metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Experiment0_RoBERTa_baseline
results: []
Experiment0_RoBERTa_baseline
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1016
- Accuracy: 0.9836
- Precision: 0.9837
- Recall: 0.9836
- F1: 0.9836
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: 16
- 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.1125 | 1.0 | 4434 | 0.1137 | 0.9735 | 0.9737 | 0.9735 | 0.9735 |
| 0.0743 | 2.0 | 8868 | 0.1086 | 0.9779 | 0.9780 | 0.9779 | 0.9779 |
| 0.0301 | 3.0 | 13302 | 0.1164 | 0.9797 | 0.9799 | 0.9797 | 0.9797 |
| 0.0058 | 4.0 | 17736 | 0.1016 | 0.9836 | 0.9837 | 0.9836 | 0.9836 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1