metadata
library_name: transformers
license: mit
base_model: dascim/juribert-tiny
tags:
- generated_from_trainer
model-index:
- name: bert-secabilite-regressor
results: []
bert-secabilite-regressor
This model is a fine-tuned version of dascim/juribert-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0255
- Model Preparation Time: 0.0004
- Mse: 0.0256
- Mae: 0.1108
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mse | Mae |
|---|---|---|---|---|---|---|
| 0.0971 | 1.0 | 108 | 0.0579 | 0.0004 | 0.0580 | 0.1952 |
| 0.0528 | 2.0 | 216 | 0.0377 | 0.0004 | 0.0379 | 0.1473 |
| 0.0423 | 3.0 | 324 | 0.0313 | 0.0004 | 0.0314 | 0.1301 |
| 0.0366 | 4.0 | 432 | 0.0284 | 0.0004 | 0.0285 | 0.1213 |
| 0.0342 | 5.0 | 540 | 0.0270 | 0.0004 | 0.0272 | 0.1163 |
| 0.032 | 6.0 | 648 | 0.0261 | 0.0004 | 0.0263 | 0.1132 |
| 0.0311 | 7.0 | 756 | 0.0257 | 0.0004 | 0.0258 | 0.1114 |
| 0.0306 | 8.0 | 864 | 0.0255 | 0.0004 | 0.0256 | 0.1108 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0
- Datasets 3.5.0
- Tokenizers 0.21.1