--- 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