cancerfarore/bert-base-uncased-CancerFarore-Model

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

• Train Loss: 0.0652
• Train End Logits Accuracy: 0.9800
• Train Start Logits Accuracy: 0.9786
• Validation Loss: 2.5446
• Validation End Logits Accuracy: 0.6075
• Validation Start Logits Accuracy: 0.6041
• Epoch: 9

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': 18960, '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 End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
1.8107	0.4921	0.4706	1.4353	0.5224	0.5220	0
1.0870	0.6675	0.6432	1.2412	0.6071	0.6127	1
0.7170	0.7809	0.7596	1.3592	0.6071	0.5950	2
0.4657	0.8583	0.8418	1.4376	0.6266	0.6187	3
0.3015	0.9095	0.8967	1.7133	0.6289	0.6233	4
0.2080	0.9388	0.9279	2.0004	0.6127	0.5999	5
0.1521	0.9534	0.9488	2.0970	0.6157	0.6067	6
0.1054	0.9666	0.9650	2.3507	0.6187	0.6120	7
0.0850	0.9741	0.9728	2.5902	0.5977	0.5977	8
0.0652	0.9800	0.9786	2.5446	0.6075	0.6041	9

Framework versions

• Transformers 4.40.1
• TensorFlow 2.15.0
• Datasets 2.19.0
• Tokenizers 0.19.1