Project_A1_CamemBERT.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AmauryFaure/project_article_1/blob/master/Project_A1_CamemBERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "SocQoXOEv9xd"
      },
      "source": [
        "# Projet Inspire :\n",
        "\n",
        "Notebook réalisé par Amaury Faure dans le cadre d'un projet d'intégration G3 réalisé avec INSPIRE, un des programmes de l'association Article 1. \n",
        "\n",
        "Ce notebook contient des explorations d'algorithmes simples de classification de messages. On utilise dans ce notebook CamemBERT pour la classification.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hd9Cger4vxIN"
      },
      "source": [
        "## Initialisation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnG6hNo0PTCh",
        "outputId": "e4bb439a-f2a7-4eae-a592-aa001f16fa85"
      },
      "source": [
        "#Connecting to Drive to get files.\n",
        "#Other options is to upload files to local machine (if using google colab)\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqifF1lXv9xt"
      },
      "source": [
        "#Importing pandas and numpy\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from joblib import dump, load"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ps97S30yv9xu"
      },
      "source": [
        "## Création du dataset d'entaînement :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "_hTvFZmTv9xu"
      },
      "source": [
        "#Get tweets\n",
        "df_to_moderate=pd.read_csv(\"/content/drive/MyDrive/article_1_data/selected_tweets.csv\")\n",
        "#Drop unused data\n",
        "df_to_moderate=df_to_moderate.drop([\"Id\",\"source\",\"Looked up word\"],axis=1)\n",
        "#Change column name\n",
        "df_to_moderate[\"content\"]=df_to_moderate[\"tweet (without @)\"]\n",
        "df_to_moderate=df_to_moderate.drop([\"tweet (without @)\"], axis=1)\n",
        "#Drop empty lines\n",
        "df_to_moderate=df_to_moderate.dropna()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5oU7Wsxv9xw"
      },
      "source": [
        "#Get regular data\n",
        "df_article1_messages=pd.read_excel(\"/content/drive/MyDrive/article_1_data/AmauryModerationAllMessagesInspireFrom3Aout2020.xlsx\", sheet_name=\"ContenuNormal\")\n",
        "#Drop unused data\n",
        "df_article1_messages=df_article1_messages.drop([\"_id\",\"sender\",\"recipients.0\",\"threadId\",\"timestamp\",\"EchantillonNormal\"], axis=1)\n",
        "#Assign label\n",
        "df_article1_messages[\"Harmful\"]=0"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZbR7w16v9xy"
      },
      "source": [
        "#Create training dataset. Balanced 50% for each class !\n",
        "df_to_moderate=pd.concat([df_article1_messages[:200],df_to_moderate],ignore_index=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t53OrpKtv9xz"
      },
      "source": [
        "## Création du dataset de test :\n",
        "\n",
        "Il y a un problème car nous n'avons pas de dataset d'exemples de message \"a modérer\" d'INSPIRE.\n",
        "\n",
        "On va donc tenter une analyse par 2 approches :\n",
        "- Test sur le dataset de tweets MLMA (sans les tweets d'entraînement)\n",
        "- Evaluation du % de messages d'Article 1 qui sont identifié comme messages à modérer actuellement et qui sont identifié comme à modéré par l'algorithme. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJa2kqhy6knU"
      },
      "source": [
        "### Création du set de test MLMA :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4ZCUPLVv9xz"
      },
      "source": [
        "test=pd.read_csv(\"/content/drive/MyDrive/article_1_data/fr_dataset_test.csv\")\n",
        "#Dropping Unused columns\n",
        "test=test.drop(test.columns[[7,8,9]],axis=1)\n",
        "test=test.drop(columns=[\"HITId\",\"directness\",\"annotator_sentiment\",\"target\",\"group\"])\n",
        "#Changing sentiment to 0 (for normal) and 1 (for else) \n",
        "test[\"sentiment\"]=[0 if test[\"sentiment\"][x]==\"normal\" else 1 for x in range(test.shape[0])]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAf3MrFYMbS9"
      },
      "source": [
        "### Création du set de test INSPIRE :\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "MAY7ELRiMaib",
