README.md

---
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](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits [UER-py](https://github.com/dbiir/UER-py/) to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

[Turc et al.](https://arxiv.org/abs/1908.08962) 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](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the link [roberta-xlarge-wwm-chinese-cluecorpussmall](https://huggingface.co/uer/roberta-xlarge-wwm-chinese-cluecorpussmall):

## How to use

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

```python
>>> 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:

```python
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:

```python
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](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 

## Training procedure

Models are pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) on [Tencent Cloud](https://cloud.tencent.com/). 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](https://github.com/fxsjy/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}
```