--- tags: - generated_from_keras_callback model-index: - name: silviacamplani/distilbert-finetuned-dapt_tapt-ner-ai results: [] --- # silviacamplani/distilbert-finetuned-dapt_tapt-ner-ai This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8595 - Validation Loss: 0.8604 - Train Precision: 0.3378 - Train Recall: 0.3833 - Train F1: 0.3591 - Train Accuracy: 0.7860 - 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': 350, '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 | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.5333 | 1.7392 | 0.0 | 0.0 | 0.0 | 0.6480 | 0 | | 1.5890 | 1.4135 | 0.0 | 0.0 | 0.0 | 0.6480 | 1 | | 1.3635 | 1.2627 | 0.0 | 0.0 | 0.0 | 0.6483 | 2 | | 1.2366 | 1.1526 | 0.1538 | 0.0920 | 0.1151 | 0.6921 | 3 | | 1.1296 | 1.0519 | 0.2147 | 0.2147 | 0.2147 | 0.7321 | 4 | | 1.0374 | 0.9753 | 0.2743 | 0.2981 | 0.2857 | 0.7621 | 5 | | 0.9639 | 0.9202 | 0.3023 | 0.3373 | 0.3188 | 0.7693 | 6 | | 0.9097 | 0.8829 | 0.3215 | 0.3714 | 0.3447 | 0.7795 | 7 | | 0.8756 | 0.8635 | 0.3280 | 0.3850 | 0.3542 | 0.7841 | 8 | | 0.8595 | 0.8604 | 0.3378 | 0.3833 | 0.3591 | 0.7860 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1