,  2006, 14110,  2103,  1010,  2019,\n",
              "           6388,  2930,  1997,  1996,  3827,  2073,  2035,  1996,  4442,  2031,\n",
              "           3221, 21430,  1998,  2227, 20546,  2015,  1010,  2061,  9394,  2003,\n",
              "           2025,  2152,  2006,  1996, 11376,  1012,  7861,  2103,  2003,  2188,\n",
              "           2000,  2116,  1012,  1012, 26030,  2015,  1010,  7486,  1010, 18542,\n",
              "          10230,  1010,  7402,  2015,  1010,  8135,  1010, 16773,  1010,  3493,\n",
              "           1998,  2062,  1012,  1012,  1012,  1012,  2061,  8040, 16093, 28331,\n",
              "           1010,  2331, 14020,  1010, 26489,  6292, 24069,  1998, 22824, 10540,\n",
              "           2024,  2196,  2521,  2185,  1012,  1026,  7987,  1013,  1028,  1026,\n",
              "           7987,  1013,  1028,  1045,  2052,  2360,  1996,  2364,  5574,  1997,\n",
              "           1996,  2265,  2003,  2349,  2000,  1996,  2755,  2008,  2009,  3632,\n",
              "           2073,  2060,  3065,  2876,  1005,  1056,  8108,  1012,  5293,  3492,\n",
              "           4620,  4993,  2005,  7731,  9501,  1010,  5293, 11084,  1010,  5293,\n",
              "           7472,  1012,  1012,  1012, 11472,  2987,  1005,  1056,  6752,  2105,\n",
              "           1012,  1996,  2034,  2792,  1045,  2412,  2387,  4930,  2033,  2004,\n",
              "           2061, 11808,  2009,  2001, 16524,  1010,  1045,  2481,  1005,  1056,\n",
              "           2360,  1045,  2001,  3201,  2005,  2009,  1010,  2021,  2004,  1045,\n",
              "           3427,  2062,  1010,  1045,  2764,  1037,  5510,  2005, 11472,  1010,\n",
              "           1998,  2288, 17730,  2000,  1996,  2152,  3798,  1997,  8425,  4808,\n",
              "           1012,  2025,  2074,  4808,  1010,  2021, 21321,  1006, 15274,  4932,\n",
              "           2040,  1005,  2222,  2022,  2853,  2041,  2005,  1037, 15519,  1010,\n",
              "          13187,  2040,  1005,  2222,  3102,  2006,  2344,  1998,  2131,  2185,\n",
              "           2007,  2009,  1010,  2092,  5450,  2098,  1010,  2690,  2465, 13187,\n",
              "           2108,  2357,  2046,  3827,  7743,  2229,  2349,  2000,  2037,  3768,\n",
              "           1997,  2395,  4813,  2030,  3827,  3325,  1007,  3666, 11472,  1010,\n",
              "           2017,  2089,  2468,  6625,  2007,  2054,  2003,  8796, 10523,  1012,\n",
              "           1012,  1012,  1012,  2008,  2015,  2065,  2017,  2064,  2131,  1999,\n",
              "           3543,  2007,  2115,  9904,  2217,  1012,   102,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0]),\n",
              "  tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0])),\n",
              " tensor(1.))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Séparons notre jeu de données en jeux d'entrainement, validation et test, avec les proportions 70% - 15% - 15%."
      ],
      "metadata": {
        "id": "65mQ-UZSO9Fl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valid_ratio = 0.15\n",
        "test_ratio = 0.15\n",
        "valid_size = int(valid_ratio * len(my_data))\n",
        "test_size = int(test_ratio * len(my_data))\n",
        "train_size = len(my_data) - valid_size - test_size\n",
        "my_data_train, my_data_valid, my_data_test = \\\n",
        "  random_split(my_data, [train_size, valid_size, test_size])\n",
        "print(train_size, valid_size, test_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGFN-tW-PboN",
        "outputId": "39fcb6eb-2844-4dde-b849-53ecc4c29826"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35000 7500 7500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Créons les Dataloaders, avec batch de 32. Le paramètre `num_workers` nous permet d'attribuer plus de processus chargés d'envoyer les données."
