Create README.md

README.md

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+---
+language: Chinese
+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 --target 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 \
+                    --factorized_embedding_parameterization --parameter_sharing \
+                    --embedding word_pos_seg --encoder transformer --mask fully_visible --target albert
+```
+
+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 --target albert 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_albert_seq512_dataset.pt \
+                    --pretrained_model_path models/cluecorpussmall_albert_base_seq128_model.bin-1000000 \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --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 \
+                    --factorized_embedding_parameterization --parameter_sharing \
+                    --embedding word_pos_seg --encoder transformer --mask fully_visible --target albert
+```
+
+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}
+}
+```
+[base]:https://huggingface.co/uer/albert-base-chinese-cluecorpussmall
+[large]:https://huggingface.co/uer/albert-large-chinese-cluecorpussmall