metadata

library_name: transformers
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-bert-lenerbr-large-v1
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.865924092409241
          - name: Recall
            type: recall
            value: 0.9027956989247312
          - name: F1
            type: f1
            value: 0.8839755738050117
          - name: Accuracy
            type: accuracy
            value: 0.9754064638030079

ner-bert-lenerbr-large-v1

This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the lener_br dataset. It achieves the following results on the evaluation set:

Loss: 0.1337
Precision: 0.8659
Recall: 0.9028
F1: 0.8840
Accuracy: 0.9754

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: 2e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0521	1.0	979	0.1236	0.8260	0.8626	0.8439	0.9676
0.0338	2.0	1958	0.2221	0.6535	0.9185	0.7636	0.9536
0.0201	3.0	2937	0.1381	0.7573	0.9084	0.8260	0.9679
0.0163	4.0	3916	0.1337	0.8659	0.9028	0.8840	0.9754
0.0132	5.0	4895	0.1462	0.8392	0.9037	0.8702	0.9717
0.0105	6.0	5874	0.1423	0.8443	0.9211	0.8810	0.9738
0.0036	7.0	6853	0.1671	0.7994	0.9168	0.8541	0.9696
0.0055	8.0	7832	0.1884	0.7808	0.9161	0.8431	0.9672
0.0023	9.0	8811	0.1772	0.8182	0.9148	0.8638	0.9718
0.0019	10.0	9790	0.1788	0.8180	0.9181	0.8651	0.9718

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1