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bert-base-nsmc – AI Model by kwj9942 | AlphaNeural AI
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kwj9942
/
bert-base-nsmc
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transformers
tf
bert
text-classification
generated_from_keras_callback
klue/bert-base
finetune
cc-by-sa-4.0
autotrain_compatible
endpoints_compatible
us
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bert-base-nsmc
This model is a fine-tuned version of
klue/bert-base
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.0247
Train Accuracy: 0.9927
Validation Loss: 0.5804
Validation Accuracy: 0.8714
Epoch: 4
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': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1058, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 117, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.3985
0.8117
0.3103
0.8674
0
0.2211
0.9126
0.3240
0.8692
1
0.1069
0.9607
0.3943
0.8736
2
0.0463
0.9857
0.5306
0.8704
3
0.0247
0.9927
0.5804
0.8714
4
Framework versions
Transformers 4.48.3
TensorFlow 2.18.0
Tokenizers 0.21.0
