distilbert-base-uncased-binaryclassification-v2
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4987
- Accuracy: 0.7486
- Precision: 0.6324
- Recall: 0.6653
- F1: 0.6484
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4802 | 1.0 | 4537 | 0.4926 | 0.7400 | 0.6494 | 0.5519 | 0.5967 |
| 0.4249 | 2.0 | 9074 | 0.4987 | 0.7486 | 0.6324 | 0.6653 | 0.6484 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1