news_classifier

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CaroTabar/news_classifier

This model is a fine-tuned version of distilbert-base-uncased on a private dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0338
• Validation Loss: 0.1342
• Train Accuracy: 0.9537
• Epoch: 4

Model description

This is a text classification model used to distinguish news topics from non-news topics.

Intended uses & limitations

More information needed

Training and evaluation data

This model is trained on a private dataset consisting of thousands of local news websites data.

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': 2e-05, 'decay_steps': 500, '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

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.15.0
• Tokenizers 0.15.0