---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-distilbert-base-cased
  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: 0.6757969952363503
    - name: F1
      type: f1
      value: 0.7481855699444998
    - name: Precision
      type: precision
      value: 0.6215273673010995
    - name: Recall
      type: recall
      value: 0.9396782841823056
---

<!-- 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-distilbert-base-cased

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1147
- Accuracy: 0.6758
- F1: 0.7482
- Precision: 0.6215
- Recall: 0.9397

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4298        | 1.0   | 3808  | 0.5010          | 0.7725   | 0.8114 | 0.6964    | 0.9718 |
| 0.4464        | 2.0   | 7616  | 0.4737          | 0.8514   | 0.8531 | 0.8493    | 0.8568 |
| 0.4296        | 3.0   | 11424 | 0.4870          | 0.8402   | 0.8424 | 0.8363    | 0.8486 |
| 0.2034        | 4.0   | 15232 | 0.5404          | 0.8493   | 0.8510 | 0.8475    | 0.8545 |
| 0.0803        | 5.0   | 19040 | 0.6954          | 0.8520   | 0.8491 | 0.8724    | 0.8269 |


### Framework versions

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