From a6f959d271324391e8636f074fbb545f4ce785ab Mon Sep 17 00:00:00 2001
From: Ludovic Moncla <moncla.ludovic@gmail.com>
Date: Tue, 14 Mar 2023 22:16:30 +0100
Subject: [PATCH] Delete Predict_Classification.ipynb
---
notebooks/Predict_Classification.ipynb | 3786 ------------------------
1 file changed, 3786 deletions(-)
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diff --git a/notebooks/Predict_Classification.ipynb b/notebooks/Predict_Classification.ipynb
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "m39L6DJ2r0zN"
- },
- "source": [
- "# BERT Predict classification\n",
- "\n",
- "## 1. Setup the environment\n",
- "\n",
- "### 1.1 Setup colab environment\n",
- "\n",
- "#### 1.1.1 Install packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "pwmZ5bBvgGNh",
- "outputId": "1a080856-4e47-4e1d-81d1-d38bb58948a5"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Collecting transformers==4.10.3\n",
- " Downloading transformers-4.10.3-py3-none-any.whl (2.8 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (2.25.1)\n",
- "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (1.21.6)\n",
- "Collecting sacremoses\n",
- " Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m880.6/880.6 KB\u001b[0m \u001b[31m45.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (23.0)\n",
- "Collecting tokenizers<0.11,>=0.10.1\n",
- " Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (4.64.1)\n",
- "Collecting huggingface-hub>=0.0.12\n",
- " Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (3.9.0)\n",
- "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (2022.6.2)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers==4.10.3) (6.0)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.0.12->transformers==4.10.3) (4.4.0)\n",
- "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.10.3) (4.0.0)\n",
- "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.10.3) (1.24.3)\n",
- "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.10.3) (2.10)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.10.3) (2022.12.7)\n",
- "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==4.10.3) (1.15.0)\n",
- "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==4.10.3) (7.1.2)\n",
- "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==4.10.3) (1.2.0)\n",
- "Building wheels for collected packages: sacremoses\n",
- " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- " Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=1a6d3101ab60a657a64074bebed597b1987c115de1092b993a013ae317d882f9\n",
- " Stored in directory: /root/.cache/pip/wheels/82/ab/9b/c15899bf659ba74f623ac776e861cf2eb8608c1825ddec66a4\n",
- "Successfully built sacremoses\n",
- "Installing collected packages: tokenizers, sacremoses, huggingface-hub, transformers\n",
- "Successfully installed huggingface-hub-0.12.0 sacremoses-0.0.53 tokenizers-0.10.3 transformers-4.10.3\n",
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Collecting sentencepiece\n",
- " Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
- "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m33.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hInstalling collected packages: sentencepiece\n",
- "Successfully installed sentencepiece-0.1.97\n"
- ]
- }
- ],
- "source": [
- "!pip install transformers==4.10.3\n",
- "!pip install sentencepiece"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "57zgbn_jr0zR"
- },
- "source": [
- "#### 1.1.2 Use more RAM"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "WF0qFN_g3ekz",
- "outputId": "56e76858-932c-42fd-ace0-37bf11c7b4ce"
- },
- "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"
- ]
- }
- ],
- "source": [
- "from psutil import virtual_memory\n",
- "ram_gb = virtual_memory().total / 1e9\n",
- "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
- "\n",
- "if ram_gb < 20:\n",
- " print('Not using a high-RAM runtime')\n",
- "else:\n",
- " print('You are using a high-RAM runtime!')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vpr71iWGr0zS"
- },
- "source": [
- "#### 1.1.3 Mount GoogleDrive"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "vL0S-s9Uofvn",
- "outputId": "dbe3e901-da63-48b5-d8c6-b8cbda503fef"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Mounted at /content/drive\n"
