roberta-fine-tuned – AI Model by Thamer | AlphaNeural AI

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Thamer/
roberta-fine-tuned

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text-classification

generated_from_keras_callback

FacebookAI/roberta-base

autotrain_compatible

endpoints_compatible

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Thamer/roberta-fine-tuned

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.3386
Train Binary Accuracy: 0.8828
Validation Loss: 0.5065
Validation Binary Accuracy: 0.8114
Train Accuracy: 0.4392
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': 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': 2e-05, 'decay_steps': 8416, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train Binary Accuracy	Validation Loss	Validation Binary Accuracy	Train Accuracy	Epoch
0.3433	0.8777	0.5065	0.8114	0.4392	0
0.3349	0.8815	0.5065	0.8114	0.4392	1
0.3376	0.8812	0.5065	0.8114	0.4392	2
0.3332	0.8816	0.5065	0.8114	0.4392	3
0.3386	0.8828	0.5065	0.8114	0.4392	4

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.3
Tokenizers 0.13.3