        "outputId": "407ccee8-fadf-47dd-fb41-e3bd4efff87e"
      },
      "source": [
        "df_a1_to_moderate=pd.read_excel(\"/content/drive/MyDrive/article_1_data/AmauryModerationAllMessagesInspireFrom3Aout2020.xlsx\", sheet_name=\"ContenuToModerate\")\n",
        "df_a1_to_moderate=df_a1_to_moderate.drop(columns=[\"_id\",\"sender\",\"recipients.0\",\"threadId\",\"timestamp\",\"EchantillonToModerate\"])\n",
        "# df_a1_to_moderate.columns=df_a1_to_moderate.columns.str.replace(\"\\n\",\" \")\n",
        "df_a1_to_moderate.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non, la physique-chimie de lycée n’a pas grand...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Merci infiniment d'avoir pris autant de temps ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bonjour ! Alors non la SVT et la biologie ce n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>salut\\nJ'aimerais savoir ce qu'est exactement ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>je sais pas si j'ai répondu à toutes tes quest...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             content\n",
              "0  Non, la physique-chimie de lycée n’a pas grand...\n",
              "1  Merci infiniment d'avoir pris autant de temps ...\n",
              "2  Bonjour ! Alors non la SVT et la biologie ce n...\n",
              "3  salut\\nJ'aimerais savoir ce qu'est exactement ...\n",
              "4  je sais pas si j'ai répondu à toutes tes quest..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvX7M3I4v9x4"
      },
      "source": [
        "### Implémentation de CamemBERT\n",
        "\n",
        "Cette implémentation va être réalisé avec Huggingface Transformers et PyTorch.\n",
        "In this attempt we are going to use transformers and PyTorch, using the latter for training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMt56ub2ycLp",
        "outputId": "8436f751-6fcd-414e-977c-fc42d525b807"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install sentencepiece"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/54/5ca07ec9569d2f232f3166de5457b63943882f7950ddfcc887732fc7fb23/transformers-4.3.3-py3-none-any.whl (1.9MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9MB 8.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
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            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 50.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/23/2ddc317b2121117bf34dd00f5b0de194158f2a44ee2bf5e47c7166878a97/tokenizers-0.10.1-cp37-cp37m-manylinux2010_x86_64.whl (3.2MB)\n",
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            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp37-none-any.whl size=893262 sha256=809e71053bd505b6aa7a3c1665fb6e8668f8d00bae42272d46aced7ce880a144\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.10.1 transformers-4.3.3\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 8.8MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9km-WdAN-Q3K"
      },
      "source": [
        "Création de variables :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdCToMlLv9x0"
      },
      "source": [
        "#Creating arrays for test_values and labels\n",
        "test_values=np.array(test[\"tweet\"])\n",
        "test_labels=np.array(test[\"sentiment\"])\n",
        "# Same but for training\n",
        "train_values=np.array(df_to_moderate[\"content\"])\n",
        "#Extra step to Making sure everything is on a string format (some numbers might not be)\n",
        "train_values=[str(train_values[i]) for i in range(len(train_values))]\n",
        "train_labels=np.array(df_to_moderate[\"Harmful\"])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7ZJ4qQFv9x4"
      },
      "source": [
        "#Importing function to split training data into validation and train\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(train_values, train_labels, test_size=.2)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wxo04C5ov9x4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "0d22a1d9660248bdaa0a2b7ccddf4f0f",
            "309b8d1071144f63b4098301670933e8",
            "09f137a7ffff4e5491bcb3cc9a61839a",
            "91a3583de1f84401a241bdd68aae29a1",
            "aa46e62439e846cc8d2f1cbe8d0a12a6",
