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Simple_BERT_Ads_Classifier – AI Model by ViditRaj | AlphaNeural AI
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ViditRaj
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Simple_BERT_Ads_Classifier
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transformers
tf
bert
text-classification
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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ViditRaj/Simple_BERT_Ads_Classifier
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.6739
Validation Loss: 0.6679
Train Accuracy: 0.6119
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': '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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 465, '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
Validation Loss
Train Accuracy
Epoch
0.6736
0.6682
0.6119
0
0.6712
0.6680
0.6119
1
0.6728
0.6679
0.6119
2
0.6688
0.6679
0.6119
3
0.6739
0.6679
0.6119
4
Framework versions
Transformers 4.26.1
TensorFlow 2.11.0
Datasets 2.10.1
Tokenizers 0.13.2
