98 lines
2.7 KiB
Plaintext
98 lines
2.7 KiB
Plaintext
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{
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"cells": [
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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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"# Gensim进阶教程：训练Doc2vec\n",
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"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",
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"\n",
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"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",
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"\n",
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"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"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
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"from nltk.tokenize import word_tokenize\n",
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"data = [\"I love machine learning. Its awesome.\",\n",
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" \"I love coding in python\",\n",
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" \"I love building chatbots\",\n",
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" \"they chat amagingly well\"]\n",
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"tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=['SENT_%s' %str(i)]) for i, _d in enumerate(data)]"
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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": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"model = Doc2Vec(size=100,\n",
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" alpha=0.01, \n",
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" min_count=2,\n",
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" dm =1)\n",
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"model.build_vocab(tagged_data)\n",
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"for epoch in range(10):\n",
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" print('iteration {0}'.format(epoch))\n",
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" model.train(tagged_data,\n",
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" total_examples=model.corpus_count,\n",
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" epochs=model.iter)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model[\"SENT_0\"]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.docvecs.most_similar(0)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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",
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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": "3.7.3"
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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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