pretrain_model
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6409
- Precision: 0.6385
- Recall: 0.6046
- F1: 0.6211
- Accuracy: 0.6354
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.6994 | 0.0061 | 250 | 0.6909 | 0.5946 | 0.1740 | 0.2692 | 0.5296 |
| 0.6935 | 0.0122 | 500 | 0.6461 | 0.6368 | 0.5923 | 0.6138 | 0.6288 |
| 0.6862 | 0.0184 | 750 | 0.6710 | 0.6268 | 0.6416 | 0.6341 | 0.6313 |
| 0.6629 | 0.0245 | 1000 | 0.8414 | 0.5772 | 0.7777 | 0.6626 | 0.6056 |
| 0.6729 | 0.0306 | 1250 | 0.6509 | 0.6373 | 0.5992 | 0.6177 | 0.6306 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3