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
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