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bert-base-sequence-classification – AI Model by runningsnake | AlphaNeural AI
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runningsnake
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bert-base-sequence-classification
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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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runningsnake/bert-base-sequence-classification
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: 0.0825
Train Accuracy: 0.9766
Validation Loss: 0.5064
Validation Accuracy: 0.8431
Epoch: 2
Model description
More information needed
Intended uses & limitations
More information needed
How to use
More information needed
Limitations and bias
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.2559
0.9057
0.5082
0.8211
0
0.1004
0.9673
0.5064
0.8431
1
0.0825
0.9766
0.5064
0.8431
2
Framework versions
Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.0
Tokenizers 0.13.3
Evaluation results
More information needed
