Update README.md

README.md

@@ -5,3 +5,89 @@ widget:
 - text: "[CLS] 青 山 削 芙 蓉 ，"
 
 ---
+
+# Chinese GPT2 Language Models
+
+## Model description
+
+This is the set of  two Chinese GPT2 language models pre-trained by [UER-py](https://www.aclweb.org/anthology/D19-3041.pdf).
+
+You can download the two Chinese GPT2 language models via HuggingFace from the links below:
+
+|     Model     |          [gpt2-chinese-poem][poem]           |     [gpt2-chinese-couplet][couplet]     |
+| :-----------: | :------------------------------------------: | :-------------------------------------: |
+| Training data | Contains about 800,000 chinese ancient poems | contains about 700,000 chinese couplets |
+
+
+
+## How to use
+
+Because the parameter ***skip_special_tokens*** is used in the ***pipelines.py*** , special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat.
+
+You can use this model directly with a pipeline for text generation:
+
+When the parameter ***skip_special_tokens***  is True:
+
+```python
+>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
+>>> from transformers import TextGenerationPipeline, 
+>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem")
+>>> text_generator = TextGenerationPipeline(model, tokenizer)   
+>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True)
+	[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 于 。 行 将 解 尘 缨 ， 于 焉 蹈 高 踵 。 我'}]
+```
+
+When the parameter ***skip_special_tokens***  is Flase:
+
+```python
+>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
+>>> from transformers import TextGenerationPipeline, 
+>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem")
+>>> text_generator = TextGenerationPipeline(model, tokenizer)   
+>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True)
+	[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 [UNK] 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 可 捧 。 银 汉 迟 不 来 ， 槎 头 欲 谁 揽 。 何'}]
+```
+
+## Training data
+
+Contains about 800,000 chinese ancient poems.
+
+## Training procedure
+
+Models are 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 200,000 steps with a sequence length of 128.
+
+```
+python3 preprocess.py --corpus_path corpora/poem.txt \
+					  --vocab_path models/google_zh_vocab.txt \  
+					  --dataset_path poem.pt --processes_num 16 \
+					  --seq_length 128 --target lm 
+```
+
+```
+python3 pretrain.py --dataset_path poem.pt \
+				    --vocab_path models/google_zh_vocab.txt \
+					--output_model_path models/poem_gpt_base_model.bin \  
+					--config_path models/bert_base_config.json --learning_rate 5e-4 \
+					--tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+					--batch_size 64 --report_steps 1000 \
+					--save_checkpoint_steps 50000 --total_steps 200000 \
+					--embedding gpt --encoder gpt2 --target lm
+
+```
+
+### 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}
+}
+```
+
+[poem]: https://huggingface.co/uer/gpt2-chinese-poem
+[couplet]: https://huggingface.co/uer/gpt2-chinese-couplet