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
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Evaluation results
- Accuracy on autextification2023self-reported0.513
- F1 on autextification2023self-reported0.678
- Precision on autextification2023self-reported0.513
- Recall on autextification2023self-reported1.000