            "5c2bb1a172064001b81bd26d5e34479e",
            "a38abd05f56a47eca12e3d94aa69139c",
            "2fc1d7e7fd194d6b9585da31599e8543"
          ]
        },
        "outputId": "cb1983d4-4854-4fbd-fee9-85ac28626cb7"
      },
      "source": [
        "#Tokeinization for camembert\n",
        "from transformers import CamembertTokenizer\n",
        "tokenizer=CamembertTokenizer.from_pretrained(\"camembert-base\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0d22a1d9660248bdaa0a2b7ccddf4f0f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=810912.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2iRHGAFv9x4"
      },
      "source": [
        "#Tokenizing\n",
        "train_encodings = tokenizer(train_texts, padding=True, truncation=True)\n",
        "val_encodings = tokenizer(val_texts, padding=True,truncation=True)\n",
        "test_encodings = tokenizer(test_values.tolist(), padding=True, truncation=True,return_tensors=\"pt\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1DSz2W_v9x5"
      },
      "source": [
        "#Importing pyTorch\n",
        "import torch\n",
        "\n",
        "class A1Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # print(self.encodings.items())\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx],dtype=torch.long)\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "train_dataset =  A1Dataset(train_encodings, train_labels)\n",
        "val_dataset =  A1Dataset(val_encodings, val_labels)\n",
        "test_dataset =  A1Dataset(test_encodings, test_labels)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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          ]
        },
        "id": "odufWocEv9x6",
        "outputId": "f2d803ba-30f6-4237-a8c9-90a75f821537"
      },
      "source": [
        "#Utility to load dataset in batch\n",
        "from torch.utils.data import DataLoader\n",
        "#Importing camembdert and AdamW Optimizer\n",
        "from transformers import CamembertForSequenceClassification, AdamW\n",
        "\n",
        "#Model declaration\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "model = CamembertForSequenceClassification.from_pretrained('camembert-base')\n",
        "model.to(device)\n",
        "model.train()\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n",
        "\n",
        "optim = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "#Training\n",
        "for epoch in range(1):\n",
        "    for batch in train_loader:\n",
        "        optim.zero_grad()\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['labels'].to(device)\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels.long())\n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "        optim.step()\n",
        "\n",
        "model.eval()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f31da896e974a229efa5e6d17f0260a",
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              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=508.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12228e640b9949f9baa8ecf10437f3f5",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=445032417.0, style=ProgressStyle(descri…"
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at camembert-base were not used when initializing CamembertForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']\n",
            "- This IS expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of CamembertForSequenceClassification were not initialized from the model checkpoint at camembert-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CamembertForSequenceClassification(\n",
              "  (roberta): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(32005, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): RobertaClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOMZkixGAAgw"
      },
      "source": [
        "\r\n",
        "\r\n",
        "#Import SGDClassifier (allows to implement logistic regression)\r\n",
        "from sklearn.linear_model import SGDClassifier\r\n",
        "#Import matplotlib for plots\r\n",
        "%matplotlib inline\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "#Import some metrics\r\n",
        "from sklearn.metrics import precision_recall_curve, f1_score, accuracy_score, roc_auc_score, confusion_matrix\r\n",
