69 lines
3.0 KiB
Markdown
69 lines
3.0 KiB
Markdown
---
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license: gpl-3.0
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tags:
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- DocVQA
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- Document Question Answering
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- Document Visual Question Answering
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datasets:
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- rubentito/mp-docvqa
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language:
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- en
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---
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# LayoutLMv3 base fine-tuned on MP-DocVQA
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This is pretrained LayoutLMv3 from [Microsoft hub](https://huggingface.co/microsoft/layoutlmv3-base) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
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This model was used as a baseline in [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
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- Results on the MP-DocVQA dataset are reported in Table 2.
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- Training hyperparameters can be found in Table 8 of Appendix D.
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## How to use
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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import torch
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from transformers import LayoutLMv3Processor, LayoutLMv3ForQuestionAnswering
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processor = LayoutLMv3Processor.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa", apply_ocr=False)
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model = LayoutLMv3ForQuestionAnswering.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa")
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image = Image.open("example.jpg").convert("RGB")
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question = "Is this a question?"
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context = ["Example"]
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boxes = [0, 0, 1000, 1000] # This is an example bounding box covering the whole image.
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document_encoding = processor(image, question, context, boxes=boxes, return_tensors="pt")
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outputs = model(**document_encoding)
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# Get the answer
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start_idx = torch.argmax(outputs.start_logits, axis=1)
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end_idx = torch.argmax(outputs.end_logits, axis=1)
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answers = self.processor.tokenizer.decode(input_tokens[start_idx: end_idx+1]).strip()
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```
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## Model results
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Extended experimentation can be found in Table 2 of [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
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You can also check the live leaderboard at the [RRC Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4).
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| Model | HF name | ANLS | APPA |
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|-----------------------------------------------------------------------------------|:--------------------------------------|:-------------:|:---------:|
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| [Bert large](https://huggingface.co/rubentito/bert-large-mpdocvqa) | rubentito/bert-large-mpdocvqa | 0.4183 | 51.6177 |
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| [Longformer base](https://huggingface.co/rubentito/longformer-base-mpdocvqa) | rubentito/longformer-base-mpdocvqa | 0.5287 | 71.1696 |
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| [BigBird ITC base](https://huggingface.co/rubentito/bigbird-base-itc-mpdocvqa) | rubentito/bigbird-base-itc-mpdocvqa | 0.4929 | 67.5433 |
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| [**LayoutLMv3 base**](https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa) | rubentito/layoutlmv3-base-mpdocvqa | 0.4538 | 51.9426 |
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| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa) | rubentito/t5-base-mpdocvqa | 0.5050 | 0.0000 |
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| Hi-VT5 | TBA | 0.6201 | 79.23 |
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## BibTeX entry
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```tex
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@article{tito2022hierarchical,
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title={Hierarchical multimodal transformers for Multi-Page DocVQA},
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author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest},
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journal={arXiv preprint arXiv:2212.05935},
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year={2022}
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}
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``` |