metadata

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

test_v6

This model is a fine-tuned version of 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