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pipeline_tag: sentence-similarity
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
---
This is a ONNX export of [`sentence-transformers/all-distilroberta-v1`](https://huggingface.co/sentence-transformers/all-distilroberta-v1).
The export was done using [HF Optimum](https://huggingface.co/docs/optimum/index):
```python
from optimum.exporters.onnx import main_export
main_export('sentence-transformers/all-distilroberta-v1', "./output", cache_dir='./cache', optimize='O1')
```
Please note, this ONNX model does not contain the mean pooling layer, it needs to be done in code afterwards or the embeddings won't work.
Code like this:
```python
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
```
See the example code from the original model in the "Usage (HuggingFace Transformers)" section.
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