metadata

language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: bertimbau-large-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: 0.9801301293674859
model-index:
  - name: Luciano/bertimbau-large-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: 0.9840898731012984
            verified: true
          - name: Precision
            type: precision
            value: 0.9895415357344292
            verified: true
          - name: Recall
            type: recall
            value: 0.9885856878370763
            verified: true
          - name: F1
            type: f1
            value: 0.9890633808488363
            verified: true
          - name: loss
            type: loss
            value: 0.10151929408311844
            verified: true

bertimbau-large-lener_br

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: 0.1271
Precision: 0.8965
Recall: 0.9198
F1: 0.9080
Accuracy: 0.9801

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
0.0674	1.0	1957	0.1349	0.7617	0.8710	0.8127	0.9594
0.0443	2.0	3914	0.1867	0.6862	0.9194	0.7858	0.9575
0.0283	3.0	5871	0.1185	0.8206	0.8766	0.8477	0.9678
0.0226	4.0	7828	0.1405	0.8072	0.8978	0.8501	0.9708
0.0141	5.0	9785	0.1898	0.7224	0.9194	0.8090	0.9629
0.01	6.0	11742	0.1655	0.9062	0.8856	0.8958	0.9741
0.012	7.0	13699	0.1271	0.8965	0.9198	0.9080	0.9801
0.0091	8.0	15656	0.1919	0.8890	0.8886	0.8888	0.9719
0.0042	9.0	17613	0.1725	0.8977	0.8985	0.8981	0.9744
0.0043	10.0	19570	0.1530	0.8878	0.9034	0.8955	0.9761
0.0042	11.0	21527	0.1635	0.8792	0.9108	0.8947	0.9774
0.0033	12.0	23484	0.2009	0.8155	0.9138	0.8619	0.9719
0.0008	13.0	25441	0.1766	0.8737	0.9135	0.8932	0.9755
0.0005	14.0	27398	0.1868	0.8616	0.9129	0.8865	0.9743
0.0014	15.0	29355	0.1910	0.8694	0.9101	0.8893	0.9746

Framework versions

Transformers 4.8.2
Pytorch 1.9.0+cu102
Datasets 1.9.0
Tokenizers 0.10.3