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bert-base-uncased-finetuned-fake-news_10000_lr_rate_1e-5 – AI Model by Francis2003 | AlphaNeural AI
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Francis2003
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bert-base-uncased-finetuned-fake-news_10000_lr_rate_1e-5
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transformers
tensorboard
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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bert-base-uncased-finetuned-fake-news_10000_lr_rate_1e-5
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: 0.0309
Precision: 0.9960
Recall: 0.9960
Acc: 0.996
F1: 0.9960
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: 1e-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
0.079
1.0
1000
0.0242
0.9954
0.9946
0.995
0.9950
0.0077
2.0
2000
0.0001
1.0
1.0
1.0
1.0
0.002
3.0
3000
0.0000
1.0
1.0
1.0
1.0
0.0005
4.0
4000
0.0000
1.0
1.0
1.0
1.0
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1
