
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.

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

### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder('tuhailong/cross-encoder')
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
>>> print(scores)
```
{"language": "zh", "tags": ["sbert"], "datasets": ["dialogue"]}
transformers pytorch safetensors bert text-classification sbert zh dataset:dialogue autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sbert #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 


# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.

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

### Usage

# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs. ## Model
model created by sentence-tansformers，model struct is cross-encoder ### Usage
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sbert #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 
 # Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs. ## Model
model created by sentence-tansformers，model struct is cross-encoder ### Usage
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #sbert #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.## Model
model created by sentence-tansformers，model struct is cross-encoder### Usage