silviacamplani/distilbert-finetuned-tapt-ner-ai

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.9093
• Validation Loss: 0.9177
• Train Precision: 0.3439
• Train Recall: 0.3697
• Train F1: 0.3563
• Train Accuracy: 0.7697
• 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
2.5750	1.7754	0.0	0.0	0.0	0.6480	0
1.6567	1.4690	0.0	0.0	0.0	0.6480	1
1.3888	1.2847	0.0	0.0	0.0	0.6480	2
1.2569	1.1744	0.0526	0.0221	0.0312	0.6751	3
1.1536	1.0884	0.2088	0.1704	0.1876	0.7240	4
1.0722	1.0281	0.2865	0.2641	0.2748	0.7431	5
1.0077	0.9782	0.3151	0.3135	0.3143	0.7553	6
0.9582	0.9437	0.3254	0.3492	0.3369	0.7661	7
0.9268	0.9242	0.3381	0.3595	0.3485	0.7689	8
0.9093	0.9177	0.3439	0.3697	0.3563	0.7697	9

Framework versions

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