MSJose2K5/awesome_wnut_model_01

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: 0.0197
• Validation Loss: 0.2603
• Train Precision: 0.6570
• Train Recall: 0.5407
• Train F1: 0.5932
• Train Accuracy: 0.9545
• Epoch: 7

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: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2120, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.3454	0.3138	0.4	0.1077	0.1697	0.9285	0
0.1470	0.2369	0.6315	0.4653	0.5358	0.9482	1
0.0884	0.2366	0.6799	0.4701	0.5559	0.9517	2
0.0601	0.2359	0.6597	0.5311	0.5885	0.9549	3
0.0418	0.2569	0.6802	0.5012	0.5771	0.9538	4
0.0319	0.2607	0.6759	0.5263	0.5918	0.9546	5
0.0237	0.2482	0.6531	0.5383	0.5902	0.9540	6
0.0197	0.2603	0.6570	0.5407	0.5932	0.9545	7

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.0