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library_name: keras |
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--- |
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## Model description |
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BERT-based model for classifying fake news written in Romanian. |
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## Intended uses & limitations |
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It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire"). |
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It also predicts if the article talks about health or politics. |
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## How to use the model |
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Load the model with: |
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```python |
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from huggingface_hub import from_pretrained_keras |
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model = from_pretrained_keras("pandrei7/fakenews-mtl") |
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``` |
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Use this tokenizer: `readerbench/RoBERT-base`. |
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The input length should be 512. You can tokenize the input like this: |
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```python |
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tokenizer( |
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your_text, |
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padding="max_length", |
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truncation=True, |
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max_length=512, |
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return_tensors="tf", |
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) |
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``` |
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## Training data |
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The model was trained and evaluated on the [fakerom](https://www.tagtog.net/fakerom/fakerom) dataset. |
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## Evaluation results |
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The accuracy of predicting fake news was roughly 75%. |
