Update README.md

README.md

@@ -1,3 +1,47 @@
 ---
 license: gpl-3.0
+tags:
+- DocVQA
+- Document Question Answering
+- Document Visual Question Answering
+datasets:
+- MP-DocVQA
+language:
+- en
 ---
+
+# BERT-BASE fine-tuned on MP-DocVQA
+
+This is BERT trained on [SinglePage DocVQA](https://arxiv.org/abs/2007.00398) 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
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bert-base-mpdocvqa")
+
+question = "Replace me by any text you'd like."
+context = "Put some context for answering"
+encoded_input = tokenizer(question, context, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## 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}
+}
+```