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afrodp95/distilbert-base-uncased-finetuned-job-skills-ner

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: 0.0923
  • Validation Loss: 0.1313
  • Train Precision: 0.3601
  • Train Recall: 0.4922
  • Train F1: 0.4159
  • Train Accuracy: 0.9522
  • Epoch: 5

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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1386, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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
0.3257 0.1935 0.3122 0.2144 0.2542 0.9521 0
0.1564 0.1464 0.3503 0.3423 0.3463 0.9546 1
0.1257 0.1365 0.3593 0.4893 0.4143 0.9522 2
0.1102 0.1318 0.3607 0.4692 0.4079 0.9521 3
0.1002 0.1305 0.3504 0.4941 0.4100 0.9515 4
0.0923 0.1313 0.3601 0.4922 0.4159 0.9522 5

Framework versions

  • Transformers 4.24.0
  • TensorFlow 2.9.2
  • Datasets 2.6.1
  • Tokenizers 0.13.2
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