bert-finetuned-mrpc – AI Model by 422christopher | AlphaNeural AI

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422christopher/
bert-finetuned-mrpc

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text-classification

generated_from_keras_callback

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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422christopher/bert-finetuned-mrpc

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: 0.2966
Train Accuracy: 0.8735
Validation Loss: 0.3793
Validation Accuracy: 0.8627
Epoch: 1

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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, '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-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.5359	0.7211	0.4508	0.8039	0
0.2966	0.8735	0.3793	0.8627	1

Framework versions

Transformers 4.34.0
TensorFlow 2.13.0
Datasets 2.14.5
Tokenizers 0.14.0