lulygavri/berto-subj

• Train Loss: 0.2648
• Validation Loss: 0.2302
• Train Accuracy: 0.8400
• Train Precision: [0.9935821 0.39460253]
• Train Precision W: 0.9301
• Train Recall: [0.82643237 0.95494063]
• Train Recall W: 0.8400
• Train F1: [0.90233174 0.55844377]
• Train F1 W: 0.8659
• Epoch: 1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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': 18106, '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: mixed_float16

Training results

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.1