
# ONNX version of sentence-transormers/all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the [Metarank](https://github.com/metarank/metarank) re-ranker
to do semantic similarity.

Check out the [main Metarank docs](https://docs.metarank.ai) on how to configure it.

TLDR:
```yaml
- type: field_match
  name: title_query_match
  rankingField: ranking.query
  itemField: item.title
  distance: cos 
  method:
    type: bert 
    model: metarank/all-mpnet-base-v2
```

## Building the model

```shell
$> pip install -r requirements.txt
$> python convert.py

============= Diagnostic Run torch.onnx.export version 2.0.0+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================

```

## License

Apache 2.0
{"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search_qa", "eli5", "snli", "multi_nli", "wikihow", "natural_questions", "trivia_qa", "embedding-data/sentence-compression", "embedding-data/flickr30k-captions", "embedding-data/altlex", "embedding-data/simple-wiki", "embedding-data/QQP", "embedding-data/SPECTER", "embedding-data/PAQ_pairs", "embedding-data/WikiAnswers"]}
sentence-transformers onnx feature-extraction sentence-similarity en dataset:s2orc dataset:flax-sentence-embeddings/stackexchange_xml dataset:ms_marco dataset:gooaq dataset:yahoo_answers_topics dataset:code_search_net dataset:search_qa dataset:eli5 dataset:snli dataset:multi_nli dataset:wikihow dataset:natural_questions dataset:trivia_qa dataset:embedding-data/sentence-compression dataset:embedding-data/flickr30k-captions dataset:embedding-data/altlex dataset:embedding-data/simple-wiki dataset:embedding-data/QQP dataset:embedding-data/SPECTER dataset:embedding-data/PAQ_pairs dataset:embedding-data/WikiAnswers license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #onnx #feature-extraction #sentence-similarity #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #license-apache-2.0 #endpoints_compatible #region-us 


# ONNX version of sentence-transormers/all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the Metarank re-ranker
to do semantic similarity.

Check out the main Metarank docs on how to configure it.

TLDR:


## Building the model



## License

Apache 2.0
# ONNX version of sentence-transormers/all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the Metarank re-ranker
to do semantic similarity.

Check out the main Metarank docs on how to configure it.

TLDR: ## Building the model ## License

Apache 2.0
TAGS
#sentence-transformers #onnx #feature-extraction #sentence-similarity #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #license-apache-2.0 #endpoints_compatible #region-us 
 # ONNX version of sentence-transormers/all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the Metarank re-ranker
to do semantic similarity.

Check out the main Metarank docs on how to configure it.

TLDR: ## Building the model ## License

Apache 2.0
TAGS
#sentence-transformers #onnx #feature-extraction #sentence-similarity #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #license-apache-2.0 #endpoints_compatible #region-us 
# ONNX version of sentence-transormers/all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. The ONNX version of this model is made for the Metarank re-ranker
to do semantic similarity.

Check out the main Metarank docs on how to configure it.

TLDR:## Building the model## License

Apache 2.0