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SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2# Download from the 🤗 Hub
3model = SentenceTransformer("sentence_transformers_model_id")
4# Run inference
5sentences = [
6 'The weather is lovely today.',
7 "It's so sunny outside!",
8 'He drove to the stadium.',
9]
10embeddings = model.encode(sentences)
11print(embeddings.shape)
12# [3, 1024]
13# Get the similarity scores for the embeddings
14similarities = model.similarity(embeddings, embeddings)
15print(similarities.shape)
16# [3, 3]