deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4254
- F1 Macro: 0.8118
- F1 Micro: 0.8346
- Accuracy Balanced: 0.8071
- Accuracy: 0.8346
- Precision Macro: 0.8175
- Recall Macro: 0.8071
- Precision Micro: 0.8346
- Recall Micro: 0.8346
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: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Accuracy Balanced | F1 Macro | F1 Micro | Validation Loss | Precision Macro | Precision Micro | Recall Macro | Recall Micro |
|---|
| 0.1959 | 1.0 | 12340 | 0.8333 | 0.7971 | 0.8067 | 0.8333 | 0.3943 | 0.8209 | 0.8333 | 0.7971 | 0.8333 |
| 0.1375 | 2.0 | 24680 | 0.4254 | 0.8118 | 0.8346 | 0.8071 | 0.8346 | 0.8175 | 0.8071 | 0.8346 | 0.8346 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
