Instructions to use d4data/bias-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4data/bias-detection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="d4data/bias-detection-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model") model = AutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model") - Notebooks
- Google Colab
- Kaggle
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README.md
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widget:
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- text: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property."
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example_title: "Biased example 1"
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- text: "
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example_title: "Biased example 2"
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- text: "There have been a protest by a group of people"
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example_title: "Non-Biased example 1"
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- text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology."
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widget:
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- text: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property."
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example_title: "Biased example 1"
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- text: "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video."
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example_title: "Biased example 2"
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- text: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion."
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example_title: "Biased example 3"
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- text: "There have been a protest by a group of people"
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example_title: "Non-Biased example 1"
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- text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology."
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