      ],
      "metadata": {
        "id": "NliQOzaAPKZA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "train_loader = DataLoader(my_data_train, batch_size, num_workers=2)\n",
        "valid_loader = DataLoader(my_data_valid, batch_size, num_workers=2)\n",
        "test_loader = DataLoader(my_data_test, batch_size, num_workers=2)"
      ],
      "metadata": {
        "id": "X9q5QTdTXizb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La classe `transformers.BertModel` nous permet des créer des instances de modèles pré-entrainés, grâce à sa méthode `from_pretrained()`, à partir d'un nom de modèle Hugging Face. Le modèle est téchargé et mis en mémoire."
      ],
      "metadata": {
        "id": "es5eTcIyPlXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bert_model = BertModel.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "avAetOfzATJG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7a7f2830098f4e3ab744d2c718d45e3c",
            "e879e75c4961420a95ffd88a5089bb3d",
            "e7e9847a48b646929c136b52ea5fd33c",
            "30eacc43938e4058bd31daee147e95f1",
            "b0514318b8fb43a090df663c5332364a",
            "963edab56e7d4422b01ee3e0aae23515",
            "7069666a59314b719a5e883f2400e9c6",
            "c264a61b383145ad85db88766b938018",
            "9726eee02e544232b0ddc97cad11dfe2",
            "c98edd214d954e1eb7455b8a092970c6",
            "3c685f76f2334baa87cc980b8780fb1a"
          ]
        },
        "outputId": "ad009950-8873-44cd-c01c-355a9ce00f23"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/45.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a7f2830098f4e3ab744d2c718d45e3c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En passant un exemple de notre jeu de données dans ce modèle, nous pouvons voir la sortie, qui est constituée de deux éléments `last_hidden_state` et `pooler_output`. Ces deux sorties contiennent :\n",
        "\n",
        "* `last_hidden_state` : les sorties cachées de la dernière couche du modèle, avec un vecteur d'une taille définie (par la modèle) pour chaque token d'entrée.\n",
        "* `pooler_output` : le vecteur caché du premier élément de la phrase, correspondant au token [CLS], et qui sert de résumé pour toute la phrase."
      ],
      "metadata": {
        "id": "xu9tur82QNDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input, _ = my_data[0]\n",
        "bert_output = bert_model(input_ids=input[0].unsqueeze(0),\n",
        "                         attention_mask=input[1].unsqueeze(0))\n",
        "bert_output.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIQ17wliDrjT",
        "outputId": "ea8ff660-46c0-47d9-c598-3775bf61107c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "odict_keys(['last_hidden_state', 'pooler_output'])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(bert_output[\"last_hidden_state\"].shape)\n",
        "print(bert_output[\"pooler_output\"].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaX-gqCpRC2-",
        "outputId": "529b17e9-d72f-44aa-81ed-76e8b406650e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 512, 256])\n",
            "torch.Size([1, 256])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nous allons maintenant créer un modèle qui consiste en :\n",
        "* Un modèle pré-entrainé de type BERT.\n",
        "* Une couche entièrement connectée qui prend le premier état caché de la sortie du modèle BERT (avec dropout) et l'envoie sur une seule sortie, nous servant ainsi de log-odds pour le sentiment positif.\n",
        "\n",
        "Notez que la taille du vecteur caché peut s'obtenir en utilisant l'attribut `bert.config.hidden_size`, permettant ainsi de définir le bon nombre de neurones dans la couche entièrement connectée."
