Entraîner un modèle…¶
Il y a plusieurs approches. Nous allons en présenter deux: w2v et glove.
Word2Vector¶
Continous bag of words (CBOW): utiliser le contexte comme input pour prévoir le mot cible.
Skip-gram: utiliser un mot comme input pour prévoir le contexte. Article original
Skip-gram¶
One hot encoding: un vecteur du même nombre des dimension que la taille du vocabulaire.
\[\begin{split}
\begin{bmatrix}
0\\
0\\
0\\
1\\
0\\
\vdots \\
0\\
\end{bmatrix}
\end{split}\]
une couche cachée
un output (une série d’autres one hot encodings)
Le but est d’ajuster les poids pour ensuite pouvoir représenter le mot avec la cocuhe cachée. Donc la tâche qu,on donne au réseau ne sert pas à entraîner un modèle capable ensuite de réaliser cette tâche (deviner les mots du contexte), mais cela servira simplement à représenter le mot.
Concrètement une ligne de la couche cachée sera notre vecteur:
http://mccormickml.com/assets/word2vec/matrix_mult_w_one_hot.png
L’idée est que si deux mots se trouvent statistiquemnt dans des contextes très similaires ils doivent être proches.
from gensim.models import Word2Vec
with open('fichiers/recherche.txt', 'r') as file:
recherche = file.read()
recherche = recherche.replace('\xa0', '')
recherche = recherche.replace('\n', ' ')
sentences = [s for s in recherche.split('.')]
sentences = [s.split(' ') for s in sentences]
model = Word2Vec(sentences=sentences)
model.wv.vocab
{'': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2550>,
'À': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2588>,
'la': <gensim.models.keyedvectors.Vocab at 0x7f170ceb25c0>,
'recherche': <gensim.models.keyedvectors.Vocab at 0x7f170ceb25f8>,
'du': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2630>,
'temps': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2668>,
'perdu': <gensim.models.keyedvectors.Vocab at 0x7f170ceb26a0>,
'Proust': <gensim.models.keyedvectors.Vocab at 0x7f170ceb26d8>,
'de': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2710>,
'le': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2748>,
'MARCEL': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2780>,
'PROUST': <gensim.models.keyedvectors.Vocab at 0x7f170ceb27b8>,
'LA': <gensim.models.keyedvectors.Vocab at 0x7f170ceb27f0>,
'RECHERCHE': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2828>,
'DU': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2860>,
'TEMPS': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2898>,
'PERDU': <gensim.models.keyedvectors.Vocab at 0x7f170ceb28d0>,
'nrf': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2908>,
'GALLIMARD': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2940>,
'-': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2978>,
'1': <gensim.models.keyedvectors.Vocab at 0x7f170ceb29b0>,
'Du': <gensim.models.keyedvectors.Vocab at 0x7f170ceb29e8>,
'côté': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2a20>,
'chez': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2a58>,
'Swann': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2a90>,
'2': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2ac8>,
'l’ombre': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2b00>,
'des': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2b38>,
'jeunes': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2b70>,
'filles': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2ba8>,
'en': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2be0>,
'fleurs': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2c18>,
'Le': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2c50>,
'Guermantes': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2c88>,
'Sodome': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2cc0>,
'et': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2cf8>,
'Gomorrhe': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2d30>,
'La': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2d68>,
'6': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2da0>,
'Albertine': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2dd8>,
'Temps': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2e10>,
'retrouvé': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2e48>,
'DE': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2e80>,
'Comme': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2eb8>,
'un': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2ef0>,
'témoignage': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2f28>,
'profonde': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2f60>,
'reconnaissance': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2f98>,
'Première': <gensim.models.keyedvectors.Vocab at 0x7f170ceb2fd0>,
'Combray': <gensim.models.keyedvectors.Vocab at 0x7f170cb16048>,
'Un': <gensim.models.keyedvectors.Vocab at 0x7f170cb16080>,
