Update README.md

README.md

@@ -1,3 +1,52 @@
 ---
 license: gpl-3.0
+tags:
+- DocVQA
+- Document Question Answering
+- Document Visual Question Answering
+datasets:
+- MP-DocVQA
+language:
+- en
 ---
+
+# T5 base fine-tuned on MP-DocVQA
+
+This is [pretrained](https://huggingface.co/t5-base) T5 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 T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = LongformerTokenizerFast.from_pretrained("rubentito/t5-base-mpdocvqa")
+model = LongformerForQuestionAnswering.from_pretrained("rubentito/t5-base-mpdocvqa")
+
+context = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
+question = "What has Huggingface done?"
+input_text = "question: {:s}  context: {:s}".format(question, context)
+
+encoding = tokenizer(input_text, return_tensors="pt")
+output = self.model.generate(**encoding)
+answer = tokenizer.decode(output['sequences'], skip_special_tokens=True)
+```
+
+## 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}
+}
+```