      ],
      "metadata": {
        "id": "pYs1ZVtcRqa5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SentClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, bert_model_name):\n",
        "    super(SentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(bert_model_name)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.full = nn.Linear(self.bert.config.hidden_size, 1)\n",
        "\n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    bert_output = self.bert(input_ids, attention_mask)\n",
        "    dropout = self.drop(bert_output[\"pooler_output\"])\n",
        "    logits = self.full(dropout)\n",
        "    return logits"
      ],
      "metadata": {
        "id": "sOBqDBskSmpG"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vérifions que notre modèle fonctionne correctement en lui envoyant une entrée de notre jeu de données."
      ],
      "metadata": {
        "id": "ow3JTfXuSxS3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_model = SentClassifier(model_name)\n",
        "my_model.to(device)\n",
        "my_model(input[0].unsqueeze(0).to(device), input[1].unsqueeze(0).to(device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axlqhTVUV4lc",
        "outputId": "6a477c6c-41ae-44c1-855b-022a4cbc2608"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.1089]], device='cuda:0', grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "L'entrainement du modèle va s'effectuer d'une manière légèrement plus complexe que les dernières fois. Nous allons utiliser ici un **ordonnanceur** pour le taux d'apprentissage, avec une instance de classe obtenu avec la fonction `transformers.get_linear_schedule_with_warmup()`. Cette instance permet de faire varier le taux d'apprentissage en fonction des itérations. Le taux d'apprentissage commence à 0, monte rapidement jusqu'à sa valeur maximale définie dans l'optimisateur, puis diminue lentement jusqu'à le nombre d'itérations maximal (`len(train_loader) * n_epochs`). Cette procédure permet de chercher plus efficacement un minimum dans la fonctionnelle. Pour en savoir plus, voir https://huggingface.co/docs/transformers/main_classes/optimizer_schedules#transformers.get_linear_schedule_with_warmup.optimizer.\n",
        "\n",
        "La fonction de perte est `torch.nn.BCEWithLogitsLoss()` qui permet de comparer une sortie entre un log-odd vs une valeur binaire."
      ],
      "metadata": {
        "id": "ax-irH6TS9TF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs = 10\n",
        "optimizer = torch.optim.AdamW(my_model.parameters(), lr=2e-5)\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=len(train_loader) * n_epochs\n",
        ")\n",
        "loss_fn = nn.BCEWithLogitsLoss()"
      ],
      "metadata": {
        "id": "ZFksIEIoWGFa"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nous allons aussi implémenter un arrêt prématuré dans notre entrainement. Pour cela, nous allons sauver le modèle (sous `checkpoint_path`) à chaque fois que celui-ci diminue la perte de validation, et nous arrêterons l'entrainement si la perte d'entrainement n'a pas diminué durant `patience` epochs. Il nous suffira de revenir au dernier modèle sauvé pour avoir celui qui présente les meilleurs performances de validation."
      ],
      "metadata": {
        "id": "brEmMODxWSyE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "patience = 3\n",
        "checkpoint_path = \"checkpoint.pt\""
      ],
      "metadata": {
        "id": "vhQoqOuQKzwj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nous allons maintenant effectuer l'entrainement. Vous pouvez lire les commentaires ci-dessous pour avoir les détails."
