readme: ---
  pipeline_tag: sentence-similarity
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # sentence-transformers/msmarco-distilbert-base-dot-prod-v3
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  
  
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["This is an example sentence", "Each sentence is converted"]
  
  model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-dot-prod-v3')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  
  ## Evaluation Results
  
  
  
  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-dot-prod-v3)
  
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
    (2): Dense({'in_features': 768, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  )
  ```
  
  ## Citing & Authors
  
  This model was trained by [sentence-transformers](https://www.sbert.net/). 
          
  If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
  ```bibtex 
  @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",
  }
  ```