'amour': <gensim.models.keyedvectors.Vocab at 0x7f170cb160b8>,
'nom': <gensim.models.keyedvectors.Vocab at 0x7f170cb160f0>,
'PARTIE': <gensim.models.keyedvectors.Vocab at 0x7f170cb16128>,
'je': <gensim.models.keyedvectors.Vocab at 0x7f170cb16160>,
'me': <gensim.models.keyedvectors.Vocab at 0x7f170cb16198>,
'suis': <gensim.models.keyedvectors.Vocab at 0x7f170cb161d0>,
'couché': <gensim.models.keyedvectors.Vocab at 0x7f170cb16208>,
'bonne': <gensim.models.keyedvectors.Vocab at 0x7f170cb16240>,
'heure': <gensim.models.keyedvectors.Vocab at 0x7f170cb16278>,
'Parfois,': <gensim.models.keyedvectors.Vocab at 0x7f170cb162b0>,
'à': <gensim.models.keyedvectors.Vocab at 0x7f170cb162e8>,
'peine': <gensim.models.keyedvectors.Vocab at 0x7f170cb16320>,
'ma': <gensim.models.keyedvectors.Vocab at 0x7f170cb16358>,
'bougie': <gensim.models.keyedvectors.Vocab at 0x7f170cb16390>,
'mes': <gensim.models.keyedvectors.Vocab at 0x7f170cb163c8>,
'yeux': <gensim.models.keyedvectors.Vocab at 0x7f170cb16400>,
'se': <gensim.models.keyedvectors.Vocab at 0x7f170cb16438>,
'fermaient': <gensim.models.keyedvectors.Vocab at 0x7f170cb16470>,
'si': <gensim.models.keyedvectors.Vocab at 0x7f170cb164a8>,
'vite': <gensim.models.keyedvectors.Vocab at 0x7f170cb164e0>,
'que': <gensim.models.keyedvectors.Vocab at 0x7f170cb16518>,
'n’avais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16550>,
'pas': <gensim.models.keyedvectors.Vocab at 0x7f170cb16588>,
'dire:': <gensim.models.keyedvectors.Vocab at 0x7f170cb165c0>,
'«Je': <gensim.models.keyedvectors.Vocab at 0x7f170cb165f8>,
'»': <gensim.models.keyedvectors.Vocab at 0x7f170cb16668>,
'Et,': <gensim.models.keyedvectors.Vocab at 0x7f170cb166a0>,
'une': <gensim.models.keyedvectors.Vocab at 0x7f170cb166d8>,
'demi-heure': <gensim.models.keyedvectors.Vocab at 0x7f170cb16710>,
'après,': <gensim.models.keyedvectors.Vocab at 0x7f170cb16748>,
'pensée': <gensim.models.keyedvectors.Vocab at 0x7f170cb16780>,
'qu’il': <gensim.models.keyedvectors.Vocab at 0x7f170cb167b8>,
'était': <gensim.models.keyedvectors.Vocab at 0x7f170cb167f0>,
'chercher': <gensim.models.keyedvectors.Vocab at 0x7f170cb16828>,
'sommeil': <gensim.models.keyedvectors.Vocab at 0x7f170cb16860>,
'voulais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16898>,
'poser': <gensim.models.keyedvectors.Vocab at 0x7f170cb168d0>,
'volume': <gensim.models.keyedvectors.Vocab at 0x7f170cb16908>,
'croyais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16940>,
'avoir': <gensim.models.keyedvectors.Vocab at 0x7f170cb16978>,
'encore': <gensim.models.keyedvectors.Vocab at 0x7f170cb169b0>,
'dans': <gensim.models.keyedvectors.Vocab at 0x7f170cb169e8>,
'les': <gensim.models.keyedvectors.Vocab at 0x7f170cb16a20>,
'mains': <gensim.models.keyedvectors.Vocab at 0x7f170cb16a58>,
'cessé': <gensim.models.keyedvectors.Vocab at 0x7f170cb16a90>,
'dormant': <gensim.models.keyedvectors.Vocab at 0x7f170cb16ac8>,
'faire': <gensim.models.keyedvectors.Vocab at 0x7f170cb16b00>,
'réflexions': <gensim.models.keyedvectors.Vocab at 0x7f170cb16b38>,
'sur': <gensim.models.keyedvectors.Vocab at 0x7f170cb16b70>,
'ce': <gensim.models.keyedvectors.Vocab at 0x7f170cb16ba8>,
'venais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16be0>,
'lire,': <gensim.models.keyedvectors.Vocab at 0x7f170cb16c18>,
'mais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16c50>,
'ces': <gensim.models.keyedvectors.Vocab at 0x7f170cb16c88>,
'avaient': <gensim.models.keyedvectors.Vocab at 0x7f170cb16cc0>,
'pris': <gensim.models.keyedvectors.Vocab at 0x7f170cb16cf8>,
'tour': <gensim.models.keyedvectors.Vocab at 0x7f170cb16d30>,
'peu': <gensim.models.keyedvectors.Vocab at 0x7f170cb16d68>,
'particulier;': <gensim.models.keyedvectors.Vocab at 0x7f170cb16da0>,
'il': <gensim.models.keyedvectors.Vocab at 0x7f170cb16dd8>,
'semblait': <gensim.models.keyedvectors.Vocab at 0x7f170cb16e10>,
'j’étais': <gensim.models.keyedvectors.Vocab at 0x7f170cb16e48>,
'moi-même': <gensim.models.keyedvectors.Vocab at 0x7f170cb16e80>,
'dont': <gensim.models.keyedvectors.Vocab at 0x7f170cb16eb8>,
'parlait': <gensim.models.keyedvectors.Vocab at 0x7f170cb16ef0>,
'église,': <gensim.models.keyedvectors.Vocab at 0x7f170cb16f28>,
'François': <gensim.models.keyedvectors.Vocab at 0x7f170cb16f60>,
'I^(er)': <gensim.models.keyedvectors.Vocab at 0x7f170cb16f98>,
'Cette': <gensim.models.keyedvectors.Vocab at 0x7f170cb16fd0>,
'croyance': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd048>,
'survivait': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd080>,
'pendant': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd0b8>,
'quelques': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd0f0>,
'secondes': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd128>,
'mon': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd160>,
