rubakha/xlnet-new

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: 0.1361
• Train Accuracy: 0.934
• Validation Loss: 0.1800
• Validation Accuracy: 0.9340
• Train Precision: 0.9357
• Train Recall: 0.934
• Train F1: 0.9335
• 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
0.6884	0.913	0.2723	0.9130	0.9152	0.913	0.9124	0
0.1980	0.9345	0.1866	0.9345	0.9360	0.9345	0.9341	1
0.1361	0.934	0.1800	0.9340	0.9357	0.934	0.9335	2

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.19.0
• Tokenizers 0.15.2