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: 3.7669
- Precision: 0.2852
- Recall: 0.2420
- F1: 0.2618
- Accuracy: 0.8806
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 |
|---|---|---|---|---|---|---|---|
| 0.0387 | 0.4292 | 100 | 3.2626 | 0.2781 | 0.2316 | 0.2527 | 0.8801 |
| 0.0432 | 0.8584 | 200 | 4.3510 | 0.3575 | 0.1485 | 0.2098 | 0.9021 |
| 0.0305 | 1.2876 | 300 | 4.4340 | 0.3663 | 0.1578 | 0.2206 | 0.9024 |
| 0.0303 | 1.7167 | 400 | 4.2810 | 0.3418 | 0.1537 | 0.2120 | 0.9000 |
| 0.0347 | 2.1459 | 500 | 4.3217 | 0.3607 | 0.1828 | 0.2426 | 0.9001 |
| 0.0235 | 2.5751 | 600 | 4.3738 | 0.3302 | 0.1817 | 0.2344 | 0.8961 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0