README.md

---
language: zh
datasets: CLUECorpusSmall
widget: 
- text: "最近一趟去北京的[MASK]几点发车"


---


# Chinese word-based RoBERTa Miniatures

## Model description

This is the set of 5 Chinese word-based 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.

Most Chinese pre-trained weights are based on Chinese character. Compared with character-based models, word-based models are faster (because of shorter sequence length) and have better performance according to our experimental results. To this end, we released the 5 Chinese word-based RoBERTa models of different sizes. 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 5 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           |
| -------- | :-----------------------: |
| **word-based RoBERTa-Tiny** | [**L=2/H=128 (Tiny)**][2_128] |
| **word-based RoBERTa-Mini** | [**L=4/H=256 (Mini)**][4_256] |
| **word-based RoBERTa-Small** | [**L=4/H=512 (Small)**][4_512] |
| **word-based RoBERTa-Medium** | [**L=8/H=512 (Medium)**][8_512] |
| **word-based RoBERTa-Base** | [**L=12/H=768 (Base)**][12_768] |

Compared with [char-based models](https://huggingface.co/uer/chinese_roberta_L-2_H-128), word-based models achieve better results in most cases. 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(char)       | 72.3            |  83.4      |     91.4         | 81.8      |    62.0         |     55.0          |    60.3         |
| **RoBERTa-Tiny(word)**   | **74.4(+2.1)**  |  **86.7**  |     **93.2**     | **82.0**  |    **66.4**     |     **58.2**      |    **59.6**     |
| RoBERTa-Mini(char)       | 75.9            |  85.7      |     93.7         | 86.1      |    63.9         |     58.3          |    67.4         |
| **RoBERTa-Mini(word)**   | **76.9(+1.0)**  |  **88.5**  |     **94.1**     | **85.4**  |    **66.9**     |     **59.2**      |    **67.3**     |
| RoBERTa-Small(char)      | 76.9            |  87.5      |     93.4         | 86.5      |    65.1         |     59.4          |    69.7         |
| **RoBERTa-Small(word)**  | **78.4(+1.5)**  |  **89.7**  |     **94.7**     | **87.4**  |    **67.6**     |     **60.9**      |    **69.8**     |
| RoBERTa-Medium(char)     | 78.0            |  88.7      |     94.8         | 88.1      |    65.6         |     59.5          |    71.2         |
| **RoBERTa-Medium(word)** | **79.1(+1.1)**  |  **90.0**  |     **95.1**     | **88.0**  |    **67.8**     |     **60.6**      |    **73.0**     |
| RoBERTa-Base(char)       | 79.7            |  90.1      |     95.2         | 89.2      |    67.0         |     60.9          |    75.5         |
| **RoBERTa-Base(word)**   | **80.4(+0.7)**  |  **91.1**  |     **95.7**     | **89.4**  |    **68.0**     |     **61.5**      |    **76.8**     |

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 (take the case of word-based RoBERTa-Medium):

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='uer/roberta-medium-word-chinese-cluecorpussmall')
>>> unmasker("[MASK]的首都是北京。")
[
    {'sequence': '中国 的首都是北京。',
     'score': 0.21525809168815613, 
     'token': 2873, 
     'token_str': '中国'}, 
    {'sequence': '北京 的首都是北京。', 
     'score': 0.15194718539714813, 
     'token': 9502, 
     'token_str': '北京'}, 
    {'sequence': '我们 的首都是北京。', 
     'score': 0.08854265511035919, 
     'token': 4215, 
     'token_str': '我们'},
    {'sequence': '美国 的首都是北京。', 
     'score': 0.06808705627918243, 
     'token': 7810, 
     'token_str': '美国'}, 
    {'sequence': '日本 的首都是北京。', 
     'score': 0.06071401759982109, 
     'token': 7788, 
     'token_str': '日本'}
]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import AlbertTokenizer, BertModel
tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-medium-word-chinese-cluecorpussmall')
model = BertModel.from_pretrained("uer/roberta-medium-word-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 AlbertTokenizer, TFBertModel
tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-medium-word-chinese-cluecorpussmall')
model = TFBertModel.from_pretrained("uer/roberta-medium-word-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)
```

Since BertTokenizer does not support sentencepiece, AlbertTokenizer is used here.

## Training data

[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. Google's [sentencepiece](https://github.com/google/sentencepiece) is used for word segmentation. The sentencepiece model is trained on CLUECorpusSmall corpus:

```
>>> import sentencepiece as spm
>>> spm.SentencePieceTrainer.train(input='cluecorpussmall.txt',
             model_prefix='cluecorpussmall_spm',
             vocab_size=100000,
             max_sentence_length=1024,
             max_sentencepiece_length=6,
             user_defined_symbols=['[MASK]','[unused1]','[unused2]',
                '[unused3]','[unused4]','[unused5]','[unused6]',
                '[unused7]','[unused8]','[unused9]','[unused10]'],
             pad_id=0,
             pad_piece='[PAD]',
             unk_id=1,
             unk_piece='[UNK]',
             bos_id=2,
             bos_piece='[CLS]',
             eos_id=3,
             eos_piece='[SEP]',
             train_extremely_large_corpus=True
            )
```

## 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.

Taking the case of word-based RoBERTa-Medium

Stage1:

```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
                      --spm_model_path models/cluecorpussmall_spm.model \
                      --dataset_path cluecorpussmall_word_seq128_dataset.pt \
                      --processes_num 32 --seq_length 128 \
                      --dynamic_masking --data_processor mlm
```

```
python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \
                    --spm_model_path models/cluecorpussmall_spm.model \
                    --config_path models/bert/medium_config.json \
                    --output_model_path models/cluecorpussmall_word_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 \
                    --data_processor mlm --target mlm
```

Stage2:

```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
                      --spm_model_path models/cluecorpussmall_spm.model \
                      --dataset_path cluecorpussmall_word_seq512_dataset.pt \
                      --processes_num 32 --seq_length 512 \
                      --dynamic_masking --data_processor mlm
```

```
python3 pretrain.py --dataset_path cluecorpussmall_word_seq512_dataset.pt \
                    --spm_model_path models/cluecorpussmall_spm.model \
                    --pretrained_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-1000000 \
                    --config_path models/bert/medium_config.json \
                    --output_model_path models/cluecorpussmall_word_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 \
                    --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_word_roberta_medium_seq512_model.bin-250000 \
                                                        --output_model_path pytorch_model.bin \
                                                        --layers_num 8 --type mlm
```

### BibTeX entry and citation info

```
@article{devlin2018bert,
  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@article{turc2019,
  title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
  author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1908.08962v2 },
  year={2019}
}

@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-word-chinese-cluecorpussmall
[4_256]:https://huggingface.co/uer/roberta-mini-word-chinese-cluecorpussmall
[4_512]:https://huggingface.co/uer/roberta-small-word-chinese-cluecorpussmall
[8_512]:https://huggingface.co/uer/roberta-medium-word-chinese-cluecorpussmall
[12_768]:https://huggingface.co/uer/roberta-base-word-chinese-cluecorpussmall