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	---
	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