- ]
- }
- ],
- "source": [
- "from google.colab import drive\n",
- "drive.mount('/content/drive')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8hzEGHl7gmzk"
- },
- "source": [
- "### 1.2 Setup GPU"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dPOU-Efhf4ui",
- "outputId": "0bb7fd0e-e2fb-4477-e5f7-b408d0a1ced7"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "There are 1 GPU(s) available.\n",
- "We will use the GPU: Tesla T4\n"
- ]
- }
- ],
- "source": [
- "import torch\n",
- "\n",
- "# If there's a GPU available...\n",
- "if torch.cuda.is_available(): \n",
- " # Tell PyTorch to use the GPU. \n",
- " device = torch.device(\"cuda\")\n",
- " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
- " print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
- "\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",
- " device = torch.device(\"cpu\")\n",
- " print('No GPU available, using the CPU instead.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wSqbrupGMc1M"
- },
- "source": [
- "### 1.3 Import librairies"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "SkErnwgMMbRj"
- },
- "outputs": [],
- "source": [
- "import pandas as pd \n",
- "import numpy as np\n",
- "\n",
- "from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, CamembertForSequenceClassification\n",
- "from torch.utils.data import TensorDataset, DataLoader, SequentialSampler"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "c5QKcXulhNJ-"
- },
- "source": [
- "## 2. Load Data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "M2awiee1r0zV"
- },
- "outputs": [],
- "source": [
- "drive_path = \"drive/MyDrive/Classification-EDdA/\"\n",
- "path = \"./\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "X1A_J8MGr0zV",
- "outputId": "ca5c966c-00a2-4d74-cd1c-576c18f98d3d"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "--2023-02-15 07:14:06-- https://geode.liris.cnrs.fr/EDdA-Classification/datasets/Parallel_datatset_articles_230215.tsv\n",
- "Resolving geode.liris.cnrs.fr (geode.liris.cnrs.fr)... 134.214.142.28\n",
- "Connecting to geode.liris.cnrs.fr (geode.liris.cnrs.fr)|134.214.142.28|:443... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 42343065 (40M) [text/tab-separated-values]\n",
- "Saving to: ‘Parallel_datatset_articles_230215.tsv’\n",
- "\n",
- "Parallel_datatset_a 100%[===================>] 40.38M 74.9MB/s in 0.5s \n",
- "\n",
- "2023-02-15 07:14:07 (74.9 MB/s) - ‘Parallel_datatset_articles_230215.tsv’ saved [42343065/42343065]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "#!wget https://geode.liris.cnrs.fr/files/datasets/EDdA/Classification/LGE_withContent.tsv\n",
- "#!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/EDdA_dataset_articles_no_superdomain.tsv\n",
- "!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/Parallel_datatset_articles_230215.tsv"
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "#filepath = \"data/LGE_withContent.tsv\"\n",
- "#filepath = \"EDdA_dataset_articles_no_superdomain.tsv\"\n",
- "filepath = \"Parallel_datatset_articles_230215.tsv\""
- ],
- "metadata": {
- "id": "eea7F4vato1x"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 333
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- "id": "erjPU3y8r0zW",
- "outputId": "e2b4a39d-a72b-4e7a-8b26-e709eb983df3"
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- "outputs": [
- {
- "output_type": "execute_result",
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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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- " }\n",
- "\n",
- " .colab-df-convert:hover {\n",
- " background-color: #E2EBFA;\n",
- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
- " 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",
- "\n",
- " <script>\n",
- " const buttonEl =\n",
- " document.querySelector('#df-be30bfa5-3524-40b4-abed-43faebfa6628 button.colab-df-convert');\n",
- " buttonEl.style.display =\n",
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
- "\n",
- " async function convertToInteractive(key) {\n",
- " const element = document.querySelector('#df-be30bfa5-3524-40b4-abed-43faebfa6628');\n",
