Update README.md

README.md

@@ -41,13 +41,17 @@ These instructions will get you running the codes of MixText.
 Please download the dataset and put them in the data folder. You can find Yahoo Answers, AG News, DB Pedia [here](https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset), IMDB [here](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
 
 ### Pre-processing the data
-We utilized [Fairseq](https://github.com/pytorch/fairseq) to perform back translation on the training dataset. Please refer to `./data/yahoo_answers_csv/back_translate.ipynb` for details.
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 For Yahoo Answer, We concatenate the question title, question content and best answer together to form the text to be classified. The pre-processed Yahoo Answer dataset can be downloaded [here](https://drive.google.com/file/d/1IoX9dp_RUHwIVA2_kJgHCWBOLHsV9V7A/view?usp=sharing). 
 
+Note that for AG News and DB Pedia, we only utilize the content (without titles) to do the classifications, and for IMDB we do not perform any pre-processing.
+
+We utilize [Fairseq](https://github.com/pytorch/fairseq) to perform back translation on the training dataset. Please refer to `./data/yahoo_answers_csv/back_translate.ipynb` for details.
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 Here, we have put two examples of back translated data, `de_1.pkl and ru_1.pkl`, in `./data/yahoo_answers_csv/` as well. You can directly use them for Yahoo Answers or generate your own back translated data followed the `./data/yahoo_answers_csv/back_translate.ipynb`.
 
-Note that for AG News and DB Pedia, we only utilize the content (without titles) to do the classifications, and for IMDB we do not perform any pre-processing.
+
 
 ### Training models
 These section contains instructions for training models on Yahoo Answers using 10 labeled data per class for training.