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Qt5Classic_Unbalance – AI Model by YakovElm | AlphaNeural AI
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YakovElm
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Qt5Classic_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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Qt5Classic_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: 0.1505
Train Accuracy: 0.9470
Validation Loss: 0.3218
Validation Accuracy: 0.9067
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
0.3372
0.8918
0.2536
0.9294
0
0.3193
0.8943
0.2479
0.9294
1
0.2871
0.8948
0.2818
0.9286
2
0.2276
0.9129
0.2921
0.9278
3
0.1505
0.9470
0.3218
0.9067
4
Framework versions
Transformers 4.29.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3
