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| from gensim import models | |
| sentence = models.doc2vec.LabeledSentence( | |
| words=[u'so`bme', u'words', u'here'], tags=["SENT_0"]) | |
| sentence1 = models.doc2vec.LabeledSentence( | |
| words=[u'here', u'we', u'go'], tags=["SENT_1"]) | |
| sentences = [sentence, sentence1] | |
| class LabeledLineSentence(object): | |
| def __init__(self, filename): | |
| self.filename = filename | |
| def __iter__(self): | |
| for uid, line in enumerate(open(filename)): | |
| yield LabeledSentence(words=line.split(), labels=['SENT_%s' % uid]) | |
| model = models.Doc2Vec(alpha=.025, min_alpha=.025, min_count=1) | |
| model.build_vocab(sentences) | |
| for epoch in range(10): | |
| model.train(sentences) | |
| model.alpha -= 0.002 # decrease the learning rate` | |
| model.min_alpha = model.alpha # fix the learning rate, no decay | |
| model.save("my_model.doc2vec") | |
| model_loaded = models.Doc2Vec.load('my_model.doc2vec') | |
| print model.docvecs.most_similar(["SENT_0"]) | |
| print model_loaded.docvecs.most_similar(["SENT_1"]) |
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