diff --git a/notebooks/Classification_BertFineTuning.ipynb b/notebooks/Classification_BertFineTuning.ipynb
index 692d3b5af2a782eef88210ca4cb8b64052dd80a1..6e533e75b3b02a47abe1c2c3ba679bcbe93de0ac 100644
--- a/notebooks/Classification_BertFineTuning.ipynb
+++ b/notebooks/Classification_BertFineTuning.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -28,17 +28,7 @@
"id": "WF0qFN_g3ekz",
"outputId": "f3a5f049-24ee-418f-fe5e-84c633234ad8"
},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Your runtime has 27.3 gigabytes of available RAM\n",
- "\n",
- "You are using a high-RAM runtime!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from psutil import virtual_memory\n",
"ram_gb = virtual_memory().total / 1e9\n",
@@ -52,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -60,15 +50,7 @@
"id": "vL0S-s9Uofvn",
"outputId": "4b7efa4d-7f09-4c8e-bc98-99e6099ede32"
},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Mounted at /content/drive\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
@@ -85,7 +67,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -95,11 +77,10 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "There are 1 GPU(s) available.\n",
- "We will use the GPU: Tesla P100-PCIE-16GB\n"
+ "We will use the GPU\n"
]
}
],
@@ -108,18 +89,18 @@
"\n",
"# If there's a GPU available...\n",
"if torch.cuda.is_available(): \n",
- "\n",
" # Tell PyTorch to use the GPU. \n",
" device = torch.device(\"cuda\")\n",
- "\n",
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
- "\n",
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
"\n",
- "# If not...\n",
+ "# for MacOS\n",
+ "elif torch.backends.mps.is_available() and torch.backends.mps.is_built():\n",
+ " device = torch.device(\"mps\")\n",
+ " print('We will use the GPU')\n",
"else:\n",
- " print('No GPU available, using the CPU instead.')\n",
- " device = torch.device(\"cpu\")"
+ " device = torch.device(\"cpu\")\n",
+ " print('No GPU available, using the CPU instead.')"
]
},
{
@@ -133,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -141,57 +122,7 @@
"id": "pwmZ5bBvgGNh",
"outputId": "fce0a8bf-1779-4079-c7ac-200ebb2678c5"
},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Collecting transformers==4.10.3\n",
- " Downloading transformers-4.10.3-py3-none-any.whl (2.8 MB)\n",
- "\u001b[K |████████████████████████████████| 2.8 MB 5.2 MB/s \n",
- "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (2.23.0)\n",
- "Collecting sacremoses\n",
- " Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
- "\u001b[K |████████████████████████████████| 895 kB 47.4 MB/s \n",
- "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (4.11.0)\n",
- "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (21.3)\n",
- "Collecting tokenizers<0.11,>=0.10.1\n",
- " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
- "\u001b[K |████████████████████████████████| 3.3 MB 49.6 MB/s \n",
- "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (2019.12.20)\n",
- "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (1.21.5)\n",
- "Collecting huggingface-hub>=0.0.12\n",
- " Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
- "\u001b[K |████████████████████████████████| 67 kB 5.8 MB/s \n",
- "\u001b[?25hCollecting pyyaml>=5.1\n",
- " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
- "\u001b[K |████████████████████████████████| 596 kB 46.7 MB/s \n",
- "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (4.62.3)\n",
- "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.10.3) (3.4.2)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.12->transformers==4.10.3) (3.10.0.2)\n",
- "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==4.10.3) (3.0.7)\n",
- "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.10.3) (3.7.0)\n",
- "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.10.3) (1.24.3)\n",
- "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.10.3) (3.0.4)\n",
- "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.10.3) (2.10)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.10.3) (2021.10.8)\n",
- "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.10.3) (1.1.0)\n",
- "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.10.3) (1.15.0)\n",
- "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.10.3) (7.1.2)\n",
- "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
