README.md

metadata

language:
  - pt
license: mit
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: Luciano/xlm-roberta-base-finetuned-lener_br
model-index:
  - name: xlm-roberta-base-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: 0.9206349206349206
            name: Precision
          - type: recall
            value: 0.9294391315585423
            name: Recall
          - type: f1
            value: 0.925016077170418
            name: F1
          - type: accuracy
            value: 0.9832504071600401
            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: 0.9832802904657313
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.986258771429967
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9897717432152019
            name: Recall
            verified: true
            verifyToken: >-
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          - type: f1
            value: 0.9880121346555324
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.1050868034362793
            name: loss
            verified: true
            verifyToken: >-
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xlm-roberta-base-finetuned-lener_br-finetuned-lener-br

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

• Loss: nan
• Precision: 0.9206
• Recall: 0.9294
• F1: 0.9250
• Accuracy: 0.9833

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
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0657	1.0	1957	nan	0.7780	0.8687	0.8209	0.9718
0.0321	2.0	3914	nan	0.8755	0.8708	0.8731	0.9793
0.0274	3.0	5871	nan	0.8096	0.9124	0.8579	0.9735
0.0216	4.0	7828	nan	0.7913	0.8842	0.8352	0.9718
0.0175	5.0	9785	nan	0.7735	0.9248	0.8424	0.9721
0.0117	6.0	11742	nan	0.9206	0.9294	0.9250	0.9833
0.0121	7.0	13699	nan	0.8988	0.9318	0.9150	0.9819
0.0086	8.0	15656	nan	0.8922	0.9175	0.9047	0.9801
0.007	9.0	17613	nan	0.8482	0.8997	0.8732	0.9769
0.0051	10.0	19570	nan	0.8730	0.9274	0.8994	0.9798
0.0045	11.0	21527	nan	0.9172	0.9051	0.9111	0.9819
0.0014	12.0	23484	nan	0.9138	0.9155	0.9147	0.9823
0.0029	13.0	25441	nan	0.9099	0.9287	0.9192	0.9834
0.0035	14.0	27398	nan	0.9019	0.9294	0.9155	0.9831
0.0005	15.0	29355	nan	0.8886	0.9343	0.9109	0.9825

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1