- " const dataTable =\n",
- " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
- " [key], {});\n",
- " if (!dataTable) return;\n",
- "\n",
- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
- " + ' to learn more about interactive tables.';\n",
- " element.innerHTML = '';\n",
- " dataTable['output_type'] = 'display_data';\n",
- " await google.colab.output.renderOutput(dataTable, element);\n",
- " const docLink = document.createElement('div');\n",
- " docLink.innerHTML = docLinkHtml;\n",
- " element.appendChild(docLink);\n",
- " }\n",
- " </script>\n",
- " </div>\n",
- " </div>\n",
- " "
- ]
- },
- "metadata": {},
- "execution_count": 13
- }
- ],
- "source": [
- "df = pd.read_csv(path + filepath, sep=\"\\t\")\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "corpus = 'LGE'\n",
- "#corpus = 'EDdA'\n",
- "data = df['content'+corpus].values\n"
- ],
- "metadata": {
- "id": "Ndw4UtgWt_MJ"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "U6KSUho7r0zX"
- },
- "source": [
- "## 3. Load model and predict\n",
- "\n",
- "### 3.1 BERT / CamemBERT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "0qDZ86qTr0zX"
- },
- "outputs": [],
- "source": [
- "model_name = \"bert-base-multilingual-cased\"\n",
- "#model_name = \"camembert-base\"\n",
- "#model_path = path + \"models/model_\" + model_name + \"_s10000.pt\"\n",
- "\n",
- "model_path = drive_path + \"models/model_\" + model_name + \"_s10000_superdomains.pt\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "KEljGX0br0zX"
- },
- "outputs": [],
- "source": [
- "def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):\n",
- "\n",
- " # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
- " input_ids_test = []\n",
- " # For every sentence...\n",
- " for sent in sentences:\n",
- " # `encode` will:\n",
- " # (1) Tokenize the sentence.\n",
- " # (2) Prepend the `[CLS]` token to the start.\n",
- " # (3) Append the `[SEP]` token to the end.\n",
- " # (4) Map tokens to their IDs.\n",
- " encoded_sent = tokenizer.encode(\n",
- " sent, # Sentence to encode.\n",
- " add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
- " # This function also supports truncation and conversion\n",
- " # to pytorch tensors, but I need to do padding, so I\n",
- " # can't use these features.\n",
- " #max_length = max_len, # Truncate all sentences.\n",
- " #return_tensors = 'pt', # Return pytorch tensors.\n",
- " )\n",
- " input_ids_test.append(encoded_sent)\n",
- "\n",
- " # Pad our input tokens\n",
- " padded_test = []\n",
- " for i in input_ids_test:\n",
- " if len(i) > max_len:\n",
- " padded_test.extend([i[:max_len]])\n",
- " else:\n",
- " padded_test.extend([i + [0] * (max_len - len(i))])\n",
- " input_ids_test = np.array(padded_test)\n",
- "\n",
- " # Create attention masks\n",
- " attention_masks = []\n",
- "\n",
- " # Create a mask of 1s for each token followed by 0s for padding\n",
- " for seq in input_ids_test:\n",
- " seq_mask = [float(i>0) for i in seq]\n",
- " attention_masks.append(seq_mask)\n",
- "\n",
- " # Convert to tensors.\n",
- " inputs = torch.tensor(input_ids_test)\n",
- " masks = torch.tensor(attention_masks)\n",
- " #set batch size\n",
- "\n",
- " # Create the DataLoader.\n",
- " data = TensorDataset(inputs, masks)\n",
- " prediction_sampler = SequentialSampler(data)\n",
- "\n",
- " return DataLoader(data, sampler=prediction_sampler, batch_size=batch_size)\n",
- "\n",
- "\n",
- "\n",
- "def predict(model, dataloader, device):\n",
- "\n",
- " # Put model in evaluation mode\n",
- " model.eval()\n",
- "\n",
- " # Tracking variables\n",
- " predictions_test , true_labels = [], []\n",
- " pred_labels_ = []\n",
- " # Predict\n",
- " for batch in dataloader:\n",
- " # Add batch to GPU\n",
- " batch = tuple(t.to(device) for t in batch)\n",
- "\n",
- " # Unpack the inputs from the dataloader\n",
- " b_input_ids, b_input_mask = batch\n",
- "\n",
- " # Telling the model not to compute or store gradients, saving memory and\n",
- " # speeding up prediction\n",
- " with torch.no_grad():\n",
- " # Forward pass, calculate logit predictions\n",
- " outputs = model(b_input_ids, token_type_ids=None,\n",
- " attention_mask=b_input_mask)\n",
- "\n",
- " logits = outputs[0]\n",
