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---
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
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