readme: ---
  pipeline_tag: sentence-similarity
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # sentence-transformers/msmarco-distilbert-base-tas-b
  
  This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of 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, util
  
  query = "How many people live in London?"
  docs = ["Around 9 Million people live in London", "London is known for its financial district"]
  
  #Load the model
  model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-tas-b')
  
  #Encode query and documents
  query_emb = model.encode(query)
  doc_emb = model.encode(docs)
  
  #Compute dot score between query and all document embeddings
  scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
  
  #Combine docs & scores
  doc_score_pairs = list(zip(docs, scores))
  
  #Sort by decreasing score
  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
  
  #Output passages & scores
  for doc, score in doc_score_pairs:
      print(score, doc)
  ```
  
  
  
  ## Usage (HuggingFace Transformers)
  Without [sentence-transformers](https://www.SBERT.net), 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.
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  #CLS Pooling - Take output from first token
  def cls_pooling(model_output):
      return model_output.last_hidden_state[:,0]
  
  #Encode text
  def encode(texts):
      # Tokenize sentences
      encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
  
      # Compute token embeddings
      with torch.no_grad():
          model_output = model(**encoded_input, return_dict=True)
  
      # Perform pooling
      embeddings = cls_pooling(model_output)
  
      return embeddings
  
  
  # Sentences we want sentence embeddings for
  query = "How many people live in London?"
  docs = ["Around 9 Million people live in London", "London is known for its financial district"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
  model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
  
  #Encode query and docs
  query_emb = encode(query)
  doc_emb = encode(docs)
  
  #Compute dot score between query and all document embeddings
  scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
  
  #Combine docs & scores
  doc_score_pairs = list(zip(docs, scores))
  
  #Sort by decreasing score
  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
  
  #Output passages & scores
  for doc, score in doc_score_pairs:
      print(score, doc)
  ```
  
  
  
  ## 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-tas-b)
  
  
  
  ## 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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  )
  ```
  
  ## Citing & Authors
  
  Have a look at: [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco)