metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base_fold_3
results: []
roberta-base_fold_3
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0312
- Accuracy: 0.9941
- F1: 0.9889
- Precision: 0.9940
- Recall: 0.9839
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: 38
- eval_batch_size: 38
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0572 | 1.0 | 3983 | 0.0345 | 0.9933 | 0.9876 | 0.9935 | 0.9817 |
| 0.0328 | 2.0 | 7966 | 0.0280 | 0.9943 | 0.9894 | 0.9942 | 0.9846 |
| 0.0293 | 3.0 | 11949 | 0.0312 | 0.9941 | 0.9889 | 0.9940 | 0.9839 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2