fix typo

README.md

@@ -42,7 +42,7 @@ pip install git+https://github.com/s-JoL/transformers.git@dev
 1. 使用HuggingFace的datasets库进行数据读取，具体流程如下
    1. 使用transform函数将不同数据集的数据统一格式为{'text': 'xxx'}
    2. 使用Tokenizer进行分词
-   3. 对长序列进行采样，目前提供三种模式，分别是：截断/采样（参考Gopher论文）/切分
+   3. 对长序列进行采样，目前提供三种模式，分别是：截断/采样（参考[Gopher论文](https://arxiv.org/abs/2112.11446)）/切分
    4. 可选：对来自不同doc的文本进行拼接。减少了数据中的pad，加速训练；在v1版本中pad占比为30%，使用拼接后pad占比降低为5%。
 2. 加入Trainer，对于预训练和指令微调都可以复用，见solver/trainer.py
 3. 统一预训练和指令微调训练入口为train_lm.py

README_en.md

@@ -43,7 +43,7 @@ This update mainly includes the following aspects, increasing the effective trai
 1. Use HuggingFace's datasets library for data reading, with the process as follows:
    1. Use the transform function to unify data formats from different datasets to {'text': 'xxx'}
    2. Tokenize using Tokenizer
-   3. Sample long sequences; currently, three modes are provided: truncation, sampling (refer to the Gopher paper), and splitting
+   3. Sample long sequences; currently, three modes are provided: truncation, sampling (refer to the [Gopher paper](https://arxiv.org/abs/2112.11446)), and splitting
    4. Optional: concatenate texts from different docs, reducing padding in the data and accelerating training. In the v1 version, padding accounted for 30%; after concatenation, padding is reduced to 5%.
 2. Add Trainer, which can be reused for both pre-training and instruction fine-tuning, see solver/trainer.py
 3. Unify the pre-training and instruction fine-tuning training entry to train_lm.py