        "#Import seaborn for plots\r\n",
        "import seaborn as sns\r\n",
        "\r\n",
        "#This function comes from : https://towardsdatascience.com/text-classification-with-extremely-small-datasets-333d322caee2\r\n",
        "#It computes F1-score\r\n",
        "def calc_f1(p_and_r):\r\n",
        "    p, r = p_and_r\r\n",
        "    return (2*p*r)/(p+r)\r\n",
        "\r\n",
        "#This function comes from : https://towardsdatascience.com/text-classification-with-extremely-small-datasets-333d322caee2\r\n",
        "#It computes some metrics\r\n",
        "def compute_metrics(y_test, y_prob, verbose=False, return_metrics=True, confusion=False):\r\n",
        "  precision, recall, threshold = precision_recall_curve(y_test, y_prob, pos_label = 1)\r\n",
        "\r\n",
        "  #Optimizing the F1-score\r\n",
        "  best_f1_index =np.argmax([calc_f1(p_r) for p_r in zip(precision, recall)])\r\n",
        "  best_threshold, best_precision, best_recall = threshold[best_f1_index], precision[best_f1_index], recall[best_f1_index]\r\n",
        "\r\n",
        "  y_test_pred = np.where(y_prob > best_threshold, 1, 0)\r\n",
        "  \r\n",
        "  f1 = f1_score(y_test, y_test_pred, pos_label = 1, average = 'binary')\r\n",
        "  roc_auc = roc_auc_score(y_test, y_prob)\r\n",
        "  acc = accuracy_score(y_test, y_test_pred)\r\n",
        "\r\n",
        "  if confusion:\r\n",
        "    cm = confusion_matrix(y_test, y_test_pred)\r\n",
        "\r\n",
        "    plt.title('Confusion Matrix')\r\n",
        "    sns.set(font_scale=1.0) #for label size\r\n",
        "    sns.heatmap(cm, annot = True, fmt = 'd', xticklabels = ['Not harmful', 'Harmful'], yticklabels = ['Not harmful', 'Harmful'], annot_kws={\"size\": 14}, cmap = 'Blues')# font size\r\n",
        "\r\n",
        "    plt.xlabel('Prediction')\r\n",
        "    plt.ylabel('Truth')\r\n",
        "\r\n",
        "  if verbose:\r\n",
        "    print('F1: {:.3f} | Pr: {:.3f} | Re: {:.3f} | AUC: {:.3f} | Accuracy: {:.3f} \\n'.format(f1, best_precision, best_recall, roc_auc, acc))\r\n",
        "    \r\n",
        "  if return_metrics:\r\n",
        "    return np.array([f1, best_precision, best_recall, roc_auc, acc])\r\n",
        "\r\n",
        "#Defining a function to do 10 times the logistic regression\r\n",
        "def evaluate_log_reg(train_features, test_features, y_train, y_test):\r\n",
        "    score=0\r\n",
        "    metrics = np.zeros(5)\r\n",
        "    for i in range(10):\r\n",
        "        log_reg=SGDClassifier(loss=\"log\", penalty='l2')\r\n",
        "        log_reg.fit(train_features,y_train)\r\n",
        "      \r\n",
        "        y_prob=log_reg.predict_proba(test_features)[:,1]\r\n",
        "        metrics+=compute_metrics(y_test,y_prob)\r\n",
        "    metrics /=10\r\n",
        "\r\n",
        "    return metrics"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Ti767eguQZry",
        "outputId": "e8c9ec5f-661e-487e-d0e9-64ff4e6f3d00"
      },
      "source": [
        "#Evaluating the model : \n",
        "with torch.no_grad():\n",
        "  input_ids = test_dataset[:]['input_ids'].to(device)\n",
        "  attention_mask = test_dataset[:]['attention_mask'].to(device)\n",
        "  labels = test_dataset[:]['labels'].to(device)\n",
        "  outputs = model(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "  metrics_camembert=compute_metrics(labels.cpu().data.numpy(),outputs[0].cpu().data.numpy()[:,1], confusion=True)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS3huvx7AQve"
      },
      "source": [
        "Il faut utiliser cette cellule pour sauvegarder le modèle dans un fichier de son choix : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuIm9sjodY9G"
      },
      "source": [
        "# model.save_pretrained(\"/content/drive/MyDrive/article_1_model/camembert_v1\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1Sa00rwsDv7"
      },
      "source": [
        "metrics_df=pd.DataFrame(metrics_camembert.reshape((1,-1)), columns=[\"f1\", \"best_precision\", \"best_recall\", \"roc_auc\", \"acc\"], index=[\"CamemBERT\"])\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "JxwhcYazbtI8",