'elle': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd198>,
'ne': <gensim.models.keyedvectors.Vocab at 0x7f170c9fd1d0>,
'raison,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1550>,
'pesait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1588>,
'comme': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1518>,
'empêchait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc14a8>,
'rendre': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1710>,
'compte': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1748>,
'n’était': <gensim.models.keyedvectors.Vocab at 0x7f170cfc17b8>,
'allumé': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1470>,
'Puis': <gensim.models.keyedvectors.Vocab at 0x7f170cfc16a0>,
'commençait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc15c0>,
'devenir': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1630>,
'après': <gensim.models.keyedvectors.Vocab at 0x7f170cfc11d0>,
'pensées': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1908>,
'd’une': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1828>,
'existence': <gensim.models.keyedvectors.Vocab at 0x7f170cfc16d8>,
'sujet': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1780>,
'livre': <gensim.models.keyedvectors.Vocab at 0x7f170cfc18d0>,
'détachait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1978>,
'moi,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1940>,
'libre': <gensim.models.keyedvectors.Vocab at 0x7f170cfc19b0>,
'm’y': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1a20>,
'appliquer': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1a58>,
'ou': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1a90>,
'non;': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1ac8>,
'aussitôt': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1b00>,
'vue': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1b38>,
'bien': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1b70>,
'étonné': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1ba8>,
'trouver': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1be0>,
'autour': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1c18>,
'moi': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1c50>,
'douce': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1c88>,
'pour': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1cc0>,
'yeux,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1cf8>,
'peut-être': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1d30>,
'plus': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1d68>,
'esprit,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1da0>,
'qui': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1dd8>,
'apparaissait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1e10>,
'chose': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1e48>,
'sans': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1e80>,
'cause,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1eb8>,
'incompréhensible,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1ef0>,
'vraiment': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1f28>,
'obscure': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1f60>,
'Je': <gensim.models.keyedvectors.Vocab at 0x7f1738c77978>,
'demandais': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1400>,
'quelle': <gensim.models.keyedvectors.Vocab at 0x7f170cfc12e8>,
'pouvait': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1358>,
'j’entendais': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1240>,
'trains': <gensim.models.keyedvectors.Vocab at 0x7f170cfc12b0>,
'qui,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1898>,
'moins': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1f98>,
'éloigné,': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1fd0>,
'chant': <gensim.models.keyedvectors.Vocab at 0x7f170cfc1668>,
'd’un': <gensim.models.keyedvectors.Vocab at 0x7f17081df9b0>,
'oiseau': <gensim.models.keyedvectors.Vocab at 0x7f17081df9e8>,
'forêt,': <gensim.models.keyedvectors.Vocab at 0x7f17081dfa20>,
'relevant': <gensim.models.keyedvectors.Vocab at 0x7f17081dfa58>,
'distances,': <gensim.models.keyedvectors.Vocab at 0x7f17081dfa90>,
'décrivait': <gensim.models.keyedvectors.Vocab at 0x7f17081dfac8>,
'l’étendue': <gensim.models.keyedvectors.Vocab at 0x7f17081dfb00>,
'campagne': <gensim.models.keyedvectors.Vocab at 0x7f17081dfb38>,
'où': <gensim.models.keyedvectors.Vocab at 0x7f17081dfb70>,
'voyageur': <gensim.models.keyedvectors.Vocab at 0x7f17081dfba8>,
'hâte': <gensim.models.keyedvectors.Vocab at 0x7f17081dfbe0>,
'vers': <gensim.models.keyedvectors.Vocab at 0x7f17081dfc18>,
'station': <gensim.models.keyedvectors.Vocab at 0x7f17081dfc50>,
'petit': <gensim.models.keyedvectors.Vocab at 0x7f17081dfc88>,
'chemin': <gensim.models.keyedvectors.Vocab at 0x7f17081dfcc0>,
