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bert-base-uncased-finetuned-answer_pol – AI Model by GCopoulos | AlphaNeural AI
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GCopoulos
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bert-base-uncased-finetuned-answer_pol
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transformers
tf
tensorboard
bert
text-classification
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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GCopoulos/bert-base-uncased-finetuned-answer_pol
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: 0.0261
Validation Loss: 0.4267
Train F1: 0.8825
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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 7e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 7e-05, 'decay_steps': 653, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '
passive_serialization
': True}, 'warmup_steps': 10, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32
Training results
Train Loss
Validation Loss
Train F1
Epoch
0.3981
0.4720
0.8450
0
0.0820
0.3742
0.8866
1
0.0261
0.4267
0.8825
2
Framework versions
Transformers 4.30.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3
