Create README.md

README.md

@@ -0,0 +1,146 @@
+---
+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}
+```