        "outputId": "dbbfc0a6-bd8a-439a-e821-87bc571971d8"
      },
      "source": [
        "metrics_df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>f1</th>\n",
              "      <th>best_precision</th>\n",
              "      <th>best_recall</th>\n",
              "      <th>roc_auc</th>\n",
              "      <th>acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CamemBERT</th>\n",
              "      <td>0.886534</td>\n",
              "      <td>0.79949</td>\n",
              "      <td>0.995243</td>\n",
              "      <td>0.681144</td>\n",
              "      <td>0.797427</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 f1  best_precision  best_recall   roc_auc       acc\n",
              "CamemBERT  0.886534         0.79949     0.995243  0.681144  0.797427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyQJg1i-OkzN"
      },
      "source": [
        "On remarque que les performances présentées ici sont très similaires à celle vues pour les algorithmes de BoW et TF-IDF.\n",
        "Chaque modèle semble donc plafonner à un certain stade, ce qui semble indiquer que le set de test est trop différent du set d'entraînement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tDCa0DmxiSt"
      },
      "source": [
        "## Analyse : Quelles implications sur les messages à modérer A1 ? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KO_9WPqD0Fjm"
      },
      "source": [
        "test_values=np.array(test[\"tweet\"])\r\n",
        "test_labels=np.array(test[\"sentiment\"])\r\n",
        "\r\n",
        "train_values=np.array(df_to_moderate[\"content\"])\r\n",
        "train_values=[str(train_values[i]) for i in range(len(train_values))]\r\n",
        "train_labels=np.array(df_to_moderate[\"Harmful\"])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zy9l36sw3tpU"
      },
      "source": [
        " ### Test avec Camembert:\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1MaKld0pLw8"
      },
      "source": [
        "a1_test = tokenizer(np.array(df_a1_to_moderate[\"content\"]).tolist(), padding=True, truncation=True)\r\n",
        "a1_test_dataset=A1Dataset(a1_test,np.zeros(df_a1_to_moderate.shape[0]))"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cr6rLV3GqxjK"
      },
      "source": [
        "model.load_state_dict(torch.load(\"/content/drive/MyDrive/article_1_data/camembert.pt\"))\r\n",
        "predictions=[]\r\n",
        "\r\n",
        "test_loader=DataLoader(a1_test_dataset, batch_size=16, shuffle=False)\r\n",
        "with torch.no_grad():\r\n",
        "  for batch in test_loader:\r\n",
        "\r\n",
        "    inputs_test=batch[\"input_ids\"].to(device)\r\n",
        "    attention_mask_test=batch[\"attention_mask\"].to(device)\r\n",
        "    outputs=model(inputs_test,attention_mask=attention_mask_test)\r\n",
        "    predictions.append(np.argmax(outputs[0].cpu().data.numpy(), axis=1).tolist())\r\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpRFc_kqAZLz",
        "outputId": "2df13920-482c-42c5-9bc2-41175b3c1a55"
      },
      "source": [
        "predictions=[item for sublist in predictions for item in sublist]\r\n",
        "\r\n",
        "print(f\"Le modèle fait ressortir {sum(predictions)} messages problématiques\")\r\n",
        "print(f\"Sur {len(predictions)} messages\")\r\n",
        "print(f\"Le modèle détecte {sum(predictions)/len(predictions)} messages à modérer\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "51\n",
            "8233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-oDzWxQZctyw",
        "outputId": "6ae3a94a-de03-4c5c-8099-b317045ba0b0"
      },
      "source": [
        "indexes_camembert=[ i for i in range(len(predictions)) if predictions[i]==1]\n",
        "df_a1_bow_moderate=df_a1_to_moderate.iloc[indexes_camembert]\n",
        "\n",
        "for i in range(51): \n",
        "  print(df_a1_bow_moderate.iloc[i][\"content\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[36, 77, 210, 316, 521, 534, 1124, 1273, 1538, 1849, 2008, 2169, 2267, 2286, 2287, 2291, 2313, 2347, 2498, 2499, 2763, 2911, 3194, 3497, 4001, 4199, 4200, 4201, 4204, 4206, 4208, 4211, 4213, 4214, 4217, 4220, 4227, 4701, 4783, 5459, 5628, 6095, 6112, 6149, 6504, 6507, 6512, 7092, 7199, 7805, 7853]\n",
            "t'es un amour merciiiiiiiiii\n",
