sentence-transformers onnx xlm-roberta 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 autotrain_compatible endpoints_compatible text-embeddings-inference region:us
---
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 intfloat/multilingual-e5-large

This is a sentence-transformers model: It maps sentences & paragraphs to a N dimensional dense vector space and can be used for tasks like clustering or semantic search. 

The model conversion was made with [onnx-convert](https://github.com/nixiesearch/onnx-convert) tool with the following parameters:

```shell
python convert.sh --model_id intfloat/multilingual-e5-large --quantize QInt8 --optimize 0
```

There are two versions of model available:

* `model.onnx` - Float32 version, with optimize=0
* `model_opt0_QInt8.onnx` - QInt8 quantized version, with optimize=0

Compared to the base/small versions of the model, this one is not optimized due to a bug in ONNX runtime: https://github.com/microsoft/onnxruntime/issues/15563

## License

Apache 2.0