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	---
	language: zh
	datasets: CLUECorpusSmall
	widget:
	- text: "中国的首都是[MASK]京"


	---


	# Chinese ALBERT

	## Model description

	This is the set of Chinese ALBERT 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.

	You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:

	\| \| Link \|
	\| -------- \| :-----------------------: \|
	\| ALBERT-Base \| [L=12/H=768 (Base)][base] \|
	\| ALBERT-Large \| [L=24/H=1024 (Large)][large] \|

	## How to use

	You can use the model directly with a pipeline for text generation:

	```python
	>>> from transformers import BertTokenizer, AlbertForMaskedLM, FillMaskPipeline
	>>> tokenizer = BertTokenizer.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
	>>> model = AlbertForMaskedLM.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
	>>> unmasker = FillMaskPipeline(model, tokenizer)
	>>> unmasker("中国的首都是[MASK]京。")
	[
	{'sequence': '中国的首都是北京。',
	'score': 0.8528032898902893,
	'token': 1266,
	'token_str': '北'},
	{'sequence': '中国的首都是南京。',
	'score': 0.07667620480060577,
	'token': 1298,
	'token_str': '南'},
	{'sequence': '中国的首都是东京。',
	'score': 0.020440367981791496,
	'token': 691,
	'token_str': '东'},
	{'sequence': '中国的首都是维京。',
	'score': 0.010197942145168781,
	'token': 5335,
	'token_str': '维'},
	{'sequence': '中国的首都是汴京。',
	'score': 0.0075391442514956,
	'token': 3745,
	'token_str': '汴'}
	]

	```

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

	```python
	from transformers import BertTokenizer, AlbertModel
	tokenizer = BertTokenizer.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
	model = AlbertModel.from_pretrained("uer/albert-base-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, TFAlbertModel
	tokenizer = BertTokenizer.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
	model = TFAlbertModel.from_pretrained("uer/albert-base-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

	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 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 ALBERT-Base

	Stage1:

	```
	python3 preprocess.py --corpus_path corpora/cluecorpussmall_bert.txt \
	--vocab_path models/google_zh_vocab.txt \
	--dataset_path cluecorpussmall_albert_seq128_dataset.pt \
	--seq_length 128 --processes_num 32 --data_processor albert
	```

	```
	python3 pretrain.py --dataset_path cluecorpussmall_albert_seq128_dataset.pt \
	--vocab_path models/google_zh_vocab.txt \
	--config_path models/albert/base_config.json \
	--output_model_path models/cluecorpussmall_albert_base_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
	```

	Stage2:

	```
	python3 preprocess.py --corpus_path corpora/cluecorpussmall_bert.txt \
	--vocab_path models/google_zh_vocab.txt \
	--dataset_path cluecorpussmall_albert_seq512_dataset.pt \
	--seq_length 512 --processes_num 32 --data_processor albert
	```

	```
	python3 pretrain.py --dataset_path cluecorpussmall_albert_seq512_dataset.pt \
	--vocab_path models/google_zh_vocab.txt \
	--pretrained_model_path models/cluecorpussmall_albert_base_seq128_model.bin-1000000 \
	--config_path models/albert/base_config.json \
	--output_model_path models/cluecorpussmall_albert_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 1e-4 --batch_size 64
	```

	Finally, we convert the pre-trained model into Huggingface's format:

	```
	python3 scripts/convert_albert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_albert_base_seq512_model.bin-1000000 \
	--output_model_path pytorch_model.bin
	```

	### BibTeX entry and citation info

	```
	@article{lan2019albert,
	title={Albert: A lite bert for self-supervised learning of language representations},
	author={Lan, Zhenzhong and Chen, Mingda and Goodman, Sebastian and Gimpel, Kevin and Sharma, Piyush and Soricut, Radu},
	journal={arXiv preprint arXiv:1909.11942},
	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/albert-base-chinese-cluecorpussmall
	[large]:https://huggingface.co/uer/albert-large-chinese-cluecorpussmall