w2v article
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TP de Word Embedding pour la revue 3EI.
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# TP de Word Embedding pour la revue 3EI.
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## Méthodes de plongement lexical
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CBOW et Skig-gram
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## Utilisation de Gensim pour Word2Vec
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https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html#sphx-glr-auto-examples-tutorials-run-word2vec-py
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wordvec.ipynb
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wordvec.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gensim.downloader as api\n",
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"wv = api.load('word2vec-google-news-300')"
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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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"source": [
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"Affichage des premiers mots du dictionnaire"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"word #0/3000000 is </s>\n",
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"word #1/3000000 is in\n",
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"word #2/3000000 is for\n",
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"word #3/3000000 is that\n",
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"word #4/3000000 is is\n"
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]
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}
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],
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"source": [
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"# Affichage de quelques mots du vocabulaire\n",
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"for index, word in enumerate(wv.index_to_key):\n",
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" if index == 5:\n",
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" break\n",
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" print(f\"word #{index}/{len(wv.index_to_key)} is {word}\")"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 1.25976562e-01 2.97851562e-02 8.60595703e-03 1.39648438e-01\n",
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" -2.56347656e-02 -3.61328125e-02 1.11816406e-01 -1.98242188e-01\n",
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" 5.12695312e-02 3.63281250e-01 -2.42187500e-01 -3.02734375e-01\n",
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" -1.77734375e-01 -2.49023438e-02 -1.67968750e-01 -1.69921875e-01\n",
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" 3.46679688e-02 5.21850586e-03 4.63867188e-02 1.28906250e-01\n",
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" 1.36718750e-01 1.12792969e-01 5.95703125e-02 1.36718750e-01\n",
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" 1.01074219e-01 -1.76757812e-01 -2.51953125e-01 5.98144531e-02\n",
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" 3.41796875e-01 -3.11279297e-02 1.04492188e-01 6.17675781e-02\n",
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" 1.24511719e-01 4.00390625e-01 -3.22265625e-01 8.39843750e-02\n",
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" 3.90625000e-02 5.85937500e-03 7.03125000e-02 1.72851562e-01\n",
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" 1.38671875e-01 -2.31445312e-01 2.83203125e-01 1.42578125e-01\n",
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" 3.41796875e-01 -2.39257812e-02 -1.09863281e-01 3.32031250e-02\n",
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" -5.46875000e-02 1.53198242e-02 -1.62109375e-01 1.58203125e-01\n",
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" -2.59765625e-01 2.01416016e-02 -1.63085938e-01 1.35803223e-03\n",
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" -1.44531250e-01 -5.68847656e-02 4.29687500e-02 -2.46582031e-02\n",
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" 1.85546875e-01 4.47265625e-01 9.58251953e-03 1.31835938e-01\n",
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" 9.86328125e-02 -1.85546875e-01 -1.00097656e-01 -1.33789062e-01\n",
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" -1.25000000e-01 2.83203125e-01 1.23046875e-01 5.32226562e-02\n",
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" -1.77734375e-01 8.59375000e-02 -2.18505859e-02 2.05078125e-02\n",
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" -1.39648438e-01 2.51464844e-02 1.38671875e-01 -1.05468750e-01\n",
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" 1.38671875e-01 8.88671875e-02 -7.51953125e-02 -2.13623047e-02\n",
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" 1.72851562e-01 4.63867188e-02 -2.65625000e-01 8.91113281e-03\n",
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" 1.49414062e-01 3.78417969e-02 2.38281250e-01 -1.24511719e-01\n",
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" -2.17773438e-01 -1.81640625e-01 2.97851562e-02 5.71289062e-02\n",
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" -2.89306641e-02 1.24511719e-02 9.66796875e-02 -2.31445312e-01\n",
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" 5.81054688e-02 6.68945312e-02 7.08007812e-02 -3.08593750e-01\n",
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" -2.14843750e-01 1.45507812e-01 -4.27734375e-01 -9.39941406e-03\n",
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" 1.54296875e-01 -7.66601562e-02 2.89062500e-01 2.77343750e-01\n",
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" -4.86373901e-04 -1.36718750e-01 3.24218750e-01 -2.46093750e-01\n",
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" -3.03649902e-03 -2.11914062e-01 1.25000000e-01 2.69531250e-01\n",
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" 2.04101562e-01 8.25195312e-02 -2.01171875e-01 -1.60156250e-01\n",
