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