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: 0.6682
- Precision: 0.1905
- Recall: 0.5929
- F1: 0.2884
- Accuracy: 0.7439
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 |
|---|---|---|---|---|---|---|---|
| 0.6693 | 0.4292 | 100 | 0.6514 | 0.1965 | 0.4060 | 0.2648 | 0.8027 |
| 0.6306 | 0.8584 | 200 | 0.6153 | 0.1557 | 0.6739 | 0.2530 | 0.6517 |
| 0.5589 | 1.2876 | 300 | 0.6298 | 0.1694 | 0.6552 | 0.2693 | 0.6887 |
| 0.552 | 1.7167 | 400 | 0.6102 | 0.1726 | 0.6355 | 0.2715 | 0.7015 |
| 0.5035 | 2.1459 | 500 | 0.6432 | 0.1808 | 0.6293 | 0.2809 | 0.7180 |
| 0.4624 | 2.5751 | 600 | 0.6507 | 0.1904 | 0.6054 | 0.2897 | 0.7402 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0