- " #print(logits)\n",
- "\n",
- " # Move logits and labels to CPU ???\n",
- " logits = logits.detach().cpu().numpy()\n",
- " #print(logits)\n",
- "\n",
- " # Store predictions and true labels\n",
- " predictions_test.append(logits)\n",
- "\n",
- " pred_labels = []\n",
- " \n",
- " for i in range(len(predictions_test)):\n",
- " # The predictions for this batch are a 2-column ndarray (one column for \"0\"\n",
- " # and one column for \"1\"). Pick the label with the highest value and turn this\n",
- " # in to a list of 0s and 1s.\n",
- " pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
- " pred_labels.append(pred_labels_i)\n",
- "\n",
- " pred_labels_ += [item for sublist in pred_labels for item in sublist]\n",
- " return pred_labels_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 162,
- "referenced_widgets": [
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- },
- "id": "eGKU1J9Ar0zY",
- "outputId": "0a5f7fe5-7b5e-4c11-8a6e-7e85e8478b92"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Loading Bert Tokenizer...\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading: 0%| | 0.00/996k [00:00<?, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
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- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading: 0%| | 0.00/29.0 [00:00<?, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "9be44ba364a344f2b6b2546ae9d61ba8"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading: 0%| | 0.00/1.96M [00:00<?, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "aa6a7a9106554f85a91150bd65c271d0"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading: 0%| | 0.00/625 [00:00<?, ?B/s]"
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- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
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- }
- },
- "metadata": {}
- }
- ],
- "source": [
- "if model_name == 'bert-base-multilingual-cased' :\n",
- " print('Loading Bert Tokenizer...')\n",
- " tokenizer = BertTokenizer.from_pretrained(model_name)\n",
- "elif model_name == 'camembert-base':\n",
- " print('Loading Camembert Tokenizer...')\n",
- " tokenizer = CamembertTokenizer.from_pretrained(model_name)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "-O6NspVTr0zZ"
- },
- "outputs": [],
- "source": [
- "data_loader = generate_dataloader(tokenizer, data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4lv8lvUar0zZ"
- },
- "source": [
- "\n",
- "https://discuss.huggingface.co/t/an-efficient-way-of-loading-a-model-that-was-saved-with-torch-save/9814\n",
- "\n",
- "https://github.com/huggingface/transformers/issues/2094\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "CN8EZst-r0zZ"
- },
- "outputs": [],
- "source": [
- "#model = torch.load(model_path, map_location=torch.device('mps'))\n",
- "#model.load_state_dict(torch.load(model_path, map_location=torch.device('mps')))\n",
- "\n",
- "model = BertForSequenceClassification.from_pretrained(model_path).to(\"cuda\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_fzgS5USJeAF"
- },
- "outputs": [],
- "source": [
- "pred = predict(model, data_loader, device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ISkijyclr0za",
- "outputId": "8120e858-9950-4380-f887-70ca47360c76"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
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- ]
- },
- "metadata": {},
- "execution_count": 32
- }
- ],
- "source": [
- "pred"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "fo6k4li1r0za"
- },
- "outputs": [],
- "source": [
- "import pickle \n",
- "#encoder_filename = \"models/label_encoder.pkl\"\n",
- "encoder_filename = \"models/label_encoder_superdomains.pkl\"\n",
- "with open(drive_path + encoder_filename, 'rb') as file:\n",
- " encoder = pickle.load(file)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "UU7qg7zVr0zb"
- },
- "outputs": [],
- "source": [
- "p2 = list(encoder.inverse_transform(pred))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "w4eHpBztr0zb"
- },
- "outputs": [],
- "source": [
- "df['superdomainBert'+corpus] = p2"
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "df[df.numero == 2835]['content'+corpus].values"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "KsJQMhCBxpSF",
