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