'suit': <gensim.models.keyedvectors.Vocab at 0x7f17081dfcf8>,
'va': <gensim.models.keyedvectors.Vocab at 0x7f17081dfd30>,
'être': <gensim.models.keyedvectors.Vocab at 0x7f17081dfd68>,
'son': <gensim.models.keyedvectors.Vocab at 0x7f17081dfda0>,
'souvenir': <gensim.models.keyedvectors.Vocab at 0x7f17081dfdd8>,
'par': <gensim.models.keyedvectors.Vocab at 0x7f17081dfe10>,
'doit': <gensim.models.keyedvectors.Vocab at 0x7f17081dfe48>,
'lieux': <gensim.models.keyedvectors.Vocab at 0x7f17081dfe80>,
'nouveaux,': <gensim.models.keyedvectors.Vocab at 0x7f17081dfeb8>,
'actes': <gensim.models.keyedvectors.Vocab at 0x7f17081dfef0>,
'causerie': <gensim.models.keyedvectors.Vocab at 0x7f17081dff28>,
'récente': <gensim.models.keyedvectors.Vocab at 0x7f17081dff60>,
'aux': <gensim.models.keyedvectors.Vocab at 0x7f17081dff98>,
'adieux': <gensim.models.keyedvectors.Vocab at 0x7f17081dffd0>,
'sous': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6048>,
'lampe': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6080>,
'étrangère': <gensim.models.keyedvectors.Vocab at 0x7f170c9a60b8>,
'suivent': <gensim.models.keyedvectors.Vocab at 0x7f170c9a60f0>,
'silence': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6128>,
'nuit,': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6160>,
'douceur': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6198>,
'prochaine': <gensim.models.keyedvectors.Vocab at 0x7f170c9a61d0>,
'retour': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6208>,
'tendrement': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6240>,
'joues': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6278>,
'contre': <gensim.models.keyedvectors.Vocab at 0x7f170c9a62b0>,
'belles': <gensim.models.keyedvectors.Vocab at 0x7f170c9a62e8>,
'pleines': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6320>,
'sont': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6358>,
'notre': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6390>,
'enfance': <gensim.models.keyedvectors.Vocab at 0x7f170c9a63c8>,
'regarder': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6400>,
'montre': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6438>,
'Bientôt': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6470>,
'minuit': <gensim.models.keyedvectors.Vocab at 0x7f170c9a64a8>,
'C’est': <gensim.models.keyedvectors.Vocab at 0x7f170c9a64e0>,
'l’instant': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6518>,
'malade': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6550>,
'a': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6588>,
'été': <gensim.models.keyedvectors.Vocab at 0x7f170c9a65c0>,
'obligé': <gensim.models.keyedvectors.Vocab at 0x7f170c9a65f8>,
'partir': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6630>,
'voyage': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6668>,
'dû': <gensim.models.keyedvectors.Vocab at 0x7f170c9a66a0>,
'coucher': <gensim.models.keyedvectors.Vocab at 0x7f170c9a66d8>,
'hôtel': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6710>,
'inconnu,': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6748>,
'réveillé': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6780>,
'apercevant': <gensim.models.keyedvectors.Vocab at 0x7f170c9a67b8>,
'porte': <gensim.models.keyedvectors.Vocab at 0x7f170c9a67f0>,
'raie': <gensim.models.keyedvectors.Vocab at 0x7f170c9a6828>,
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'légèrement': <gensim.models.keyedvectors.Vocab at 0x7f170c9aeba8>,
'tant': <gensim.models.keyedvectors.Vocab at 0x7f170c9aebe0>,
'montrer': <gensim.models.keyedvectors.Vocab at 0x7f170c9aec18>,
'réserver': <gensim.models.keyedvectors.Vocab at 0x7f170c9aec50>,
'lit;': <gensim.models.keyedvectors.Vocab at 0x7f170c9aec88>,
'contraire': <gensim.models.keyedvectors.Vocab at 0x7f170c9aecc0>,
'celle,': <gensim.models.keyedvectors.Vocab at 0x7f170c9aecf8>,
'élevée': <gensim.models.keyedvectors.Vocab at 0x7f170c9aed30>,
'hauteur': <gensim.models.keyedvectors.Vocab at 0x7f170c9aed68>,
'deux': <gensim.models.keyedvectors.Vocab at 0x7f170c9aeda0>,
'étages': <gensim.models.keyedvectors.Vocab at 0x7f170c9aedd8>,
'partiellement': <gensim.models.keyedvectors.Vocab at 0x7f170c9aee10>,
'dès': <gensim.models.keyedvectors.Vocab at 0x7f170c9aee48>,
'seconde,': <gensim.models.keyedvectors.Vocab at 0x7f170c9aee80>,
'l’odeur': <gensim.models.keyedvectors.Vocab at 0x7f170c9aeeb8>,
'inconnue': <gensim.models.keyedvectors.Vocab at 0x7f170c9aeef0>,
'convaincu': <gensim.models.keyedvectors.Vocab at 0x7f170c9aef28>,
'l’hostilité': <gensim.models.keyedvectors.Vocab at 0x7f170c9aef60>,
'rideaux': <gensim.models.keyedvectors.Vocab at 0x7f170c9aef98>,
'violets': <gensim.models.keyedvectors.Vocab at 0x7f170c9aefd0>,
'indifférence': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4048>,
