distilbert-base-uncased-binaryclassification-test1
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: 0.4215
- Accuracy: 0.8358
- Precision: 0.8442
- Recall: 0.9319
- F1: 0.8859
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4919 | 1.0 | 230 | 0.4459 | 0.7819 | 0.8369 | 0.8459 | 0.8414 |
| 0.296 | 2.0 | 460 | 0.3710 | 0.8235 | 0.8462 | 0.9068 | 0.8754 |
| 0.3076 | 3.0 | 690 | 0.4215 | 0.8358 | 0.8442 | 0.9319 | 0.8859 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1