readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  datasets:
  - flax-sentence-embeddings/stackexchange_xml
  - ms_marco
  - gooaq
  - yahoo_answers_topics
  - search_qa
  - eli5
  - natural_questions
  - trivia_qa
  - embedding-data/QQP
  - embedding-data/PAQ_pairs
  - embedding-data/Amazon-QA
  - embedding-data/WikiAnswers
  
  ---
  
  # multi-qa-mpnet-base-dot-v1
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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-mpnet-base-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-mpnet-base-dot-v1")
  model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-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 | 768 |
  | 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 pas