---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- fancy_dataset
metrics:
- accuracy
model-index:
- name: longformer-sep_tok
  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: 0.8209896449174101
---

<!-- 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-sep_tok

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: 0.4200
- Claim: {'precision': 0.8410830848577645, 'recall': 0.7810956443646161, 'f1-score': 0.8099802103139188, 'support': 13362.0}
- Majorclaim: {'precision': 0.6330965315503552, 'recall': 0.6943171402383135, 'f1-score': 0.6622950819672131, 'support': 2182.0}
- Premise: {'precision': 0.8362706950484474, 'recall': 0.8864536999595632, 'f1-score': 0.8606312814070352, 'support': 12365.0}
- Accuracy: 0.8210
- Macro avg: {'precision': 0.7701501038188557, 'recall': 0.7872888281874976, 'f1-score': 0.7776355245627223, 'support': 27909.0}
- Weighted avg: {'precision': 0.8226900267292406, 'recall': 0.8209896449174101, 'f1-score': 0.8208746007977725, '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                                                                                                          | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.5303          | {'precision': 0.7396921017402945, 'recall': 0.8270468492740608, 'f1-score': 0.780934209596495, 'support': 13362.0}  | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2182.0}                                               | {'precision': 0.8185673529184979, 'recall': 0.8585523655479175, 'f1-score': 0.8380832083366226, 'support': 12365.0} | 0.7763   | {'precision': 0.5194198182195975, 'recall': 0.5618664049406594, 'f1-score': 0.5396724726443726, 'support': 27909.0} | {'precision': 0.716806448897884, 'recall': 0.7763445483535777, 'f1-score': 0.7451983868899174, 'support': 27909.0}  |
| No log        | 2.0   | 82   | 0.4493          | {'precision': 0.8127147766323024, 'recall': 0.7787756323903607, 'f1-score': 0.7953833218680731, 'support': 13362.0} | {'precision': 0.7305801376597837, 'recall': 0.34051329055912005, 'f1-score': 0.4645201625507971, 'support': 2182.0} | {'precision': 0.8051533219761499, 'recall': 0.9173473513950667, 'f1-score': 0.8575964918912788, 'support': 12365.0} | 0.8059   | {'precision': 0.7828160787560786, 'recall': 0.6788787581148492, 'f1-score': 0.7058333254367164, 'support': 27909.0} | {'precision': 0.8029431915141912, 'recall': 0.8059049052277043, 'f1-score': 0.797078919478401, 'support': 27909.0}  |
| No log        | 3.0   | 123  | 0.4200          | {'precision': 0.8410830848577645, 'recall': 0.7810956443646161, 'f1-score': 0.8099802103139188, 'support': 13362.0} | {'precision': 0.6330965315503552, 'recall': 0.6943171402383135, 'f1-score': 0.6622950819672131, 'support': 2182.0}  | {'precision': 0.8362706950484474, 'recall': 0.8864536999595632, 'f1-score': 0.8606312814070352, 'support': 12365.0} | 0.8210   | {'precision': 0.7701501038188557, 'recall': 0.7872888281874976, 'f1-score': 0.7776355245627223, 'support': 27909.0} | {'precision': 0.8226900267292406, 'recall': 0.8209896449174101, 'f1-score': 0.8208746007977725, 'support': 27909.0} |


### Framework versions

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