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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f4bdb240-9099-411b-a859-daaf3269c900",
+ "metadata": {},
+ "source": [
+ "## Implémentation de l'algorithme BPE\n",
+ "L'objectif de ce premier TP est de mettre en place l'algorithme de tokenization BPE. Pour rappel le principe consiste à rassembler les mots ou ``tokens'' apparaissant le plus de fois successivement.\n",
+ "\n",
+ "Par exemple si l'on considère le corpus contenant les mots de la table suivante (avec le nombre d’occurrences de chaque mot) :\n",
+ "\n",
+ "| mots | occurence |\n",
+ "|------|-----------|\n",
+ "| manger | 2 |\n",
+ "| voter | 3 |\n",
+ "| lent | 1 |\n",
+ "| lentement | 2 |\n",
+ "\n",
+ "Et que les \"tokens\" initiaux sont les lettres de l'alphabet alors, c'est le sufixe \"er\" qui sera ajouté à la liste des sous-mots (tokens) dans un premier temps.\n",
+ "\n",
+ "Les étapes pour réaliser l'algorithme BPE sont les suivantes :\n",
+ "1. Télécharger un corpus de textes (ici une page wikipedia)\n",
+ "2. Découper le texte en mots (en utilisant le caractère \"espace\") et compter le nombre d’occurrences de chaque mot\n",
+ "3. Initialiser le dictionnaire de mots avec les tokens initiaux (les lettres de l’alphabet)\n",
+ "4. Faire tourner l'algorithme BPE (apprendre le vocabulaire)\n",
+ "5. Tester la décomposition en tokens sur des phrases choisies (on applique les règles de fusion)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee88b07e-4de8-436e-9dc7-f405c69c9042",
+ "metadata": {},
+ "source": [
+ "### Étape 1: Télécharger un corpus"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5352d48f-27ca-41ad-9265-0e128578a5ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.request\n",
+ "import re\n",
+ "import numpy as np\n",
+ "import collections\n",
+ "import json\n",
+ "url_request = 'https://fr.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&explaintext&redirects=1&titles=Gr%C3%A8ce_antique'\n",
+ "raw_page = urllib.request.urlopen(url_request)\n",
+ "json_page = json.load(raw_page)\n",
+ "\n",
+ "with open('wikitest.json', 'w') as f:\n",
+ " json.dump(json_page, f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8b6b7a2-74b4-4ba2-aa4b-93885a286fb2",
+ "metadata": {},
+ "source": [
+ "### Étape 2 : Découper le texte en mots\n",
+ "\n",
+ "Pour découper le texte en mot nous utiliserons la regex suivante ```r'(\\b[^\\s]+\\b)'```. Pour compter les mots nous utiliserons l'objet Counter de python. \n",
+ "1. Stocker dans **count_words** chaque mot ainsi que le nombre d’occurrences\n",
+ "2. En regardant la documentation donnez les 10 mots les plus fréquents (vous les stockerez dans most_commons_words)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6d7341ff-61bf-4a5a-9d0d-4ae8fd8797ce",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['de', 'la', 'et', 'des', 'les', 'Ã ', 'le', 'en', 'qui', 'dans']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "\n",
+ "corpus = list(json_page['query']['pages'].values())[0]['extract']\n",
+ "word_regex = re.compile(r'(\\b[^\\s]+\\b)')\n",
+ "words = word_regex.findall(corpus)\n",
+ "\n",
+ "count_words = collections.Counter(words)\n",
+ "most_commons_words = [k for k, v in count_words.most_common(10)]\n",
+ "\n",
+ "most_commons_words"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a7a38b8-97f5-4851-b642-f7b40c023491",
+ "metadata": {},
+ "source": [
+ "### Étape 3 : Initialiser le dictionnaire de mots avec les tokens initiaux (les lettre de l'aplhabet)\n",
+ "\n",
+ "Créer le vocabulaire initial dans la variable vocab. Combien de tokens initiaux avez-vous ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "901f7ea5-9d71-4f8c-95bf-32b098c3241c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab = list({char for word in count_words.keys() for char in word })\n",
+ "vocab.sort()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "efd79ab8-b1b1-4802-ac80-39b12cb5917f",
+ "metadata": {},
+ "source": [
