metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
pipeline_tag: text-classification
model-index:
- name: fator-fallacy-detector
results: []
fator-fallacy-detector
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7968
- Accuracy: 0.8598
- F1 Macro: 0.6798
- F1 Weighted: 0.7825
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 2.0353 | 1.0 | 69 | 2.2417 | 0.4041 | 0.3083 | 0.4288 |
| 1.1018 | 2.0 | 138 | 1.8271 | 0.5619 | 0.5319 | 0.5691 |
| 1.0166 | 3.0 | 207 | 1.0606 | 0.7808 | 0.6107 | 0.6679 |
| 0.7968 | 4.0 | 276 | 0.9268 | 0.8598 | 0.6798 | 0.7825 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2