Update README.md

README.md

@@ -15,4 +15,37 @@ This is BigBird-base trained on TriviaQA from [Google hub](https://huggingface.c
 
 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
+- 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 BigBirdForQuestionAnswering
+
+# by default its in `block_sparse` mode with num_random_blocks=3, block_size=64
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA")
+
+# you can change `attention_type` to full attention like this:
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA", attention_type="original_full")
+
+# you can change `block_size` & `num_random_blocks` like this:
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA", block_size=16, num_random_blocks=2)
+
+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}
+}
+```