readme: ---
  license: cc-by-sa-4.0
  pipeline_tag: fill-mask
  language: en
  arxiv: 2210.05529
  tags:
  - long-documents
  datasets:
  - c4
  model-index:
  - name: kiddothe2b/adhoc-hierarchical-transformer-base-4096
    results: []
  ---
  
  # Hierarchical Attention Transformer (HAT) / kiddothe2b/adhoc-hierarchical-transformer-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/2210.05529). 
  
  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.
  
  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.
  
  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
  
  The model is intended to be fine-tuned on a downstream task.
  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.
  
  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-hierarchical-transformer-base-4096", trust_remote_code=True)
  doc_classifier = AutoModelforSequenceClassification("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True)
  ```
  
  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).
  
  
  ## 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/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},
  }
  ```
  
  
  