'pendule': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4080>,
'n’eusse': <gensim.models.keyedvectors.Vocab at 0x7f170c9b40b8>,
'là;': <gensim.models.keyedvectors.Vocab at 0x7f170c9b40f0>,
'étrange': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4128>,
'impitoyable': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4160>,
'glace': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4198>,
'pieds': <gensim.models.keyedvectors.Vocab at 0x7f170c9b41d0>,
'obliquement': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4208>,
'creusait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4240>,
'vif': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4278>,
'plénitude': <gensim.models.keyedvectors.Vocab at 0x7f170c9b42b0>,
'champ': <gensim.models.keyedvectors.Vocab at 0x7f170c9b42e8>,
'accoutumé': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4320>,
'heures': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4358>,
'prendre': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4390>,
'exactement': <gensim.models.keyedvectors.Vocab at 0x7f170c9b43c8>,
'arriver': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4400>,
'remplir': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4438>,
'jusqu’en': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4470>,
'gigantesque': <gensim.models.keyedvectors.Vocab at 0x7f170c9b44a8>,
'souffert': <gensim.models.keyedvectors.Vocab at 0x7f170c9b44e0>,
'dures': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4518>,
'étendu': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4550>,
'l’oreille': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4588>,
'cœur': <gensim.models.keyedvectors.Vocab at 0x7f170c9b45c0>,
'l’habitude': <gensim.models.keyedvectors.Vocab at 0x7f170c9b45f8>,
'changé': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4630>,
'couleur': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4668>,
'rideaux,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b46a0>,
'taire': <gensim.models.keyedvectors.Vocab at 0x7f170c9b46d8>,
'enseigné': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4710>,
'pitié': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4748>,
'oblique': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4780>,
'cruelle,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b47b8>,
'sinon': <gensim.models.keyedvectors.Vocab at 0x7f170c9b47f0>,
'chassé': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4828>,
'complètement,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4860>,
'diminué': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4898>,
'apparente': <gensim.models.keyedvectors.Vocab at 0x7f170c9b48d0>,
'habile': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4908>,
'lente,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4940>,
'commence': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4978>,
'laisser': <gensim.models.keyedvectors.Vocab at 0x7f170c9b49b0>,
'semaines': <gensim.models.keyedvectors.Vocab at 0x7f170c9b49e8>,
'malgré': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4a20>,
'heureux': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4a58>,
'trouver,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4a90>,
'car': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4ac8>,
'réduit': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4b00>,
'seuls': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4b38>,
'serait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4b70>,
'impuissant': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4ba8>,
'Certes,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4be0>,
'dernière': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4c18>,
'fois': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4c50>,
'bon': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4c88>,
'ange': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4cc0>,
'arrêté': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4cf8>,
'm’avait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4d30>,
'mis': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4d68>,
'l’obscurité': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4da0>,
'commode,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4dd8>,
'bureau,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4e10>,
'rue': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4e48>,
'portes': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4e80>,
'beau': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4eb8>,
'demeures': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4ef0>,
'l’ignorance': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4f28>,
'présenté': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4f60>,
'distincte,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4f98>,
