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