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ia-detection-tiny-random-gptj

Text Classification
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ia-detection-tiny-random-gptj
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---
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-tiny-random-gptj
  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: 0.633198973983144
    - name: F1
      type: f1
      value: 0.7005683517798384
    - name: Precision
      type: precision
      value: 0.6022888003086023
    - name: Recall
      type: recall
      value: 0.8371760500446828
---

<!-- 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-tiny-random-gptj

This model is a fine-tuned version of [ydshieh/tiny-random-gptj-for-sequence-classification](https://huggingface.co/ydshieh/tiny-random-gptj-for-sequence-classification) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7313
- Accuracy: 0.6332
- F1: 0.7006
- Precision: 0.6023
- Recall: 0.8372

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6038        | 1.0   | 3808  | 0.5362          | 0.7309   | 0.7270 | 0.7294    | 0.7246 |
| 0.5303        | 2.0   | 7616  | 0.5109          | 0.7465   | 0.7358 | 0.7592    | 0.7139 |
| 0.4588        | 3.0   | 11424 | 0.5258          | 0.7424   | 0.7568 | 0.7097    | 0.8106 |
| 0.4459        | 4.0   | 15232 | 0.5137          | 0.7477   | 0.7428 | 0.7491    | 0.7366 |
| 0.3586        | 5.0   | 19040 | 0.5062          | 0.7572   | 0.7452 | 0.7745    | 0.7180 |
| 0.4072        | 6.0   | 22848 | 0.5264          | 0.7539   | 0.7565 | 0.7407    | 0.7730 |


### Framework versions

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