distilbert-base-uncased-finetuned-intel-llm-yn-dataset

This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.3401
• Train Accuracy: 0.8595
• Validation Loss: 0.4899
• Validation Accuracy: 0.7858
• 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 2946, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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	Epoch
0.6333	0.6752	0.5191	0.7486	0
0.4562	0.7870	0.4849	0.7898	1
0.3401	0.8595	0.4899	0.7858	2

Framework versions

• Transformers 4.34.0
• TensorFlow 2.12.0
• Datasets 2.14.5
• Tokenizers 0.14.0