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bert-teste4 – AI Model by vladjr | AlphaNeural AI
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vladjr
/
bert-teste4
like
0
transformers
tf
bert
text-classification
generated_from_keras_callback
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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vladjr/bert-teste4
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: 0.3511
Validation Loss: 0.5113
Train Accuracy: 0.75
Epoch: 7
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': 200, '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
0.4295
0.5164
0.7438
0
0.3839
0.5113
0.75
1
0.3366
0.5113
0.75
2
0.3391
0.5113
0.75
3
0.3534
0.5113
0.75
4
0.3536
0.5113
0.75
5
0.3546
0.5113
0.75
6
0.3511
0.5113
0.75
7
Framework versions
Transformers 4.34.1
TensorFlow 2.13.0
Datasets 2.14.5
Tokenizers 0.14.1
