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fake-news-classification-distilbert-fine-tuned – AI Model by harshhmaniya | AlphaNeural AI
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harshhmaniya
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fake-news-classification-distilbert-fine-tuned
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transformers
tf
distilbert
text-classification
generated_from_keras_callback
en
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Fake News Classification DistilBERT Fine-Tuned
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Model Architecture
Base Model:
TFDistilBertForSequenceClassification
Number of Labels:
2
Framework:
TensorFlow with Hugging Face Transformers
Training Configuration
Optimizer:
Adam
Learning Rate:
0.001
Loss Function:
Sparse Categorical Crossentropy
Epochs:
3
Batch Size:
8
Data Split:
The dataset was loaded from the Kaggle fake news detection files (“True.csv” and “Fake.csv”).
(Note: The notebook internally handles the train/validation/test split.)
Performance Metrics
After fine-tuning, the model achieved the following evaluation results (as logged during training):
Training Accuracy:
~99.5%
Validation Accuracy:
~99.8%
Testing Accuracy:
~99.7%
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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32
Framework versions
Transformers 4.48.3
TensorFlow 2.18.0
Tokenizers 0.21.0
