Update README.md

README.md

@@ -6,21 +6,19 @@ widget:
 
 ---
 
-# Chinese GPT2 Language Model
+# Chinese Poem GPT2 Model
 
 ## Model description
 
-This model is used to generate Chinese ancient poems and is pre-trained by [UER-py](https://www.aclweb.org/anthology/D19-3041.pdf).
+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].
 
-You can download this model 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
 
-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 the model directly with a pipeline for text generation:
 
-You can use this model directly with a pipeline for text generation:
-
-When the parameter ***skip_special_tokens***  is True:
+When the parameter skip_special_tokens is True:
 
 ```python
 >>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
@@ -32,7 +30,7 @@ When the parameter ***skip_special_tokens***  is 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:
+When the parameter skip_special_tokens is False:
 
 ```python
 >>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
@@ -46,11 +44,11 @@ When the parameter ***skip_special_tokens***  is Flase:
 
 ## Training data
 
-Contains about 800,000 chinese ancient poems.
+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
 
-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.
+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 \