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bert – AI Model by vedantjumle | AlphaNeural AI
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vedantjumle
/
bert
like
0
transformers
tf
distilbert
text-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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vedantjumle/bert
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.3131
Validation Loss: 0.6052
Train Accuracy: 0.8499
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2325, '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
Validation Loss
Train Accuracy
Epoch
2.3808
1.2434
0.7405
0
0.8940
0.7746
0.8247
1
0.5247
0.6507
0.8432
2
0.3778
0.6127
0.8499
3
0.3131
0.6052
0.8499
4
Framework versions
Transformers 4.34.0
TensorFlow 2.13.0
Datasets 2.14.5
Tokenizers 0.14.1
