CS221-xlm-roberta-base-ukr-finetuned-finetuned-ukr-tapt
This model is a fine-tuned version of Kuongan/xlm-roberta-base-ukr-finetuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0540
- F1: 0.8996
- Roc Auc: 0.9476
- Accuracy: 0.9139
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 |
|---|---|---|---|---|---|---|
| 0.0991 | 1.0 | 237 | 0.0540 | 0.8996 | 0.9476 | 0.9139 |
| 0.063 | 2.0 | 474 | 0.0494 | 0.8996 | 0.9349 | 0.9147 |
| 0.0564 | 3.0 | 711 | 0.0589 | 0.8891 | 0.9324 | 0.9013 |
| 0.0446 | 4.0 | 948 | 0.0759 | 0.8857 | 0.9314 | 0.8863 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0