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Cataloged from sentence-transformers/msmarco-MiniLM-L12-v3
All models by sentence-transformers →This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
pip install -U sentence-transformersfrom sentence_transformers import SentenceTransformersentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L12-v3')embeddings = model.encode(sentences)print(embeddings)from transformers import AutoTokenizer, AutoModelimport torch
#Mean Pooling - Take attention mask into account for correct averagingdef mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings forsentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hubtokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-MiniLM-L12-v3')model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L12-v3')
# Tokenize sentencesencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddingswith torch.no_grad(): model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")print(sentence_embeddings)SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}))@inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084",}