diff --git a/notebooks/ConfusionMatrix.ipynb b/notebooks/ConfusionMatrix.ipynb
index 14a33f76f893983994f1d5ce8219a44875640c25..968d72d9630c8838a893f7e8834f85f60e74175c 100644
--- a/notebooks/ConfusionMatrix.ipynb
+++ b/notebooks/ConfusionMatrix.ipynb
@@ -1,26 +1,12 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "EDdA-Classification_Generate_ConfusionMatrix.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "F-x2Ei_TdhSs"
       },
+      "outputs": [],
       "source": [
         "train_path = 'training_set.tsv'\n",
         "validation_path = 'validation_set.tsv'\n",
@@ -31,9 +17,7 @@
         "\n",
         "minOfInstancePerClass = 0\n",
         "maxOfInstancePerClass = 10000"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -46,6 +30,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -53,23 +38,15 @@
         "id": "WoNGyMbFdsh1",
         "outputId": "c5542219-0412-4e16-9779-122d5f99a1e2"
       },
+      "outputs": [],
       "source": [
         "from google.colab import drive\n",
         "drive.mount('/content/drive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -77,6 +54,7 @@
         "id": "1LXBuRs9kOOc",
         "outputId": "1f5fe407-4a46-4b96-8124-1a0c334616df"
       },
+      "outputs": [],
       "source": [
         "import pandas as pd\n",
         "import numpy as np\n",
@@ -90,36 +68,15 @@
         "import nltk\n",
         "nltk.download('stopwords')\n",
         "nltk.download('punkt')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "True"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "FNPXtQ19kbco"
       },
+      "outputs": [],
       "source": [
         "def resample_classes(df, classColumnName, numberOfInstances):\n",
         "  #random numberOfInstances elements\n",
@@ -127,9 +84,7 @@
         "  fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]\n",
         "  return df.groupby(classColumnName, as_index=False).apply(fn)\n",
         "    "
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -142,6 +97,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -149,59 +105,20 @@
         "id": "8-1HRF3Vhr3y",
         "outputId": "bd5f5881-363f-41a9-ade7-33bbd1158adb"
       },
+      "outputs": [],
       "source": [
         "!wget https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/training_set.tsv\n",
         "!wget https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/validation_set.tsv\n",
         "!wget https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/test_set.tsv"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--2021-11-26 08:17:56--  https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/training_set.tsv\n",
-            "Resolving projet.liris.cnrs.fr (projet.liris.cnrs.fr)... 134.214.142.28\n",
-            "Connecting to projet.liris.cnrs.fr (projet.liris.cnrs.fr)|134.214.142.28|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 189925180 (181M) [text/tab-separated-values]\n",
-            "Saving to: ‘training_set.tsv’\n",
-            "\n",
-            "training_set.tsv    100%[===================>] 181.13M  31.9MB/s    in 6.3s    \n",
-            "\n",
-            "2021-11-26 08:18:02 (28.9 MB/s) - ‘training_set.tsv’ saved [189925180/189925180]\n",
-            "\n",
-            "--2021-11-26 08:18:03--  https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/validation_set.tsv\n",
-            "Resolving projet.liris.cnrs.fr (projet.liris.cnrs.fr)... 134.214.142.28\n",
-            "Connecting to projet.liris.cnrs.fr (projet.liris.cnrs.fr)|134.214.142.28|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 67474385 (64M) [text/tab-separated-values]\n",
-            "Saving to: ‘validation_set.tsv’\n",
-            "\n",
-            "validation_set.tsv  100%[===================>]  64.35M  24.4MB/s    in 2.6s    \n",
-            "\n",
-            "2021-11-26 08:18:06 (24.4 MB/s) - ‘validation_set.tsv’ saved [67474385/67474385]\n",
-            "\n",
-            "--2021-11-26 08:18:06--  https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/test_set.tsv\n",
-            "Resolving projet.liris.cnrs.fr (projet.liris.cnrs.fr)... 134.214.142.28\n",
-            "Connecting to projet.liris.cnrs.fr (projet.liris.cnrs.fr)|134.214.142.28|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 79961640 (76M) [text/tab-separated-values]\n",
-            "Saving to: ‘test_set.tsv’\n",
-            "\n",
-            "test_set.tsv        100%[===================>]  76.26M  25.5MB/s    in 3.0s    \n",
-            "\n",
-            "2021-11-26 08:18:09 (25.5 MB/s) - ‘test_set.tsv’ saved [79961640/79961640]\n",
-            "\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "P_Psa_NhhyAA"
       },
+      "outputs": [],
       "source": [
         "\n",
         "df_test = pd.read_csv(test_path, sep=\"\\t\")\n",
@@ -210,20 +127,16 @@
         "\n",
         "\n",
