README.md

metadata

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 re-ranker to do semantic similarity.

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

TLDR:

- 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

$> 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