language: zh
datasets: CLUECorpusSmall
widget:
- text: 北京是[MASK]国的首都。
Chinese Xlarge Whole Word Masking RoBERTa Model
Model description
This is an xlarge Chinese Whole Word Masking RoBERTa model 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 xlarge Chinese Whole Word Masking RoBERTa model. 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 model either from the UER-py Modelzoo page, or via HuggingFace from the link roberta-xlarge-wwm-chinese-cluecorpussmall:
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-xlarge-wwm-chinese-cluecorpussmall')
>>> unmasker("北京是[MASK]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-xlarge-wwm-chinese-cluecorpussmall')
model = BertModel.from_pretrained("uer/roberta-xlarge-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-xlarge-wwm-chinese-cluecorpussmall')
model = TFBertModel.from_pretrained("uer/roberta-xlarge-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 TencentPretrain on Tencent Cloud. We pre-train 500,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512.
jieba is used as word segmentation tool.
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
deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/bert/xlarge_config.json \
--output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 500000 --save_checkpoint_steps 50000 --report_steps 500 \
--learning_rate 2e-5 --batch_size 128 --deep_init \
--whole_word_masking --deepspeed_checkpoint_activations \
--data_processor mlm --target mlm
Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
python3 models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/ \
models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin
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
deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq512_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/bert/xlarge_config.json \
--pretrained_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin \
--output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 500 \
--learning_rate 5e-5 --batch_size 32 \
--whole_word_masking --deepspeed_checkpoint_activations \
--data_processor mlm --target mlm
Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
python3 models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/ \
models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin
Finally, we convert the pre-trained model into Huggingface's format:
python3 scripts/convert_bert_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 36 --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}