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Question:
- Encoder: ViT5-base
- Max length: 32
- Pre-Processing: lower, remove special character
Image:
- Encoder: VIT-base
- Pre-Processing: None
OCR:
Text Detection: Paddle OCR
Text Recognition: VietOCR
- Threshold: 0.8
Max length: 128
Post-processing: group layout, divide=4
Answer:
- Max length: 56
Result:
- Dev:
- CIDEr: 3.4616
- BLEU: 0.4689
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# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("truong-xuan-linh/VQA-vit5") model = AutoModelForSeq2SeqLM.from_pretrained("truong-xuan-linh/VQA-vit5", device_map="auto")