- " Attempting uninstall: pyyaml\n",
- " Found existing installation: PyYAML 3.13\n",
- " Uninstalling PyYAML-3.13:\n",
- " Successfully uninstalled PyYAML-3.13\n",
- "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.10.3\n",
- "Collecting sentencepiece\n",
- " Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
- "\u001b[K |████████████████████████████████| 1.2 MB 5.1 MB/s \n",
- "\u001b[?25hInstalling collected packages: sentencepiece\n",
- "Successfully installed sentencepiece-0.1.96\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install transformers==4.10.3\n",
"!pip install sentencepiece"
@@ -208,7 +139,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {
"id": "SkErnwgMMbRj"
},
@@ -250,7 +181,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 3,
"metadata": {
"id": "WkIVcabUgxIl"
},
@@ -290,7 +221,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -298,36 +229,7 @@
"id": "jdCdUVOTZrqh",
"outputId": "ac52be55-ed4b-4c50-dc8c-9124ca6b71e5"
},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "--2022-02-18 07:03:04-- 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: 175634219 (167M) [text/tab-separated-values]\n",
- "Saving to: ‘training_set.tsv’\n",
- "\n",
- "training_set.tsv 100%[===================>] 167.50M 27.7MB/s in 6.8s \n",
- "\n",
- "2022-02-18 07:03:11 (24.7 MB/s) - ‘training_set.tsv’ saved [175634219/175634219]\n",
- "\n",
- "--2022-02-18 07:03:11-- 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: 42730598 (41M) [text/tab-separated-values]\n",
- "Saving to: ‘test_set.tsv’\n",
- "\n",
- "test_set.tsv 100%[===================>] 40.75M 19.5MB/s in 2.1s \n",
- "\n",
- "2022-02-18 07:03:14 (19.5 MB/s) - ‘test_set.tsv’ saved [42730598/42730598]\n",
- "\n"
- ]
- }
- ],
+ "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/test_set.tsv"
@@ -344,36 +246,32 @@
},
{
"cell_type": "code",
- "source": [
- "train_path = 'training_set.tsv'\n",
- "test_path = 'test_set.tsv'"
- ],
+ "execution_count": 4,
"metadata": {
"id": "7JEnKknRoClH"
},
- "execution_count": 7,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "train_path = '../data/training_set.tsv'\n",
+ "test_path = '../data/test_set.tsv'"
+ ]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {
- "id": "5u1acjunhoxe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
+ "id": "5u1acjunhoxe",
"outputId": "3038048d-6506-473d-85c9-2d3ebdcc6a72"
},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/html": [
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- " <div class=\"colab-df-container\">\n",
- " <div>\n",
+ "<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
@@ -489,97 +387,48 @@
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "</div>\n",
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- " </svg>\n",
- " </button>\n",
- " \n",
- " <style>\n",
- " .colab-df-container {\n",
- " display:flex;\n",
- " flex-wrap:wrap;\n",
- " gap: 12px;\n",
- " }\n",
- "\n",
- " .colab-df-convert {\n",
- " background-color: #E8F0FE;\n",
- " border: none;\n",
- " border-radius: 50%;\n",
- " cursor: pointer;\n",
- " display: none;\n",
- " fill: #1967D2;\n",
- " height: 32px;\n",
- " padding: 0 0 0 0;\n",
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- " fill: #174EA6;\n",
- " }\n",
- "\n",
- " [theme=dark] .colab-df-convert {\n",
- " background-color: #3B4455;\n",
- " fill: #D2E3FC;\n",
- " }\n",
- "\n",
- " [theme=dark] .colab-df-convert:hover {\n",
- " background-color: #434B5C;\n",
- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
- " fill: #FFFFFF;\n",
- " }\n",
- " </style>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " volume numero head normClass \\\n",
+ "0 15 5 SENACULE Antiquité romaine \n",
+ "1 8 3509 Investir Marine \n",
+ "2 2 3428 BOYARDS, ou BOJARES, ou BOJARDS Histoire moderne \n",
+ "3 2 6532 Cerceau Tonnelier \n",
+ "4 8 1827 HIERONYMITES, ou HERMITES DE S. JEROME unclassified \n",
"\n",
- " <script>\n",
- " const buttonEl =\n",
- " document.querySelector('#df-5a25a6b1-21af-4b77-8ab1-a5df357d33f1 button.colab-df-convert');\n",
- " buttonEl.style.display =\n",
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " classEDdA author id_enccre domaine_enccre \\\n",
+ "0 Antiq. rom. Jaucourt v15-4-0 antiquité \n",
+ "1 Marine. Le Blond v8-2689-1 marine \n",
+ "2 Hist. mod. Mallet v2-2041-0 histoire \n",