'croire': <gensim.models.keyedvectors.Vocab at 0x7f170c9b4fd0>,
'présence': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7048>,
'possible,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7080>,
'donné': <gensim.models.keyedvectors.Vocab at 0x7f170c9b70b8>,
'généralement': <gensim.models.keyedvectors.Vocab at 0x7f170c9b70f0>,
'cherchais': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7128>,
'grande': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7160>,
'd’autrefois': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7198>,
'grand’tante,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b71d0>,
'Balbec,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7208>,
'Paris,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7240>,
'Doncières,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7278>,
'Venise,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b72b0>,
'ailleurs': <gensim.models.keyedvectors.Vocab at 0x7f170c9b72e8>,
'encore,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7320>,
'lieux,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7358>,
'personnes': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7390>,
'j’y': <gensim.models.keyedvectors.Vocab at 0x7f170c9b73c8>,
'connues,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7400>,
'vu': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7438>,
'd’elles,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7470>,
'm’en': <gensim.models.keyedvectors.Vocab at 0x7f170c9b74a8>,
'raconté': <gensim.models.keyedvectors.Vocab at 0x7f170c9b74e0>,
'fin': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7518>,
'l’après-midi,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7550>,
'longtemps': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7588>,
'faudrait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b75c0>,
'mettre': <gensim.models.keyedvectors.Vocab at 0x7f170c9b75f8>,
'rester,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7630>,
'dormir,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7668>,
'mère': <gensim.models.keyedvectors.Vocab at 0x7f170c9b76a0>,
'grand’mère,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b76d8>,
'redevenait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7710>,
'fixe': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7748>,
'douloureux': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7780>,
'préoccupations': <gensim.models.keyedvectors.Vocab at 0x7f170c9b77b8>,
'On': <gensim.models.keyedvectors.Vocab at 0x7f170c9b77f0>,
'distraire': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7828>,
'l’air': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7860>,
'lanterne': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7898>,
'magique,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b78d0>,
'dont,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7908>,
'attendant': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7940>,
'et,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7978>,
'premiers': <gensim.models.keyedvectors.Vocab at 0x7f170c9b79b0>,
'architectes': <gensim.models.keyedvectors.Vocab at 0x7f170c9b79e8>,
'maîtres': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7a20>,
'l’âge': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7a58>,
'gothique,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7a90>,
'substituait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7ac8>,
'apparitions': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7b00>,
'vitrail': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7b38>,
'momentané': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7b70>,
'tristesse': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7ba8>,
'n’en': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7be0>,
'parce': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7c18>,
'rien': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7c50>,
'changement': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7c88>,
'détruisait': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7cc0>,
'quoi,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7cf8>,
'sauf': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7d30>,
'supplice': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7d68>,
'coucher,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7da0>,
'm’était': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7dd8>,
'devenue': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7e10>,
'supportable': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7e48>,
'Maintenant': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7e80>,
'reconnaissais': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7eb8>,
'étais': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7ef0>,
'inquiet,': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7f28>,
