lamia6001/xlnet-base

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

• Train Loss: 0.1395
• Train Accuracy: 0.936
• Validation Loss: 0.1985
• Validation Accuracy: 0.9360
• Train Precision: 0.9386
• Train Recall: 0.936
• Train F1: 0.9353
• Epoch: 2

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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Train Precision	Train Recall	Train F1	Epoch
0.6647	0.9205	0.2528	0.9205	0.9226	0.9205	0.9200	0
0.1997	0.931	0.1945	0.9310	0.9330	0.931	0.9305	1
0.1395	0.936	0.1985	0.9360	0.9386	0.936	0.9353	2

Framework versions

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