      ],
      "metadata": {
        "id": "wlvqRRSkYWm6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Valeur initiale de la perte minimale obtenue (artificiellement\n",
        "# élevée initialement) pour l'arrêt prématuré\n",
        "lowest_valid_loss = 1e5\n",
        "# Le conteur de patience\n",
        "patience_counter = 0\n",
        "# La boucle sur les epochs\n",
        "for epoch in range(n_epochs):\n",
        "\n",
        "  print(f\"Epoch {epoch + 1}/{n_epochs}\")\n",
        "\n",
        "  # --- Training\n",
        "\n",
        "  my_model.train()\n",
        "  sum_train_loss = 0\n",
        "  sum_train_acc = 0\n",
        "  # La boucle sur le loader d'entrainement\n",
        "  for (input_ids, masks), outputs in tqdm(train_loader):\n",
        "    input_ids = input_ids.to(device)\n",
        "    masks = masks.to(device)\n",
        "    outputs = outputs.to(device).unsqueeze(1)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    logits = my_model(input_ids, masks)\n",
        "    loss = loss_fn(logits, outputs)\n",
        "    loss.backward()\n",
        "    # On procède ici au gradient clipping, pour être sur que celui-ci\n",
        "    # ne déborde pas.\n",
        "    nn.utils.clip_grad_norm_(my_model.parameters(), max_norm=1.0)\n",
        "    # Notre optimisateur et notre ordonnanceur son mis à jour\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "\n",
        "    sum_train_loss += loss.detach().item()\n",
        "    sum_train_acc += ((logits.flatten() > 0) ==\n",
        "                      outputs.flatten()).detach().sum().item()\n",
        "\n",
        "  # --- Validation\n",
        "\n",
        "  my_model.eval()\n",
        "  sum_valid_loss = 0\n",
        "  sum_valid_acc = 0\n",
        "  for (input_ids, masks), outputs in tqdm(valid_loader):\n",
        "    input_ids = input_ids.to(device)\n",
        "    masks = masks.to(device)\n",
        "    outputs = outputs.to(device).unsqueeze(1)\n",
        "\n",
        "    logits = my_model(input_ids, masks)\n",
        "    loss = loss_fn(logits, outputs)\n",
        "\n",
        "    sum_valid_loss += loss.detach()\n",
        "    sum_valid_acc += ((logits.flatten() > 0) ==\n",
        "                      outputs.flatten()).detach().sum().item()\n",
        "\n",
        "  train_loss = sum_train_loss/len(train_loader)\n",
        "  train_acc = sum_train_acc/len(train_loader.dataset)\n",
        "  valid_loss = sum_valid_loss/len(valid_loader)\n",
        "  valid_acc = sum_valid_acc/len(valid_loader.dataset)\n",
        "\n",
        "  print(f\"Train loss: {train_loss:.4f}, Valid loss: {valid_loss:.4f}\")\n",
        "  print(f\"Train accuracy: {train_acc:.2%}, Valid accuracy: {valid_acc:.2%}\")\n",
        "\n",
        "  # --- Pour l'arrêt prématuré\n",
        "\n",
        "  # Si la perte de validation est plus petite que la dernière enregistrée,\n",
        "  # on effectue un checkpoint du modèle\n",
        "  if valid_loss < lowest_valid_loss:\n",
        "    print(f\"***{valid_loss:.4f} < {lowest_valid_loss:.4f}, saving.***\")\n",
        "    torch.save({\"model_state_dict\": my_model.state_dict(),\n",
        "                \"optimizer_state_dict\": optimizer.state_dict(),\n",
        "                \"scheduler_state_dict\": scheduler.state_dict()},\n",
        "               checkpoint_path)\n",
        "    lowest_valid_loss = valid_loss\n",
        "    patience_counter = 0\n",
        "  # Sinon, on augmente le compteur de patience\n",
        "  else:\n",
        "    patience_counter += 1\n",
        "\n",
        "  # Si le compteur de patience atteint la limite, on arrête l'entrainement\n",
        "  if patience_counter >= patience:\n",
        "    print(\"Early stopping\")\n",
