README.md

---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
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

---

# ONNX version of cross-encoder/mcmarco-MiniLM-L6-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-MiniLM-L6-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