Create README.md

README.md

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+# Vit2-DistilGPT2
+This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training)
+
+## Usage
+```python
+import Image
+from transformers import AutoModel, GPT2Tokenizer, ViTFeatureExtractor
+
+model = AutoModel.from_pretrained("sachin/vit2distilgpt2")
+vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+
+# make sure GPT2 appends EOS in begin and end
+def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+    outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+    return outputs
+    
+GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens
+gpt2_tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+# set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id
+gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token
+
+image = (Image.open(image_path).convert("RGB"), return_tensors="pt").pixel_values
+encoder_outputs = model.generate(image.unsqueeze(0))
+generated_sentences = gpt2_tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True)
+```
+Note that the output sentence may be repeated, hence a post processing step may be required.
+
+## Bias Warning
+This model may be biased due to dataset, lack of long training and the model itself. The following gender bias is an example.
+![](https://i.imgur.com/9zVN022.png)