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---
license: cc-by-nc-sa-4.0
pipeline_tag: fill-mask
language: en
tags:
- long_documents
- hierarchical_transformers
datasets:
- wikipedia
model-index:
- name: kiddothe2b/hierarchical-transformer-LC1-mini-1024
results: []
---
# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-LC1-mini-1024
## 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 been warm-started re-using the weights of miniature BERT [(Turc et al., 2019)](https://arxiv.org/abs/1908.08962), and continued pre-trained for MLM following the paradigm of Longformer released by [Beltagy et al. (2020)](](https://arxiv.org/abs/1908.08962)). It supports sequences of length up to 1,024.
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.
## 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?other=hierarchical-transformer) 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 use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
mlm_model = pipeline('fill-mask', model='kiddothe2b/hierarchical-transformer-LC1-mini-1024', trust_remote_code=True)
mlm_model("Hello I'm a <mask> model.")
```
You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hierarchical-transformer-LC1-mini-1024', 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 [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3959 | 0.2 | 10000 | 2.2258 |
| 2.3395 | 0.4 | 20000 | 2.1738 |
| 2.3082 | 0.6 | 30000 | 2.1404 |
| 2.273 | 0.8 | 40000 | 2.1145 |
| 2.262 | 1.14 | 50000 | 2.1004 |
### 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},
}
```
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