Update README.md

README.md

@@ -33,39 +33,45 @@ Training data contains 3,000,000 ancient Chinese which are collected by [daizhig
 
 ## 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 500,000 steps with a sequence length of 320. We use extended vocabulary to handle out-of-vocabulary words. The Chinese character that occurs greater than or equal to 100 in ancient Chinese corpus is added to the vocabulary.
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 500,000 steps with a sequence length of 320. We use extended vocabulary to handle out-of-vocabulary words. The Chinese character that occurs greater than or equal to 100 in ancient Chinese corpus is added to the vocabulary.
 
 ```
-python3 preprocess.py --corpus_path corpora/ancient_chinese.txt \
-                      --vocab_path models/google_zh_vocab.txt \
-                      --dataset_path ancient_chinese_dataset.pt --processes_num 16 \
+python3 preprocess.py --corpus_path corpora/ancient_chinese.txt \\
+                      --vocab_path models/google_zh_vocab.txt \\
+                      --dataset_path ancient_chinese_dataset.pt --processes_num 16 \\
                       --seq_length 320 --target lm
 ```
 
 ```
-python3 pretrain.py --dataset_path ancient_chinese_dataset.pt \
-                    --vocab_path models/google_zh_vocab.txt \
-                    --config_path models/bert_base_config.json \
-                    --output_model_path models/ancient_chinese_base_model.bin \
-                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
-                    --total_steps 500000 --save_checkpoint_steps 100000 --report_steps 10000 \
-                    --learning_rate 5e-4 --batch_size 32 \
-                    --embedding word_pos --remove_embedding_layernorm \
-                    --encoder transformer --mask causal --layernorm_positioning pre \
+python3 pretrain.py --dataset_path ancient_chinese_dataset.pt \\
+                    --vocab_path models/google_zh_vocab.txt \\
+                    --config_path models/bert_base_config.json \\
+                    --output_model_path models/ancient_chinese_base_model.bin \\
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \\
+                    --total_steps 500000 --save_checkpoint_steps 100000 --report_steps 10000 \\
+                    --learning_rate 5e-4 --batch_size 32 \\
+                    --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 ancient_chinese_base_model.bin-500000 \
-                                                        --output_model_path pytorch_model.bin \
+python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path ancient_chinese_base_model.bin-500000 \\
+                                                        --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},