# Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs. 
## Model
model is chinese-roberta-wwm-ext
### Usage
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> model = AutoModel.from_pretrained("tuhailong/chinese-roberta-wwm-ext")
>>> tokenizer = AutoTokenizer.from_pretrained("tuhailong/chinese-roberta-wwm-ext")
>>> sentences_str_list = ["今天天气不错的","天气不错的"]
>>> inputs = tokenizer(sentences_str_list,return_tensors="pt", padding='max_length', truncation=True, max_length=32)
>>> outputs = model(**inputs)
```
{"language": "zh", "tags": ["chinese-roberta-wwm-ext"], "datasets": ["dialogue"]}
transformers pytorch bert fill-mask chinese-roberta-wwm-ext zh dataset:dialogue autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #chinese-roberta-wwm-ext #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 

# Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs. 
## Model
model is chinese-roberta-wwm-ext
### Usage

# Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs. ## Model
model is chinese-roberta-wwm-ext ### Usage
TAGS
#transformers #pytorch #bert #fill-mask #chinese-roberta-wwm-ext #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 
 # Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs. ## Model
model is chinese-roberta-wwm-ext ### Usage
passage: TAGS
#transformers #pytorch #bert #fill-mask #chinese-roberta-wwm-ext #zh #dataset-dialogue #autotrain_compatible #endpoints_compatible #region-us 
# Data
unsupervise train data is E-commerce dialogue, about 20w sentence pairs.## Model
model is chinese-roberta-wwm-ext### Usage