Code Issues Pull requests Score documents using embedding-vectors dot-product or cosine-similarity with ES Lucene engine elasticsearchvectorlucenecosine-similaritydot-productembedding-vectors UpdatedOct 30, 2023 Java Dicklesworthstone/fast_vector_similarity ...
>> import gensim >> from owlready2 import * >> model = gensim.models.Word2Vec.load(word2vec_embedding_file) >> onto = get_ontology(onto_file).load() >> classes = list(onto.classes()) >> c = classes[0] >> c.iri in model.wv.index_to_key >> iri_v = model.wv.get_vector(...
2])b=tf.get_variable(name='b',shape=[3,3,2],initializer=tf.random_normal_initializer)index_b=tf.Variable([[0,1,1],[2,2,0]])withtf.Session()assess:sess.run(tf.global_variables_initializer())print(sess.run(tf.gather_nd(a,index_a)))print(sess.run(b))print(sess...
# TODO: make it with torch instead of numpy def get_position_angle_vec(position): # this part calculate the position In brackets return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] sinusoid_table = np.array([get_position_angle_vec(pos_i...