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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
-  results: []
----
-
-# Hierarchical Attention Transformer (HAT) / 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), 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.
-
-## 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=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 use this model directly for masked language modeling:
-
-```python
-from transformers import AutoTokenizer, AutoModelforForMaskedLM
-tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-mlm_model = AutoModelforForMaskedLM("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-```
-
-You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
-
-```python
-from transformers import AutoTokenizer, AutoModelforSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-```
-
-## 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 and has been continued pre-trained for additional 50k steps in long sequences (> 1024 subwords) of [C4](https://huggingface.co/datasets/c4) (Raffel et al., 2020).
-
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
-- distributed_type: tpu
-- num_devices: 8
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 128
-- total_eval_batch_size: 16
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- training_steps: 50000
-
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| 1.7437        | 0.2   | 10000 | 1.6370          |
-| 1.6994        | 0.4   | 20000 | 1.6054          |
-| 1.6726        | 0.6   | 30000 | 1.5718          |
-| 1.644         | 0.8   | 40000 | 1.5526          |
-| 1.6299        | 1.0   | 50000 | 1.5368          |
-
-
-### 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},
-}
-```
-
-