            "*inébranlable est un caprice de mon correcteur automatique\n",
            "Hello Léane* !! correcteur farceur :)\n",
            "woaw t’es grave deter mdrr\n",
            "https://drive.google.com/file/d/1lkHW998wL4xGX-ptekBuJ09iroz_PMdz/view?usp=drivesdk\n",
            "Merci, tes réponses sont très complètes ! TooGoodToGo va devenir mon meilleur ami ahah\n",
            "La communication aha\n",
            "Après paris j’y penses paaas vrmnt pck j’veux pas partir non plus loin de chez moi, je suis vers cannes 06\n",
            "Quel hasard que tu soit à pissarro !\n",
            "D’accord merveaucoup pour l’aide\n",
            "Insta : nonoo__o \n",
            ":)\n",
            "Derien, il y a pas de quoi\n",
            "Mais oui sinon c’est abusé je suis d’accord mdrr\n",
            "Ok vas-y\n",
            "et oui vas-y passes sur insta\n",
            "Ah ouais ! En fait c’est compliqué ct’histoire mdrr\n",
            "mon insta c’est myrfrh_\n",
            "*Mélodie* oups sorry\n",
            "Ouais t’as raison ajoute moi sur snap youyoune93 je réponds à tes messages la bas c’est mieux\n",
            "Ouais c’est vrai que les notes veulent à la fois tout et rien dire mdrr\n",
            "T’a*\n",
            "Mais trop drôle ahahah\n",
            "ah bah niquel\n",
            "mais on vit le fait que ce soit bétonner de partout ? XD\n",
            "je tecoute!\n",
            "Et j'affiche pas mon corps sur mes reseaux et encore moins a quelqu un que je connais pas physiquement\n",
            "Grave 😂 elle va pas se laisser marcher dessus\n",
            "Ahh elle a du caractere la petite\n",
            "Pdt au moins 10 mess je t ai dit nonn nonn ....\n",
            "Mais la frnachement t as abuse\n",
            "En plus je suis sure t un bon gars\n",
            "Je sais meme pas pq t'insiste\n",
            "Y a un moment quand on dit non c non\n",
            "Okkay ouais t aller trop loin\n",
            "Bah ouais fallair pas forcer\n",
            "Snap vitoo-59 (juge pas aha) 😁\n",
            "Non mais attends déjà que j’ai l’impression d’être vieux à côtés des nouveaux a arrivants  alors si même ma génération me vouvoie 😂😭\n",
            "Ahh chacun son boulot mais le mien me plaît bien 😊\n",
            "\n",
            "C’est ce qu’il faut, éviter les blessures bêtes pcq y’a que ça qui peut bloquer \n",
            "Ouah une patriote surmotivée 😍😂\n",
            "voici mon snap : wawa.dia\n",
            "https://drive.google.com/file/d/1lkHW998wL4xGX-ptekBuJ09iroz_PMdz/view?usp=drivesdk\n",
            "SVT** et non pas svr dans le 2e message pardon\n",
            "Et mon insta c’est axel.dnr\n",
            "* époque moderne pour la renaissance\n",
            "Quand même, sinon personne ne tiendrait le coup 😂\n",
            "Deriiiien\n",
            "ajoute sur snap yasmp si j'ai un problème je te signale Mehdi pas de bêtise !\n",
            "S'il te plaît j'y suis jamais je veux bien ton snap\n",
            "Voici mon Facebook SZarah kr\n",
            "Oh okay\n",
            "Mon insta c’est sn.mariam\n",
            "Escuse moi\n",
            "Son instagram c’est @maellfe\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzY9oF0AfwL1"
      },
      "source": [
        "Beaucoup moins de messages ressortent, et surtout les messages semblent plus cohérent (présence de snapchat, insta, facebook ou encore smiley). On note cependant que cela reste des messages relativement \"innocents\".\n",
        "\n",
        "On détecte de plus seulement X % de messages à modérer, pour rappel l'algortihme actuel en fait ressortir 20%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HbuUIbmPSL2"
      },
      "source": [
        "## Test approche Stopwords\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "dtGuIV_TSOtq",
        "outputId": "b64dfd04-8c6c-4a17-f9b4-cc50c10681b4"
      },
      "source": [
        "stopwords_df=pd.read_csv(\"/content/drive/MyDrive/article_1_data/stopwords.csv\",header=None,names=[\"word\"])\n",
        "stopwords_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>arabes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>baisable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>biatch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bifle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bite</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       word\n",
