我有一段将词转换为向量的代码。以下是我的代码：

# word_to_vec_demo.pyfrom gensim.models import word2vecimport logginglogging.basicConfig(format='%(asctime)s : \%(levelname)s : %(message)s', level=logging.INFO)sentences = [['In', 'the', 'beginning', 'Abba','Yahweh', 'created', 'the','heaven', 'and', 'the', 'earth.', 'And', 'the', 'earth', 'was','without', 'form,', 'and', 'void;', 'and', 'darkness', 'was','upon', 'the', 'face', 'of', 'the', 'deep.', 'And', 'the','Spirit', 'of', 'Yahweh', 'moved', 'upon', 'the', 'face',  'of','the', 'waters.']]model = word2vec.Word2Vec(sentences, size=10, min_count=1)print("Vector for \'earth\' is: \n")print(model.wv['earth'])print("\nEnd demo")

输出结果是

Vector for 'earth' is: [-0.00402722  0.0034133   0.01583795  0.01997946  0.04112177  0.00291858-0.03854967  0.01581967 -0.02399057  0.00539708]

是否可以从向量数组编码为词？如果可以，我该如何在Python中实现？

回答：

你可以使用模型中的similar_by_vector()方法来查找与向量最相似的前N个词。希望这对你有帮助。