deft-glitter-211
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3537
- Accuracy: 0.3164
- Precision: 0.4989
- Recall: 0.3164
- F1: 0.3690
- Roc Auc: 0.6235
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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 1.4081 | 1.0 | 17 | 1.3928 | 0.2435 | 0.4923 | 0.2435 | 0.3213 | 0.5510 |
| 1.3773 | 2.0 | 34 | 1.3727 | 0.2682 | 0.4966 | 0.2682 | 0.3358 | 0.5862 |
| 1.3568 | 3.0 | 51 | 1.3597 | 0.3008 | 0.4996 | 0.3008 | 0.3587 | 0.6121 |
| 1.3458 | 4.0 | 68 | 1.3544 | 0.3151 | 0.4995 | 0.3151 | 0.3677 | 0.6220 |
| 1.3409 | 5.0 | 85 | 1.3537 | 0.3164 | 0.4989 | 0.3164 | 0.3690 | 0.6235 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0