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 ---
 license: gpl-3.0
+tags:
+- DocVQA
+- Document Question Answering
+- Document Visual Question Answering
+datasets:
+- MP-DocVQA
+language:
+- en
 ---
+
+# LayoutLMv3 base fine-tuned on MP-DocVQA
+
+This is pretrained LayoutLMv3 from [Microsoft hub](https://huggingface.co/microsoft/layoutlmv3-base) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
+
+
+This model was used as a baseline in [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
+- Results on the MP-DocVQA dataset are reported in Table 2.
+- Training hyperparameters can be found in Table 8 of Appendix D.
+
+
+## How to use
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+import torch
+from transformers import LayoutLMv3Processor, LayoutLMv3ForQuestionAnswering
+
+processor = LayoutLMv3Processor.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa", apply_ocr=False)
+model = LayoutLMv3ForQuestionAnswering.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa")
+
+image = Image.open("example.jpg").convert("RGB")
+question = "Is this a question?"
+context = ["Example"]
+boxes = [0, 0, 1000, 1000]  # This is an example bounding box covering the whole image.
+document_encoding = processor(image, question, context, boxes=boxes, return_tensors="pt")
+outputs = model(**document_encoding)
+
+# Get the answer
+start_idx = torch.argmax(outputs.start_logits, axis=1)
+end_idx = torch.argmax(outputs.end_logits, axis=1)
+answers = self.processor.tokenizer.decode(input_tokens[start_idx: end_idx+1]).strip()
+```
+
+## BibTeX entry
+
+```tex
+@article{tito2022hierarchical,
+  title={Hierarchical multimodal transformers for Multi-Page DocVQA},
+  author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest},
+  journal={arXiv preprint arXiv:2212.05935},
+  year={2022}
+}
+```
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