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End of training
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metadata
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
  - generated_from_trainer
datasets:
  - harem
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER_harem_bert-large-portuguese-cased
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: harem
          type: harem
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.7077353867693384
          - name: Recall
            type: recall
            value: 0.7553231228987672
          - name: F1
            type: f1
            value: 0.7307553306830503
          - name: Accuracy
            type: accuracy
            value: 0.9551379448220711

NER_harem_bert-large-portuguese-cased

This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the harem dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2487
  • Precision: 0.7077
  • Recall: 0.7553
  • F1: 0.7308
  • Accuracy: 0.9551

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 300

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 16 0.6334 0.0163 0.0078 0.0106 0.8468
No log 2.0 32 0.4537 0.2614 0.3112 0.2841 0.8826
No log 3.0 48 0.3117 0.5262 0.5671 0.5458 0.9231
No log 4.0 64 0.2421 0.5852 0.6631 0.6217 0.9385
No log 5.0 80 0.2099 0.5950 0.6855 0.6370 0.9479
No log 6.0 96 0.2153 0.6810 0.7464 0.7122 0.9551
No log 7.0 112 0.2270 0.6894 0.7198 0.7043 0.9546
No log 8.0 128 0.2213 0.6918 0.7437 0.7168 0.9554
No log 9.0 144 0.2299 0.7021 0.7564 0.7283 0.9545
No log 10.0 160 0.2256 0.7002 0.7591 0.7284 0.9562
No log 11.0 176 0.2169 0.7100 0.7736 0.7404 0.9568
No log 12.0 192 0.2266 0.6981 0.7740 0.7341 0.9571
No log 13.0 208 0.2322 0.7093 0.7620 0.7347 0.9570
No log 14.0 224 0.2487 0.7077 0.7553 0.7308 0.9551

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2