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Hyperledger10Classic_256 – AI Model by YakovElm | AlphaNeural AI
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YakovElm
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Hyperledger10Classic_256
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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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Hyperledger10Classic_256
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: 0.3173
Train Accuracy: 0.8817
Validation Loss: 0.3725
Validation Accuracy: 0.8600
Epoch: 2
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': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.3559
0.8834
0.3700
0.8600
0
0.3334
0.8838
0.3598
0.8600
1
0.3173
0.8817
0.3725
0.8600
2
Framework versions
Transformers 4.29.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3
