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metadata
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-multilingual-uncased-finetuned-ner-lenerBR
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.8678256070640177
          - name: Recall
            type: recall
            value: 0.8758006126427179
          - name: F1
            type: f1
            value: 0.8717948717948718
          - name: Accuracy
            type: accuracy
            value: 0.9706569722150091

bert-base-multilingual-uncased-finetuned-ner-lenerBR

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

  • Loss: 0.1568
  • Precision: 0.8678
  • Recall: 0.8758
  • F1: 0.8718
  • Accuracy: 0.9707

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 245 0.1819 0.7691 0.8118 0.7899 0.9585
No log 2.0 490 0.1487 0.7383 0.8098 0.7724 0.9586
0.1325 3.0 735 0.1532 0.8662 0.8777 0.8719 0.9683
0.1325 4.0 980 0.1470 0.8770 0.8800 0.8785 0.9698
0.0233 5.0 1225 0.1155 0.8493 0.8839 0.8663 0.9750
0.0233 6.0 1470 0.1727 0.8874 0.8822 0.8848 0.9701
0.0126 7.0 1715 0.1698 0.8890 0.8853 0.8871 0.9710
0.0126 8.0 1960 0.1687 0.8651 0.8783 0.8716 0.9702
0.0076 9.0 2205 0.1593 0.8077 0.8797 0.8422 0.9668
0.0076 10.0 2450 0.1568 0.8678 0.8758 0.8718 0.9707

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.1.2
  • Datasets 2.19.1
  • Tokenizers 0.19.1