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" -3.78417969e-02 -1.20117188e-01 1.15234375e-01 -4.10156250e-02\n",
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" -3.95507812e-02 -8.98437500e-02 6.34765625e-03 2.03125000e-01\n",
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" 1.86523438e-01 2.73437500e-01 6.29882812e-02 1.41601562e-01\n",
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" -9.81445312e-02 1.38671875e-01 1.82617188e-01 1.73828125e-01\n",
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" 1.73828125e-01 -2.37304688e-01 1.78710938e-01 6.34765625e-02\n",
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" 2.36328125e-01 -2.08984375e-01 8.74023438e-02 -1.66015625e-01\n",
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" -7.91015625e-02 2.43164062e-01 -8.88671875e-02 1.26953125e-01\n",
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" -2.16796875e-01 -1.73828125e-01 -3.59375000e-01 -8.25195312e-02\n",
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" -6.49414062e-02 5.07812500e-02 1.35742188e-01 -7.47070312e-02\n",
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" -1.64062500e-01 1.15356445e-02 4.45312500e-01 -2.15820312e-01\n",
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" -1.11328125e-01 -1.92382812e-01 1.70898438e-01 -1.25000000e-01\n",
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" 2.65502930e-03 1.92382812e-01 -1.74804688e-01 1.39648438e-01\n",
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" 2.92968750e-01 1.13281250e-01 5.95703125e-02 -6.39648438e-02\n",
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" 9.96093750e-02 -2.72216797e-02 1.96533203e-02 4.27246094e-02\n",
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" -2.46093750e-01 6.39648438e-02 -2.25585938e-01 -1.68945312e-01\n",
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" 2.89916992e-03 8.20312500e-02 3.41796875e-01 4.32128906e-02\n",
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" 1.32812500e-01 1.42578125e-01 7.61718750e-02 5.98144531e-02\n",
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" -1.19140625e-01 2.74658203e-03 -6.29882812e-02 -2.72216797e-02\n",
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" -4.82177734e-03 -8.20312500e-02 -2.49023438e-02 -4.00390625e-01\n",
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" -1.06933594e-01 4.24804688e-02 7.76367188e-02 -1.16699219e-01\n",
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" 7.37304688e-02 -9.22851562e-02 1.07910156e-01 1.58203125e-01\n",
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" 4.24804688e-02 1.26953125e-01 3.61328125e-02 2.67578125e-01\n",
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" -1.01074219e-01 -3.02734375e-01 -5.76171875e-02 5.05371094e-02\n",
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" 5.26428223e-04 -2.07031250e-01 -1.38671875e-01 -8.97216797e-03\n",
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" -2.78320312e-02 -1.41601562e-01 2.07031250e-01 -1.58203125e-01\n",
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" 1.27929688e-01 1.49414062e-01 -2.24609375e-02 -8.44726562e-02\n",
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" 1.22558594e-01 2.15820312e-01 -2.13867188e-01 -3.12500000e-01\n",
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" -3.73046875e-01 4.08935547e-03 1.07421875e-01 1.06933594e-01\n",
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" 7.32421875e-02 8.97216797e-03 -3.88183594e-02 -1.29882812e-01\n",
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" 1.49414062e-01 -2.14843750e-01 -1.83868408e-03 9.91210938e-02\n",
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" 1.57226562e-01 -1.14257812e-01 -2.05078125e-01 9.91210938e-02\n",
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" 3.69140625e-01 -1.97265625e-01 3.54003906e-02 1.09375000e-01\n",
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" 1.31835938e-01 1.66992188e-01 2.35351562e-01 1.04980469e-01\n",
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" -4.96093750e-01 -1.64062500e-01 -1.56250000e-01 -5.22460938e-02\n",
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" 1.03027344e-01 2.43164062e-01 -1.88476562e-01 5.07812500e-02\n",
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" -9.37500000e-02 -6.68945312e-02 2.27050781e-02 7.61718750e-02\n",
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" 2.89062500e-01 3.10546875e-01 -5.37109375e-02 2.28515625e-01\n",
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" 2.51464844e-02 6.78710938e-02 -1.21093750e-01 -2.15820312e-01\n",
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" -2.73437500e-01 -3.07617188e-02 -3.37890625e-01 1.53320312e-01\n",
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" 2.33398438e-01 -2.08007812e-01 3.73046875e-01 8.20312500e-02\n",
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" 2.51953125e-01 -7.61718750e-02 -4.66308594e-02 -2.23388672e-02\n",
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" 2.99072266e-02 -5.93261719e-02 -4.66918945e-03 -2.44140625e-01\n",
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" -2.09960938e-01 -2.87109375e-01 -4.54101562e-02 -1.77734375e-01\n",
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" -2.79296875e-01 -8.59375000e-02 9.13085938e-02 2.51953125e-01]\n"
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]
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}
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],
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"source": [
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"# Conversion du mot en vecteur\n",
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"vec_king = wv['king']\n",
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"print(vec_king)"
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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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"source": [
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"Similarités entre les mots"
