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Hyperledger20Classic_Unbalance – AI Model by YakovElm | AlphaNeural AI
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YakovElm
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Hyperledger20Classic_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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Hyperledger20Classic_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: 0.1783
Train Accuracy: 0.9315
Validation Loss: 0.3472
Validation Accuracy: 0.8776
Epoch: 3
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
0.2858
0.9104
0.2927
0.8983
0
0.2677
0.9153
0.2946
0.8983
1
0.2325
0.9170
0.3256
0.8600
2
0.1783
0.9315
0.3472
0.8776
3
Framework versions
Transformers 4.29.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3