+ "3 Tonneliers unsigned v2-4266-3 tonnelier \n",
+ "4 unclassified unsigned v8-1404-0 histoireecclésiastique \n",
"\n",
- " async function convertToInteractive(key) {\n",
- " const element = document.querySelector('#df-5a25a6b1-21af-4b77-8ab1-a5df357d33f1');\n",
- " const dataTable =\n",
- " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
- " [key], {});\n",
- " if (!dataTable) return;\n",
+ " ensemble_domaine_enccre content \\\n",
+ "0 Antiquité SENACULE, s. m. (Antiq. rom.) senaculum:\\nlieu... \n",
+ "1 Marine Investir, (Marine.) se dit parmi les matelots\\... \n",
+ "2 Histoire BOYARDS, ou BOJARES, ou BOJARDS, s. m.\\npl. (H... \n",
+ "3 Métiers Cerceau, c'est un lien de bois qui se plie fac... \n",
+ "4 Religion HIERONYMITES, ou HERMITES DE S. JEROME, Voyez ... \n",
"\n",
- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
- " + ' to learn more about interactive tables.';\n",
- " element.innerHTML = '';\n",
- " dataTable['output_type'] = 'display_data';\n",
- " await google.colab.output.renderOutput(dataTable, element);\n",
- " const docLink = document.createElement('div');\n",
- " docLink.innerHTML = docLinkHtml;\n",
- " element.appendChild(docLink);\n",
- " }\n",
- " </script>\n",
- " </div>\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- " volume numero ... firstParagraph nb_words\n",
- "0 15 5 ... senacule s. m. senaculum \\n lieu où tenoit s... 91\n",
- "1 8 3509 ... investir parmi matelot \\n méditerranée échou... 30\n",
- "2 2 3428 ... boyard bojares bojards s. m. \\n pl nom donne... 218\n",
- "3 2 6532 ... cerceau lien bois plie facilement \\n servent... 229\n",
- "4 8 1827 ... hieronymites hermites s. jerome jeronymites he... 34\n",
+ " contentWithoutClass \\\n",
+ "0 senacule s. m. senaculum \\n lieu où tenoit s... \n",
+ "1 investir parmi matelot \\n méditerranée échou... \n",
+ "2 boyard bojares bojards s. m. \\n pl nom donne... \n",
+ "3 cerceau lien bois plie facilement \\n servent... \n",
+ "4 hieronymites hermites s. jerome jeronymites he... \n",
"\n",
- "[5 rows x 13 columns]"
+ " firstParagraph nb_words \n",
+ "0 senacule s. m. senaculum \\n lieu où tenoit s... 91 \n",
+ "1 investir parmi matelot \\n méditerranée échou... 30 \n",
+ "2 boyard bojares bojards s. m. \\n pl nom donne... 218 \n",
+ "3 cerceau lien bois plie facilement \\n servent... 229 \n",
+ "4 hieronymites hermites s. jerome jeronymites he... 34 "
]
},
+ "execution_count": 5,
"metadata": {},
- "execution_count": 9
+ "output_type": "execute_result"
}
],
"source": [
@@ -589,7 +438,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -599,8 +448,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(46807, 13)\n"
]
@@ -612,60 +461,61 @@
},
{
"cell_type": "markdown",
- "source": [
- "## Configuration"
- ],
"metadata": {
"id": "dADYGtTcn4AB"
- }
+ },
+ "source": [
+ "## Configuration"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "I0OrfFsBn4Io"
+ },
+ "outputs": [],
"source": [
"columnText = 'contentWithoutClass'\n",
"columnClass = 'ensemble_domaine_enccre'\n",
"\n",
"maxOfInstancePerClass = 10000\n",
"\n",
- "#model_chosen = \"bert\"\n",
- "model_chosen = \"camembert\"\n",
+ "model_chosen = \"bert\"\n",
+ "#model_chosen = \"camembert\"\n",
"\n",
- "batch_size = 8 # 16 or 32 recommended\n",
+ "batch_size = 16 # 16 or 32 recommended\n",
"max_len = 512\n",
"\n",
- "path = \"drive/MyDrive/Classification-EDdA/\"\n",
+ "#path = \"drive/MyDrive/Classification-EDdA/\"\n",
+ "path = \"../models/new/\"\n",
"encoder_filename = \"label_encoder.pkl\""
- ],
- "metadata": {
- "id": "I0OrfFsBn4Io"
- },
- "execution_count": 30,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "## Preprocessing"
- ],
"metadata": {
"id": "t3brU-Yvn4XS"
- }
+ },
+ "source": [
+ "## Preprocessing"
+ ]
},
{
"cell_type": "code",
- "source": [
- "if maxOfInstancePerClass != 10000:\n",
- " df_train = resample_classes(df_train, columnClass, maxOfInstancePerClass)"
- ],
+ "execution_count": null,
"metadata": {
"id": "aQCLJE4Jtm7v"
},
- "execution_count": 31,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "if maxOfInstancePerClass != 10000:\n",
+ " df_train = resample_classes(df_train, columnClass, maxOfInstancePerClass)"
+ ]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 25,
"metadata": {
"id": "zrjZvs2dhzAy"
},
@@ -693,7 +543,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 26,
