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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license: mit
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- 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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“We’re prepared to take our response to the next level in addressing this virus, and we urge every American to take monkeypox seriously and to take responsibility to help us tackle this virus,” Becerra said at a Thursday news briefing.
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# Fake News Classification Distilbert 🤗
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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.
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license: mit
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- 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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---
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# Fake News Classification Distilbert 🤗
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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.
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