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roberta-base-word-chinese-cluecorpussmall

Fill-Mask Transformers PyTorch TensorFlow JAX
Chinese bert Inference Endpoints
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  1. README.md +23 -1
  2. pytorch_model.bin +1 -1
  3. tf_model.h5 +1 -1
README.md CHANGED
@@ -34,7 +34,29 @@ You can use this model directly with a pipeline for masked language modeling:
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  ```python
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  >>> from transformers import pipeline
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  >>> unmasker = pipeline('fill-mask', model='uer/roberta-base-word-chinese-cluecorpussmall')
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- >>> unmasker("中国的首都是[MASK]。")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  BertTokenizer does not support sentencepiece, so we use AlbertTokenizer here.
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  ```python
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  >>> from transformers import pipeline
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  >>> unmasker = pipeline('fill-mask', model='uer/roberta-base-word-chinese-cluecorpussmall')
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+ >>> unmasker("[MASK]的首都是北京。")
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+ [
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+ {'sequence': '中国 的首都是北京。',
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+ 'score': 0.21525809168815613,
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+ 'token': 2873,
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+ 'token_str': '中国'},
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+ {'sequence': '北京 的首都是北京。',
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+ 'score': 0.15194718539714813,
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+ 'token': 9502,
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+ 'token_str': '北京'},
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+ {'sequence': '我们 的首都是北京。',
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+ 'score': 0.08854265511035919,
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+ 'token': 4215,
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+ 'token_str': '我们'},
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+ {'sequence': '美国 的首都是北京。',
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+ 'score': 0.06808705627918243,
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+ 'token': 7810,
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+ 'token_str': '美国'},
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+ {'sequence': '日本 的首都是北京。',
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+ 'score': 0.06071401759982109,
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+ 'token': 7788,
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+ 'token_str': '日本'}
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+ ]
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  ```
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  BertTokenizer does not support sentencepiece, so we use AlbertTokenizer here.
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