| --- |
| 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-L12-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-L12-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 |
