CS221-xlm-roberta-base-esp-finetuned-finetuned-esp-tapt
This model is a fine-tuned version of Kuongan/xlm-roberta-base-esp-finetuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0895
- F1: 0.9428
- Roc Auc: 0.9591
- Accuracy: 0.8559
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 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_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1242 | 1.0 | 187 | 0.0895 | 0.9428 | 0.9591 | 0.8559 |
| 0.1169 | 2.0 | 374 | 0.0948 | 0.9350 | 0.9557 | 0.8328 |
| 0.0874 | 3.0 | 561 | 0.0925 | 0.9426 | 0.9647 | 0.8488 |
| 0.0677 | 4.0 | 748 | 0.0946 | 0.9422 | 0.9626 | 0.8498 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0