diff --git a/notebooks/Predict_LGE.ipynb b/notebooks/Predict_LGE.ipynb
deleted file mode 100644
index 590b96f33c0ac3d0189bca0d140af93e42786011..0000000000000000000000000000000000000000
--- a/notebooks/Predict_LGE.ipynb
+++ /dev/null
@@ -1,1883 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "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": "fce0a8bf-1779-4079-c7ac-200ebb2678c5"
- },
- "outputs": [],
- "source": [
- "!pip install transformers==4.10.3\n",
- "!pip install sentencepiece"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 1.1.2 Use more RAM"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "WF0qFN_g3ekz",
- "outputId": "f3a5f049-24ee-418f-fe5e-84c633234ad8"
- },
- "outputs": [],
- "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": {},
- "source": [
- "#### 1.1.3 Mount GoogleDrive"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "vL0S-s9Uofvn",
- "outputId": "4b7efa4d-7f09-4c8e-bc98-99e6099ede32"
- },
- "outputs": [],
- "source": [
- "from google.colab import drive\n",
- "drive.mount('/content/drive')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wSqbrupGMc1M"
- },
- "source": [
- "### 1.2 Import librairies"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "SkErnwgMMbRj"
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import pandas as pd \n",
- "import numpy as np\n",
- "import pickle \n",
- "import torch\n",
- "from tqdm import tqdm\n",
- "\n",
- "from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, CamembertForSequenceClassification\n",
- "from torch.utils.data import TensorDataset, DataLoader, SequentialSampler"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8hzEGHl7gmzk"
- },
- "source": [
- "### 1.3 Setup GPU"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dPOU-Efhf4ui",
- "outputId": "121dd21e-f98c-483d-d6d1-2838f732a4e2"
- },
- "outputs": [],
- "source": [
- " # 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",
- " gpu_name = \"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",
- "# for MacOS\n",
- "elif torch.backends.mps.is_available() and torch.backends.mps.is_built():\n",
- " device = torch.device(\"mps\")\n",
- " gpu_name = \"mps\"\n",
- " print('We will use the GPU')\n",
- "else:\n",
- " device = torch.device(\"cpu\")\n",
- " gpu_name = \"cpu\"\n",
- " print('No GPU available, using the CPU instead.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Utils"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "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",
- "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",
- " 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_\n",
- "\n",
- "\n",
- "def text_folder_to_dataframe(path):\n",
- "\n",
- " data = []\n",
- " # id,tome,filename,nb_words,content,domain\n",
- "\n",
- " for tome in sorted(os.listdir(path)):\n",
- " try:\n",
- " for article in tqdm(sorted(os.listdir(path + \"/\" + tome))):\n",
- " filename = article[:-4]\n",
- " id = tome + filename\n",
- "\n",
- " if article[-4:] == \".txt\":\n",
- " with open(path + \"/\" + tome + \"/\" + article) as f:\n",
- " content = f.read()\n",
- "\n",
- " data.append([id, tome, filename, content, len(content.split(' '))])\n",
- " except NotADirectoryError:\n",
- " pass\n",
- " return pd.DataFrame(data, columns=['id', 'tome', 'filename', 'content', 'nb_words'])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "c5QKcXulhNJ-"
- },
- "source": [
- "## 3. Load Data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.1 LGE (Nakala)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!wget https://api.nakala.fr/data/10.34847/nkl.74eb1xfd/e522413b58b04ab7c283f8fa68642e9cb69ab5c5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!unzip e522413b58b04ab7c283f8fa68642e9cb69ab5c5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "input_path = \"/Users/lmoncla/Documents/Data/Corpus/LGE/Text\"\n",
- "#input_path = \"./Text\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE = text_folder_to_dataframe(input_path)\n",
- "#df_LGE = pd.read_csv(path + \"data/LGE_withContent.tsv\", sep=\"\\t\")\n",
- "data_LGE = df_LGE[\"content\"].values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Load model and predict\n",
- "\n",
- "### 3.1 BERT / CamemBERT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#path = \"drive/MyDrive/Classification-EDdA/\"\n",
- "path = \"../\"\n",
- "model_name = \"bert-base-multilingual-cased\"\n",
- "model_path = path + \"models/model_\" + model_name + \"_s10000.pt\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print('Loading Bert Tokenizer...')\n",
- "tokenizer = BertTokenizer.from_pretrained(model_name)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_loader = generate_dataloader(tokenizer, data_LGE)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = BertForSequenceClassification.from_pretrained(model_path).to(gpu_name) #.to(\"cuda\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "_fzgS5USJeAF",
- "outputId": "be4a5506-76ed-4eef-bb3c-fe2bb77c6e4d"
- },
- "outputs": [],
- "source": [
- "pred = predict(model, data_loader, device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "encoder_filename = \"models/label_encoder.pkl\"\n",
- "with open(path + encoder_filename, 'rb') as file:\n",
- " encoder = pickle.load(file)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "p2 = list(encoder.inverse_transform(pred))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE['domain'] = p2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE.head(50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "filepath = path + \"results_LGE/LGE-metadata-withContent.csv\"\n",
- "df_LGE.to_csv(filepath, sep=\"\\,\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_LGE.drop(columns=['content'], inplace=True)\n",
- "filepath = path + \"results_LGE/LGE-metadata.csv\"\n",
- "df_LGE.to_csv(filepath, sep=\"\\,\")"
- ]
- }
- ],
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