transformers pytorch safetensors longformer fill-mask long_documents en dataset:c4 arxiv:2004.05150 arxiv:2210.05529 arxiv:1908.08962 license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- c4
language: en
license: cc-by-sa-4.0
pipeline_tag: fill-mask
tags:
- long_documents
model-index:
- name: kiddothe2b/longformer-mini-1024
  results: []
---

# Longformer / longformer-mini-1024

## Model description

[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents.  This version of Longformer is 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 miniature BERT (Turc et al., 2019), 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.

Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.

## 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=longformer) 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/longformer-mini-1024', trust_remote_code=True)
mlm_model("Hello I'm a <mask> model.")
```

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/longformer-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/longformer-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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7067        | 0.2   | 10000 | 1.5923          | 0.6714   |
| 1.6532        | 0.4   | 20000 | 1.5494          | 0.6784   |
| 1.622         | 0.6   | 30000 | 1.5208          | 0.6830   |
| 1.588         | 0.8   | 40000 | 1.4880          | 0.6876   |
| 1.5682        | 1.0   | 50000 | 1.4680          | 0.6908   |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- 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},
}
```

Also cite the original work: [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).

```
@article{Beltagy2020Longformer,
  title={Longformer: The Long-Document Transformer},
  author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
  journal={arXiv:2004.05150},
  year={2020},
}
```

