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Assignment_1_intro_word_vectors/Gensim Doc2vec.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gensim进阶教程：训练Doc2vec\n",
+    "Doc2vec是Mikolov在word2vec基础上提出的另一个用于计算长文本向量的工具。它的工作原理与word2vec极为相似——只是将长文本作为一个特殊的token id引入训练语料中。\n",
+    "\n",
+    "在Gensim中，doc2vec也是继承于word2vec的一个子类。因此，无论是API的参数接口还是调用文本向量的方式，doc2vec与word2vec都极为相似。\n",
+    "\n",
+    "主要的区别是在对输入数据的预处理上。Doc2vec接受一个由LabeledSentence对象组成的迭代器作为其构造函数的输入参数。其中，LabeledSentence是Gensim内建的一个类，它接受两个List作为其初始化的参数：word list和label list。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "data = [\"I love machine learning. Its awesome.\",\n",
+    "        \"I love coding in python\",\n",
+    "        \"I love building chatbots\",\n",
+    "        \"they chat amagingly well\"]\n",
+    "tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=['SENT_%s' %str(i)]) for i, _d in enumerate(data)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Doc2Vec(size=100,\n",
+    "                alpha=0.01, \n",
+    "                min_count=2,\n",
+    "                dm =1)\n",
+    "model.build_vocab(tagged_data)\n",
+    "for epoch in range(10):\n",
+    "    print('iteration {0}'.format(epoch))\n",
+    "    model.train(tagged_data,\n",
+    "                total_examples=model.corpus_count,\n",
+    "                epochs=model.iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model[\"SENT_0\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.docvecs.most_similar(0)"
+   ]
+  },
+  {
+   "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
+}