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
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README.md
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# Fake News Classification Distilbert 🤗
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This model was trained on thousands of news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news.
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# Sources
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Dataset used: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
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# Fake News Classification Distilbert 🤗
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This model was trained on thousands of news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news.
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Labels:
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0 : Fake News
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1 : Real News
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# Sources
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Dataset used: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
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