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, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

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 in reproducing the results, we used a 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 Modelzoo 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	book_review	chnsenticorp	lcqmc	tnews(CLUE)	iflytek(CLUE)	ocnli(CLUE)
RoBERTa-Tiny-WWM	72.2	83.7	91.8	81.8	62.1	55.4	58.6
RoBERTa-Mini-WWM	76.3	86.4	93.0	86.8	64.4	58.7	68.8
RoBERTa-Small-WWM	77.6	88.1	93.8	87.2	65.2	59.6	71.4
RoBERTa-Medium-WWM	78.6	89.3	94.4	88.8	66.0	59.9	73.2
RoBERTa-Base-WWM	80.2	90.6	95.8	89.4	67.5	61.8	76.2
RoBERTa-Large-WWM	81.1	91.1	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_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-250000 \
                                                        --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}
}

@article{zhao2023tencentpretrain,
  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
  journal={ACL 2023},
  pages={217},
  year={2023}