Update README.md

README.md

@@ -13,21 +13,21 @@ widget:
 
 This is the set of Chinese T5 models pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
 
-The Text-to-Text Transfer Transformer (T5) leverages a unified text-to-text format and attains state-of-the-art results on a wide variety of English-language NLP tasks. Following their paper, we released a series of Chinese T5 models.
+The Text-to-Text Transfer Transformer (T5) leverages a unified text-to-text format and attains state-of-the-art results on a wide variety of English-language NLP tasks. Following their work, we released a series of Chinese T5 models.
 
 |          |           Link           |
 | -------- | :-----------------------: |
 | **T5-Small**  | [**L=6/H=512 (Small)**][small] |
 | **T5-Base**  | [**L=12/H=768 (Base)**][base] |
 
-In T5, spans of the input sequence are masked by so-called sentinel token. Each sentinel token represents a unique mask token for the input sequence and should start with `<extra_id_0>`, `<extra_id_1>`, … up to `<extra_id_199>`. However, `<extra_id_xxx>` is separated into multiple parts in Huggingface's Hosted inference API. Therefore, we replace `<extra_id_xxx>` with `extraxxx` in vocabulary and BertTokenizer regards `extraxxx` as one sentinel token.
+In T5, spans of the input sequence are masked by so-called sentinel token. Each sentinel token represents a unique mask token for the input sequence and should start with `<extra_id_0>`, `<extra_id_1>`, … up to `<extra_id_99>`. However, `<extra_id_xxx>` is separated into multiple parts in Huggingface's Hosted inference API. Therefore, we replace `<extra_id_xxx>` with `extraxxx` in vocabulary and BertTokenizer regards `extraxxx` as one sentinel token.
 
 ## How to use
 
-You can use the model directly with a pipeline for text2text generation:
+You can use this model directly with a pipeline for text2text generation (take the case of T5-Small):
 
 ```python
->>> from transformers import BertTokenizer, T5ForConditionalGeneration,Text2TextGenerationPipeline
+>>> from transformers import BertTokenizer, T5ForConditionalGeneration, Text2TextGenerationPipeline
 >>> tokenizer = BertTokenizer.from_pretrained("uer/t5-small-chinese-cluecorpussmall")
 >>> model = T5ForConditionalGeneration.from_pretrained("uer/t5-small-chinese-cluecorpussmall")
 >>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
@@ -41,7 +41,9 @@ You can use the model directly with a pipeline for text2text generation:
 
 ## Training procedure
 
-The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. 
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes.
+
+Taking the case of T5-Small
 
 Stage1: