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--- |
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language: zh |
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datasets: CLUECorpusSmall |
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widget: |
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- text: "北京是[MASK]国的首都。" |
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--- |
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# Chinese RoBERTa Miniatures |
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## Model description |
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This is the set of 24 Chinese 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. |
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[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 24 Chinese RoBERTa models. In order to facilitate users in reproducing the results, we used a publicly available corpus and provided all training details. |
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You can download the 24 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: |
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| | H=128 | H=256 | H=512 | H=768 | |
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| -------- | :-----------------------: | :-----------------------: | :-------------------------: | :-------------------------: | |
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| **L=2** | [**2/128 (Tiny)**][2_128] | [2/256][2_256] | [2/512][2_512] | [2/768][2_768] | |
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| **L=4** | [4/128][4_128] | [**4/256 (Mini)**][4_256] | [**4/512 (Small)**][4_512] | [4/768][4_768] | |
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| **L=6** | [6/128][6_128] | [6/256][6_256] | [6/512][6_512] | [6/768][6_768] | |
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| **L=8** | [8/128][8_128] | [8/256][8_256] | [**8/512 (Medium)**][8_512] | [8/768][8_768] | |
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| **L=10** | [10/128][10_128] | [10/256][10_256] | [10/512][10_512] | [10/768][10_768] | |
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| **L=12** | [12/128][12_128] | [12/256][12_256] | [12/512][12_512] | [**12/768 (Base)**][12_768] | |
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Here are scores on the devlopment set of six Chinese tasks: |
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| Model | Score | book_review | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) | |
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| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: | |
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| RoBERTa-Tiny | 72.3 | 83.4 | 91.4 | 81.8 | 62.0 | 55.0 | 60.3 | |
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| RoBERTa-Mini | 75.9 | 85.7 | 93.7 | 86.1 | 63.9 | 58.3 | 67.4 | |
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| RoBERTa-Small | 76.9 | 87.5 | 93.4 | 86.5 | 65.1 | 59.4 | 69.7 | |
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| RoBERTa-Medium | 78.0 | 88.7 | 94.8 | 88.1 | 65.6 | 59.5 | 71.2 | |
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| RoBERTa-Base | 79.7 | 90.1 | 95.2 | 89.2 | 67.0 | 60.9 | 75.5 | |
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For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128: |
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- epochs: 3, 5, 8 |
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- batch sizes: 32, 64 |
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- learning rates: 3e-5, 1e-4, 3e-4 |
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## How to use |
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You can use this model directly with a pipeline for masked language modeling (take the case of RoBERTa-Medium): |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='uer/chinese_roberta_L-8_H-512') |
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>>> unmasker("中国的首都是[MASK]京。") |
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[ |
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{'sequence': '[CLS] 中 国 的 首 都 是 北 京 。 [SEP]', |
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'score': 0.8701988458633423, |
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'token': 1266, |
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'token_str': '北'}, |
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{'sequence': '[CLS] 中 国 的 首 都 是 南 京 。 [SEP]', |
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'score': 0.1194809079170227, |
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'token': 1298, |
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'token_str': '南'}, |
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{'sequence': '[CLS] 中 国 的 首 都 是 东 京 。 [SEP]', |
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'score': 0.0037803512532263994, |
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'token': 691, |
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'token_str': '东'}, |
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{'sequence': '[CLS] 中 国 的 首 都 是 普 京 。 [SEP]', |
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'score': 0.0017127094324678183, |
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'token': 3249, |
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'token_str': '普'}, |
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{'sequence': '[CLS] 中 国 的 首 都 是 望 京 。 [SEP]', |
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'score': 0.001687526935711503, |
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'token': 3307, |
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'token_str': '望'} |
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] |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import BertTokenizer, BertModel |
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tokenizer = BertTokenizer.from_pretrained('uer/chinese_roberta_L-8_H-512') |
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model = BertModel.from_pretrained("uer/chinese_roberta_L-8_H-512") |
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text = "用你喜欢的任何文本替换我。" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import BertTokenizer, TFBertModel |
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tokenizer = BertTokenizer.from_pretrained('uer/chinese_roberta_L-8_H-512') |
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model = TFBertModel.from_pretrained("uer/chinese_roberta_L-8_H-512") |
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text = "用你喜欢的任何文本替换我。" |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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## Training data |
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[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. We found that models pre-trained on CLUECorpusSmall outperform those pre-trained on CLUECorpus2020, although CLUECorpus2020 is much larger than CLUECorpusSmall. |
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## Training procedure |
