README.md

@@ -1,12 +1,5 @@
 ---
-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
@@ -16,36 +9,17 @@ 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
 
-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])
-```
+(to do)
 
 ## Training data
 

preprocessor_config.json

@@ -1,18 +0,0 @@
-{
-  "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

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

tokenizer_config.json

@@ -1 +1 @@
-{"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"}
+{"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"}