sentiment-model
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: 0.7470
- Accuracy: 0.6598
- F1 Weighted: 0.6493
- F1 Macro: 0.6493
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: 32
- eval_batch_size: 32
- 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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Macro |
|---|---|---|---|---|---|---|
| 1.0498 | 1.0 | 58 | 0.8737 | 0.6080 | 0.5529 | 0.5529 |
| 0.8304 | 2.0 | 116 | 0.7226 | 0.6975 | 0.6881 | 0.6881 |
| 0.6785 | 3.0 | 174 | 0.7117 | 0.6821 | 0.6736 | 0.6736 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.23.1