silviacamplani/distilbert-finetuned-ner-ai

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.8962
• Validation Loss: 0.9088
• Train Precision: 0.3895
• Train Recall: 0.3901
• Train F1: 0.3898
• Train Accuracy: 0.7558
• 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
2.5761	1.7934	0.0	0.0	0.0	0.6480	0
1.7098	1.5860	0.0	0.0	0.0	0.6480	1
1.4692	1.3213	0.0	0.0	0.0	0.6480	2
1.2755	1.1859	0.1154	0.0460	0.0658	0.6789	3
1.1561	1.0921	0.2878	0.2010	0.2367	0.7192	4
1.0652	1.0170	0.3250	0.2862	0.3043	0.7354	5
0.9936	0.9649	0.3489	0.3305	0.3395	0.7462	6
0.9442	0.9340	0.3845	0.3799	0.3822	0.7549	7
0.9097	0.9168	0.3866	0.3748	0.3806	0.7556	8
0.8962	0.9088	0.3895	0.3901	0.3898	0.7558	9

Framework versions

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