---
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