              "0    arabes\n",
              "1  baisable\n",
              "2    biatch\n",
              "3     bifle\n",
              "4      bite"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "0SwPodh1S__X",
        "outputId": "df6f034f-f40d-410c-e50e-26788f551a62"
      },
      "source": [
        "df_a1_to_moderate_stopwords=df_a1_to_moderate\n",
        "df_a1_to_moderate_stopwords[\"stopwords\"]=False\n",
        "df_a1_to_moderate_stopwords[\"stopwordsCount\"]=0\n",
        "df_a1_to_moderate_stopwords.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>stopwordsCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non, la physique-chimie de lycée n’a pas grand...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Merci infiniment d'avoir pris autant de temps ...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bonjour ! Alors non la SVT et la biologie ce n...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>salut\\nJ'aimerais savoir ce qu'est exactement ...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>je sais pas si j'ai répondu à toutes tes quest...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             content  stopwords  stopwordsCount\n",
              "0  Non, la physique-chimie de lycée n’a pas grand...      False               0\n",
              "1  Merci infiniment d'avoir pris autant de temps ...      False               0\n",
              "2  Bonjour ! Alors non la SVT et la biologie ce n...      False               0\n",
              "3  salut\\nJ'aimerais savoir ce qu'est exactement ...      False               0\n",
              "4  je sais pas si j'ai répondu à toutes tes quest...      False               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rto0SZetP8qP"
      },
      "source": [
        "  df_a1_to_moderate[\"content\"]\n",
        "\n",
        "for a1_index, a1_row in df_a1_to_moderate_stopwords.iterrows():\n",
        "  for s_index, s_row in stopwords_df.iterrows():\n",
        "    if (s_row[\"word\"] in a1_row[\"content\"]):\n",
        "      df_a1_to_moderate_stopwords.at[a1_index,\"stopwords\"]=True\n",
        "\n",
        "      df_a1_to_moderate_stopwords.at[a1_index,\"stopwordsCount\"]=df_a1_to_moderate_stopwords.at[a1_index,\"stopwordsCount\"]+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "jZyEWBhTQ_f1",
        "outputId": "aee8616a-8ed1-4bdc-b474-96e816ba27c0"
      },
      "source": [
        "df_a1_to_moderate_stopwords.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>stopwordsCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non, la physique-chimie de lycée n’a pas grand...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Merci infiniment d'avoir pris autant de temps ...</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bonjour ! Alors non la SVT et la biologie ce n...</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>salut\\nJ'aimerais savoir ce qu'est exactement ...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>je sais pas si j'ai répondu à toutes tes quest...</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             content  stopwords  stopwordsCount\n",
              "0  Non, la physique-chimie de lycée n’a pas grand...      False               0\n",
              "1  Merci infiniment d'avoir pris autant de temps ...       True               1\n",
              "2  Bonjour ! Alors non la SVT et la biologie ce n...       True               1\n",
              "3  salut\\nJ'aimerais savoir ce qu'est exactement ...      False               0\n",
              "4  je sais pas si j'ai répondu à toutes tes quest...      False               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB_odvdWURqw",
        "outputId": "53d209f1-a998-4ce9-e782-a0a50eb52f4b"
      },
      "source": [
        "df_a1_to_moderate_stopwords.stopwords.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    6529\n",
              "True     1704\n",
              "Name: stopwords, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83aY1dIFVGAz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}