metadata

language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: bertimbau-base-lener_br
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          args: lener_br
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9692504609383333
model-index:
  - name: Luciano/bertimbau-base-lener_br
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9824282794418222
            verified: true
          - name: Precision
            type: precision
            value: 0.9877557596262284
            verified: true
          - name: Recall
            type: recall
            value: 0.9870401674313772
            verified: true
          - name: F1
            type: f1
            value: 0.9873978338768773
            verified: true
          - name: loss
            type: loss
            value: 0.11542011797428131
            verified: true

bertimbau-base-lener_br

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

Loss: 0.2298
Precision: 0.8501
Recall: 0.9138
F1: 0.8808
Accuracy: 0.9693

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0686	1.0	1957	0.1399	0.7759	0.8669	0.8189	0.9641
0.0437	2.0	3914	0.1457	0.7997	0.8938	0.8441	0.9623
0.0313	3.0	5871	0.1675	0.8466	0.8744	0.8603	0.9651
0.0201	4.0	7828	0.1621	0.8713	0.8839	0.8775	0.9718
0.0137	5.0	9785	0.1811	0.7783	0.9159	0.8415	0.9645
0.0105	6.0	11742	0.1836	0.8568	0.9009	0.8783	0.9692
0.0105	7.0	13699	0.1649	0.8339	0.9125	0.8714	0.9725
0.0059	8.0	15656	0.2298	0.8501	0.9138	0.8808	0.9693
0.0051	9.0	17613	0.2210	0.8437	0.9045	0.8731	0.9693
0.0061	10.0	19570	0.2499	0.8627	0.8946	0.8784	0.9681
0.0041	11.0	21527	0.1985	0.8560	0.9052	0.8799	0.9720
0.003	12.0	23484	0.2204	0.8498	0.9065	0.8772	0.9699
0.0014	13.0	25441	0.2152	0.8425	0.9067	0.8734	0.9709
0.0005	14.0	27398	0.2317	0.8553	0.8987	0.8765	0.9705
0.0015	15.0	29355	0.2436	0.8543	0.8989	0.8760	0.9700

Framework versions

Transformers 4.8.2
Pytorch 1.9.0+cu102
Datasets 1.9.0
Tokenizers 0.10.3