576 lines
18 KiB
Plaintext
576 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NXTSugt6ieXh"
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},
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"source": [
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"## Entraîner un modèle CBoW\n",
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"\n",
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"Ce notebook fait partie du [Curriculum AI pour Débutants](http://aka.ms/ai-beginners)\n",
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"\n",
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"Dans cet exemple, nous allons apprendre à entraîner un modèle de langage CBoW pour obtenir notre propre espace d'embedding Word2Vec. Nous utiliserons le jeu de données AG News comme source de texte.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import torch\n",
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"import torchtext\n",
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"import os\n",
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"import collections\n",
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"import builtins\n",
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"import random\n",
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"import numpy as np"
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],
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"metadata": {
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"id": "q-UiiJUKaxHj"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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],
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"metadata": {
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"id": "TFbR8CZaTZ1q"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Tout d'abord, chargeons notre ensemble de données et définissons le tokenizer et le vocabulaire. Nous allons définir `vocab_size` à 5000 pour limiter un peu les calculs.\n"
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],
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"metadata": {
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"id": "HIwC7lI5T-ov"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"def load_dataset(ngrams = 1, min_freq = 1, vocab_size = 5000 , lines_cnt = 500):\n",
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" tokenizer = torchtext.data.utils.get_tokenizer('basic_english')\n",
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" print(\"Loading dataset...\")\n",
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" test_dataset, train_dataset = torchtext.datasets.AG_NEWS(root='./data')\n",
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" train_dataset = list(train_dataset)\n",
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" test_dataset = list(test_dataset)\n",
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" classes = ['World', 'Sports', 'Business', 'Sci/Tech']\n",
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" print('Building vocab...')\n",
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" counter = collections.Counter()\n",
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" for i, (_, line) in enumerate(train_dataset):\n",
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" counter.update(torchtext.data.utils.ngrams_iterator(tokenizer(line),ngrams=ngrams))\n",
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" if i == lines_cnt:\n",
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" break\n",
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" vocab = torchtext.vocab.Vocab(collections.Counter(dict(counter.most_common(vocab_size))), min_freq=min_freq)\n",
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" return train_dataset, test_dataset, classes, vocab, tokenizer"
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],
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"metadata": {
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"id": "wdZuygtgiuLG"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"train_dataset, test_dataset, _, vocab, tokenizer = load_dataset()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4d1nU1gsivGu",
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"outputId": "949fe272-ae0e-49f5-c373-6703458b3a74"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Loading dataset...\n",
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"Building vocab...\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"def encode(x, vocabulary, tokenizer = tokenizer):\n",
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" return [vocabulary[s] for s in tokenizer(x)]"
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],
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"metadata": {
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"id": "1XDYNhG8ToFV"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "LIlQk6_PaHVY"
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},
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"source": [
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"## Modèle CBoW\n",
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"\n",
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"CBoW apprend à prédire un mot en se basant sur les $2N$ mots voisins. Par exemple, lorsque $N=1$, nous obtiendrons les paires suivantes à partir de la phrase *I like to train networks* : (like, I), (I, like), (to, like), (like, to), (train, to), (to, train), (networks, train), (train, networks). Ici, le premier mot est le mot voisin utilisé comme entrée, et le second mot est celui que nous cherchons à prédire.\n",
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"\n",
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"Pour construire un réseau capable de prédire le mot suivant, nous devrons fournir le mot voisin comme entrée et obtenir le numéro du mot en sortie. L'architecture du réseau CBoW est la suivante :\n",
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"\n",
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"* Le mot d'entrée est passé à travers la couche d'embedding. Cette même couche d'embedding sera notre embedding Word2Vec, nous la définirons donc séparément comme variable `embedder`. Dans cet exemple, nous utiliserons une taille d'embedding de 30, bien que vous puissiez expérimenter avec des dimensions plus élevées (le Word2Vec réel utilise 300).\n",
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"* Le vecteur d'embedding sera ensuite passé à une couche linéaire qui prédira le mot en sortie. Cette couche contient donc `vocab_size` neurones.\n",
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"\n",
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"Pour la sortie, si nous utilisons `CrossEntropyLoss` comme fonction de perte, nous devrons également fournir uniquement les numéros des mots comme résultats attendus, sans encodage one-hot.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"vocab_size = len(vocab)\n",
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"\n",
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"embedder = torch.nn.Embedding(num_embeddings = vocab_size, embedding_dim = 30)\n",
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"model = torch.nn.Sequential(\n",
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" embedder,\n",
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" torch.nn.Linear(in_features = 30, out_features = vocab_size),\n",
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")\n",
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"\n",
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"print(model)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "akKTcKQKkfl2",
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"outputId": "da687e3e-a8ec-4c1a-e456-ab8cd6ac7dad"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Sequential(\n",
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" (0): Embedding(5002, 30)\n",
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" (1): Linear(in_features=30, out_features=5002, bias=True)\n",
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")\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Nud6jgGPaHVa"
