A fine-tuning BERT model for detect and to binary classify fake news articles.
This release tries to classify fake news in fake or not-fake (binary classification) in a simple web app (Fake-Detector) with micro-framework Flask. It use BERT (Google) as pre-training model and It has been fine-tuned in a specific model, trained by two different dataset (AG News and fake). I merged the two datasets keeping two columns: text | type; text shows me articles while type could be 0 (news) or 1 (fake news).
It has achieved an 0,99 accuracy in test set and exploited this accuracy result to this binary article classification.
First needs to install this requirements:
- torch
- numpy
- flask
- Jinja2
After that, you must get secret_key for Flask session and change it at line 12 in app.py file. Then, you must simple run with:
python app.py
After that, you could see different "FutureWarning: Passing (type,1) ..." caused by a version of numpy. You can simply ignore it. Finally you will see Fake-Detector to classify a text of article with a score for a fake or not-fake.
Below you can see a demo image of homepage and an examples score about a The Onion's article.
BERT-Base, Multilingual Cased (New, recommended), which include different language support (104 languages) with 12-layer, 768-hidden, 12-heads, 110M parameters.
- @jalammar, for his fantastic visual article about Transformer (useful for grasp BERT) Jay Alammar .
- @chrisjmccormick for his amazing BERT Research Series.