README.md

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:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: train
          args: lener_br
        metrics:
          - type: precision
            value: 0.9122490993309316
            name: Precision
          - type: recall
            value: 0.9162574308606876
            name: Recall
          - type: f1
            value: 0.9142488716956804
            name: F1
          - type: accuracy
            value: 0.982592974434832
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
        metrics:
          - type: accuracy
            value: 0.982592974434832
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9882345251323615
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9881214973122232
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9881780079902612
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.16145998239517212
            name: loss
            verified: true
            verifyToken: >-
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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