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@@ -83,7 +83,7 @@ NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLI
 In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). <a href="https://github.com/facebookresearch/fairscale">Gradient checkpoint</a> can also be activated in the config file to reduce GPU memory usage. 
 
 ### Pre-train:
-1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
+1. Prepare training json files where each json file contains a list. Each item in the list is a dictionary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
 2. In configs/pretrain.yaml, set 'train_file' as the paths for the json files .
 3. Pre-train the model using 8 A100 GPUs:
 <pre>python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain </pre>