        "    break\n",
        "\n",
        "# On charge le dernier modèle sauvé\n",
        "checkpoint_dict = torch.load(checkpoint_path, map_location=torch.device(device))\n",
        "my_model.load_state_dict(checkpoint_dict[\"model_state_dict\"])\n",
        "optimizer.load_state_dict(checkpoint_dict[\"optimizer_state_dict\"])\n",
        "scheduler.load_state_dict(checkpoint_dict[\"scheduler_state_dict\"])"
      ],
      "metadata": {
        "id": "kKlA3zsZYTCc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712,
          "referenced_widgets": [
            "73f0524be3a74d1eb11cbab7f1f8c691",
            "3e01fdbc7d5d47889b173d265cb88cc4",
            "a216bb268ea7406f9d626a35c8fad67d",
            "59e94470189d443ea9e259bd0e580120",
            "83ab91cf786c46de8326e430c19bda40",
            "28fa08d387b940a092b04b7f492a1cee",
            "ca57ebfed44e450c92ac30c5e6cc8432",
            "96d12c00f67744dda474bcc248621d49",
            "8029fd489038487c87adddc2fad45943",
            "5a2f35fba9f6440e90a19681ae95af32",
            "6da5493bf2184e21994c81d9623f7409"
          ]
        },
        "outputId": "5b4dba0e-dda9-4355-9b7c-3b4c03e3dd68"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 2/1094 [00:01<12:02,  1.51it/s]"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/45.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73f0524be3a74d1eb11cbab7f1f8c691"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:31<00:00,  4.03it/s]\n",
            "100%|██████████| 235/235 [00:47<00:00,  4.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.3938, Valid loss: 0.2969\n",
            "Train accuracy: 81.99%, Valid accuracy: 87.67%\n",
            "***0.2969 < 100000.0000, saving.***\n",
            "Epoch 2/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:32<00:00,  4.01it/s]\n",
            "100%|██████████| 235/235 [00:47<00:00,  4.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.2638, Valid loss: 0.2588\n",
            "Train accuracy: 89.31%, Valid accuracy: 89.72%\n",
            "***0.2588 < 0.2969, saving.***\n",
            "Epoch 3/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:30<00:00,  4.05it/s]\n",
            "100%|██████████| 235/235 [00:46<00:00,  5.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.2182, Valid loss: 0.2568\n",
            "Train accuracy: 91.69%, Valid accuracy: 90.27%\n",
            "***0.2568 < 0.2588, saving.***\n",
            "Epoch 4/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:26<00:00,  4.10it/s]\n",
            "100%|██████████| 235/235 [00:47<00:00,  4.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.1863, Valid loss: 0.2604\n",
            "Train accuracy: 93.30%, Valid accuracy: 91.01%\n",
            "Epoch 5/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:28<00:00,  4.08it/s]\n",
            "100%|██████████| 235/235 [00:47<00:00,  4.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.1649, Valid loss: 0.2892\n",
            "Train accuracy: 94.22%, Valid accuracy: 90.89%\n",
            "Epoch 6/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1094/1094 [04:35<00:00,  3.97it/s]\n",
            "100%|██████████| 235/235 [00:47<00:00,  4.99it/s]\n",
            "<ipython-input-21-5e4bcd53ca84>:84: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  checkpoint_dict = torch.load(checkpoint_path, map_location=torch.device(device))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.1444, Valid loss: 0.2911\n",
            "Train accuracy: 95.14%, Valid accuracy: 90.89%\n",
            "Early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Effectuons maintenant l'évaluation du modèle sur le jeu de test. On va également sauver chaque sortie en terme de probabilité, afin de voir la répartition de ces dernières."