"metadata": {
"id": "Xt_PhH_6h1_3"
},
@@ -705,7 +555,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -715,7 +565,6 @@
},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"array(['senacule s. m. senaculum \\n lieu où tenoit sénat rome \\n sénacules endroit où corps illustre assembloit \\n capitole forum \\n porte capène troisieme près temple \\n bellone cirque flaminien empereur héliogabale bâtir lieu assemblée dame \\n lieu appellé senaculum matronarum d. j.',\n",
@@ -728,8 +577,9 @@
" dtype=object)"
]
},
+ "execution_count": 27,
"metadata": {},
- "execution_count": 34
+ "output_type": "execute_result"
}
],
"source": [
@@ -748,7 +598,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 28,
"metadata": {
"id": "YZ5PhEYZiCEA"
},
@@ -764,7 +614,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -810,53 +660,11 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "Loading CamemBERT tokenizer...\n"
+ "Loading BERT tokenizer...\n"
]
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "274e505b5f354efc8de3ef26cc43e617",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "Downloading: 0%| | 0.00/811k [00:00<?, ?B/s]"
- ]
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0279837673b446b09aac18346213eb7e",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "Downloading: 0%| | 0.00/1.40M [00:00<?, ?B/s]"
- ]
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8f467553598f4dcc9abf55da79c11018",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "Downloading: 0%| | 0.00/508 [00:00<?, ?B/s]"
- ]
- },
- "metadata": {}
}
],
"source": [
@@ -871,7 +679,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -881,10 +689,10 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
- "Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors\n"
+ "Token indices sequence length is longer than the specified maximum sequence length for this model (667 > 512). Running this sequence through the model will result in indexing errors\n"
]
}
],
@@ -917,7 +725,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -927,10 +735,10 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "Max sentence length train: 38073\n"
+ "Max sentence length train: 45443\n"
]
}
],
@@ -940,7 +748,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {
"id": "xh1TQJyvjOx5"
},
@@ -961,7 +769,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"metadata": {
"id": "ZiwY6gn0jUkD"
},
@@ -985,7 +793,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"metadata": {
"id": "oBTR5AfAjXJe"
},
@@ -1001,7 +809,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"metadata": {
"id": "b9Mw5kq3jhTb"
},
@@ -1018,7 +826,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": null,
"metadata": {
"id": "UfFWzbENjnkw"
},
@@ -1046,7 +854,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -1070,334 +878,333 @@
},
"outputs": [
{
- "output_type": "display_data",
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d09d664839d04303b8fef9ef895f6e4f",
- "version_minor": 0,
- "version_major": 2
- },
- "text/plain": [
- "Downloading: 0%| | 0.00/445M [00:00<?, ?B/s]"
- ]
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
- "Some weights of the model checkpoint at camembert-base were not used when initializing CamembertForSequenceClassification: ['lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight']\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.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
+ "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias']\n",
+ "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
- "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",
+ "BertForSequenceClassification(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(119547, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
- " (encoder): RobertaEncoder(\n",
+ " (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
