readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  ---
  
  # multi-qa-MiniLM-L6-dot-v1
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
  
  
  ## 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/multi-qa-MiniLM-L6-dot-v1')
  
  #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 correct 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/multi-qa-MiniLM-L6-dot-v1")
  model = AutoModel.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-dot-v1")
  
  #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)
  ```
  
  ## Technical Details
  
  In the following some technical details how this model must be used:
  
  | Setting | Value |
  | --- | :---: |
  | Dimensions | 384 |
  | Produces normalized embeddings | No |
  | Pooling-Method | CLS pooling |
  | Suitable score functions | dot-product (e.g. `util.dot_score`) |
  
  ----
  
  
  ## Background
  
  The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
  contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
  
  We developped this model during the 
  [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
  organized by Hugging Face. We developped this model as part of the project:
  [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
  
  ## Intended uses
  
  Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
  
  Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text. 
  
  
  
  ## Training procedure
  
  The full tra