+ "### Étape 4 : Apprendre le tokenizer\n",
+ "Pour apprendre le tokenizer nous avons besoins de plusieurs fonctions:\n",
+ "1. Une fonction pour calculer la fréquence de chacune des pairs de ``tokens''\n",
+ "2. Une fonction pour fusionner un paire\n",
+ "\n",
+ "Plusieurs variables sont nécessaires:\n",
+ "1. **vocab** contenant le vocabulaire courant\n",
+ "2. **merge_rules** contenant toutes les règles de fusion (un dictionnaire contenant comme clef un couple de tokens à fusionner et le résultat de la fusion des tokens). Par exemple : {('e', 's'), 'es', ('en', 't') :'ent'}\n",
+ "3. **splits** Un dictionnaire contenant le découpage courant du corpus avec pour clef le mot et comme valeur la liste des \"tokens\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8357b6bd-9414-4263-9bf5-ef249614cb04",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'La': ['L', 'a'],\n",
+ " 'Grèce': ['G', 'r', 'è', 'c', 'e'],\n",
+ " 'antique': ['a', 'n', 't', 'i', 'q', 'u', 'e'],\n",
+ " 'est': ['e', 's', 't'],\n",
+ " 'une': ['u', 'n', 'e']}"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# En première étape splits contient les mots décomposer en caractères\n",
+ "splits = {word: [c for c in word] for word in count_words.keys()}\n",
+ "{k: splits[k] for k in list(splits.keys())[:5]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "722df20d-fae1-41f1-96bc-fecb6b10a0ca",
+ "metadata": {},
+ "source": [
+ "#### Calculer la fréquences des pairs de tokens\n",
+ "Créer un fonction **compute_pair_freqs** qui étant donné les mots décomposés en tokens (dictionnaire splits) et la fréquence des mots retourne la fréquence de chaque couple de tokens (attention seulement les sous-mots successifs). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "cb995e01-74f4-432f-9ddb-5c676afc32c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_pair_freqs(splits, word_freqs):\n",
+ " pair_freqs = {}\n",
+ " for word, freq in word_freqs.items():\n",
+ " split = splits[word]\n",
+ " if len(split) == 1:\n",
+ " continue\n",
+ " for i in range(len(split) - 1):\n",
+ " pair = (split[i], split[i + 1])\n",
+ " if(pair not in pair_freqs):\n",
+ " pair_freqs[pair] = 0\n",
+ " pair_freqs[pair] += freq\n",
+ " return pair_freqs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d806c606-8cd3-43ff-8e14-aea74f9d4172",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{('L', 'a'): 188,\n",
+ " ('G', 'r'): 266,\n",
+ " ('r', 'è'): 279,\n",
+ " ('è', 'c'): 316,\n",
+ " ('c', 'e'): 1342}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pair_freqs = compute_pair_freqs(splits, count_words)\n",
+ "{k: pair_freqs[k] for k in list(pair_freqs.keys())[:5]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72e05d19-df89-4b0b-a2ad-ba5af727b1e5",
+ "metadata": {},
+ "source": [
+ "#### Retrouver la paire la plus fréquente et fusionner une pair \n",
+ "1. Créer une fonction **most_frequent(pair_freqs)** retournant la paire de token la plus fréquente.\n",
+ "2. Créer une fonction **merge_pair()** qui étant donnée une paire, l'objet splits retourne la nouvelle séparation en token des données (splits))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c608944a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "9b60759e-0bb4-4969-bc86-ee39fd3dc7bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(('e', 's'), 6895)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def most_frequent(pair_freqs):\n",
+ " return max(pair_freqs.items(), key=lambda x: x[1])\n",
+ "most_frequent(pair_freqs)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "dd44467a-326b-499c-9995-8661fd102bec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def merge_pair(a, b, splits):\n",
+ " for word in splits.keys():\n",
+ " split = splits[word]\n",
+ " if len(split) == 1:\n",
+ " continue\n",
+ " i = 0\n",
+ " while i < len(split) - 1:\n",
+ " if split[i] == a and split[i + 1] == b:\n",
+ " split = split[:i] + [a + b] + split[i + 2 :]\n",
+ " else:\n",
