model_cards for Chinese Couplet and Poem GPT2 models (#8620)

model_cards/uer/gpt2-chinese-couplet/README.md

+---
+language: zh 
+widget:
+- text: "[CLS]国 色 天 香 ， 姹 紫 嫣 红 ， 碧 水 青 云 欣 共 赏 -"
+
+
+---
+
+# Chinese Couplet GPT2 Model
+
+## Model description
+
+The model is used to generate Chinese couplets. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-couplet][couplet].
+
+Since 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.
+
+## How to use
+
+You can use the 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-couplet")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet")
+>>> text_generator = TextGenerationPipeline(model, tokenizer)   
+>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
+	[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}]
+```
+
+When the parameter skip_special_tokens is False:
+
+```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]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
+	[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 我 酒 不 辞 [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP]'}]
+```
+
+## Training data
+
+Contains 700,000 Chinese couplets collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset).
+
+## 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 25,000  steps with a sequence length of 64.
+
+```
+python3 preprocess.py --corpus_path corpora/couplet.txt \
+		      --vocab_path models/google_zh_vocab.txt \  
+		      --dataset_path couplet.pt --processes_num 16 \
+	              --seq_length 64 --target lm 
+```
+
+```
+python3 pretrain.py --dataset_path couplet.pt \
+	            --vocab_path models/google_zh_vocab.txt \
+		    --output_model_path models/couplet_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 5000 --total_steps 25000 \
+		    --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}
+}
+```
+
+[couplet]: https://huggingface.co/uer/gpt2-chinese-couplet
+

model_cards/uer/gpt2-chinese-poem/README.md

+---
+language: zh 
+widget:
+- text: "[CLS] 万 叠 春 山 积 雨 晴 ，"
+- text: "[CLS] 青 山 削 芙 蓉 ，"
+
+
+---
+
+# Chinese Poem GPT2 Model
+
+## Model description
+
+The model is used to generate Chinese ancient poems. You can download the model  either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-poem][poem].
+
+Since 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.
+
+## How to use
+
+You can use the 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 False:
+
+```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 800,000 Chinese ancient poems collected by [chinese-poetry](https://github.com/chinese-poetry/chinese-poetry) and [Poetry](https://github.com/Werneror/Poetry) projects.
+
+## Training procedure
+
+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 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