lulygavri/sub1-wsp

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

• Train Loss: 0.0192
• Validation Loss: 0.2130
• Train Accuracy: 0.9492
• Train Precision: [0.96024845 0.88152174]
• Train Precision W: 0.9478
• Train Recall: [0.98025362 0.78357488]
• Train Recall W: 0.9492
• Train F1: [0.97014792 0.82966752]
• Train F1 W: 0.9480
• Epoch: 5

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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 340, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Train Precision	Train Precision W	Train Recall	Train Recall W	Train F1	Train F1 W	Epoch
0.4028	0.2959	0.8429	[0.84274809 1. ]	0.8676	[1. 0.00483092]	0.8429	[0.91466446 0.00961538]	0.7718	1
0.1572	0.1686	0.9315	[0.94412331 0.8463357 ]	0.9287	[0.97644928 0.69178744]	0.9315	[0.96001425 0.76129718]	0.9286	2
0.1028	0.2179	0.9222	[0.92357119 0.90951638]	0.9214	[0.98949275 0.56328502]	0.9222	[0.95539619 0.69570406]	0.9144	3
0.0554	0.1492	0.9475	[0.96816208 0.83641675]	0.9474	[0.96956522 0.82995169]	0.9475	[0.96886314 0.83317168]	0.9474	4
0.0192	0.2130	0.9492	[0.96024845 0.88152174]	0.9478	[0.98025362 0.78357488]	0.9492	[0.97014792 0.82966752]	0.9480	5

Framework versions

• Transformers 4.38.1
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2