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},
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"source": [
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"## Préparation des données d'entraînement\n",
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"\n",
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"Programmons maintenant la fonction principale qui calculera les paires de mots CBoW à partir du texte. Cette fonction nous permettra de spécifier la taille de la fenêtre et renverra un ensemble de paires - mot d'entrée et mot de sortie. Notez que cette fonction peut être utilisée sur des mots, ainsi que sur des vecteurs/tenseurs - ce qui nous permettra d'encoder le texte avant de le transmettre à la fonction `to_cbow`.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "x-dsXygOieXn",
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"outputId": "c2218280-e540-40ba-9546-efe48d0d714f"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[['like', 'I'], ['to', 'I'], ['I', 'like'], ['to', 'like'], ['train', 'like'], ['I', 'to'], ['like', 'to'], ['train', 'to'], ['networks', 'to'], ['like', 'train'], ['to', 'train'], ['networks', 'train'], ['to', 'networks'], ['train', 'networks']]\n",
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"[[232, 172], [5, 172], [172, 232], [5, 232], [0, 232], [172, 5], [232, 5], [0, 5], [1202, 5], [232, 0], [5, 0], [1202, 0], [5, 1202], [0, 1202]]\n"
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]
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}
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],
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"source": [
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"def to_cbow(sent,window_size=2):\n",
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" res = []\n",
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" for i,x in enumerate(sent):\n",
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" for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n",
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" if i!=j:\n",
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" res.append([sent[j],x])\n",
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" return res\n",
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"\n",
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"print(to_cbow(['I','like','to','train','networks']))\n",
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"print(to_cbow(encode('I like to train networks', vocab)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "XVaaDLjaaHVb"
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},
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"source": [
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"Préparons le jeu de données d'entraînement. Nous allons parcourir toutes les actualités, appeler `to_cbow` pour obtenir la liste des paires de mots, et ajouter ces paires à `X` et `Y`. Par souci de temps, nous ne considérerons que les 10 000 premiers articles - vous pouvez facilement supprimer cette limitation si vous avez plus de temps à attendre et souhaitez obtenir de meilleures représentations :)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "54b-Gd9TieXo"
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},
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"outputs": [],
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"source": [
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"X = []\n",
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"Y = []\n",
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"for i, x in zip(range(10000), train_dataset):\n",
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" for w1, w2 in to_cbow(encode(x[1], vocab), window_size = 5):\n",
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" X.append(w1)\n",
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" Y.append(w2)\n",
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"\n",
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"X = torch.tensor(X)\n",
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"Y = torch.tensor(Y)"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Nous convertirons également ces données en un seul ensemble de données et créerons un dataloader :\n"
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],
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"metadata": {
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"id": "cwWy0PzXWhN5"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"class SimpleIterableDataset(torch.utils.data.IterableDataset):\n",
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" def __init__(self, X, Y):\n",
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" super(SimpleIterableDataset).__init__()\n",
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" self.data = []\n",
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" for i in range(len(X)):\n",
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" self.data.append( (Y[i], X[i]) )\n",
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" random.shuffle(self.data)\n",
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"\n",
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" def __iter__(self):\n",
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" return iter(self.data)"
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],
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"metadata": {
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"id": "mfoAcGPFZU8p"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "e4NQ_-5waHVc"
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},
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"source": [
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"Nous convertirons également ces données en un seul ensemble de données et créerons un dataloader :\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "AbLUcojlieXo"
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},
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"outputs": [],
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"source": [
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"ds = SimpleIterableDataset(X, Y)\n",
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"dl = torch.utils.data.DataLoader(ds, batch_size = 256)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pKQr7sXeaHVc"
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},
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"source": [
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"Maintenant, passons à l'entraînement proprement dit. Nous utiliserons l'optimiseur `SGD` avec un taux d'apprentissage assez élevé. Vous pouvez également essayer d'utiliser d'autres optimiseurs, comme `Adam`. Nous allons entraîner pendant 10 époques pour commencer - et vous pouvez relancer cette cellule si vous souhaitez une perte encore plus faible.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"def train_epoch(net, dataloader, lr = 0.01, optimizer = None, loss_fn = torch.nn.CrossEntropyLoss(), epochs = None, report_freq = 1):\n",
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" optimizer = optimizer or torch.optim.Adam(net.parameters(), lr = lr)\n",
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" loss_fn = loss_fn.to(device)\n",
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" net.train()\n",
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"\n",
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" for i in range(epochs):\n",
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" total_loss, j = 0, 0, \n",
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" for labels, features in dataloader:\n",
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" optimizer.zero_grad()\n",
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" features, labels = features.to(device), labels.to(device)\n",
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" out = net(features)\n",
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" loss = loss_fn(out, labels)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" total_loss += loss\n",
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" j += 1\n",
