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vladjr/bert_ner_tf_pln

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.1067
  • Validation Loss: 0.1824
  • Train Precision: 0.8287
  • Train Recall: 0.8193
  • Train F1: 0.8240
  • Train Accuracy: 0.9458
  • Epoch: 2

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:

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4674, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Validation Loss Train Precision Train Recall Train F1 Train Accuracy Epoch
0.1890 0.1811 0.8290 0.8053 0.8170 0.9445 0
0.1250 0.1739 0.8243 0.8188 0.8216 0.9457 1
0.1067 0.1824 0.8287 0.8193 0.8240 0.9458 2

Framework versions

  • Transformers 4.35.2
  • TensorFlow 2.14.0
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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