---
license: cc-by-nc-sa-4.0
pipeline_tag: fill-mask
language: en
tags:
- long_documents
datasets:
- c4
model-index:
- name: kiddothe2b/adhoc-hat-base-4096
  results: []
---

# Hierarchical Attention Transformer (HAT) / adhoc-hat-base-4096

## 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/xxx). 

The model has not been warm-started re-using the weights of RoBERTa (Liu et al., 2019), BUT has not been continued pre-trained. It supports sequences of length up to 4,096.

HAT use a hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think segments as paragraphs or sentences.

Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/adhoc-hat-base-4096](https://huggingface.co/kiddothe2b/adhoc-hat-base-4096).

## Intended uses & limitations

You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=hat) to look for fine-tuned versions on a task that
interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification or question answering.

## How to use

You can fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:

```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/adhoc-hat-base-4096", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/adhoc-hat-base-4096', trust_remote_code=True)
```

Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/adhoc-hat-base-4096](https://huggingface.co/kiddothe2b/adhoc-hat-base-4096).


## Limitations and bias

The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.


## Training procedure

### Training and evaluation data

The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint.

### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6


##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/xxx)

```
@misc{chalkidis-etal-2022-hat,
  url = {https://arxiv.org/abs/xxx},
  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},
}
```