full_finetune
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0222
- Mae: 11.9511
- Rmse: 14.9036
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: 5e-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
- lr_scheduler_warmup_steps: 50
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse |
|---|---|---|---|---|---|
| 0.0445 | 1.0 | 43 | 0.0424 | 17.0126 | 20.5993 |
| 0.0361 | 2.0 | 86 | 0.0373 | 15.7193 | 19.3194 |
| 0.0194 | 3.0 | 129 | 0.0271 | 13.5765 | 16.4728 |
| 0.0165 | 4.0 | 172 | 0.0286 | 13.281 | 16.9142 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 2.19.0
- Tokenizers 0.22.2
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Model tree for LucasLisboadev/full_finetune
Base model
distilbert/distilbert-base-uncased