         "data_eval = df_test[columnText].tolist()\n"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "AfsjFx1L_ddl"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "markdown",
@@ -236,20 +149,22 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "I-BT_jRs74tI"
       },
+      "outputs": [],
       "source": [
         "!wget https://projet.liris.cnrs.fr/geode/EDdA-Classification/datasets/EDdA_dataframe_withContent.tsv"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "0NrbzDu66-k3"
       },
+      "outputs": [],
       "source": [
         "\n",
         "## test sortie pour Katie avec la classification de tous les articles\n",
@@ -258,9 +173,7 @@
         "\n",
         "\n",
         "data_eval = df[columnText].tolist()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -273,35 +186,37 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "SHCqMPk8iPZS"
       },
+      "outputs": [],
       "source": [
         "classifier_name = \"sgd\"       # sgd | lr | rfc | svm | bayes | bert-base-multilingual | camembert-base\n",
         "vectorizer_name = \"tf_idf\"    # bagofwords | tf_idf | doc2vec"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "oJ2xKgoVSQFC"
       },
+      "outputs": [],
       "source": [
         "# récupérer les modèles depuis le serveur\n",
         "\n",
         "\n",
         "# récupéréer les modèles depuis Google Drive\n"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "xI_4exathQdd"
       },
+      "outputs": [],
       "source": [
         "if classifier_name in [\"sgd\", \"lr\", \"rfc\", \"svm\", \"bayes\"]:\n",
         "\n",
@@ -330,12 +245,11 @@
         "\n",
         "  model = torch.load(clf_file_name)\n",
         "\n"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -343,36 +257,14 @@
         "id": "jJjCGPTFjC78",
         "outputId": "099e267e-8f5e-4c85-ef8e-b6bb60104c8d"
       },
+      "outputs": [],
       "source": [
         "df_test[columnClass]"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0               Commerce\n",
-              "1                    NaN\n",
-              "2                 Marine\n",
-              "3             Géographie\n",
-              "4               Histoire\n",
-              "              ...       \n",
-              "15849         Géographie\n",
-              "15850                NaN\n",
-              "15851    Arts et métiers\n",
-              "15852           Anatomie\n",
-              "15853                NaN\n",
-              "Name: ensemble_domaine_enccre, Length: 15854, dtype: object"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -381,6 +273,7 @@
         "id": "o2J8mU_djDsm",
         "outputId": "aa2784b6-623d-4605-cdfb-93e2b6adb3c1"
       },
+      "outputs": [],
       "source": [
         "plot_confusion_matrix(clf, vec_data, df_test[columnClass], normalize=\"true\", include_values=False, xticks_rotation=\"vertical\", cmap=plt.cm.Blues)\n",
         "name = classifier_name + '_' +vectorizer_name + '_s' + str(maxOfInstancePerClass) +\".png\"\n",
@@ -391,119 +284,84 @@
         "plt.rcParams[\"font.size\"] = 10\n",
         "\n",
         "plt.savefig(pathSave, bbox_inches='tight')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "sgd_tf_idf_s10000.png\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 720x720 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "5KnaRf855lsv"
       },
+      "outputs": [],
       "source": [
         "# ajouter le code pour faire la prediction avec les modèles BERT\n",
         "\n",
         "\n"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "llGjT-xsUvR4"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "3dGPXQSLUvUn"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "NQyuDQw_JOwB"
       },
+      "outputs": [],
       "source": [
         "y_pred = clf.predict(vec_data)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "zgNKwbp_eYos"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "dZGxg_OreYrO"
       },
+      "outputs": [],
       "source": [
         "df_test = df.copy()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "g8FfxZ7bKwCe"
       },
+      "outputs": [],
       "source": [
         "df_test['classification'] = y_pred"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -511,36 +369,25 @@
         "id": "hRcYKfdIK0Tm",
         "outputId": "db988435-9716-4cf5-a754-04bc5356369f"
       },
+      "outputs": [],
       "source": [
         "df_test.shape"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(61738, 14)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 29
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "nlV3yXcCMb8v"
       },
+      "outputs": [],
       "source": [
         "df_test.head()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -548,38 +395,27 @@
         "id": "GuotNONXMXgt",
         "outputId": "5fb34593-c97d-4401-a617-b25aa8f7e49c"
       },
+      "outputs": [],
       "source": [
