README.md

---
library_name: transformers
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- stab-gurevych-essays
metrics:
- accuracy
model-index:
- name: longformer-simple
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: stab-gurevych-essays
      type: stab-gurevych-essays
      config: simple
      split: train[0%:20%]
      args: simple
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8751580602166706
---

<!-- 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 stab-gurevych-essays dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3326
- Claim: {'precision': 0.6375421311900441, 'recall': 0.6000488042947779, 'f1-score': 0.6182275298554368, 'support': 4098.0}
- Majorclaim: {'precision': 0.8534005037783375, 'recall': 0.7853500231803431, 'f1-score': 0.8179623370352487, 'support': 2157.0}
- O: {'precision': 0.9584632404706829, 'recall': 0.9674144756877474, 'f1-score': 0.9629180559765586, 'support': 9851.0}
- Premise: {'precision': 0.884906500445236, 'recall': 0.9064994298745724, 'f1-score': 0.8955728286583305, 'support': 13155.0}
- Accuracy: 0.8752
- Macro avg: {'precision': 0.8335780939710751, 'recall': 0.8148281832593602, 'f1-score': 0.8236701878813937, 'support': 29261.0}
- Weighted avg: {'precision': 0.8727042457708365, 'recall': 0.8751580602166706, 'f1-score': 0.8736819489681839, 'support': 29261.0}

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                 | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.5843          | {'precision': 0.48984771573604063, 'recall': 0.14128843338213762, 'f1-score': 0.21931818181818183, 'support': 4098.0} | {'precision': 0.5396243701328447, 'recall': 0.5461288827074641, 'f1-score': 0.5428571428571428, 'support': 2157.0} | {'precision': 0.8916069169126951, 'recall': 0.858390011166379, 'f1-score': 0.8746832169640548, 'support': 9851.0}  | {'precision': 0.7743724104313917, 'recall': 0.9660965412390726, 'f1-score': 0.8596746372645179, 'support': 13155.0} | 0.7834   | {'precision': 0.673862853303243, 'recall': 0.6279759671237634, 'f1-score': 0.6241332947259743, 'support': 29261.0}  | {'precision': 0.7566882370115429, 'recall': 0.7833635214107515, 'f1-score': 0.7516910901801511, 'support': 29261.0} |
| No log        | 2.0   | 82   | 0.4171          | {'precision': 0.5352343493936415, 'recall': 0.39848706686188384, 'f1-score': 0.45684711148412366, 'support': 4098.0}  | {'precision': 0.8575712143928036, 'recall': 0.5303662494204914, 'f1-score': 0.6553995989687769, 'support': 2157.0} | {'precision': 0.9516030844155844, 'recall': 0.952086082631205, 'f1-score': 0.9518445222509768, 'support': 9851.0}  | {'precision': 0.8249001331557922, 'recall': 0.9418472063854048, 'f1-score': 0.8795031055900621, 'support': 13155.0} | 0.8389   | {'precision': 0.7923271953394554, 'recall': 0.7056966513247462, 'f1-score': 0.7358985845734849, 'support': 29261.0} | {'precision': 0.8293966272342979, 'recall': 0.8388640169508903, 'f1-score': 0.8281446341741303, 'support': 29261.0} |
| No log        | 3.0   | 123  | 0.3525          | {'precision': 0.6357409713574097, 'recall': 0.49829184968277207, 'f1-score': 0.5586867305061559, 'support': 4098.0}   | {'precision': 0.7525641025641026, 'recall': 0.8164116828929068, 'f1-score': 0.7831887925283523, 'support': 2157.0} | {'precision': 0.9471273523847455, 'recall': 0.9655872500253782, 'f1-score': 0.956268221574344, 'support': 9851.0}  | {'precision': 0.8749451192741109, 'recall': 0.9089319650323071, 'f1-score': 0.8916147794638529, 'support': 13155.0} | 0.8637   | {'precision': 0.8025943863950922, 'recall': 0.797305686908341, 'f1-score': 0.7974396310181762, 'support': 29261.0}  | {'precision': 0.8567240306977373, 'recall': 0.863675199070435, 'f1-score': 0.8587617347894375, 'support': 29261.0}  |
| No log        | 4.0   | 164  | 0.3385          | {'precision': 0.6185015290519877, 'recall': 0.5922401171303074, 'f1-score': 0.6050860134629769, 'support': 4098.0}    | {'precision': 0.7913082842915347, 'recall': 0.8103847936949466, 'f1-score': 0.8007329363261567, 'support': 2157.0} | {'precision': 0.9529177057356608, 'recall': 0.9697492640341082, 'f1-score': 0.9612598108271282, 'support': 9851.0} | {'precision': 0.8938411050904373, 'recall': 0.8903078677309008, 'f1-score': 0.8920709878894051, 'support': 13155.0} | 0.8694   | {'precision': 0.8141421560424051, 'recall': 0.8156705106475658, 'f1-score': 0.8147874371264168, 'support': 29261.0} | {'precision': 0.8676102420265399, 'recall': 0.8694166296435528, 'f1-score': 0.8684387980236479, 'support': 29261.0} |
| No log        | 5.0   | 205  | 0.3326          | {'precision': 0.6375421311900441, 'recall': 0.6000488042947779, 'f1-score': 0.6182275298554368, 'support': 4098.0}    | {'precision': 0.8534005037783375, 'recall': 0.7853500231803431, 'f1-score': 0.8179623370352487, 'support': 2157.0} | {'precision': 0.9584632404706829, 'recall': 0.9674144756877474, 'f1-score': 0.9629180559765586, 'support': 9851.0} | {'precision': 0.884906500445236, 'recall': 0.9064994298745724, 'f1-score': 0.8955728286583305, 'support': 13155.0}  | 0.8752   | {'precision': 0.8335780939710751, 'recall': 0.8148281832593602, 'f1-score': 0.8236701878813937, 'support': 29261.0} | {'precision': 0.8727042457708365, 'recall': 0.8751580602166706, 'f1-score': 0.8736819489681839, 'support': 29261.0} |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 2.19.1
- Tokenizers 0.20.1