      ],
      "metadata": {
        "id": "yN3Wij4nbuEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_model.eval()\n",
        "sum_test_loss = 0\n",
        "sum_test_acc = 0\n",
        "real_sent = []\n",
        "pred_sent = []\n",
        "for (input_ids, masks), outputs in tqdm(test_loader):\n",
        "  input_ids = input_ids.to(device)\n",
        "  masks = masks.to(device)\n",
        "  outputs = outputs.to(device).unsqueeze(1)\n",
        "\n",
        "  logits = my_model(input_ids, masks).detach()\n",
        "  loss = loss_fn(logits, outputs).detach()\n",
        "  sum_test_acc += ((logits.flatten() > 0) == outputs.flatten()).sum().item()\n",
        "  probs = torch.sigmoid(logits)\n",
        "\n",
        "  real_sent.extend([real_prob.item() for real_prob in outputs.flatten()])\n",
        "  pred_sent.extend([pred_prob.item() for pred_prob in probs.flatten()])\n",
        "  sum_test_loss += loss.item()\n",
        "\n",
        "print(f\"\\nLoss : {sum_test_loss/len(test_loader):.4f}\")\n",
        "print(f\"Accuracy : {sum_test_acc/len(test_loader.dataset):.2%}\")"
      ],
      "metadata": {
        "id": "hQ_XU4zAiAxT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff66bb81-4438-4a87-cdc2-de1bf024e0ef"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 235/235 [00:47<00:00,  4.96it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Loss : 0.2549\n",
            "Accuracy : 90.63%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On peut tracer les boxplots des probabilités prédites pour les critiques négatives et positives. Il y a quelques critiques (par rapport à la taille du jeu de test) avec une probabilité contre-intuitive par rapport à leur appréciation, mais la majorité semble concentré sur les pôles adéquats."
      ],
      "metadata": {
        "id": "ig6cSCd7cFyh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "real_sent = np.array(real_sent)\n",
        "pred_sent = np.array(pred_sent)\n",
        "unique_real_sent = list(set(real_sent))\n",
        "binned_pred_sent = [np.array(pred_sent)[np.where(real_sent == sent)[0]]\n",
        "                    for sent in unique_real_sent]\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = [12, 9]\n",
        "plt.boxplot(binned_pred_sent, positions=unique_real_sent)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZKsTbx6djbZ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "outputId": "4b8a5dc2-b714-479b-e851-79a46704f25b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On sauve maintenant le modèle dans le drive, afin de pouvoir l'utiliser dans la deuxième partie sans la phase d'entrainement."
      ],
      "metadata": {
        "id": "LPwhVscOcbLG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/\" \\\n",
        "#              \"TP12/sent_analyser_bert_base.pt\"\n",
        "model_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/\" \\\n",
        "             \"TP12/sent_analyser_bert_mini.pt\"\n",
        "torch.save({\"model_state_dict\": my_model.state_dict(),\n",
        "            \"optimizer_tat_dict\": optimizer.state_dict(),\n",
        "            \"scheduler_state_dict\": scheduler.state_dict()},\n",
        "            model_path)"
      ],
      "metadata": {
        "id": "pgJ7Kojseybs"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "E2f6kR64h6IG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Utilisation du modèle pour l'analyse des sentiments\n",
        "\n",
        "Passons maintenant à la partie où nous allons utiliser notre modèle d'analyse des sentiments sur le corpus des Misérables.\n",
        "\n",
        "Comme cette deuxième partie peut être utilisée de manière indépendante de la première partie, nous allons redéfinir ici plusieurs éléments, tout en rechargeant le modèle entrainé."
