| 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 sentence-transormers/all-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 |