lulygavri/sub2-trans

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: 0.0341
• Validation Loss: 0.0805
• Train Accuracy: 0.9762
• Train Precision: [0.96558916 0.99786325]
• Train Precision W: 0.9769
• Train Recall: [0.99892125 0.934 ]
• Train Recall W: 0.9762
• Train F1: [0.98197243 0.96487603]
• Train F1 W: 0.9760
• 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': 3436, '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
0.2010	0.7379	0.7400	[0.71450617 0.99236641]	0.8119	[0.99892125 0.26 ]	0.7400	[0.83310841 0.41204437]	0.6856	1
0.1127	0.4136	0.7982	[0.76339654 0.9953271 ]	0.8447	[0.99892125 0.426 ]	0.7982	[0.86542056 0.59663866]	0.7712	2
0.0818	0.1851	0.9411	[0.91691395 1. ]	0.9460	[1. 0.832]	0.9411	[0.95665635 0.90829694]	0.9397	3
0.0511	0.1053	0.9671	[0.9526749 0.9978022]	0.9685	[0.99892125 0.908 ]	0.9671	[0.97525013 0.95078534]	0.9667	4
0.0341	0.0805	0.9762	[0.96558916 0.99786325]	0.9769	[0.99892125 0.934 ]	0.9762	[0.98197243 0.96487603]	0.9760	5

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2