|
|
--- |
|
|
library_name: transformers |
|
|
license: mit |
|
|
base_model: dascim/juribert-tiny |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: bert-secabilite-regressor |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# bert-secabilite-regressor |
|
|
|
|
|
This model is a fine-tuned version of [dascim/juribert-tiny](https://huggingface.co/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 |
|
|
|
