Better prompts for widget samples (#2)

- Better prompts for widget samples (5a1d3ba58fe705a59c0d26d94e7e10e1dbcce846)
- What's the animal doing (67d4cf273bc9974aee5d87d3f768f590a408e5ce)


Co-authored-by: Mishig Davaadorj <mishig@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,12 @@
 ---
+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
@@ -9,17 +16,36 @@ Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by
 
 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.
 
-## Model description
-
-(to do)
-
 ## Intended uses & limitations
 
 You can use the raw model for visual question answering. 
 
 ### How to use
 
-(to do)
+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
 

preprocessor_config.json

@@ -0,0 +1,18 @@
+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViltFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 3,
+  "size": 384,
+  "size_divisor": 32
+}

special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

@@ -1 +1 @@
-{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "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": 512, "special_tokens_map_file": null, "tokenizer_file": "/root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4", "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}
+{"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"}