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Cataloged from sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
All models by sentence-transformers →This is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 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.
Have a look at: DPR Model
pip install -U sentence-transformersfrom sentence_transformers import SentenceTransformersentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base')embeddings = model.encode(sentences)print(embeddings)from transformers import AutoTokenizer, AutoModelimport torch
def cls_pooling(model_output, attention_mask): return model_output[0][:,0]
# 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/facebook-dpr-ctx_encoder-single-nq-base')model = AutoModel.from_pretrained('sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base')
# 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 = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")print(sentence_embeddings)SentenceTransformer( (0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}))