--- 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](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits 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 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](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: | | Link | | -------- | :-----------------------: | | **Tiny** | [**2/128 (Tiny)**][2_128] | | **Mini** | [**4/256 (Mini)**][4_256] | | **Small** | [**4/512 (Small)**][4_512] | | **Medium** | [**8/512 (Medium)**][8_512] | | **Base** | [**12/768 (Base)**][12_768] | | **Large** | [**24/1024 (Large)**][24_1024] | 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: ```python >>> 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: ```python 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: ```python 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](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. ## Training procedure Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). 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](https://github.com/fxsjy/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} ``` [2_128]:https://huggingface.co/uer/roberta-tiny-wwm-chinese-cluecorpussmall [4_256]:https://huggingface.co/uer/roberta-mini-wwm-chinese-cluecorpussmall [4_512]:https://huggingface.co/uer/roberta-small-wwm-chinese-cluecorpussmall [8_512]:https://huggingface.co/uer/roberta-medium-wwm-chinese-cluecorpussmall [12_768]:https://huggingface.co/uer/roberta-base-wwm-chinese-cluecorpussmall [24_1024]:https://huggingface.co/uer/roberta-large-wwm-chinese-cluecorpussmall