|
--- |
|
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 |