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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": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[('lumber', 0.678311824798584), ('timber', 0.6622092127799988), ('softwoods', 0.649677038192749)]\n",
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"[('fleur', 0.6820620894432068), ('jardin', 0.6616703271865845), (\"c'est_le\", 0.6514866352081299)]\n"
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]
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}
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],
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"source": [
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"# N mots les plus proches du mot cible\n",
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"print(wv.most_similar(positive=['wood'], topn=3))\n",
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"print(wv.most_similar(positive=['bois'], topn=3))"
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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": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"'wood'\t'pine'\tSimilarité : 0.59\n",
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"'wood'\t'leaf'\tSimilarité : 0.30\n",
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"'wood'\t'plank'\tSimilarité : 0.26\n"
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]
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}
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],
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"source": [
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"# Similarité entre des paires de mots\n",
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"pairs = [\n",
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" ('wood', 'pine'), \n",
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" ('wood', 'leaf'),\n",
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" ('wood', 'plank') \n",
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"]\n",
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"for w1, w2 in pairs:\n",
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" print('%r\\t%r\\tSimilarité : %.2f' % (w1, w2, wv.similarity(w1, w2)))"
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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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"source": [
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"Le modèle a été entrainé en anglais donc il ne fonctionne pas dans les autres langues"
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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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"source": [
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"Exemple d'opération sur les mots"
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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": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[('mother', 0.8671472072601318)]\n"
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]
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}
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],
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"source": [
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"# Opérations sur les mots\n",
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"vec_father = wv['father']\n",
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"vec_man = wv['man']\n",
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"vec_woman = wv['woman']\n",
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"\n",
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"result = wv.most_similar(positive=(vec_father - vec_man + vec_woman), topn=1)\n",
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"print(result)"
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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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"source": [
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"On obtient bien le mot \"maman\" "
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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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"source": [
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"On peut également jouer au jeu de l'intru"
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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": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"iron\n"
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]
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}
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],
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"source": [
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"print(wv.doesnt_match(['wood', 'oak', 'tree', 'iron', 'leaf']))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.4 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.4"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -8,4 +8,11 @@ wv = api.load('word2vec-google-news-300')
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# print(f"word #{index}/{len(wv.index_to_key)} is {word}")
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print(wv.most_similar(positive=['car'], topn=5))
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print(wv.most_similar(positive=['voiture'], topn=5))
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print(wv.most_similar(positive=['voiture'], topn=5))
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vec_father = wv['father']
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vec_man = wv['man']
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vec_woman = wv['woman']
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result = wv.most_similar(positive=(vec_father - vec_man + vec_woman), topn=1)
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print(result)
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