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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. |
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Taking the case of RoBERTa-Medium |
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Stage1: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path cluecorpussmall_seq128_dataset.pt \ |
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--processes_num 32 --seq_length 128 \ |
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--dynamic_masking --data_processor mlm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--config_path models/bert/medium_config.json \ |
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--output_model_path models/cluecorpussmall_roberta_medium_seq128_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ |
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--learning_rate 1e-4 --batch_size 64 \ |
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--data_processor mlm --target mlm |
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``` |
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Stage2: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path cluecorpussmall_seq512_dataset.pt \ |
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--processes_num 32 --seq_length 512 \ |
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--dynamic_masking --data_processor mlm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_seq512_dataset.pt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--pretrained_model_path models/cluecorpussmall_roberta_medium_seq128_model.bin-1000000 \ |
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--config_path models/bert/medium_config.json \ |
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--output_model_path models/cluecorpussmall_roberta_medium_seq512_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ |
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--learning_rate 5e-5 --batch_size 16 \ |
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--data_processor mlm --target mlm |
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``` |
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Finally, we convert the pre-trained model into Huggingface's format: |
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``` |
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python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_roberta_medium_seq512_model.bin-250000 \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 8 --type mlm |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{devlin2018bert, |
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title={Bert: Pre-training of deep bidirectional transformers for language understanding}, |
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author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, |
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journal={arXiv preprint arXiv:1810.04805}, |
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year={2018} |
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} |
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@article{liu2019roberta, |
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title={Roberta: A robustly optimized bert pretraining approach}, |
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author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, |
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journal={arXiv preprint arXiv:1907.11692}, |
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year={2019} |
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} |
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@article{turc2019, |
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title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, |
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author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, |
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journal={arXiv preprint arXiv:1908.08962v2 }, |
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year={2019} |
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} |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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} |
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``` |
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[2_128]:https://huggingface.co/uer/chinese_roberta_L-2_H-128 |
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[2_256]:https://huggingface.co/uer/chinese_roberta_L-2_H-256 |
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[2_512]:https://huggingface.co/uer/chinese_roberta_L-2_H-512 |
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[2_768]:https://huggingface.co/uer/chinese_roberta_L-2_H-768 |
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[4_128]:https://huggingface.co/uer/chinese_roberta_L-4_H-128 |
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[4_256]:https://huggingface.co/uer/chinese_roberta_L-4_H-256 |
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[4_512]:https://huggingface.co/uer/chinese_roberta_L-4_H-512 |
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[4_768]:https://huggingface.co/uer/chinese_roberta_L-4_H-768 |
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[6_128]:https://huggingface.co/uer/chinese_roberta_L-6_H-128 |
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[6_256]:https://huggingface.co/uer/chinese_roberta_L-6_H-256 |
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[6_512]:https://huggingface.co/uer/chinese_roberta_L-6_H-512 |
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[6_768]:https://huggingface.co/uer/chinese_roberta_L-6_H-768 |
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[8_128]:https://huggingface.co/uer/chinese_roberta_L-8_H-128 |
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[8_256]:https://huggingface.co/uer/chinese_roberta_L-8_H-256 |
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[8_512]:https://huggingface.co/uer/chinese_roberta_L-8_H-512 |
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[8_768]:https://huggingface.co/uer/chinese_roberta_L-8_H-768 |
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[10_128]:https://huggingface.co/uer/chinese_roberta_L-10_H-128 |
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[10_256]:https://huggingface.co/uer/chinese_roberta_L-10_H-256 |
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[10_512]:https://huggingface.co/uer/chinese_roberta_L-10_H-512 |
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[10_768]:https://huggingface.co/uer/chinese_roberta_L-10_H-768 |
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[12_128]:https://huggingface.co/uer/chinese_roberta_L-12_H-128 |
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[12_256]:https://huggingface.co/uer/chinese_roberta_L-12_H-256 |
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[12_512]:https://huggingface.co/uer/chinese_roberta_L-12_H-512 |
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[12_768]:https://huggingface.co/uer/chinese_roberta_L-12_H-768 |
