learn_pytorch_54_pytorch自然语言处理实战前的准备之gensim词嵌入.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 什么是词嵌入word embedding？  \n",
    "词嵌入方法/算法从文本语料库学习实值向量表示。 解决：词怎么用向量表示的问题！  \n",
    "词嵌入的2种表示方法：  \n",
    "(1) Google的word2vec     \n",
    "(2) Standford的Glove     \n",
    "注意：词嵌入需要大量的文本！！！  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 gensim中的word2vec  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec   \n",
    "sentences = [\n",
    "   ['this', 'is', 'gensim', 'tutorial', 'for', 'free'],\n",
    "   ['this', 'is', 'the', 'tutorials' 'point', 'website'],\n",
    "   ['you', 'can', 'read', 'technical','tutorials', 'for','free'],\n",
    "   ['we', 'are', 'implementing','word2vec'],\n",
    "   ['learn', 'full', 'gensim', 'tutorial']\n",
    "]\n",
    "\n",
    "model = Word2Vec(sentences, min_count=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec(vocab=20, size=100, alpha=0.025)\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['this', 'is', 'gensim', 'tutorial', 'for', 'free', 'the', 'tutorialspoint', 'website', 'you', 'can', 'read', 'technical', 'tutorials', 'we', 'are', 'implementing', 'word2vec', 'learn', 'full']\n"
     ]
    }
   ],
   "source": [
    "words = list(model.wv.vocab)\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00306002  0.00075905 -0.00141364 -0.00499561 -0.00190462 -0.00214869\n",
      " -0.00079073 -0.00427876  0.00371061 -0.00250087  0.00168984 -0.00396709\n",
      " -0.00300621  0.00250296 -0.00408328  0.00420155  0.0036866   0.00252063\n",
      " -0.00143976 -0.00496841 -0.00071836 -0.003299   -0.00455939  0.00363592\n",
      " -0.00466755  0.00438139  0.00040535  0.00187105  0.00360228 -0.00232076\n",
      "  0.00149902  0.00156562 -0.00262082  0.00023976  0.00236297 -0.00351344\n",
      " -0.00054606  0.0024303   0.00131988  0.00307537  0.00013842 -0.00122192\n",
      " -0.00408617 -0.00190329  0.00095237 -0.003611   -0.00437814  0.0006808\n",
      " -0.00376589  0.00326931 -0.00167317 -0.00089797 -0.00234703 -0.00121301\n",
      " -0.00012303  0.00275227  0.00154004 -0.00129077  0.00462849  0.00449333\n",
      "  0.00297474  0.00379989  0.0007763  -0.00089003  0.00415985  0.00434766\n",
      "  0.00182243  0.00347096 -0.00138332 -0.00220014 -0.00214198 -0.00071778\n",
      " -0.00036296  0.00434354  0.00349193 -0.00031091  0.00252123 -0.00375613\n",
      "  0.00021581  0.00475968  0.00097439 -0.0019076  -0.00225459 -0.00197006\n",
      " -0.00132931 -0.00177294  0.0041848   0.0019552  -0.00483219  0.0024246\n",
      "  0.00079086  0.00370788  0.00301908 -0.00141492 -0.00481021 -0.00375668\n",
      " -0.00160457 -0.00301417 -0.00151121  0.00053291]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "print(model['tutorial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model.save('model.bin')  \n",
    "new_model = Word2Vec.load('model.bin')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gensim.models import Word2Vec\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "sentences = [\n",
    "   ['this', 'is', 'gensim', 'tutorial', 'for', 'free'],\n",
    "\t['this', 'is', 'the', 'tutorials' 'point', 'website'],\n",
    "\t['you', 'can', 'read', 'technical','tutorials', 'for','free'],\n",
    "\t['we', 'are', 'implementing','word2vec'],\n",
    "\t['learn', 'full', 'gensim', 'tutorial']\n",
    "]\n",
    "model = Word2Vec(sentences, min_count=1)\n",
    "X = model[model.wv.vocab]\n",
    "pca = PCA(n_components=2)\n",
    "result = pca.fit_transform(X)\n",
    "pyplot.scatter(result[:, 0], result[:, 1])\n",
    "words = list(model.wv.vocab)\n",
    "for i, word in enumerate(words):\n",
    "   pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}