Update README.md

README.md

@@ -3,10 +3,14 @@ license: apache-2.0
 ---
 
 # OFA-huge-vqa
+
+## Introduction
 This is the **huge** version of OFA model finetuned for **VQA**. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework.
 
 The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. 
 
+
+## How to use
 To use it in transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers. Install the transformers and download the models as shown below.
 ```
 git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git
@@ -15,7 +19,7 @@ git clone https://huggingface.co/OFA-Sys/OFA-huge-vqa
 ```
 After, refer the path to OFA-large to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. 
 
-```
+```python
 >>> from PIL import Image
 >>> from torchvision import transforms
 >>> from transformers import OFATokenizer, OFAModel
@@ -39,7 +43,7 @@ After, refer the path to OFA-large to `ckpt_dir`, and prepare an image for the t
 >>> patch_img = patch_resize_transform(img).unsqueeze(0)
 
 
->>> # using the generator of fairseq version
+# using the generator of fairseq version
 >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=True)
 >>> generator = sequence_generator.SequenceGenerator(
                     tokenizer=tokenizer,
@@ -53,7 +57,7 @@ After, refer the path to OFA-large to `ckpt_dir`, and prepare an image for the t
 >>> gen_output = generator.generate([model], data)
 >>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))]
 
->>> # using the generator of huggingface version
+# using the generator of huggingface version
 >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False)
 >>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3)