MSJose2K5/awesome_wnut_model_02

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: 0.0172
• Validation Loss: 0.2735
• Train Precision: 0.6564
• Train Recall: 0.5347
• Train F1: 0.5893
• Train Accuracy: 0.9542
• Epoch: 8

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
0.3380	0.3161	0.6	0.1005	0.1721	0.9275	0
0.1553	0.2490	0.6637	0.4438	0.5319	0.9463	1
0.0969	0.2498	0.6953	0.4749	0.5643	0.9517	2
0.0620	0.2336	0.6395	0.5072	0.5657	0.9530	3
0.0440	0.2565	0.6578	0.5012	0.5689	0.9526	4
0.0313	0.2572	0.6423	0.5263	0.5786	0.9532	5
0.0254	0.2580	0.6446	0.5359	0.5852	0.9547	6
0.0208	0.2726	0.6538	0.5263	0.5832	0.9540	7
0.0172	0.2735	0.6564	0.5347	0.5893	0.9542	8

Framework versions

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