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" if i % report_freq == 0:\n",
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" print(f\"Epoch: {i+1}: loss={total_loss.item()/j}\")\n",
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"\n",
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" return total_loss.item()/j"
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],
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"metadata": {
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"id": "HeeCYKr_KF1w"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"train_epoch(net = model, dataloader = dl, optimizer = torch.optim.SGD(model.parameters(), lr = 0.1), loss_fn = torch.nn.CrossEntropyLoss(), epochs = 10)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "KVgwGtDHgDlT",
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"outputId": "2447833f-f0e3-4566-c33d-addbfe2f451d"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Epoch: 1: loss=5.664632366860172\n",
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"Epoch: 2: loss=5.632101973960962\n",
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"Epoch: 3: loss=5.610399051405015\n",
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"Epoch: 4: loss=5.594621561080262\n",
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"Epoch: 5: loss=5.582538017415446\n",
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"Epoch: 6: loss=5.572900234519603\n",
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"Epoch: 7: loss=5.564951676341915\n",
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"Epoch: 8: loss=5.558288112064614\n",
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"Epoch: 9: loss=5.552576955031129\n",
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"Epoch: 10: loss=5.547634165194347\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"5.547634165194347"
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]
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},
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"metadata": {},
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"execution_count": 16
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "W8u2qXZmaHVd"
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},
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"source": [
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"## Essayer Word2Vec\n",
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"\n",
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"Pour utiliser Word2Vec, extrayons les vecteurs correspondant à tous les mots de notre vocabulaire :\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "r8TatcXjkU_t"
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},
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"outputs": [],
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"source": [
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"vectors = torch.stack([embedder(torch.tensor(vocab[s])) for s in vocab.itos], 0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3OcX21UOaHVd"
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},
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"source": [
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"Voyons, par exemple, comment le mot **Paris** est encodé en un vecteur :\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bz6tAeLzieXp",
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"outputId": "5b20850e-4342-45e9-f840-cfac2b4d61d8"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"tensor([-0.0915, 2.1224, -0.0281, -0.6819, 1.1219, 0.6458, -1.3704, -1.3314,\n",
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" -1.1437, 0.4496, 0.2301, -0.3515, -0.8485, 1.0481, 0.4386, -0.8949,\n",
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" 0.5644, 1.0939, -2.5096, 3.2949, -0.2601, -0.8640, 0.1421, -0.0804,\n",
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" -0.5083, -1.0560, 0.9753, -0.5949, -1.6046, 0.5774],\n",
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" grad_fn=<EmbeddingBackward>)\n"
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]
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}
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],
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"source": [
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"paris_vec = embedder(torch.tensor(vocab['paris']))\n",
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"print(paris_vec)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pHTJlaeYaHVd"
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},
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"source": [
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"Il est intéressant d'utiliser Word2Vec pour rechercher des synonymes. La fonction suivante retournera les `n` mots les plus proches d'une entrée donnée. Pour les trouver, nous calculons la norme de $|w_i - v|$, où $v$ est le vecteur correspondant à notre mot d'entrée, et $w_i$ est l'encodage du $i$-ème mot dans le vocabulaire. Nous trions ensuite le tableau et retournons les indices correspondants en utilisant `argsort`, puis prenons les premiers `n` éléments de la liste, qui encodent les positions des mots les plus proches dans le vocabulaire.\n"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "NlZyi-_olFar",
|
|
"outputId": "b5dbb163-88c4-4d5a-eaf2-6751f700e98c"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['microsoft', 'quoted', 'lp', 'rate', 'top']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 56
|
|
}
|
|
],
|
|
"source": [
|
|
"def close_words(x, n = 5):\n",
|
|
" vec = embedder(torch.tensor(vocab[x]))\n",
|
|
" top5 = np.linalg.norm(vectors.detach().numpy() - vec.detach().numpy(), axis = 1).argsort()[:n]\n",
|
|
" return [ vocab.itos[x] for x in top5 ]\n",
|
|
"\n",
|
|
"close_words('microsoft')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "-dQq7xeAln0U",
|
|
"outputId": "66f768c3-c248-4bfd-ce4f-c8ffc6d0dd0d"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['basketball', 'lot', 'sinai', 'states', 'healthdaynews']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 51
|
|
}
|
|
],
|
|
"source": [
|
|
"close_words('basketball')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "fJXqK26b29sa",
|
|
"outputId": "78f0baba-ffd0-485a-dd87-0a12bedfd7fa"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['funds', 'travel', 'sydney', 'japan', 'business']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 77
|
|
}
|
|
],
|
|
"source": [
|
|
"close_words('funds')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "My0VeTDd3Ji8"
|
|
},
|
|
"source": [
|
|
"## À retenir\n",
|
|
"\n",
|
|
"En utilisant des techniques astucieuses comme CBoW, nous pouvons entraîner un modèle Word2Vec. Vous pouvez également essayer d'entraîner un modèle skip-gram, conçu pour prédire les mots voisins à partir du mot central, et observer ses performances.\n"
|
|
]
|
|
},
|
|
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle effectuée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
|
|
]
|
|
}
|
|
],
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|
"metadata": {
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|
"colab": {
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|
"collapsed_sections": [],
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"name": "CBoW-PyTorch.ipynb",
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"provenance": []
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},
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"interpreter": {
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"hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f"
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"display_name": "Python 3.8.12 ('py38')",
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"name": "python3"
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"translation_date": "2025-08-31T15:14:16+00:00",
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"source_file": "lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb",
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"language_code": "fr"
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}
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