readme: ---
  license: cc-by-sa-4.0
  pipeline_tag: fill-mask
  language: en
  arxiv: 
  tags:
  - long_documents
  datasets:
  - c4
  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},
  }
  ```
  
  