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Bert-fake-news-detector – AI Model by Youssef-El-SaYed | AlphaNeural AI
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Youssef-El-SaYed
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Bert-fake-news-detector
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Model card
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fake_news_model
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0251
Accuracy: 0.9958
F1: 0.9959
Precision: 0.9984
Recall: 0.9935
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:
learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
0.0323
1.0
3155
0.0264
0.9935
0.9937
0.9967
0.9906
0.0118
2.0
6310
0.0274
0.9945
0.9947
0.9980
0.9914
0.0021
3.0
9465
0.0246
0.9958
0.9959
0.9977
0.9942
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2
