readme: ---
  license: apache-2.0
  tags:
  - generated_from_keras_callback
  model-index:
  - name: silviacamplani/distilbert-finetuned-ner-music
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # silviacamplani/distilbert-finetuned-ner-music
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 0.6767
  - Validation Loss: 0.7802
  - Train Precision: 0.5256
  - Train Recall: 0.5824
  - Train F1: 0.5525
  - Train Accuracy: 0.8017
  - Epoch: 9
  
  ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 370, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
  - training_precision: mixed_float16
  
  ### Training results
  
  | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
  |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
  | 2.6671     | 2.0032          | 0.0             | 0.0          | 0.0      | 0.5482         | 0     |
  | 1.7401     | 1.5194          | 0.1820          | 0.0693       | 0.1004   | 0.5902         | 1     |
  | 1.3487     | 1.2627          | 0.2628          | 0.2952       | 0.2781   | 0.6766         | 2     |
  | 1.1390     | 1.0990          | 0.4018          | 0.4527       | 0.4257   | 0.7181         | 3     |
  | 0.9823     | 0.9837          | 0.4575          | 0.4887       | 0.4726   | 0.7311         | 4     |
  | 0.8741     | 0.9022          | 0.5008          | 0.5338       | 0.5168   | 0.7544         | 5     |
  | 0.7904     | 0.8449          | 0.5085          | 0.5626       | 0.5342   | 0.7776         | 6     |
  | 0.7327     | 0.8097          | 0.5211          | 0.5779       | 0.5480   | 0.7917         | 7     |
  | 0.7000     | 0.7872          | 0.5281          | 0.5842       | 0.5547   | 0.7975         | 8     |
  | 0.6767     | 0.7802          | 0.5256          | 0.5824       | 0.5525   | 0.8017         | 9     |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - TensorFlow 2.6.4
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  