- " (0): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (0): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (1): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (1): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (2): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (2): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (3): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (3): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (4): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (4): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (5): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (5): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (6): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (6): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (7): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (7): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (8): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (8): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (9): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (9): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (10): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (10): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (11): RobertaLayer(\n",
- " (attention): RobertaAttention(\n",
- " (self): RobertaSelfAttention(\n",
+ " (11): BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSelfAttention(\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",
+ " (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
- " (intermediate): RobertaIntermediate(\n",
+ " (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
" )\n",
- " (output): RobertaOutput(\n",
+ " (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
- " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
+ " (pooler): BertPooler(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (activation): Tanh()\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=38, bias=True)\n",
- " )\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " (classifier): Linear(in_features=768, out_features=38, bias=True)\n",
")"
]
},
+ "execution_count": 20,
"metadata": {},
- "execution_count": 44
+ "output_type": "execute_result"
}
],
"source": [
@@ -1423,16 +1230,26 @@
" )\n",
"\n",
"# Tell pytorch to run this model on the GPU.\n",
- "model.cuda()"
+ "#model.cuda()\n",
+ "model.to(\"mps\")"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"metadata": {
"id": "xd_cG-8pj4Iw"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/homebrew/Caskroom/miniforge/base/envs/geode-classification-py39/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
"source": [
"#Note: AdamW is a class from the huggingface library (as opposed to pytorch) \n",
"# I believe the 'W' stands for 'Weight Decay fix\"\n",
@@ -1444,7 +1261,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": null,
"metadata": {
"id": "65G-uHuLj4_6"
},
@@ -1474,107 +1291,26 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"\n",
"======== Epoch 1 / 4 ========\n",
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+ "Training...\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [23], line 92\u001b[0m\n\u001b[1;32m 88\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n\u001b[1;32m 90\u001b[0m \u001b[39m# Clip the norm of the gradients to 1.0.\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[39m# This is to help prevent the \"exploding gradients\" problem.\u001b[39;00m\n\u001b[0;32m---> 92\u001b[0m torch\u001b[39m.\u001b[39;49mnn\u001b[39m.\u001b[39;49mutils\u001b[39m.\u001b[39;49mclip_grad_norm_(model\u001b[39m.\u001b[39;49mparameters(), \u001b[39m1.0\u001b[39;49m)\n\u001b[1;32m 94\u001b[0m \u001b[39m# Update parameters and take a step using the computed gradient.\u001b[39;00m\n\u001b[1;32m 95\u001b[0m \u001b[39m# The optimizer dictates the \"update rule\"--how the parameters are\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[39m# modified based on their gradients, the learning rate, etc.\u001b[39;00m\n\u001b[1;32m 97\u001b[0m optimizer\u001b[39m.\u001b[39mstep()\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/geode-classification-py39/lib/python3.9/site-packages/torch/nn/utils/clip_grad.py:43\u001b[0m, in \u001b[0;36mclip_grad_norm_\u001b[0;34m(parameters, max_norm, norm_type, error_if_nonfinite)\u001b[0m\n\u001b[1;32m 41\u001b[0m total_norm \u001b[39m=\u001b[39m norms[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(norms) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m torch\u001b[39m.\u001b[39mmax(torch\u001b[39m.\u001b[39mstack(norms))\n\u001b[1;32m 42\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 43\u001b[0m total_norm \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mnorm(torch\u001b[39m.