metadata

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, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, 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, or via HuggingFace from the links below:

	Link
ALBERT-Base	L=12/H=768 (Base)
ALBERT-Large	L=24/H=1024 (Large)

How to use

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

>>> 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:

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:

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 is used as training data.

Training procedure

The model is pre-trained by UER-py on Tencent Cloud. 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.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.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}