readme: ---
  language: en
  license: mit
  pipeline_tag: sentence-similarity
  tags:
  - feature-extraction
  - sentence-similarity
  - sentence-transformers
  ---
  
  # Multi QA MPNet base model for Semantic Search
  
  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.
  
  This model uses [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base).
  
  ## Training Data
  We use the concatenation from multiple datasets to fine-tune this model. In total we have about 215M (question, answer) pairs. The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
  
  
  | Dataset                    | Number of training tuples  |
  |--------------------------------------------------------|:--------------------------:|
  | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers |  77,427,422 |
  | [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
  | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges  | 25,316,456 |
  | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges  |  21,396,559 |
  | [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine |  17,579,773 |
  | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet  | 3,012,496 |
  | [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839 
  | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
  | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
  | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
  | [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
  | [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
  | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
  | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
  | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
  | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset |  87,599 |
  | [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
  | **Total** | **214,988,242** |
  
  ## Technical Details
  In the following some technical details how this model must be used:
  
  | Setting | Value |
  | --- | :---: |
  | Dimensions | 768 |
  | Produces normalized embeddings | Yes |
  | Pooling-Method | Mean pooling |
  | Suitable score functions | dot-product, cosine-similarity, or euclidean distance |
  
  Note: This model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
  
  ## Usage and Performance
  The trained model can be used like this:
  ```python
  from sentence_transformers import SentenceTransformer, util
  
  question = "That is a happy person"
  contexts = [
      "That is a happy dog",
      "That is a very happy person",
      "Today is a sunny day"
  ]
  
  # Load the model
  model = SentenceTransformer('navteca//multi-qa-mpnet-base-cos-v1')
  
  # Encode question and contexts
  question_emb = model.encode(question)
  contexts_emb = model.encode(contexts)
  
  # Compute dot score between question and all contexts embeddings
  result = util.dot_score(question_emb, contexts_emb)[0].cpu().tolist()
  
  print(result)
  
  #[
  #  0.60806852579116820,
  #  0.94949364662170410,
  #  0.29836517572402954
  #]
  