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albert-base-v2-fakenews-discriminator – AI Model by XSY | AlphaNeural AI
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XSY
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albert-base-v2-fakenews-discriminator
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transformers
pytorch
albert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset
https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
I use title and label to train the classifier
label_0 : Fake news label_1 : Real news
This model is a fine-tuned version of
albert-base-v2
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0910
Accuracy: 0.9758
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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.0452
1.0
1768
0.0910
0.9758
Framework versions
Transformers 4.12.3
Pytorch 1.10.0+cu111
Datasets 1.15.1
Tokenizers 0.10.3
