---
license: mit
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-bert-tiny
  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: 0.699019787467937
    - name: F1
      type: f1
      value: 0.7522153927372828
    - name: Precision
      type: precision
      value: 0.6506621436492922
    - name: Recall
      type: recall
      value: 0.891331546023235
---

<!-- 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-bert-tiny

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9775
- Accuracy: 0.6990
- F1: 0.7522
- Precision: 0.6507
- Recall: 0.8913

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3606        | 1.0   | 3808  | 0.4135          | 0.8068   | 0.8126 | 0.7795    | 0.8486 |
| 0.39          | 2.0   | 7616  | 0.4197          | 0.8213   | 0.8147 | 0.8344    | 0.7959 |
| 0.386         | 3.0   | 11424 | 0.5145          | 0.8210   | 0.8249 | 0.7977    | 0.8540 |
| 0.277         | 4.0   | 15232 | 0.7962          | 0.8080   | 0.7887 | 0.8633    | 0.7259 |
| 0.1913        | 5.0   | 19040 | 0.8833          | 0.8115   | 0.8001 | 0.8396    | 0.7642 |
| 0.2053        | 6.0   | 22848 | 0.9313          | 0.8180   | 0.8070 | 0.8468    | 0.7708 |


### Framework versions

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