\u001b[39mstack([torch\u001b[39m.\u001b[39mnorm(g\u001b[39m.\u001b[39mdetach(), norm_type)\u001b[39m.\u001b[39mto(device) \u001b[39mfor\u001b[39;00m g \u001b[39min\u001b[39;00m grads]), norm_type)\n\u001b[1;32m 44\u001b[0m \u001b[39mif\u001b[39;00m error_if_nonfinite \u001b[39mand\u001b[39;00m torch\u001b[39m.\u001b[39mlogical_or(total_norm\u001b[39m.\u001b[39misnan(), total_norm\u001b[39m.\u001b[39misinf()):\n\u001b[1;32m 45\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m 46\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mThe total norm of order \u001b[39m\u001b[39m{\u001b[39;00mnorm_type\u001b[39m}\u001b[39;00m\u001b[39m for gradients from \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 47\u001b[0m \u001b[39m'\u001b[39m\u001b[39m`parameters` is non-finite, so it cannot be clipped. To disable \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 48\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthis error and scale the gradients by the non-finite norm anyway, \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 49\u001b[0m \u001b[39m'\u001b[39m\u001b[39mset `error_if_nonfinite=False`\u001b[39m\u001b[39m'\u001b[39m)\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/geode-classification-py39/lib/python3.9/site-packages/torch/nn/utils/clip_grad.py:43\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 41\u001b[0m total_norm \u001b[39m=\u001b[39m norms[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(norms) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m torch\u001b[39m.\u001b[39mmax(torch\u001b[39m.\u001b[39mstack(norms))\n\u001b[1;32m 42\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 43\u001b[0m total_norm \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mnorm(torch\u001b[39m.\u001b[39mstack([torch\u001b[39m.\u001b[39;49mnorm(g\u001b[39m.\u001b[39;49mdetach(), norm_type)\u001b[39m.\u001b[39mto(device) \u001b[39mfor\u001b[39;00m g \u001b[39min\u001b[39;00m grads]), norm_type)\n\u001b[1;32m 44\u001b[0m \u001b[39mif\u001b[39;00m error_if_nonfinite \u001b[39mand\u001b[39;00m torch\u001b[39m.\u001b[39mlogical_or(total_norm\u001b[39m.\u001b[39misnan(), total_norm\u001b[39m.\u001b[39misinf()):\n\u001b[1;32m 45\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m 46\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mThe total norm of order \u001b[39m\u001b[39m{\u001b[39;00mnorm_type\u001b[39m}\u001b[39;00m\u001b[39m for gradients from \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 47\u001b[0m \u001b[39m'\u001b[39m\u001b[39m`parameters` is non-finite, so it cannot be clipped. To disable \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 48\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthis error and scale the gradients by the non-finite norm anyway, \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 49\u001b[0m \u001b[39m'\u001b[39m\u001b[39mset `error_if_nonfinite=False`\u001b[39m\u001b[39m'\u001b[39m)\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/geode-classification-py39/lib/python3.9/site-packages/torch/functional.py:1485\u001b[0m, in \u001b[0;36mnorm\u001b[0;34m(input, p, dim, keepdim, out, dtype)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(p, \u001b[39mstr\u001b[39m):\n\u001b[1;32m 1484\u001b[0m _dim \u001b[39m=\u001b[39m [i \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(ndim)] \u001b[39m# noqa: C416 TODO: rewrite as list(range(m))\u001b[39;00m\n\u001b[0;32m-> 1485\u001b[0m \u001b[39mreturn\u001b[39;00m _VF\u001b[39m.\u001b[39;49mnorm(\u001b[39minput\u001b[39;49m, p, dim\u001b[39m=\u001b[39;49m_dim, keepdim\u001b[39m=\u001b[39;49mkeepdim) \u001b[39m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 1487\u001b[0m \u001b[39m# TODO: when https://github.com/pytorch/pytorch/issues/33782 is fixed\u001b[39;00m\n\u001b[1;32m 1488\u001b[0m \u001b[39m# remove the overloads where dim is an int and replace with BraodcastingList1\u001b[39;00m\n\u001b[1;32m 1489\u001b[0m \u001b[39m# and remove next four lines, replace _dim with dim\u001b[39;00m\n\u001b[1;32m 1490\u001b[0m \u001b[39mif\u001b[39;00m dim \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
@@ -1619,7 +1355,7 @@
" for step, batch in enumerate(train_dataloader):\n",
"\n",
" # Progress update every 40 batches.\n",
- " if step % 40 == 0 and not step == 0:\n",
+ " if step % 5 == 0 and not step == 0:\n",
