|
--- |
|
language: zh |
|
datasets: CLUECorpusSmall |
|
widget: |
|
- text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。" |
|
|
|
|
|
--- |
|
|
|
# Chinese BART |
|
|
|
## Model description |
|
|
|
This model is 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. |
|
|
|
You can download the set of Chinese BART models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: |
|
|
|
| | Link | |
|
| ----------------- | :----------------------------: | |
|
| **BART-Base** | [**L=6/H=768 (Base)**][base] | |
|
| **BART-Large** | [**L=12/H=1024 (Large)**][large] | |
|
|
|
## How to use |
|
|
|
You can use this model directly with a pipeline for text2text generation (take the case of BART-Base): |
|
|
|
```python |
|
>>> from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline |
|
>>> tokenizer = BertTokenizer.from_pretrained("uer/bart-base-chinese-cluecorpussmall") |
|
>>> model = BartForConditionalGeneration.from_pretrained("uer/bart-base-chinese-cluecorpussmall") |
|
>>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) |
|
>>> text2text_generator("中国的首都是[MASK]京", max_length=50, do_sample=False) |
|
[{'generated_text': '中 国 的 首 都 是 北 京'}] |
|
``` |
|
|
|
## Training data |
|
|
|
[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. |
|
|
|
## Training procedure |
|
|
|
The model is 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 512. |
|
Taking the case of BART-Base |
|
|
|
``` |
|
python3 preprocess.py --corpus_path corpora/cluecorpussmall_bert.txt \ |
|
--vocab_path models/google_zh_vocab.txt \ |
|
--dataset_path cluecorpussmall_bart_seq512_dataset.pt \ |
|
--processes_num 32 --seq_length 512 \ |
|
--data_processor bart |
|
``` |
|
|
|
``` |
|
python3 pretrain.py --dataset_path cluecorpussmall_bart_seq512_dataset.pt \ |
|
--vocab_path models/google_zh_vocab.txt \ |
|
--config_path models/bart/base_config.json \ |
|
--output_model_path models/cluecorpussmall_bart_base_seq512_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 5e-5 --batch_size 8 \ |
|
--span_masking --span_max_length 3 |
|
``` |
|
|
|
Finally, we convert the pre-trained model into Huggingface's format: |
|
|
|
``` |
|
python3 scripts/convert_bart_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_bart_base_seq512_model.bin-1000000 \ |
|
--output_model_path pytorch_model.bin \ |
|
--layers_num 6 |
|
``` |
|
|
|
|
|
### BibTeX entry and citation info |
|
|
|
``` |
|
@article{lewis2019bart, |
|
title={Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension}, |
|
author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke}, |
|
journal={arXiv preprint arXiv:1910.13461}, |
|
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} |
|
``` |
|
|
|
[base]:https://huggingface.co/uer/bart-base-chinese-cluecorpussmall |
|
[large]:https://huggingface.co/uer/bart-large-chinese-cluecorpussmall |
