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ia-detection-bart-base

Text Classification
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ia-detection-bart-base
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-bart-base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: autextification2023
      type: autextification2023
      config: detection_en
      split: train
      args: detection_en
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7699248809087578
    - name: F1
      type: f1
      value: 0.7727252160535721
    - name: Precision
      type: precision
      value: 0.7826047108422692
    - name: Recall
      type: recall
      value: 0.7630920464700626
---

<!-- 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. -->

# ia-detection-bart-base

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6968
- Accuracy: 0.7699
- F1: 0.7727
- Precision: 0.7826
- Recall: 0.7631

## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5243        | 1.0   | 3808  | 0.4726          | 0.7861   | 0.7309 | 0.9685    | 0.5869 |
| 0.627         | 2.0   | 7616  | 0.6362          | 0.6151   | 0.7120 | 0.5653    | 0.9618 |
| 0.6919        | 3.0   | 11424 | 0.7017          | 0.5052   | 0.0    | 0.0       | 0.0    |
| 0.7018        | 4.0   | 15232 | 0.6932          | 0.5052   | 0.0    | 0.0       | 0.0    |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3