         "df_test.loc[(df_test['ensemble_domaine_enccre'] != df_test['classification'])].shape"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(8597, 14)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 30
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "raw7PJrtMsDx"
       },
+      "outputs": [],
       "source": [
         "\n",
         "\n",
         "df_test['class_is_true'] = df_test['ensemble_domaine_enccre'] == df_test['classification']"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -588,174 +424,25 @@
         "id": "qDD13-3dOSgK",
         "outputId": "a309b603-8179-48ff-ad55-f3599f0dc699"
       },
+      "outputs": [],
       "source": [
         "df_test.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>volume</th>\n",
-              "      <th>numero</th>\n",
-              "      <th>head</th>\n",
-              "      <th>normClass</th>\n",
-              "      <th>classEDdA</th>\n",
-              "      <th>author</th>\n",
-              "      <th>id_enccre</th>\n",
-              "      <th>domaine_enccre</th>\n",
-              "      <th>ensemble_domaine_enccre</th>\n",
-              "      <th>content</th>\n",
-              "      <th>contentWithoutClass</th>\n",
-              "      <th>firstParagraph</th>\n",
-              "      <th>nb_word</th>\n",
-              "      <th>classification</th>\n",
-              "      <th>class_is_true</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1</td>\n",
-              "      <td>5</td>\n",
-              "      <td>A, a &amp; a</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>ordre Encyclopéd. Entend. Science de l'homme, ...</td>\n",
-              "      <td>Dumarsais5</td>\n",
-              "      <td>v1-1-0</td>\n",
-              "      <td>grammaire</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>A, a &amp; a s.m. (ordre Encyclopéd.\\nEntend. Scie...</td>\n",
-              "      <td>A, a &amp; a s.m. (ordre Encyclopéd.\\nEntend. Scie...</td>\n",
-              "      <td>A, a &amp; a s.m. (ordre Encyclopéd.\\nEntend. Scie...</td>\n",
-              "      <td>711</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1</td>\n",
-              "      <td>6</td>\n",
-              "      <td>A</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>Dumarsais5</td>\n",
-              "      <td>v1-1-1</td>\n",
-              "      <td>grammaire</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>A, mot, est 1. la troisieme personne du présen...</td>\n",
-              "      <td>A, mot, est 1. la troisieme personne du présen...</td>\n",
-              "      <td>A, mot, est 1. la troisieme personne du présen...</td>\n",
-              "      <td>238</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>1</td>\n",
-              "      <td>7</td>\n",
-              "      <td>A</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>Dumarsais</td>\n",
-              "      <td>v1-1-2</td>\n",
-              "      <td>grammaire</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>A, préposition vient du latin à, à dextris, à ...</td>\n",
-              "      <td>A, préposition vient du latin à, à dextris, à ...</td>\n",
-              "      <td>A, préposition vient du latin à, à dextris, à ...</td>\n",
-              "      <td>1980</td>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>1</td>\n",
-              "      <td>10</td>\n",
-              "      <td>A, numismatique ou monétaire</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>Mallet</td>\n",
-              "      <td>v1-1-5</td>\n",
-              "      <td>numismatique</td>\n",
-              "      <td>Médailles</td>\n",
-              "      <td>A, numismatique ou monétaire, sur le revers de...</td>\n",
-              "      <td>A, numismatique ou monétaire, sur le revers de...</td>\n",
-              "      <td>A, numismatique ou monétaire, sur le revers de...</td>\n",
-              "      <td>112</td>\n",
-              "      <td>Médailles</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>1</td>\n",
-              "      <td>11</td>\n",
-              "      <td>A, lapidaire</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>unclassified</td>\n",
-              "      <td>Mallet</td>\n",
-              "      <td>v1-1-6</td>\n",
-              "      <td>inscriptions</td>\n",
-              "      <td>Histoire</td>\n",
-              "      <td>A, lapidaire, dans les anciennes inscriptions ...</td>\n",
-              "      <td>A, lapidaire, dans les anciennes inscriptions ...</td>\n",
-              "      <td>A, lapidaire, dans les anciennes inscriptions ...</td>\n",
-              "      <td>80</td>\n",
-              "      <td>Histoire</td>\n",
-              "      <td>True</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   volume  numero  ... classification class_is_true\n",
-              "3       1       5  ...      Grammaire          True\n",
