bert-base-uncased-finetuned-fake-news_10000 – AI Model by Francis2003 | AlphaNeural AI

You can deploy this model and start earning money today!

Francis2003/
bert-base-uncased-finetuned-fake-news_10000

like

0

text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

Views

No views yet

bert-base-uncased-finetuned-fake-news_10000

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: 0.0222
Precision: 0.9969
Recall: 0.9971
Acc: 0.997
F1: 0.9970

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: 8
eval_batch_size: 8
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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Acc	F1
0.0706	1.0	1000	0.0237	0.9963	0.9957	0.996	0.9960
0.0122	2.0	2000	0.0000	1.0	1.0	1.0	1.0
0.0017	3.0	3000	0.0085	0.9991	0.9989	0.999	0.9990
0.0015	4.0	4000	0.0125	0.9979	0.9981	0.998	0.9980

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1