Views
No views yet
1from sentence_transformers import SentenceTransformer
2sentences = ["This is an example sentence", "Each sentence is converted"]
3
4model = SentenceTransformer('lambdaofgod/paperswithcode_word2vec')
5embeddings = model.encode(sentences)
6print(embeddings)SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(147043, 200)
)
(1): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)