Update README.md

README.md

@@ -1 +1,147 @@
-The model is coming soon.
+---
+language: Chinese
+datasets: CLUECorpusSmall
+widget: 
+- text: "中国的首都是[MASK]"
+
+
+
+---
+
+
+# Chinese RoBERTa-base-word Model
+
+## Model description
+
+We use sentencepiece model to train this roberta base model. You can download the model via HuggingFace from the link [roberta-base-word-chinese-cluecorpussmall](https://huggingface.co/uer/roberta-base-word-chinese-cluecorpussmall).
+
+## How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='uer/roberta-base-word-chinese-cluecorpussmall')
+>>> unmasker("中国的首都是[MASK]。")
+```
+
+BertTokenizer does not support sentencepiece, so we use AlbertTokenizer here.
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import AlbertTokenizer, BertModel
+tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-base-word-chinese-cluecorpussmall')
+model = BertModel.from_pretrained("uer/roberta-base-word-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 AlbertTokenizer, TFBertModel
+tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-base-word-chinese-cluecorpussmall')
+model = TFBertModel.from_pretrained("uer/roberta-base-word-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
+
+We use google's **[sentencepiece](https://github.com/google/sentencepiece)** to train the sentencepiece model.
+
+```
+>>> import sentencepiece as spm
+>>> spm.SentencePieceTrainer.train(input='CLUEsmall_shuf.txt',
+             model_prefix='clue_6',
+             vocab_size=100000,
+             max_sentence_length=1024,
+             max_sentencepiece_length=6,
+             user_defined_symbols=['[MASK]','[unused1]','[unused2]',
+                '[unused3]','[unused4]','[unused5]','[unused6]',
+                '[unused7]','[unused8]','[unused9]','[unused10]'],
+             pad_id=0,
+             pad_piece='[PAD]',
+             unk_id=1,
+             unk_piece='[UNK]',
+             bos_id=2,
+             bos_piece='[CLS]',
+             eos_id=3,
+             eos_piece='[SEP]',
+             train_extremely_large_corpus=True
+            )
+```
+
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). 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. 
+
+Stage1:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --spm_model_path models/clue_6.model \
+                      --dataset_path cluecorpussmall_seq128_dataset.pt \
+                      --processes_num 32 --seq_length 128 \
+                      --dynamic_masking --target mlm
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \
+                    --spm_model_path models/clue_6.model \
+                    --config_path models/bert/base_config.json \
+                    --output_model_path models/cluecorpussmall_word_roberta_base_128.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 \
+                    --embedding word_pos_seg --encoder transformer --mask fully_visible \
+                    --target mlm --tie_weights
+```
+
+Stage2:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --spm_model_path models/clue_6.model \
+                      --dataset_path cluecorpussmall_seq512_dataset.pt \
+                      --processes_num 32 --seq_length 512 \
+                      --dynamic_masking --target mlm
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \
+					--pretrained_model_path models/cluecorpussmall_word_roberta_base_128.bin-1000000 \
+                    --spm_model_path models/clue_6.model \
+                    --config_path models/bert/base_config.json \
+                    --output_model_path models/cluecorpussmall_word_roberta_base_512.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
+                    --learning_rate 5e-5 --batch_size 16 \
+                    --embedding word_pos_seg --encoder transformer --mask fully_visible \
+                    --target mlm --tie_weights
+```
+
+Finally, we convert the pre-trained model into Huggingface's format:
+
+```
+python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_word_roberta_base_512.bin-250000 \
+                                                        --output_model_path pytorch_model.bin \
+                                                        --layers_num 12 --target mlm
+```
+
+### BibTeX entry and citation info
+
+```
+@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}
+}
+```