---
license: mit
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-deberta-v3-small
  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: 0.6245419567607182
    - name: F1
      type: f1
      value: 0.7308134379823322
    - name: Precision
      type: precision
      value: 0.5776958621047713
    - name: Recall
      type: recall
      value: 0.9943699731903485
---

<!-- 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-deberta-v3-small

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0506
- Accuracy: 0.6245
- F1: 0.7308
- Precision: 0.5777
- Recall: 0.9944

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2303        | 1.0   | 3808  | 0.3607          | 0.8984   | 0.8934 | 0.9231    | 0.8655 |
| 0.1757        | 2.0   | 7616  | 0.5627          | 0.8606   | 0.8731 | 0.7903    | 0.9754 |
| 0.0372        | 3.0   | 11424 | 0.4746          | 0.8978   | 0.9014 | 0.8575    | 0.9502 |
| 0.1016        | 4.0   | 15232 | 0.6520          | 0.8910   | 0.8932 | 0.8620    | 0.9267 |
| 0.0871        | 5.0   | 19040 | 0.7452          | 0.8730   | 0.8797 | 0.8235    | 0.9441 |
| 0.0002        | 6.0   | 22848 | 0.7724          | 0.8942   | 0.8942 | 0.8802    | 0.9087 |


### Framework versions

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