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README.md

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+---
+tags:
+- visual-question-answering
+license: apache-2.0
+widget:
+- text: "What's the animal doing?"
+  src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
+- text: "What is on top of the building?"
+  src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"
+---
+
+# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
+
+Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
+Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). 
+
+Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Intended uses & limitations
+
+You can use the raw model for visual question answering. 
+
+### How to use
+
+Here is how to use this model in PyTorch:
+
+```python
+from transformers import ViltProcessor, ViltForQuestionAnswering
+import requests
+from PIL import Image
+
+# prepare image + question
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+text = "How many cats are there?"
+
+processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+# prepare inputs
+encoding = processor(image, text, return_tensors="pt")
+
+# forward pass
+outputs = model(**encoding)
+logits = outputs.logits
+idx = logits.argmax(-1).item()
+print("Predicted answer:", model.config.id2label[idx])
+```
+
+## Training data
+
+(to do)
+
+## Training procedure
+
+### Preprocessing
+
+(to do)
+
+### Pretraining
+
+(to do)
+
+## Evaluation results
+
+(to do)
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{kim2021vilt,
+      title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, 
+      author={Wonjae Kim and Bokyung Son and Ildoo Kim},
+      year={2021},
+      eprint={2102.03334},
+      archivePrefix={arXiv},
+      primaryClass={stat.ML}
+}
+```

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 40, "special_tokens_map_file": null, "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}