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bert-base-nsmc – AI Model by jjoggoddalgi | AlphaNeural AI
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jjoggoddalgi
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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
text-embeddings-inference
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: 0.0271
Train Accuracy: 0.9913
Validation Loss: 0.5408
Validation Accuracy: 0.8754
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': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.4063
0.8061
0.3151
0.8648
0
0.2178
0.9151
0.3074
0.8724
1
0.1017
0.9651
0.3884
0.8694
2
0.0473
0.9860
0.5086
0.8746
3
0.0271
0.9913
0.5408
0.8754
4
Framework versions
Transformers 4.57.1
TensorFlow 2.19.0
Datasets 4.0.0
Tokenizers 0.22.1
