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--- |
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license: cc-by-sa-4.0 |
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pipeline_tag: fill-mask |
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arxiv: 2210.05529 |
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language: en |
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tags: |
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- long-documents |
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datasets: |
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- c4 |
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model-index: |
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- name: kiddothe2b/longformer-base-4096 |
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results: [] |
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--- |
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# Longformer / longformer-base-4096 |
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## Model description |
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[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. This version of Longformer presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). |
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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 original Longformer released by Beltagy et al. (2020). It supports sequences of length up to 4,096. |
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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. |
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## Intended uses & limitations |
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You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. |
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See the [model hub](https://huggingface.co/models?filter=longformer) to look for fine-tuned versions on a task that |
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interests you. |
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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. |
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## How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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from transformers import pipeline |
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mlm_model = pipeline('fill-mask', model='kiddothe2b/longformer-base-4096', trust_remote_code=True) |
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mlm_model("Hello I'm a <mask> model.") |
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``` |
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You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks: |
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```python |
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from transformers import AutoTokenizer, AutoModelforSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/longformer-base-4096", trust_remote_code=True) |
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doc_classifier = AutoModelforSequenceClassification("kiddothe2b/longformer-base-4096", trust_remote_code=True) |
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``` |
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## Limitations and bias |
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The training data used for this model contains a lot of unfiltered content from the internet, which is far from |
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neutral. Therefore, the model can have biased predictions. |
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## Training procedure |
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### Training and evaluation data |
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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). |
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### Training hyperparameters |
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TThe following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 50000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.7067 | 0.2 | 10000 | 1.5923 | 0.6714 | |
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| 1.6532 | 0.4 | 20000 | 1.5494 | 0.6784 | |
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| 1.622 | 0.6 | 30000 | 1.5208 | 0.6830 | |
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| 1.588 | 0.8 | 40000 | 1.4880 | 0.6876 | |
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| 1.5682 | 1.0 | 50000 | 1.4680 | 0.6908 | |
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### Framework versions |
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- Transformers 4.19.0.dev0 |
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- Pytorch 1.11.0 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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## Citing |
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If you use HAT in your research, please cite: |
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[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). |
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``` |
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@misc{chalkidis-etal-2022-hat, |
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url = {https://arxiv.org/abs/2210.05529}, |
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author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond}, |
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title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification}, |
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publisher = {arXiv}, |
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year = {2022}, |
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} |
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``` |
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Also cite the original work: [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150). |
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``` |
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@article{Beltagy2020Longformer, |
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title={Longformer: The Long-Document Transformer}, |
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author={Iz Beltagy and Matthew E. Peters and Arman Cohan}, |
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journal={arXiv:2004.05150}, |
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year={2020}, |
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} |
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``` |
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