Create README.md

README.md

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+---
+license: cc-by-sa-4.0
+pipeline_tag: fill-mask
+arxiv: 2210.05529
+language: en
+thumbnail: https://github.com/coastalcph/hierarchical-transformers/raw/main/data/figures/hat_encoder.png
+tags:
+- long-documents
+datasets:
+- c4
+model-index:
+- name: kiddothe2b/hierarchical-transformer-base-4096-v2
+  results: []
+---
+
+# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-base-4096-v2
+
+## Disclaimer 🚧 ⚠️
+This is an experimental version of HAT, trying to make HAT a native part of Transformers library. Please use ONLY [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096) for the moment.
+
+## Model description
+
+This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). 
+
+The model has been warm-started re-using the weights of RoBERTa (Liu et al., 2019), and continued pre-trained for MLM in long sequences following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 4,096.
+
+HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
+
+
+## Citing
+
+If you use HAT in your research, please cite:
+
+[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
+
+```
+@misc{chalkidis-etal-2022-hat,
+  url = {https://arxiv.org/abs/2210.05529},
+  author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
+  title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
+  publisher = {arXiv},
+  year = {2022},
+}
+```
+
+