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metadata
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).

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.

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 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:

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.

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 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

@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},
}