Python实战：Word2Vec与GloVe应用指南

基于Python的Word2Vec和GloVe实例

以下是一些基于Python的Word2Vec和GloVe实现示例，涵盖训练、可视化、应用等多个方面。示例代码基于gensim、glove-python等库，可直接运行或调整参数使用。

训练Word2Vec模型

from gensim.models import Word2Vecsentences = [[\"cat\", \"say\", \"meow\"], [\"dog\", \"say\", \"woof\"]]model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)model.save(\"word2vec.model\")

加载预训练Word2Vec模型

from gensim.models import KeyedVectorsmodel = KeyedVectors.load_word2vec_format(\"GoogleNews-vectors-negative300.bin\", binary=True)print(model.most_similar(\"king\"))

训练GloVe模型

from glove import Corpus, Glovecorpus = Corpus()texts = [[\"hello\", \"world\"], [\"machine\", \"learning\"]]corpus.fit(texts, window=10)glove = Glove(no_components=100)glove.fit(corpus.matrix, epochs=30)glove.add_dictionary(corpus.dictionary)

词向量可视化（PCA降维）

import matplotlib.pyplot as pltfrom sklearn.decomposition import PCAwords = [\"king\", \"queen\", \"man\", \"woman\"]vectors = [model[w] for w in words]pca = PCA(n_components=2)result = pca.fit_transform(vectors)plt.scatter(result[:, 0], result[:, 1])for i, word in enumerate(words): plt.annotate(word, xy=(result[i, 0], result[i, 1]))plt.show()

计算词语相似度

similarity = model.similarity(\"woman\", \"man\")print(f\"Cosine similarity: {similarity:.4f}\")

词语类比推理

result = model.most_similar(positive=[\"woman\", \"king\"], negative=[\"man\"])print(f\"woman + king - man = {result[0][0]} (score: {result[0][1]:.4f})\")

查找不相关词语

odd_one = model.doesnt_match([\"breakfast\", \"cereal\", \"dinner\", \"lunch\"])print(f\"Odd one out: {odd_one}\")

增量训练Word2Vec

new_sentences = [[\"python\", \"programming\"], [\"java\", \"coding\"]]model.build_vocab(new_sentences, update=True)model.train(new_sentences, total_examples=model.corpus_count, epochs=5)

使用预训练GloVe向量

import numpy as npdef load_glove(file): embeddings = {} with open(file, \'r\', encoding=\'utf8\') as f: for line in f: values = line.split() word = values[0] vector = np.asarray(values[1:], dtype=\'float32\') embeddings[word] = vector return embeddingsglove_vectors = load_glove(\"glove.6B.100d.txt\")

句子向量化（均值法）

import numpy as npdef sentence_vector(sentence, model): words = [w