metadata
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-large-ToM0
results: []
roberta-large-ToM0
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4166
- Accuracy: 0.8908
- F1: 0.9132
- Precision: 0.8772
- Recall: 0.9524
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: 2015
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5872 | 1.0 | 93 | 0.3094 | 0.8718 | 0.8864 | 0.9070 | 0.8667 |
| 0.3277 | 2.0 | 186 | 0.4200 | 0.9103 | 0.9263 | 0.88 | 0.9778 |
| 0.2431 | 3.0 | 279 | 0.5804 | 0.8590 | 0.8791 | 0.8696 | 0.8889 |
| 0.135 | 4.0 | 372 | 0.5604 | 0.8846 | 0.9032 | 0.875 | 0.9333 |
| 0.0747 | 5.0 | 465 | 0.6683 | 0.8846 | 0.9032 | 0.875 | 0.9333 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0