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--- |
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license: cc-by-sa-4.0 |
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pipeline_tag: fill-mask |
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language: en |
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arxiv: 2210.05529 |
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tags: |
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- long-documents |
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datasets: |
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- c4 |
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model-index: |
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- name: kiddothe2b/adhoc-hierarchical-transformer-base-4096 |
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results: [] |
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--- |
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# Hierarchical Attention Transformer (HAT) / kiddothe2b/adhoc-hierarchical-transformer-base-4096 |
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## Model description |
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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). |
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The model has 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. |
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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. |
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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). |
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## Intended uses & limitations |
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The model is intended to be fine-tuned on a downstream task. |
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See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT, or fine-tuned versions on a task that interests you. |
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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. |
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## How to use |
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You can fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True) |
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doc_classifier = AutoModelForSequenceClassification("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True) |
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``` |
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Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096). |
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## Limitations and bias |
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The training data used for this model contains a lot of unfiltered content from the internet, which is far from |
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neutral. Therefore, the model can have biased predictions. |
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## Training procedure |
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### Training and evaluation data |
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The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint. |
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### Framework versions |
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- Transformers 4.19.0.dev0 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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## Citing |
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If you use HAT in your research, please cite: |
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[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). |
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``` |
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@misc{chalkidis-etal-2022-hat, |
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url = {https://arxiv.org/abs/2210.05529}, |
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author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond}, |
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title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification}, |
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publisher = {arXiv}, |
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year = {2022}, |
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} |
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``` |
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