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pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2sentences = ["This is an example sentence", "Each sentence is converted"]
3
4model = SentenceTransformer('mteb-pt/average_pt_nilc_word2vec_skip_s1000')
5embeddings = model.encode(sentences)
6print(embeddings)SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929607, 1000)
)
(1): Pooling({'word_embedding_dimension': 1000, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)1@inproceedings{hartmann2017portuguese,
2 title = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
3 author = {Hartmann, Nathan S and
4 Fonseca, Erick R and
5 Shulby, Christopher D and
6 Treviso, Marcos V and
7 Rodrigues, J{'{e}}ssica S and
8 Alu{'{\i}}sio, Sandra Maria},
9 year = {2017},
10 publisher = {SBC},
11 booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
12 url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
13}