metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model
results: []
model
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.8504
- Precision: 0.2663
- Recall: 0.2503
- F1: 0.2580
- Accuracy: 0.8740
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: 4
- eval_batch_size: 4
- 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: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0738 | 0.4292 | 100 | 3.5742 | 0.3714 | 0.1080 | 0.1673 | 0.9059 |
| 0.0518 | 0.8584 | 200 | 3.6916 | 0.4130 | 0.1059 | 0.1686 | 0.9086 |
| 0.0464 | 1.2876 | 300 | 2.9332 | 0.3185 | 0.2461 | 0.2777 | 0.8879 |
| 0.0313 | 1.7167 | 400 | 3.4018 | 0.3495 | 0.1568 | 0.2165 | 0.9007 |
| 0.0262 | 2.1459 | 500 | 3.6431 | 0.3581 | 0.1599 | 0.2211 | 0.9014 |
| 0.0374 | 2.5751 | 600 | 3.2736 | 0.3184 | 0.2139 | 0.2559 | 0.8911 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0