- "outputId": "2ffa7475-e6de-4c42-a413-22c0d4b2d45f"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "array([\"\\nQueue, terme de Chancellerie, ce mot se dit de la\\nmaniere de sceller les lettres. Une lettre est scellée à \\nsimple queue, quand le sceau est attaché à un coin du\\nparchemin de la lettre qu'on a fendu exprès ; & elle\\nest scellée à double queue, quand le sceau est pendant\\nà une bande en double de parchemin passée au-travers de la lettre, comme on fait dans les expéditions\\nimportantes.\\n\",\n",
- " \"\\nPiquer, v. act. (Charp. & Maçon.) piquer en Charpenterie, c'est marquer un piece de bois, pour la\\ntailler & la façonner. Piquer en Maçonnerie, c'est\\nrustiquer le parement ou les lits d'une pierre, c'est-à -dire que piquer signifie en fait de moilon le tailler\\ngrossierement ; on emploie le moilon piqué de la sorte\\naux voûtes de caves, aux puits & aux murs de clôture.\\nPiquer signifie aussi faire sur les matériaux destinés à \\nla construction extérieure les bâtimens, les petits\\npoints ou creux nécessaires pour leur servir d'ornement ; \\non pique de cette maniere la pierre de taille,\\n\\nle grès & le moilon particulierement pour l'ordre\\ntoscan. (D. J.)\\n\"],\n",
- " dtype=object)"
- ]
- },
- "metadata": {},
- "execution_count": 34
- }
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 797
- },
- "id": "OCy54lRLr0zb",
- "outputId": "a42d8a75-48b9-431a-9b8e-71e4d7018c6b"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " idLGE tomeLGE rankLGE \\\n",
- "0 aam-0 1 63 \n",
- "1 abaco-0 1 92 \n",
- "2 abacot-0 1 96 \n",
- "3 abaddon-0 1 104 \n",
- "4 abandonnement-0 1 138 \n",
- "5 abantes-0 1 143 \n",
- "6 abaque-0 1 146 \n",
- "7 abaremo-temo-0 1 152 \n",
- "8 abares-0 1 153 \n",
- "9 abarim-0 1 154 \n",
- "\n",
- " contentLGE volumeEDdA numeroEDdA \\\n",
- "0 AAM. Mesure de capacité pour les liquides en u... 1 31 \n",
- "1 ABACO, architecte italien du xvi siècle (V. La... 1 42 \n",
- "2 ABACOT. Double couronne que portaient autrefoi... 1 44 \n",
- "3 ABADDONou APOLYON le Destructeur. « Elles\\nava... 1 46 \n",
- "4 ABANDONNEMENT. I. Droit civil. — Ce mot est un... 1 75 \n",
- "5 ABANTES. Peuplade d’origine douteuse que l’on ... 1 81 \n",
- "6 ABAQUE. I. Antiquité.— Dans l’antiquité on don... 1 84 \n",
- "7 ABAREMO-TEMO(Bot.). Nom sous lequel Pison\\n(Br... 1 90 \n",
- "8 ABARES. Nom de deux peuples distincts, habitan... 1 91 \n",
- "9 ABARIM. Chaîne de montagnes de la Palestine au... 1 92 \n",
- "\n",
- " headEDdA authorEDdA normclassEDdA \\\n",
- "0 AAM Diderot unclassified \n",
- "1 ABACO d'Alembert unclassified \n",
- "2 ABACOT Diderot unclassified \n",
- "3 ABADDON Diderot unclassified \n",
- "4 ABANDONNEMENT Toussaint Droit \n",
- "5 ABANTES Diderot unclassified \n",
- "6 ABAQUE d'Alembert2 unclassified \n",
- "7 ABAREMO-TEMO Diderot unclassified \n",
- "8 ABARES Diderot unclassified \n",
- "9 ABARIM Diderot unclassified \n",
- "\n",
- " contentEDdA nbWordsEDdA \\\n",
- "0 \\n* AAM, s. mesure des Liquides, en usage à Am... 18 \n",
- "1 \\nABACO, s. m. Quelques anciens Auteurs se ser... 26 \n",
- "2 \\n* ABACOT, s. m. nom de l'ancienne parure dè\\... 22 \n",
- "3 \\n* ABADDON, s. m. vient d'abad, perdre. C'est... 25 \n",
- "4 \\nABANDONNEMENT, s. m. en Droit, est le délais... 77 \n",
- "5 \\n* ABANTES, s. m. pl. Peuples de Thrace qui p... 26 \n",
- "6 \\nABAQUE, s. m. chez les anciens Mathématicien... 52 \n",
- "7 \\n* ABAREMO-TEMO, s. m. arbre qui croît, dit-o... 55 \n",
- "8 \\n* ABARES, restes de la Nation des Huns qui s... 24 \n",
- "9 \\n* ABARIM, montagne de l'Arabie d'où Moyse vi... 23 \n",
- "\n",
- " superdomainEDdA superdomainBertEDdA superdomainBertLGE \n",
- "0 Unclassified Commerce Commerce \n",
- "1 Unclassified Physique Beaux-arts \n",
- "2 Unclassified Histoire Histoire \n",
- "3 Unclassified Histoire Religion \n",
- "4 Droit Jurisprudence Droit Jurisprudence Droit Jurisprudence \n",
- "5 Unclassified Histoire Histoire \n",
- "6 Unclassified Physique Histoire \n",
- "7 Unclassified Histoire naturelle Histoire naturelle \n",
- "8 Unclassified Histoire Géographie \n",
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