meta_data/README_s42_e4.md

---
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- essays_su_g
metrics:
- accuracy
model-index:
- name: longformer-simple
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: essays_su_g
      type: essays_su_g
      config: simple
      split: train[80%:100%]
      args: simple
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8299721206415873
---

<!-- 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 essays_su_g dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4321
- Claim: {'precision': 0.5835557928457021, 'recall': 0.52447216890595, 'f1-score': 0.5524387161991408, 'support': 4168.0}
- Majorclaim: {'precision': 0.6944444444444444, 'recall': 0.824814126394052, 'f1-score': 0.754035683942226, 'support': 2152.0}
- O: {'precision': 0.934596507248031, 'recall': 0.8874918707999133, 'f1-score': 0.9104353143937287, 'support': 9226.0}
- Premise: {'precision': 0.8580758203249442, 'recall': 0.8924045390540877, 'f1-score': 0.8749035689634171, 'support': 12073.0}
- Accuracy: 0.8300
- Macro avg: {'precision': 0.7676681412157805, 'recall': 0.7822956762885007, 'f1-score': 0.7729533208746281, 'support': 27619.0}
- Weighted avg: {'precision': 0.8294594932357695, 'recall': 0.8299721206415873, 'f1-score': 0.8286917107662684, 'support': 27619.0}

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                                          | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.6033          | {'precision': 0.4527056753189617, 'recall': 0.2468809980806142, 'f1-score': 0.31951560316721006, 'support': 4168.0}  | {'precision': 0.5835601524224279, 'recall': 0.49814126394052044, 'f1-score': 0.5374780646778641, 'support': 2152.0} | {'precision': 0.8875888965359028, 'recall': 0.8387166702796445, 'f1-score': 0.862460989745876, 'support': 9226.0}  | {'precision': 0.7685754850922859, 'recall': 0.9416052348215025, 'f1-score': 0.8463371054198927, 'support': 12073.0} | 0.7678   | {'precision': 0.6731075523423946, 'recall': 0.6313360417805705, 'f1-score': 0.6414479407527107, 'support': 27619.0} | {'precision': 0.7462473548536118, 'recall': 0.7678409790361708, 'f1-score': 0.7481547773024915, 'support': 27619.0} |
| No log        | 2.0   | 82   | 0.4684          | {'precision': 0.5774099318403116, 'recall': 0.42682341650671785, 'f1-score': 0.49082632087184436, 'support': 4168.0} | {'precision': 0.6601866251944012, 'recall': 0.7890334572490706, 'f1-score': 0.7188823031329382, 'support': 2152.0}  | {'precision': 0.9429934406678593, 'recall': 0.8570344678083677, 'f1-score': 0.8979615013343932, 'support': 9226.0} | {'precision': 0.8198954421618437, 'recall': 0.9223059720036445, 'f1-score': 0.8680907460824822, 'support': 12073.0} | 0.8153   | {'precision': 0.7501213599661041, 'recall': 0.7487993283919501, 'f1-score': 0.7439402178554144, 'support': 27619.0} | {'precision': 0.8119780357779204, 'recall': 0.815344509214671, 'f1-score': 0.809509801603999, 'support': 27619.0}   |
| No log        | 3.0   | 123  | 0.4395          | {'precision': 0.5962599632127529, 'recall': 0.4666506717850288, 'f1-score': 0.5235531628532973, 'support': 4168.0}   | {'precision': 0.7146464646464646, 'recall': 0.7890334572490706, 'f1-score': 0.75, 'support': 2152.0}                | {'precision': 0.9242167175658862, 'recall': 0.885649252113592, 'f1-score': 0.9045220567886201, 'support': 9226.0}  | {'precision': 0.8378995433789954, 'recall': 0.9119522902344074, 'f1-score': 0.873358981477809, 'support': 12073.0}  | 0.8264   | {'precision': 0.7682556722010248, 'recall': 0.7633214178455248, 'f1-score': 0.7628585502799315, 'support': 27619.0} | {'precision': 0.820663866978074, 'recall': 0.8263876317028133, 'f1-score': 0.8213676477094007, 'support': 27619.0}  |
| No log        | 4.0   | 164  | 0.4321          | {'precision': 0.5835557928457021, 'recall': 0.52447216890595, 'f1-score': 0.5524387161991408, 'support': 4168.0}     | {'precision': 0.6944444444444444, 'recall': 0.824814126394052, 'f1-score': 0.754035683942226, 'support': 2152.0}    | {'precision': 0.934596507248031, 'recall': 0.8874918707999133, 'f1-score': 0.9104353143937287, 'support': 9226.0}  | {'precision': 0.8580758203249442, 'recall': 0.8924045390540877, 'f1-score': 0.8749035689634171, 'support': 12073.0} | 0.8300   | {'precision': 0.7676681412157805, 'recall': 0.7822956762885007, 'f1-score': 0.7729533208746281, 'support': 27619.0} | {'precision': 0.8294594932357695, 'recall': 0.8299721206415873, 'f1-score': 0.8286917107662684, 'support': 27619.0} |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2