news_classifier – AI Model by CaroTabar | AlphaNeural AI

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CaroTabar/
news_classifier

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text-classification

generated_from_keras_callback

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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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

Train Loss	Validation Loss	Train Accuracy	Epoch
0.5483	0.3554	0.7778	0
0.3266	0.2402	0.9537	1
0.1956	0.1917	0.9167	2
0.0954	0.1408	0.9352	3
0.0338	0.1342	0.9537	4

Framework versions

Transformers 4.35.2
TensorFlow 2.15.0
Datasets 2.15.0
Tokenizers 0.15.0