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Hyperledger10Classic_Unbalance – AI Model by YakovElm | AlphaNeural AI
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YakovElm
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Hyperledger10Classic_Unbalance
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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_Unbalance
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: 0.2891
Train Accuracy: 0.8893
Validation Loss: 0.3834
Validation Accuracy: 0.8423
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': 0.001, '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
0.3712
0.8762
0.3778
0.8600
0
0.3430
0.8838
0.3757
0.8600
1
0.3360
0.8834
0.3762
0.8600
2
0.3265
0.8834
0.3813
0.8600
3
0.2891
0.8893
0.3834
0.8423
4
Framework versions
Transformers 4.29.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3