+ " i += 1\n",
+ " splits[word] = split\n",
+ " return splits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "462376c4-6abc-4d4c-8bb6-a7e4d5bb96bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['g', 'r', 'e', 'c', 'q', 'u', 'es']\n"
+ ]
+ }
+ ],
+ "source": [
+ "new_splits = merge_pair(*most_frequent(pair_freqs)[0], splits)\n",
+ "print(new_splits['grecques'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a9cdfeb-ade8-4ac6-9cd5-9049090ca142",
+ "metadata": {},
+ "source": [
+ "#### Appliquer l'algorithme jusqu'a obtenir la taille du vocabulaire souhaitée\n",
+ "Créer un objet BPE qui prend en argument un corpus, un nombre de mots et applique l'algorithme BPE. L'algorithme stocke dans l'attribut vocab le vcocabulaire final et dans merge_rule les règles de fusion."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "c0367f37-9b40-4ae7-8ec6-ca7ab86ae687",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BPE:\n",
+ " def __init__(self, corpus, vocabulary_size=500):\n",
+ " super().__init__()\n",
+ " self.word_regex = re.compile(r'(\\b[^\\s]+\\b)')\n",
+ " words = self.word_regex.findall(corpus)\n",
+ "\n",
+ " # counting words\n",
+ " count_words = collections.Counter(words)\n",
+ " # create initial vocab\n",
+ " self.vocab = list({char for word in count_words.keys() for char in word })\n",
+ " self.vocab.sort()\n",
+ " # create the initial split\n",
+ " splits = {word: [c for c in word] for word in count_words.keys()}\n",
+ " # initialise merge_rules\n",
+ " self.merge_rules = {}\n",
+ " while len(self.vocab) < vocabulary_size:\n",
+ " pair_freqs = compute_pair_freqs(splits, count_words)\n",
+ " # I considered the format (('e', 's'), 6848) for best_pair\n",
+ " best_pair = most_frequent(pair_freqs)\n",
+ " \n",
+ " splits = merge_pair(*best_pair[0], splits)\n",
+ "\n",
+ " self.merge_rules[best_pair[0]] = best_pair[0][0] + best_pair[0][1]\n",
+ " self.vocab.append(''.join(best_pair[0]))\n",
+ "\n",
+ " def tokenize(self, text):\n",
+ " words = self.word_regex.findall(text)\n",
+ " splits = [[l for l in word] for word in words]\n",
+ " for pair, merge in self.merge_rules.items():\n",
+ " for idx, split in enumerate(splits):\n",
+ " i = 0\n",
+ " while i < len(split) - 1:\n",
+ " if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
+ " split = split[:i] + [merge] + split[i + 2 :]\n",
+ " else:\n",
+ " i += 1\n",
+ " splits[idx] = split\n",
+ " \n",
+ " return sum(splits, [])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "c282a2a1-f23c-4b22-b858-35a0049250c1",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "my_bpe = BPE(corpus)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "b225b59b-6103-4b3f-b776-ea2b71714d64",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['culture', 'grecques', 'dévelop', 'p', 'ée', 'en', 'Grèce']"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "texte = '''culture grecques développée en Grèce '''\n",
+ "my_bpe.tokenize(texte)[:12]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6f478a3-5f52-43c4-9b48-0e912304985a",
+ "metadata": {},
+ "source": [
+ "#### Testez en modifiant les paramètres ou le corpus\n",
+ "Tester l'algorithme avec différents hyper-paramètres ou données"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "e33bdef2-8392-44f8-a846-81249fd60ac9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{('e', 's'): 'es',\n",
+ " ('n', 't'): 'nt',\n",
+ " ('q', 'u'): 'qu',\n",
+ " ('r', 'e'): 're',\n",
+ " ('o', 'n'): 'on',\n",
+ " ('d', 'e'): 'de',\n",
+ " ('l', 'e'): 'le',\n",
+ " ('l', 'a'): 'la',\n",
+ " ('t', 'i'): 'ti',\n",
+ " ('i', 's'): 'is',\n",
+ " ('e', 'nt'): 'ent',\n",
+ " ('e', 'n'): 'en'}"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "{k: my_bpe.merge_rules[k] for k in list(my_bpe.merge_rules.keys())[:12]}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7513567a-5237-4dad-9500-bf2cded9b8a2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2ccb41e3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}