'd’hôtel': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7f60>,
'fusse': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7f98>,
'arrivé': <gensim.models.keyedvectors.Vocab at 0x7f170c9b7fd0>,
'descendant': <gensim.models.keyedvectors.Vocab at 0x7f17081e2048>,
'fer': <gensim.models.keyedvectors.Vocab at 0x7f17081e2080>,
'Au': <gensim.models.keyedvectors.Vocab at 0x7f17081e20b8>,
'cheval,': <gensim.models.keyedvectors.Vocab at 0x7f17081e20f0>,
'affreux': <gensim.models.keyedvectors.Vocab at 0x7f17081e2128>,
'sortait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2160>,
'forêt': <gensim.models.keyedvectors.Vocab at 0x7f17081e2198>,
'vert': <gensim.models.keyedvectors.Vocab at 0x7f17081e21d0>,
'sombre': <gensim.models.keyedvectors.Vocab at 0x7f17081e2208>,
'pente': <gensim.models.keyedvectors.Vocab at 0x7f17081e2240>,
'colline,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2278>,
's’avançait': <gensim.models.keyedvectors.Vocab at 0x7f17081e22b0>,
'château': <gensim.models.keyedvectors.Vocab at 0x7f17081e22e8>,
'pauvre': <gensim.models.keyedvectors.Vocab at 0x7f17081e2320>,
'Geneviève': <gensim.models.keyedvectors.Vocab at 0x7f17081e2358>,
'Brabant': <gensim.models.keyedvectors.Vocab at 0x7f17081e2390>,
'Ce': <gensim.models.keyedvectors.Vocab at 0x7f17081e23c8>,
'coupé': <gensim.models.keyedvectors.Vocab at 0x7f17081e2400>,
'ligne': <gensim.models.keyedvectors.Vocab at 0x7f17081e2438>,
'courbe': <gensim.models.keyedvectors.Vocab at 0x7f17081e2470>,
'guère': <gensim.models.keyedvectors.Vocab at 0x7f17081e24a8>,
'limite': <gensim.models.keyedvectors.Vocab at 0x7f17081e24e0>,
'glissait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2518>,
'entre': <gensim.models.keyedvectors.Vocab at 0x7f17081e2550>,
'coulisses': <gensim.models.keyedvectors.Vocab at 0x7f17081e2588>,
'qu’un': <gensim.models.keyedvectors.Vocab at 0x7f17081e25c0>,
'pan': <gensim.models.keyedvectors.Vocab at 0x7f17081e25f8>,
'château,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2630>,
'devant': <gensim.models.keyedvectors.Vocab at 0x7f17081e2668>,
'rêvait': <gensim.models.keyedvectors.Vocab at 0x7f17081e26a0>,
'portait': <gensim.models.keyedvectors.Vocab at 0x7f17081e26d8>,
'ceinture': <gensim.models.keyedvectors.Vocab at 0x7f17081e2710>,
'bleue': <gensim.models.keyedvectors.Vocab at 0x7f17081e2748>,
'jaunes,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2780>,
'attendu': <gensim.models.keyedvectors.Vocab at 0x7f17081e27b8>,
'connaître': <gensim.models.keyedvectors.Vocab at 0x7f17081e27f0>,
'couleur,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2828>,
'car,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2860>,
'verres': <gensim.models.keyedvectors.Vocab at 0x7f17081e2898>,
'sonorité': <gensim.models.keyedvectors.Vocab at 0x7f17081e28d0>,
'l’avait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2908>,
'montrée': <gensim.models.keyedvectors.Vocab at 0x7f17081e2940>,
'évidence': <gensim.models.keyedvectors.Vocab at 0x7f17081e2978>,
'Golo': <gensim.models.keyedvectors.Vocab at 0x7f17081e29b0>,
's’arrêtait': <gensim.models.keyedvectors.Vocab at 0x7f17081e29e8>,
'écouter': <gensim.models.keyedvectors.Vocab at 0x7f17081e2a20>,
'lu': <gensim.models.keyedvectors.Vocab at 0x7f17081e2a58>,
'haute': <gensim.models.keyedvectors.Vocab at 0x7f17081e2a90>,
'voix': <gensim.models.keyedvectors.Vocab at 0x7f17081e2ac8>,
'comprendre': <gensim.models.keyedvectors.Vocab at 0x7f17081e2b00>,
'parfaitement,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2b38>,
'n’excluait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2b70>,
'certaine': <gensim.models.keyedvectors.Vocab at 0x7f17081e2ba8>,
'indications': <gensim.models.keyedvectors.Vocab at 0x7f17081e2be0>,
's’éloignait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2c18>,
'lente': <gensim.models.keyedvectors.Vocab at 0x7f17081e2c50>,
'Si': <gensim.models.keyedvectors.Vocab at 0x7f17081e2c88>,
'distinguais': <gensim.models.keyedvectors.Vocab at 0x7f17081e2cc0>,
'continuait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2cf8>,
's’avancer': <gensim.models.keyedvectors.Vocab at 0x7f17081e2d30>,
'fenêtre,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2d68>,
'lui-même,': <gensim.models.keyedvectors.Vocab at 0x7f17081e2da0>,
'essence': <gensim.models.keyedvectors.Vocab at 0x7f17081e2dd8>,
'aussi': <gensim.models.keyedvectors.Vocab at 0x7f17081e2e10>,
'surnaturelle': <gensim.models.keyedvectors.Vocab at 0x7f17081e2e48>,
'celui': <gensim.models.keyedvectors.Vocab at 0x7f17081e2e80>,
's’arrangeait': <gensim.models.keyedvectors.Vocab at 0x7f17081e2eb8>,
'obstacle': <gensim.models.keyedvectors.Vocab at 0x7f17081e2ef0>,
...}
model.most_similar('madeleine')
/home/marcello/.local/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
"""Entry point for launching an IPython kernel.
