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--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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language: en |
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license: apache-2.0 |
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datasets: |
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- s2orc |
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- flax-sentence-embeddings/stackexchange_xml |
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- ms_marco |
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- gooaq |
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- yahoo_answers_topics |
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- code_search_net |
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- search_qa |
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- eli5 |
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- snli |
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- multi_nli |
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- wikihow |
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- natural_questions |
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- trivia_qa |
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- embedding-data/sentence-compression |
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- embedding-data/flickr30k-captions |
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- embedding-data/altlex |
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- embedding-data/simple-wiki |
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- embedding-data/QQP |
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- embedding-data/SPECTER |
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- embedding-data/PAQ_pairs |
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- embedding-data/WikiAnswers |
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--- |
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# ONNX version of intfloat/multilingual-e5-small |
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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 |
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to do semantic similarity. |
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Check out the [main Metarank docs](https://docs.metarank.ai) on how to configure it. |
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TLDR: |
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```yaml |
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- type: field_match |
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name: title_query_match |
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rankingField: ranking.query |
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itemField: item.title |
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distance: cos |
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method: |
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type: bi-encoder |
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model: metarank/multilingual-e5-small |
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``` |
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## License |
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Apache 2.0 |
