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distilbert-finetuning-fakenews
This model is a fine-tuned version of distilbert-base-uncased on an external dataset. It achieves the following results on the evaluation set:
- Loss: 0.2804
- Accuracy: 0.8833
- F1: 0.9014
Model description
More information needed
Intended uses & limitations
A DistilBERT model is trained on an external dataset (Spanish Fake and Real News) to detect fake news in spanish.
Training and evaluation data
Dataset obtained from: https://www.kaggle.com/datasets/zulanac/fake-and-real-news, under a CC BY-SA 4.0 license. Author: Fabricio A. Zules. <p>For compatibility reasons with the model, it was necessary to change 'texto' and 'clase' headers to 'text' and 'label'; and 'fake' and 'true' values (from class/label), were replaced by '0' and '1' values.</p>
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2