-              "4       1       6  ...      Grammaire          True\n",
-              "5       1       7  ...      Grammaire          True\n",
-              "8       1      10  ...      Médailles          True\n",
-              "9       1      11  ...       Histoire          True\n",
-              "\n",
-              "[5 rows x 15 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 32
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "qsAd_w_iO9LZ"
       },
+      "outputs": [],
       "source": [
         "df_test.to_csv('result_classification_sgdtfidf_21.11.24.csv', index=False)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -763,58 +450,45 @@
         "id": "H4XfLD3EaaTe",
         "outputId": "50c60efb-6670-4bd2-8c2d-f7c309fb0932"
       },
+      "outputs": [],
       "source": [
         "df_test.loc[(df_test['ensemble_domaine_enccre'] == 'Géographie') & (df_test['class_is_true'] == False )].shape"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(95, 15)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 32
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "J3Nbs6zMCnWh"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "s6xTROC7CnZA"
       },
+      "outputs": [],
       "source": [
         "## test de sortie des scores (proba) pour chaque classe"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "1TyETcoyCnbU"
       },
+      "outputs": [],
       "source": [
         "y_pred_proba = clf.predict_proba(vec_data)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -822,35 +496,14 @@
         "id": "2W4i8nrLC61s",
         "outputId": "86373732-4a06-487f-db1b-0a2e867974fa"
       },
+      "outputs": [],
       "source": [
         "clf.classes_"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['Agriculture - Economie rustique', 'Anatomie', 'Antiquité',\n",
-              "       'Architecture', 'Arts et métiers', 'Beaux-arts',\n",
-              "       'Belles-lettres - Poésie', 'Blason', 'Caractères', 'Chasse',\n",
-              "       'Chimie', 'Commerce', 'Droit - Jurisprudence',\n",
-              "       'Economie domestique', 'Grammaire', 'Géographie', 'Histoire',\n",
-              "       'Histoire naturelle', 'Jeu', 'Marine', 'Maréchage - Manège',\n",
-              "       'Mathématiques', 'Mesure', 'Militaire (Art) - Guerre - Arme',\n",
-              "       'Minéralogie', 'Monnaie', 'Musique', 'Médailles',\n",
-              "       'Médecine - Chirurgie', 'Métiers', 'Pharmacie', 'Philosophie',\n",
-              "       'Physique - [Sciences physico-mathématiques]', 'Politique',\n",
-              "       'Pêche', 'Religion', 'Spectacle', 'Superstition'], dtype='<U43')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 47
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -859,28 +512,14 @@
         "id": "tiecHJyTC66o",
         "outputId": "bf846387-9964-418d-d122-9bc032c60266"
       },
+      "outputs": [],
       "source": [
         "data_eval[0]"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "\"\\nLes pins ont encore le mérite de l'agrément ; ils\\nconservent pendant toute l'année leurs feuilles, qui\\ndans la plûpart des especes sont d'une très-belle verdure.\\nCes arbres sont d'une belle stature, & d'un accroissement \\nrégulier ; ils ne sont sujets ni aux insectes,\\n\\n\\nni à aucune maladie ; enfin plusieurs de ces pins sont\\nde la plus belle apparence au printems, par la couleur \\nvive des chatons dont ils sont chargés. Voyez sur\\nla culture du pin, le dictionnaire des Jardiniers de\\nM. Miller, & pour tous égards, le traité des arbres\\nde M. Duhamel, qui est entré dans des détails intéressans \\nsur cet arbre.\\n\""
-            ]
-          },
-          "metadata": {},
-          "execution_count": 44
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -888,32 +527,12 @@
         "id": "cFkSivM2Cndt",
         "outputId": "8fda16d7-04cc-4609-8fa6-7995a4ffd01c"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0.38404935, 0.        , 0.        , 0.        , 0.        ,\n",
-              "       0.01376867, 0.10553505, 0.        , 0.        , 0.        ,\n",
-              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-              "       0.        , 0.00485592, 0.47335577, 0.        , 0.        ,\n",
-              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-              "       0.        , 0.        , 0.        , 0.01843524, 0.        ,\n",
-              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-              "       0.        , 0.        , 0.        ])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 42
-        }
-      ]
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -922,50 +541,32 @@
         "id": "3dG5qbPoCngN",
         "outputId": "0ad887fe-dd94-4d4d-856a-b45b8091d650"
       },
+      "outputs": [],
       "source": [
         "clf.classes_[np.argmax(y_pred_proba[0], axis=0)]"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'Histoire naturelle'"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 49
