Instructions to use therealcyberlord/fake-news-classification-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use therealcyberlord/fake-news-classification-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="therealcyberlord/fake-news-classification-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("therealcyberlord/fake-news-classification-distilbert") model = AutoModelForSequenceClassification.from_pretrained("therealcyberlord/fake-news-classification-distilbert") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| widget: | |
| - text: "Health and Human Services Secretary Xavier Becerra declared the monkeypox outbreak a public health emergency on Thursday in an effort to galvanize awareness and unlock additional flexibility and funding to fight the virus’s spread." | |
| # Fake News Classification Distilbert 🤗 | |
| This model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news. | |
| 0 : Fake News, 1 : Real News | |
| # Sources | |
| Dataset used: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset | |
| Base Distilbert: https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english | |