
# 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
#]

{"language": "en", "license": "mit", "tags": ["feature-extraction", "sentence-similarity", "sentence-transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch mpnet feature-extraction sentence-similarity en license:mit endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #license-mit #endpoints_compatible #region-us 

Multi QA MPNet base model for Semantic Search
=============================================


This is a sentence-transformers 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'.


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 using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.



Technical Details
-----------------


In the following some technical details how this model must be used:



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 = URL(question)
contexts\_emb = URL(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
===================


#]



TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #license-mit #endpoints_compatible #region-us 

passage: TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #license-mit #endpoints_compatible #region-us 
