---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- fancy_dataset
metrics:
- accuracy
model-index:
- name: longformer-simple
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: fancy_dataset
      type: fancy_dataset
      config: simple
      split: test
      args: simple
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8226736894908453
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# longformer-simple

This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4587
- Claim: {'precision': 0.5772692208794035, 'recall': 0.5279868297271872, 'f1-score': 0.5515292961552635, 'support': 4252.0}
- Majorclaim: {'precision': 0.6656682890303257, 'recall': 0.8148487626031164, 'f1-score': 0.7327426334226252, 'support': 2182.0}
- O: {'precision': 0.9301160937855679, 'recall': 0.8810781671159029, 'f1-score': 0.9049332816566081, 'support': 9275.0}
- Premise: {'precision': 0.8568813181564913, 'recall': 0.8823770491803279, 'f1-score': 0.8694423131284578, 'support': 12200.0}
- Accuracy: 0.8227
- Macro avg: {'precision': 0.757483730462947, 'recall': 0.7765727021566337, 'f1-score': 0.7646618810907386, 'support': 27909.0}
- Weighted avg: {'precision': 0.8236703495364839, 'recall': 0.8226736894908453, 'f1-score': 0.8221147085496641, 'support': 27909.0}

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.5844          | {'precision': 0.4909433962264151, 'recall': 0.30597365945437444, 'f1-score': 0.37699217618081715, 'support': 4252.0} | {'precision': 0.5969423210562891, 'recall': 0.3936755270394134, 'f1-score': 0.4744545705606187, 'support': 2182.0} | {'precision': 0.825323567773653, 'recall': 0.886900269541779, 'f1-score': 0.8550046772684753, 'support': 9275.0}   | {'precision': 0.8012704829278856, 'recall': 0.9098360655737705, 'f1-score': 0.8521091620926572, 'support': 12200.0} | 0.7699   | {'precision': 0.6786199419960607, 'recall': 0.6240963804023343, 'f1-score': 0.639640146525642, 'support': 27909.0}  | {'precision': 0.7460100844931877, 'recall': 0.7698591852090724, 'f1-score': 0.7511602266394221, 'support': 27909.0} |
| No log        | 2.0   | 82   | 0.4763          | {'precision': 0.5736620565243535, 'recall': 0.4487300094073377, 'f1-score': 0.5035629453681709, 'support': 4252.0}   | {'precision': 0.7095454545454546, 'recall': 0.7153987167736022, 'f1-score': 0.7124600638977636, 'support': 2182.0} | {'precision': 0.8935006435006435, 'recall': 0.8982210242587602, 'f1-score': 0.8958546158395613, 'support': 9275.0} | {'precision': 0.8362814916915537, 'recall': 0.8951639344262295, 'f1-score': 0.8647214854111407, 'support': 12200.0} | 0.8141   | {'precision': 0.7532474115655013, 'recall': 0.7393784212164823, 'f1-score': 0.7441497776291592, 'support': 27909.0} | {'precision': 0.8053779036606528, 'recall': 0.8141101436812498, 'f1-score': 0.80814042735527, 'support': 27909.0}   |
| No log        | 3.0   | 123  | 0.4587          | {'precision': 0.5772692208794035, 'recall': 0.5279868297271872, 'f1-score': 0.5515292961552635, 'support': 4252.0}   | {'precision': 0.6656682890303257, 'recall': 0.8148487626031164, 'f1-score': 0.7327426334226252, 'support': 2182.0} | {'precision': 0.9301160937855679, 'recall': 0.8810781671159029, 'f1-score': 0.9049332816566081, 'support': 9275.0} | {'precision': 0.8568813181564913, 'recall': 0.8823770491803279, 'f1-score': 0.8694423131284578, 'support': 12200.0} | 0.8227   | {'precision': 0.757483730462947, 'recall': 0.7765727021566337, 'f1-score': 0.7646618810907386, 'support': 27909.0}  | {'precision': 0.8236703495364839, 'recall': 0.8226736894908453, 'f1-score': 0.8221147085496641, 'support': 27909.0} |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1