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "qsrY1g6mCniF"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "gFywr71BCnkt"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -974,208 +575,69 @@
         "id": "_Gews6OdbN3d",
         "outputId": "03b7bb01-51be-4d35-f090-84f02b697366"
       },
+      "outputs": [],
       "source": [
         "df_test.loc[(df_test['ensemble_domaine_enccre'] == 'Géographie') & (df_test['class_is_true'] == False )].groupby(by=[\"classification\"]).size().reset_index(name='counts').sort_values(by='counts', ascending=False)"
-      ],
-      "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>classification</th>\n",
-              "      <th>counts</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>Histoire</td>\n",
-              "      <td>19</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>Histoire naturelle</td>\n",
-              "      <td>11</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Antiquité</td>\n",
-              "      <td>10</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Belles-lettres - Poésie</td>\n",
-              "      <td>9</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>Religion</td>\n",
-              "      <td>9</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>Physique - [Sciences physico-mathématiques]</td>\n",
-              "      <td>8</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>Droit - Jurisprudence</td>\n",
-              "      <td>5</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>Commerce</td>\n",
-              "      <td>4</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>Grammaire</td>\n",
-              "      <td>4</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>Philosophie</td>\n",
-              "      <td>3</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>Marine</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11</th>\n",
-              "      <td>Mathématiques</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14</th>\n",
-              "      <td>Médailles</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15</th>\n",
-              "      <td>Médecine - Chirurgie</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Chimie</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Beaux-arts</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Architecture</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>Militaire (Art) - Guerre - Arme</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>13</th>\n",
-              "      <td>Musique</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                 classification  counts\n",
-              "8                                      Histoire      19\n",
-              "9                            Histoire naturelle      11\n",
-              "0                                     Antiquité      10\n",
-              "3                       Belles-lettres - Poésie       9\n",
-              "18                                     Religion       9\n",
-              "17  Physique - [Sciences physico-mathématiques]       8\n",
-              "6                         Droit - Jurisprudence       5\n",
-              "5                                      Commerce       4\n",
-              "7                                     Grammaire       4\n",
-              "16                                  Philosophie       3\n",
-              "10                                       Marine       2\n",
-              "11                                Mathématiques       2\n",
-              "14                                    Médailles       2\n",
-              "15                         Médecine - Chirurgie       2\n",
-              "4                                        Chimie       1\n",
-              "2                                    Beaux-arts       1\n",
-              "1                                  Architecture       1\n",
-              "12              Militaire (Art) - Guerre - Arme       1\n",
-              "13                                      Musique       1"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 39
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "IF_N5qRqdsmj"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "C_OcQ-uudso3"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "dgFIEa0Pdsre"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "tHX62GU4dsue"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "EDdA-Classification_Generate_ConfusionMatrix.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.13 ('stanza-lexicoscope-py39')",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.13"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "68d5f9281eab57a7f4901cb150f4c691b1d08935474a18f188e0e3e8f8f412b7"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}