
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.

## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder

### Usage
```python
>>> from sentence_transformers import SentenceTransformer, util
>>> model = SentenceTransformer("tuhailong/bi_encoder_roberta-wwm-ext", device="cuda:1") 
>>> model.max_seq_length=32
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> embeddings1 = model.encode([sentences[0]], convert_to_tensor=True)
>>> embeddings2 = model.encode([sentences[1]], convert_to_tensor=True)
>>> scores = util.cos_sim(embeddings1, embeddings2).cpu().numpy()
>>> print(scores)
```

#### Code
train code from https://github.com/TTurn/bi-encoder

##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-config.json", "2_Dense-config.json" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/config.json", "2_Dense/config.json" and "2_Dense/pytorch_model.bin".
{"language": "zh", "tags": ["sbert"], "datasets": ["dialogue"]}
transformers pytorch bert feature-extraction sbert zh dataset:dialogue endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #sbert #zh #dataset-dialogue #endpoints_compatible #region-us 


# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.

## Model
model created by sentence-tansformers，model struct is bi-encoder

### Usage


#### Code
train code from URL

##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-URL", "2_Dense-URL" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/URL", "2_Dense/URL" and "2_Dense/pytorch_model.bin".
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model
model created by sentence-tansformers，model struct is bi-encoder ### Usage #### Code
train code from URL ##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-URL", "2_Dense-URL" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/URL", "2_Dense/URL" and "2_Dense/pytorch_model.bin".
TAGS
#transformers #pytorch #bert #feature-extraction #sbert #zh #dataset-dialogue #endpoints_compatible #region-us 
 # Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model
model created by sentence-tansformers，model struct is bi-encoder ### Usage #### Code
train code from URL ##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-URL", "2_Dense-URL" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/URL", "2_Dense/URL" and "2_Dense/pytorch_model.bin".
passage: TAGS
#transformers #pytorch #bert #feature-extraction #sbert #zh #dataset-dialogue #endpoints_compatible #region-us 
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.## Model
model created by sentence-tansformers，model struct is bi-encoder### Usage#### Code
train code from URL##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-URL", "2_Dense-URL" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/URL", "2_Dense/URL" and "2_Dense/pytorch_model.bin".