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
File size: 683 Bytes
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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."
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# 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
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