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BERT_model – AI Model by ungjus | AlphaNeural AI
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ungjus
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BERT_model
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transformers
pytorch
tensorboard
distilbert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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BERT_model
This model is a fine-tuned version of
distilbert-base-uncased
trained on a
Fake News Dataset
It achieves the following results on the evaluation set:
Loss: 0.1862
Accuracy: 0.9696
LABEL_0 = Fake news
LABEL_1 = Real News
Model description
More information needed
Intended uses & limitations
This model was created for the purposes of UW IMT 575 project.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.1916
1.0
4490
0.1435
0.9622
0.0668
2.0
8980
0.1761
0.9650
0.0713
3.0
13470
0.1862
0.9696
Framework versions
Transformers 4.28.0
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3
