language: zh
datasets: CLUECorpusSmall
widget:
- text: 北京是[MASK]国的首都。
Chinese Whole Word Masking RoBERTa Miniatures
Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py.
Turc et al. have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 6 Chinese Whole Word Masking RoBERTa models. In order to facilitate users to reproduce the results, we used the publicly available corpus and word segmentation tool, and provided all training details.
You can download the 6 Chinese RoBERTa miniatures either from the UER-py Github page, or via HuggingFace from the links below:
| Link | |
|---|---|
| Tiny | 2/128 (Tiny) |
| Mini | 4/256 (Mini) |
| Small | 4/512 (Small) |
| Medium | 8/512 (Medium) |
| Base | 12/768 (Base) |
| Large | 24/1024 (Large) |
Here are scores on the devlopment set of six Chinese tasks:
| Model | Score | douban | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) |
|---|---|---|---|---|---|---|---|
| RoBERTa-Tiny-WWM | 72.1 | 82.8 | 91.8 | 81.8 | 62.1 | 55.4 | 58.6 |
| RoBERTa-Mini-WWM | 76.1 | 84.9 | 93.0 | 86.8 | 64.4 | 58.7 | 68.8 |
| RoBERTa-Small-WWM | 77.3 | 86.8 | 93.8 | 87.2 | 65.2 | 59.6 | 71.4 |
| RoBERTa-Medium-WWM | 78.4 | 88.2 | 94.4 | 88.8 | 66.0 | 59.9 | 73.2 |
| RoBERTa-Base-WWM | 80.1 | 90.0 | 95.8 | 89.4 | 67.5 | 61.8 | 76.2 |
| RoBERTa-Large-WWM | 81.0 | 90.4 | 95.8 | 90.0 | 68.5 | 62.1 | 79.1 |
For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128:
- epochs: 3, 5, 8
- batch sizes: 32, 64
- learning rates: 3e-5, 1e-4, 3e-4
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='uer/roberta-tiny-wwm-chinese-cluecorpussmall')
>>> unmasker("北京是[MASK]国的首都。")
[
{'score': 0.294228732585907,
'token': 704,
'token_str': '中',
'sequence': '北 京 是 中 国 的 首 都 。'},
{'score': 0.19691626727581024,
'token': 1266,
'token_str': '北',
'sequence': '北 京 是 北 国 的 首 都 。'},
{'score': 0.1070084273815155,
'token': 7506,
'token_str': '韩',
'sequence': '北 京 是 韩 国 的 首 都 。'},
{'score': 0.031527262181043625,
'token': 2769,
'token_str': '我',
'sequence': '北 京 是 我 国 的 首 都 。'},
{'score': 0.023054633289575577,
'token': 1298,
'token_str': '南',
'sequence': '北 京 是 南 国 的 首 都 。'}
]
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('uer/roberta-base-wwm-chinese-cluecorpussmall')
model = BertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
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('uer/roberta-base-wwm-chinese-cluecorpussmall')
model = TFBertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
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
CLUECorpusSmall is used as training data.
Training procedure
Models are pre-trained by UER-py on Tencent Cloud. We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes.
jieba is used as word segmentation tool.
Taking the case of Whole Word Masking RoBERTa-Medium
Stage1:
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_seq128_dataset.pt \
--processes_num 32 --seq_length 128 \
--dynamic_masking --data_processor mlm
python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/bert/medium_config.json \
--output_model_path models/cluecorpussmall_wwm_roberta_medium_seq128_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
--learning_rate 1e-4 --batch_size 64 \
--whole_word_masking \
--data_processor mlm --target mlm
Stage2:
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_seq512_dataset.pt \
--processes_num 32 --seq_length 512 \
--dynamic_masking --data_processor mlm
python3 pretrain.py --dataset_path cluecorpussmall_seq512_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--pretrained_model_path models/cluecorpussmall_wwm_roberta_medium_seq128_model.bin-1000000 \
--config_path models/bert/medium_config.json \
--output_model_path models/cluecorpussmall_wwm_roberta_medium_seq512_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
--learning_rate 5e-5 --batch_size 16 \
--whole_word_masking \
--data_processor mlm --target mlm
Finally, we convert the pre-trained model into Huggingface's format:
python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_wwm_roberta_medium_seq512_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 8 --type mlm
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}
}