metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - autextification2023
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: ia-detection-roberta-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: 0.512550384756321
          - name: F1
            type: f1
            value: 0.6777299981830295
          - name: Precision
            type: precision
            value: 0.512550384756321
          - name: Recall
            type: recall
            value: 1

ia-detection-roberta-base

This model is a fine-tuned version of roberta-base on the autextification2023 dataset. It achieves the following results on the evaluation set:

Loss: 0.6928
Accuracy: 0.5126
F1: 0.6777
Precision: 0.5126
Recall: 1.0

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
0.7021	1.0	3808	0.6950	0.5052	0.0	0.0	0.0
0.6936	2.0	7616	0.6937	0.4948	0.6621	0.4948	1.0
0.692	3.0	11424	0.6936	0.5052	0.0	0.0	0.0
0.6988	4.0	15232	0.6952	0.4948	0.6621	0.4948	1.0
0.6951	5.0	19040	0.6931	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