[('comédie', 0.9520220160484314),
('matinale', 0.9289866089820862),
('fraîcheur', 0.9280268549919128),
('déchéance', 0.9277333617210388),
('audience', 0.9262043833732605),
('direction,', 0.9231734275817871),
('cathédrale,', 0.9196324944496155),
('chaleur,', 0.918372631072998),
('tapisserie,', 0.9182485342025757),
('source', 0.9173475503921509)]
#un peu de paramètres: min_count= combien de fois un terme doit apparaître pour être mis dans le vocabulaire
model_sg = Word2Vec(sentences=sentences, min_count=1, sg=1, window=10)
model_sg.most_similar('madeleine')
/home/marcello/.local/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
"""Entry point for launching an IPython kernel.
[('solitaire', 0.9496662616729736),
('décoration', 0.9432491064071655),
('magique,', 0.9428556561470032),
('Roussainville,', 0.9383882284164429),
('lecture,', 0.9381389021873474),
('tracé', 0.9377898573875427),
('tempête', 0.9358904957771301),
('boîte', 0.9357473850250244),
('blessure', 0.9343767166137695),
('fermée,', 0.9340600967407227)]
CBOW¶
Le principe est juste inversé: on prend en entrée le contexte pour deviner un mot cible.
Skip-Gram fonctionne bien avec de petits ensembles de données et peut mieux représenter les mots moins fréquents.
CBOW s’entraîne plus rapidement que Skip-Gram, et peut mieux représenter les mots plus fréquents.
model_cbow = Word2Vec(sentences=sentences, min_count=1, sg=0, window=10)
model_cbow.most_similar('madeleine')
/home/marcello/.local/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
"""Entry point for launching an IPython kernel.
[('fenêtre', 0.9436046481132507),
('noire,', 0.9424415230751038),
('remplie', 0.9380182027816772),
('fenêtre,', 0.9375181794166565),
('voisine', 0.9338272213935852),
('étincelante', 0.9334274530410767),
('silhouette', 0.9321422576904297),
('servante,', 0.9317981004714966),
('pierre,', 0.9308982491493225),
('station', 0.9301747679710388)]
model.most_similar('temps')
/home/marcello/.local/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
"""Entry point for launching an IPython kernel.
[('temps,', 0.8334174156188965),
('sens', 0.6833239197731018),
('train', 0.6688373684883118),
('chose', 0.6642275452613831),
('dehors', 0.6556645035743713),
('pays', 0.649366021156311),
('cas', 0.6439028978347778),
('souvenir', 0.6433770656585693),
('désir', 0.6371811032295227),
('plaisir', 0.6370058655738831)]
Glove¶
Au lieu que prendre en compte le vocabulaire comme input on part de la matrice des co-occurrences des mots. Donc GloVe (Global Vectors) prend en compte les statistiques d’occurences globales et non seulement locales (comme w2v).
Unlike the matrix factorization methods, the shallow window-based methods suffer from the disadvantage that they do not operate directly on the co-occurrence statistics of the corpus. Instead, these models scan context windows across the entire corpus, which fails to take advantage of the vast amount of repetition in the data.
Par exemple, admettons d’avoir la phrase:
La littérature est un art.
W2v sera capable de rendre compte du fait qu’il y a un lien entre “littérature” et “art”. Mais il ne saura pas profitter d’une autre information; combien de fois, dans l’ensemble du corpus, le mot “littérature” et le mot “art” se touvent ensemble?
GloVe ne se requiert pas un réseau de neurones. Les vecteurs sont déterminés sur la base de la probabilité de co-occurence des mots selon la formule:
\[
F(w_i, w_j, \tilde{w_k}) = \frac{Pi_k}{Pj_k}
\]