metadata

language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-large-finetuned-lener_br-finetuned-lener-br
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: train
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.9122490993309316
          - name: Recall
            type: recall
            value: 0.9162574308606876
          - name: F1
            type: f1
            value: 0.9142488716956804
          - name: Accuracy
            type: accuracy
            value: 0.982592974434832
      - 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: 0.9852529735242637
            verified: true
          - name: Precision
            type: precision
            value: 0.9881750977971472
            verified: true
          - name: Recall
            type: recall
            value: 0.988704516705112
            verified: true
          - name: F1
            type: f1
            value: 0.9884397363605254
            verified: true
          - name: loss
            type: loss
            value: 0.1301041841506958
            verified: true

xlm-roberta-large-finetuned-lener_br-finetuned-lener-br

This model is a fine-tuned version of Luciano/xlm-roberta-large-finetuned-lener_br on the lener_br dataset. It achieves the following results on the evaluation set:

Loss: nan
Precision: 0.9122
Recall: 0.9163
F1: 0.9142
Accuracy: 0.9826

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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.068	1.0	3914	nan	0.6196	0.8604	0.7204	0.9568
0.0767	2.0	7828	nan	0.8270	0.8710	0.8484	0.9693
0.0257	3.0	11742	nan	0.7243	0.9005	0.8029	0.9639
0.0193	4.0	15656	nan	0.9010	0.8984	0.8997	0.9821
0.0156	5.0	19570	nan	0.7150	0.9121	0.8016	0.9641
0.0165	6.0	23484	nan	0.7640	0.8796	0.8177	0.9691
0.0225	7.0	27398	nan	0.8851	0.9098	0.8973	0.9803
0.016	8.0	31312	nan	0.9081	0.9015	0.9048	0.9792
0.0078	9.0	35226	nan	0.8941	0.8863	0.8902	0.9788
0.0061	10.0	39140	nan	0.9026	0.9002	0.9014	0.9804
0.0057	11.0	43054	nan	0.8793	0.9018	0.8904	0.9769
0.0044	12.0	46968	nan	0.8790	0.9033	0.8910	0.9785
0.0043	13.0	50882	nan	0.9122	0.9163	0.9142	0.9826
0.0003	14.0	54796	nan	0.9032	0.9070	0.9051	0.9807
0.0025	15.0	58710	nan	0.8903	0.9085	0.8993	0.9798

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1