metadata

language: Chinese
datasets: CLUECorpus

Chinese RoBERTa Miniatures

Model description

This is the set of 24 Chinese RoBERTa models pre-trained by UER-py.

You can download the 24 Chinese RoBERTa miniatures either from the UER-py Github page, or via HuggingFace from the links below:

	H=128	H=256	H=512	H=768
L=2	2/128 (Tiny)	[2/256]	[2/512]	[2/768]
L=4	[4/128]	[4/256 (Mini)]	[4/512 (Small)]	[4/768]
L=6	[6/128]	[6/256]	[6/512]	[6/768]
L=8	[8/128]	[8/256]	[8/512 (Medium)]	[8/768]
L=10	[10/128]	[10/256]	[10/512]	[10/768]
L=12	[12/128]	[12/256]	[12/512]	[12/768 (Base)]

How to use

You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='hhou435/chinese_roberta_L-2_H-128')
>>> unmasker("中国的首都是[MASK]京。")
[
    {'sequence': '[CLS] 中 国 的 首 都 是 北 京 。 [SEP]', 
     'score': 0.9427323937416077, 
     'token': 1266, 
     'token_str': '北'}, 
    {'sequence': '[CLS] 中 国 的 首 都 是 南 京 。 [SEP]',
     'score': 0.029202355071902275, 
     'token': 1298,
     'token_str': '南'}, 
    {'sequence': '[CLS] 中 国 的 首 都 是 东 京 。 [SEP]',
     'score': 0.00977553054690361,
     'token': 691, 
     'token_str': '东'}, 
    {'sequence': '[CLS] 中 国 的 首 都 是 葡 京 。 [SEP]',
     'score': 0.00489805219694972,
     'token': 5868, 
     'token_str': '葡'},
    {'sequence': '[CLS] 中 国 的 首 都 是 新 京 。 [SEP]',
     'score': 0.0027360401581972837, 
     'token': 3173, 
     'token_str': '新'}
]

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('hhou435/chinese_roberta_L-2_H-128')
model = BertModel.from_pretrained("hhou435/chinese_roberta_L-2_H-128")
text = "用你喜欢的任何文本替换我。"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

and in TensorFlow:

from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('hhou435/chinese_roberta_L-2_H-128')
model = TFBertModel.from_pretrained("hhou435/chinese_roberta_L-2_H-128")
text = "用你喜欢的任何文本替换我。"
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

Training data

CLUECorpus2020 and CLUECorpusSmall are used as training corpus.

Training procedure

Training details can be found in UER-py.

BibTeX entry and citation info

@article{zhao2019uer,
  title={UER: An Open-Source Toolkit for Pre-training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP-IJCNLP 2019},
  pages={241},
  year={2019}
}