" # Calculate elapsed time in minutes.\n",
" elapsed = format_time(time.time() - t0)\n",
" \n",
@@ -1723,7 +1459,18 @@
},
"outputs": [],
"source": [
- "torch.save(model, model_path)"
+ "#torch.save(model, model_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.save_pretrained(model_path)\n",
+ "tokenizer.save_pretrained(model_path)\n",
+ "#ludo: changement de la façon de sauver le modèle"
]
},
{
@@ -1737,13 +1484,14 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"id": "cEycmiS8Cnjw"
},
"outputs": [],
"source": [
- "model = torch.load(model_path)"
+ "#model = torch.load(model_path)\n",
+ "model = BertForSequenceClassification.from_pretrained(model_path).to(\"mps\") #.to(\"cuda\")"
]
},
{
@@ -1757,7 +1505,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {
"id": "K9qdtYexIIvk"
},
@@ -1959,9 +1707,7 @@
"id": "cVdM4eT6I8g2"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -1970,9 +1716,7 @@
"id": "HzxyFO3knanV"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -1981,9 +1725,7 @@
"id": "KDRPPw4Wnap7"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -1992,9 +1734,7 @@
"id": "DX81R2dcnasF"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -2003,9 +1743,7 @@
"id": "wgfqJFVeJMK1"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -2014,9 +1752,7 @@
"id": "GqEf5_41JMNZ"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -2050,25 +1786,7 @@
"id": "_fzgS5USJeAF",
"outputId": "be4a5506-76ed-4eef-bb3c-fe2bb77c6e4d"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--2021-09-30 19:38:22-- https://projet.liris.cnrs.fr/geode/files/datasets/EDdA/Classification/LGE_withContent.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: 356197 (348K) [text/tab-separated-values]\n",
- "Saving to: ‘LGE_withContent.tsv’\n",
- "\n",
- "LGE_withContent.tsv 100%[===================>] 347.85K 567KB/s in 0.6s \n",
- "\n",
- "2021-09-30 19:38:24 (567 KB/s) - ‘LGE_withContent.tsv’ saved [356197/356197]\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!wget https://projet.liris.cnrs.fr/geode/files/datasets/EDdA/Classification/LGE_withContent.tsv"
]
@@ -2099,102 +1817,7 @@
"id": "9qJDTU-6vzkk",
"outputId": "1b279f0e-7715-4d23-f524-08e8ba327f6c"
},
- "outputs": [
- {
- "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>id</th>\n",
- " <th>tome</th>\n",
- " <th>rank</th>\n",
- " <th>domain</th>\n",
- " <th>remark</th>\n",
- " <th>content</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>abrabeses-0</td>\n",
- " <td>1</td>\n",
- " <td>623</td>\n",
- " <td>geography</td>\n",
- " <td>NaN</td>\n",
- " <td>ABRABESES. Village d’Espagne de la prov. de Za...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>accius-0</td>\n",
- " <td>1</td>\n",
- " <td>1076</td>\n",
- " <td>biography</td>\n",
- " <td>NaN</td>\n",
- " <td>ACCIUS, L. ou L. ATTIUS (170-94 av. J.-C.), po...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>achenbach-2</td>\n",
- " <td>1</td>\n",
- " <td>1357</td>\n",
- " <td>biography</td>\n",
- " <td>NaN</td>\n",
- " <td>ACHENBACH(Henri), administrateur prussien, né ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>acireale-0</td>\n",
- " <td>1</td>\n",
- " <td>1513</td>\n",
- " <td>geography</td>\n",
- " <td>NaN</td>\n",
- " <td>ACIREALE. Yille de Sicile, de la province et d...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>actée-0</td>\n",
- " <td>1</td>\n",
- " <td>1731</td>\n",
- " <td>botany</td>\n",
- " <td>NaN</td>\n",
- " <td>ACTÉE(Actœa L.). Genre de plantes de la famill...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " id tome ... remark content\n",
- "0 abrabeses-0 1 ... NaN ABRABESES. Village d’Espagne de la prov. de Za...\n",
- "1 accius-0 1 ... NaN ACCIUS, L. ou L. ATTIUS (170-94 av. J.-C.), po...\n",
- "2 achenbach-2 1 ... NaN ACHENBACH(Henri), administrateur prussien, né ...\n",
- "3 acireale-0 1 ... NaN ACIREALE. Yille de Sicile, de la province et d...\n",
- "4 actée-0 1 ... NaN ACTÉE(Actœa L.). Genre de plantes de la famill...\n",
- "\n",
- "[5 rows x 6 columns]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df_LGE.head()"
]
@@ -2209,18 +1832,7 @@
"id": "71-fP61-OOwQ",
"outputId": "ef08b49e-0a9f-4653-e303-3163250af35b"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(310, 6)"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df_LGE.shape"
]
@@ -2367,22 +1979,7 @@