      ],
      "metadata": {
        "id": "LYYA1-H6cv4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Le nom du modèle à charger\n",
        "\n",
        "model_name = \"prajjwal1/bert-mini\"\n",
        "model_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/\" \\\n",
        "              \"TP12/sent_analyser_bert_mini.pt\"\n",
        "# model_name = \"bert-base-uncased\"\n",
        "# model_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/\" \\\n",
        "#              \"TP12/sent_analyser_bert_base.pt\"\n",
        "\n",
        "# --- La longueur maximale des séquences\n",
        "\n",
        "max_length = 512\n",
        "\n",
        "# --- Définition du modèle et chargement\n",
        "\n",
        "class SentClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, bert_model_name):\n",
        "    super(SentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(bert_model_name)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.full = nn.Linear(self.bert.config.hidden_size, 1)\n",
        "\n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    bert_output = self.bert(input_ids, attention_mask)\n",
        "    dropout = self.drop(bert_output[\"pooler_output\"])\n",
        "    logits = self.full(dropout)\n",
        "    return logits\n",
        "\n",
        "sent_classif = SentClassifier(model_name)\n",
        "model_dict = torch.load(model_path, map_location=torch.device(device))\n",
        "sent_classif.load_state_dict(model_dict[\"model_state_dict\"])\n",
        "\n",
        "# --- Le tokenisateur\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# --- la transformation\n",
        "\n",
        "def my_transform(text, tokenizer, max_length):\n",
        "  # L'encodage\n",
        "  encoding = tokenizer.encode_plus(text, truncation=True, max_length=max_length,\n",
        "      add_special_tokens=True, return_token_type_ids=False, return_tensors=\"pt\",\n",
        "      return_attention_mask=True, padding=\"max_length\")\n",
        "  # Retour de l'encodage et du masque\n",
        "  return encoding[\"input_ids\"].flatten(), encoding[\"attention_mask\"].flatten()"
      ],
      "metadata": {
        "id": "TjTI4Qmzh3Y7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c76584f-87db-44ff-98af-4a37467b37c6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-a10ba73fb937>:31: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model_dict = torch.load(model_path, map_location=torch.device(device))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Définissons maintenant une classe héritée de `torch.utils.data.Dataset` qui permet de donner des entrées compatibles avec notre modèle à partir d'un livre de notre corpus. Pour cela, nous divisons notre livre en blocs, de au plus `max_length` tokens, et transformons ces blocs en tenseurs grâce à notre transformation."
      ],
      "metadata": {
        "id": "LQJovF-i36MM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDocument(Dataset):\n",
        "    def __init__(self, file_path, tokenizer, max_length, transform):\n",
        "        self.file_path = file_path\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "        self.transform = transform\n",
        "\n",
        "        with open(self.file_path, \"r\") as f:\n",
        "          self.text = f.read()\n",
        "\n",
        "        # On encode avec le tokenizer pour estimer le nombre de tokens\n",
        "        tokens = self.tokenizer.encode_plus(self.text)[\"input_ids\"]\n",
        "        n_tokens = len(tokens)\n",
        "        # Le nombre de blocs\n",
        "        n_blocs = int(n_tokens / max_length) + 1\n",
        "        # Les blocs résultant (avec ids des tokens)\n",
        "        id_blocs = np.array_split(tokens[1:-1], n_blocs)\n",
        "        # On converti à nouveau en texte\n",
        "        self.blocs = [\" \".join(self.tokenizer.convert_ids_to_tokens(id_bloc))\n",
        "                      for id_bloc in id_blocs]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.blocs)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.transform(self.blocs[idx], self.tokenizer, self.max_length)"
      ],
      "metadata": {
        "id": "U2JFXlyI09iS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On teste notre classe Dataset avec le premier livre."
      ],
      "metadata": {
        "id": "IBh6ZyC85Ry8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/TP10/\" \\\n",
        "            \"by_book/book_01.txt\"\n",
        "doc_data = MyDocument(file_path, tokenizer, max_length, my_transform)"
      ],
      "metadata": {
        "id": "NJlqIjtky5xc"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "L'entrée et le masque correspondant au premier bloc sont affichés."
      ],
      "metadata": {
        "id": "-hQN2flb5WUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc_data[0]"
      ],
      "metadata": {
        "id": "L0Stsckrzj5A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5660821-47a2-428c-c17b-44f6070e676c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "          2002,   102]),\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regardons si nous pouvons passer une entrée dans notre modèle, pour voir si tout fonctionne."
      ],
      "metadata": {
        "id": "Vm7hrfA45i9t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input, mask = doc_data[0]\n",
        "sent_classif.eval()\n",
        "sent_classif(input.unsqueeze(0), mask.unsqueeze(0))"
      ],
      "metadata": {
        "id": "7UdWUJ8P0Qnx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc41ccf1-e176-414f-a058-3b6402b8c7c6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3.3615]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nous créons maintenant une fonction qui prend une instance de notre classe Dataset en entrée et permet d'estimer le sentiment de chaque bloc."
