pytorch bert arxiv:2104.08821 transformers
.gitattributes README.md config.json eval_results.txt optimizer.pt pytorch_model.bin scheduler.pt special_tokens_map.json tokenizer_config.json train_results.txt trainer_state.json training_args.bin vocab.txt
This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821)

- Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates.   
- Positive sentences are generated by paraphrasing and back-translate.   
- Negative sentences are randomly selected in general domain.  

### Extract sentence representation
```
from transformers import AutoTokenizer, AutoModel  
tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_sup_simcse")
model = AutoModel.from_pretrained("demdecuong/stroke_sup_simcse")

text = "What are disease related to red stroke's causes?"
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)[1]
```
### Build up embedding for database

```
database = [
    'What is the daily checklist for stroke returning home',
    'What are some tips for stroke adapt new life',
    'What  should I consider when using nursing-home care'
]

embedding = torch.zeros((len(database),768))

for i in range(len(database)):
  inputs = tokenizer(database[i], return_tensors="pt")
  outputs = model(**inputs)[1]
  embedding[i] = outputs

print(embedding.shape)
```

### Result
On our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation. 
- SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain
- SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain


| Model  | Top-1 Accuracy |
| ------------- | ------------- |
| SimCSE supervised (author)  | 75.83  |
| SimCSE unsupervised (ours)  | 76.66  |
| SimCSE supervised + 100k (ours)  | 73.33  |
| SimCSE supervised + 42k (ours)  | 75.83  |