"id": "O9eer_kgI8rC",
"outputId": "94ea7418-14a8-4918-e210-caf0018f5989"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading Bert Tokenizer...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Token indices sequence length is longer than the specified maximum sequence length for this model (1204 > 512). Running this sequence through the model will result in indexing errors\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"data_loader = generate_prediction_dataloader('bert-base-multilingual-cased', data_LGE)\n",
"#data_loader = generate_prediction_dataloader('camembert-base', data_LGE)"
@@ -2398,16 +1995,7 @@
"id": "sFpAwbrBwF2h",
"outputId": "8d210732-619d-41f0-b6e2-ad9d06a85069"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "There are 1 GPU(s) available.\n",
- "We will use the GPU: Tesla P100-PCIE-16GB\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"p = predict_class_bertFineTuning( model, data_loader )"
]
@@ -2422,18 +2010,7 @@
"id": "51HF6-8UPSTc",
"outputId": "26bff792-eb8d-4e1a-efa4-a7a6c9d32bf9"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "310"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"len(p)"
]
@@ -2445,9 +2022,7 @@
"id": "rFFGhaCvQHfh"
},
"outputs": [],
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "code",
@@ -2459,18 +2034,7 @@
"id": "qgJ-O4rcQHiI",
"outputId": "bfe93dd6-4d89-4d5c-be0d-45e1c98c6b14"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "LabelEncoder()"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Il faudrait enregistrer l'encoder, \n",
"# sinon on est obligé de le refaire à partir du jeu d'entrainement pour récupérer le noms des classes.\n",
@@ -2498,327 +2062,7 @@
"id": "6ek7suq9QHqE",
"outputId": "6636983a-7eba-48c8-d884-f8fb437294dc"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Chimie',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Mathématiques',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Musique',\n",
- " 'Commerce',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Physique - [Sciences physico-mathématiques]',\n",
- " 'Histoire naturelle',\n",
- " 'Chimie',\n",
- " 'Histoire',\n",
- " 'Physique - [Sciences physico-mathématiques]',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Histoire',\n",
- " 'Histoire naturelle',\n",
- " 'Médecine - Chirurgie',\n",
- " 'Géographie',\n",
- " 'Architecture',\n",
- " 'Histoire naturelle',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Arts et métiers',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Marine',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Architecture',\n",
- " 'Histoire naturelle',\n",
- " 'Beaux-arts',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Beaux-arts',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Médecine - Chirurgie',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Chimie',\n",
- " 'Géographie',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Religion',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Commerce',\n",
- " 'Agriculture - Economie rustique',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Jeu',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Beaux-arts',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Beaux-arts',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Géographie',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Histoire naturelle',\n",
- " 'Histoire',\n",
- " 'Histoire naturelle',\n",
- " 'Commerce',\n",
- " 'Histoire',\n",
- " 'Militaire (Art) - Guerre - Arme',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Commerce',\n",
- " 'Géographie',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Religion',\n",
- " 'Géographie',\n",
- " 'Commerce',\n",
- " 'Agriculture - Economie rustique',\n",
- " 'Histoire',\n",
- " 'Géographie',\n",
- " 'Géographie',\n",
- " 'Métiers',\n",
- " 'Belles-lettres - Poésie',\n",
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- "grid_gap": null,
"max_width": null,
- "order": null,
- "_view_module_version": "1.2.0",
- "grid_template_areas": null,
- "object_position": null,
+ "min_height": null,
+ "min_width": null,
"object_fit": null,
- "grid_auto_columns": null,
- "margin": null,
- "display": null,
- "left": null
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
}
}
@@ -4364,4 +3520,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}