Update README.md

README.md

@@ -11,7 +11,7 @@ widget:
 
 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.
+Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, the output results of Hosted inference API (right) may not be properly displayed..
 
 ## How to use
 
@@ -25,7 +25,7 @@ When the parameter skip_special_tokens is True:
 >>> 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'}]
+    [{'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:
@@ -36,7 +36,7 @@ When the parameter skip_special_tokens is False:
 >>> 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 我 酒 不 辞 [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP]'}]
+    [{'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
@@ -45,40 +45,44 @@ Training data contains 700,000 Chinese couplets which are collected by [couplet-
 
 ## 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 25,000 steps with a sequence length of 64.
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). 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_dataset.pt --processes_num 16 \
-					  --seq_length 64 --target lm 
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path couplet_dataset.pt --processes_num 16 \
+                      --seq_length 64 --target lm 
 ```
 
 ```
 python3 pretrain.py --dataset_path couplet_dataset.pt \
-				    --vocab_path models/google_zh_vocab.txt \
-				    --config_path models/gpt2/config.json \
-					--output_model_path models/couplet_gpt2_model.bin \
-					--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
-					--total_steps 25000 --save_checkpoint_steps 5000 --report_steps 1000 \
-					--learning_rate 5e-4 --batch_size 64 \
-					--embedding word_pos --remove_embedding_layernorm \
-					--encoder transformer --mask causal --layernorm_positioning pre \
-					--target lm --tie_weight
-					
-
+                    --vocab_path models/google_zh_vocab.txt \
+                    --config_path models/gpt2/config.json \
+                    --output_model_path models/couplet_gpt2_model.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 25000 --save_checkpoint_steps 5000 --report_steps 1000 \
+                    --learning_rate 5e-4 --batch_size 64 \
+                    --embedding word_pos --remove_embedding_layernorm \
+                    --encoder transformer --mask causal --layernorm_positioning pre \
+                    --target lm --tie_weight
 ```
 
 Finally, we convert the pre-trained model into Huggingface's format:
 ```
 python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path couplet_gpt2_model.bin-25000 \
-														--output_model_path pytorch_model.bin \
-														--layers_num 12
+                                                        --output_model_path pytorch_model.bin \
+                                                        --layers_num 12
 ```
 
 ### BibTeX entry and citation info
 
 ```
+@article{radford2019language,
+  title={Language Models are Unsupervised Multitask Learners},
+  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
+  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},