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 cluecorpussmall_albert_base_seq512_model.bin-250000 \
--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}