silviacamplani/distilbert-finetuned-tapt-ner-music

This model is a fine-tuned version of silviacamplani/distilbert-finetuned-tapt-lm-ai on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.6932
• Validation Loss: 0.7886
• Train Precision: 0.5347
• Train Recall: 0.5896
• Train F1: 0.5608
• Train Accuracy: 0.8078
• 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
2.7047	2.0137	0.0	0.0	0.0	0.5482	0
1.7222	1.5112	0.0	0.0	0.0	0.5561	1
1.3564	1.2817	0.2382	0.2592	0.2483	0.6686	2
1.1641	1.1378	0.3605	0.3816	0.3708	0.7043	3
1.0188	1.0187	0.4583	0.4950	0.4760	0.7409	4
0.8983	0.9267	0.4946	0.5383	0.5155	0.7638	5
0.8117	0.8649	0.5152	0.5653	0.5391	0.7816	6
0.7550	0.8206	0.5283	0.5806	0.5532	0.8007	7
0.7132	0.7964	0.5326	0.5887	0.5592	0.8049	8
0.6932	0.7886	0.5347	0.5896	0.5608	0.8078	9

Framework versions

• Transformers 4.20.1
• TensorFlow 2.6.4
• Datasets 2.1.0
• Tokenizers 0.12.1