from text2vec import Word2Vec
defcompute_emb(model):
# Embed a list of sentences
sentences = [
'卡',
'银行卡',
'如何更换花呗绑定银行卡',
'花呗更改绑定银行卡',
'This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.',
'敏捷的棕色狐狸跳过了懒狗',
]
sentence_embeddings = model.encode(sentences, show_progress_bar=True, normalize_embeddings=True)
print(type(sentence_embeddings), sentence_embeddings.shape)
# The result is a list of sentence embeddings as numpy arraysfor sentence, embedding inzip(sentences, sentence_embeddings):
print("Sentence:", sentence)
print("Embedding shape:", embedding.shape)
print("Embedding head:", embedding[:10])
print()
if __name__ == "__main__":
# 中文词向量模型(word2vec),中文字面匹配任务和冷启动适用
w2v_model = Word2Vec("w2v-light-tencent-chinese")
compute_emb(w2v_model)
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