[update] readme

README.md

@@ -427,7 +427,7 @@ python train_xxx.py --use_wandb
 ## Ⅰ Tokenizer
 
 分词器将单词从自然语言通过“词典”映射到`0, 1, 36`这样的数字，可以理解为数字就代表了单词在“词典”中的页码。
-可以选择自己构造词表训练一个“词典”，代码可见`./scripts/train_tokenizer.py`（仅供学习参考，若非必要无需再自行训练，MiniMind已自带tokenizer）。
+可以选择自己构造词表训练一个“词典”，代码可见`./trainer/train_tokenizer.py`（仅供学习参考，若非必要无需再自行训练，MiniMind已自带tokenizer）。
 或者选择比较出名的开源大模型分词器，
 正如同直接用新华/牛津词典的优点是token编码压缩率很好，缺点是页数太多，动辄数十万个词汇短语；
 自己训练的分词器，优点是词表长度和内容随意控制，缺点是压缩率很低（例如"hello"也许会被拆分为"h e l l o"

README_en.md

@@ -426,7 +426,7 @@ By adding the `--use_wandb` parameter, you can record the training process. Afte
 ## Ⅰ Tokenizer
 
 Tokenizer maps words from natural language to numbers like `0, 1, 36` through a "dictionary," which can be understood as numbers representing the page number of the word in the "dictionary."
-You can choose to construct your own vocabulary table to train a "dictionary." The code can be found in `./scripts/train_tokenizer.py` (for learning reference only. It's not necessary to train one yourself unless required. MiniMind comes with a built-in tokenizer).
+You can choose to construct your own vocabulary table to train a "dictionary." The code can be found in `./trainer/train_tokenizer.py` (for learning reference only. It's not necessary to train one yourself unless required. MiniMind comes with a built-in tokenizer).
 Or you can choose tokenizers from well-known open-source large models.
 Just as using Xinhua/Oxford dictionaries directly has the advantage of good token encoding compression, but the disadvantage of having too many pages—tens of thousands of word phrases;
 A self-trained tokenizer has the advantage of freely controlling vocabulary length and content, but the disadvantage of low compression ratio (for example, "hello" might be split into "h e l l o"