      ],
      "metadata": {
        "id": "kyswABe25ode"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sentiments(file_data, model, batch_size):\n",
        "  file_data_loader = DataLoader(file_data, batch_size)\n",
        "\n",
        "  file_sents = []\n",
        "  model.eval()\n",
        "  for inputs, masks in file_data_loader:\n",
        "    logits = model(inputs, masks).detach()\n",
        "    probs = torch.sigmoid(logits)\n",
        "    file_sents.extend(probs.flatten().tolist())\n",
        "\n",
        "  return file_sents"
      ],
      "metadata": {
        "id": "GV7t44c1O7c0"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va executer cette fonction sur tout notre corpus, et sauvegarder les sentiments prédits dans une liste (livre) de liste (blocs)."
      ],
      "metadata": {
        "id": "CIg-nbs153bG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corpus_path = \"/content/drive/MyDrive/Colab Notebooks/ml_data/TP10/by_book\"\n",
        "batch_size = 64\n",
        "\n",
        "file_names = os.listdir(corpus_path)\n",
        "file_names.sort()\n",
        "\n",
        "corpus_sents = []\n",
        "for i, file_name in tqdm(enumerate(file_names)):\n",
        "\n",
        "  file_path = f\"{corpus_path}/{file_name}\"\n",
        "  file_data = MyDocument(file_path, tokenizer, max_length, my_transform)\n",
        "  file_sents = get_sentiments(file_data, sent_classif, batch_size)\n",
        "\n",
        "  corpus_sents.append(file_sents)"
      ],
      "metadata": {
        "id": "g4TLPIsbDiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22241ea8-846d-4627-daa9-b394b0e0828e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "49it [02:08,  2.63s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On affiche le sentiment calculé sur chaque bloc, avec une moyenne mobile sur une fenêtre de `ma_window_size`. De plus, les sentiments moyens de chaque livre et du corpus sont calculés."
      ],
      "metadata": {
        "id": "8EkGdfcg6C8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ma_window_size = 5\n",
        "\n",
        "nx_grid = int(np.sqrt(len(corpus_sents)))\n",
        "ny_grid= int(np.ceil(len(corpus_sents) / nx_grid))\n",
        "plt.rcParams[\"figure.figsize\"] = [ny_grid*2, nx_grid*1.5]\n",
        "\n",
        "fig = plt.figure()\n",
        "grid_layout = GridSpec(nx_grid, ny_grid)\n",
        "\n",
        "total_sent, total_part = 0, 0\n",
        "for i, file_sents in enumerate(corpus_sents):\n",
        "\n",
        "  # Pour calculer la moyenne mobile\n",
        "  mov_avg_df = pd.Series(file_sents).rolling(ma_window_size, min_periods=1)\n",
        "  mov_avg = mov_avg_df.mean().to_list()\n",
        "\n",
        "  x_range = list(range(1, len(file_sents) + 1))\n",
        "\n",
        "  ax = fig.add_subplot(grid_layout[i])\n",
        "  ax.bar(x_range, file_sents, alpha=0.5)\n",
        "  ax.plot(x_range, mov_avg, color=\"red\")\n",
        "  ax.set_ylim([0, 1])\n",
        "  ax.set_title(f\"Livre {i}, Sent={np.mean(file_sents):.3f}\")\n",
        "\n",
        "  # On fait la somme du sentiment et comptons le nombre de blocs\n",
        "  total_sent += np.sum(file_sents)\n",
        "  total_part += len(file_sents)\n",
        "\n",
        "grid_layout.tight_layout(fig)\n",
        "\n",
        "print(f\"Sentiment moyen total = {total_sent/total_part:.3f}\")"
      ],
      "metadata": {
        "id": "S2xWHZ0rp4xr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6aaf2a98-f8ed-4a8f-adab-5836231fe003"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentiment moyen total = 0.535\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1050 with 49 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "KdnZ_HtXOcZh"
      }
    }
  ]
}