---
library_name: transformers
base_model: models/distill-robertalex-3L-trained
tags:
- generated_from_trainer
datasets:
- adalbertojunior/entities
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_v6
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: adalbertojunior/entities
      type: adalbertojunior/entities
      config: segmentacao
      split: validation
      args: segmentacao
    metrics:
    - name: Precision
      type: precision
      value: 0.7678083439606486
    - name: Recall
      type: recall
      value: 0.8550415905863258
    - name: F1
      type: f1
      value: 0.8090804377039739
    - name: Accuracy
      type: accuracy
      value: 0.9699217442249749
---

<!-- 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. -->

# test_v6

This model is a fine-tuned version of [models/distill-robertalex-3L-trained](https://huggingface.co/models/distill-robertalex-3L-trained) on the adalbertojunior/entities dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1536
- Precision: 0.7678
- Recall: 0.8550
- F1: 0.8091
- Accuracy: 0.9699

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0925        | 19.3898 | 7000 | 0.1536          | 0.7678    | 0.8550 | 0.8091 | 0.9699   |

### Test set results

| Label               | Precision | Recall | F1-Score | Support |
|---------------------|-----------|--------|----------|---------|
| ATRIBUICAO         | 0.82      | 0.82   | 0.82     | 221     |
| DECISAO            | 0.81      | 0.82   | 0.82     | 544     |
| FUNCAO             | 0.94      | 0.89   | 0.91     | 486     |
| FUNDAMENTO         | 0.89      | 0.83   | 0.86     | 1501    |
| LOCAL              | 0.85      | 0.84   | 0.85     | 245     |
| ORGANIZACAO        | 0.90      | 0.86   | 0.88     | 626     |
| PEDIDO             | 0.86      | 0.81   | 0.83     | 4341    |
| PESSOA             | 0.95      | 0.94   | 0.95     | 654     |
| REFLEXO            | 0.85      | 0.84   | 0.85     | 358     |
| TIPO_ACAO          | 0.93      | 0.89   | 0.91     | 341     |
| TRIBUNAL           | 0.96      | 0.92   | 0.94     | 190     |
| VALOR_ACORDO       | 0.91      | 0.71   | 0.79     | 41      |
| VALOR_CAUSA        | 0.89      | 0.92   | 0.90     | 62      |
| VALOR_CONDENACAO   | 0.89      | 0.76   | 0.82     | 72      |
| VALOR_CUSTAS       | 0.95      | 0.93   | 0.94     | 134     |
| VALOR_PEDIDO       | 0.94      | 0.81   | 0.87     | 308     |
| VARA               | 0.95      | 0.96   | 0.96     | 81      |
| **micro avg**      | 0.88      | 0.84   | 0.86     | 10205   |
| **macro avg**      | 0.90      | 0.86   | 0.88     | 10205   |
| **weighted avg**   | 0.88      | 0.84   | 0.86     | 10205   |



### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.0.1
- Tokenizers 0.21.0