readme: ---
  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. You can download the model either from the [UER-py Github page](https://github.com/dbiir/UER-py/), 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.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
  ``