Instructions to use ARISCOT/Digital_Literacy_Fact_Checker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARISCOT/Digital_Literacy_Fact_Checker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ARISCOT/Digital_Literacy_Fact_Checker")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ARISCOT/Digital_Literacy_Fact_Checker", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: apache-2.0
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base_model:
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datasets:
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- Intel/misinformation-guard
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- ucsbnlp/liar
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language:
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- en
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- fr
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- nlp
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- news
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widget:
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- text:
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example_title:
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- text:
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example_title:
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---
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# 1. Load the different "Subject Experts"
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license: apache-2.0
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base_model:
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- facebook/roberta-base
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- meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers.js
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datasets:
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- Intel/misinformation-guard
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- ucsbnlp/liar
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- Isotonic/human_assistant_conversation
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- fever/fever
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- Holmeister/Climate-Fever-TR
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- 34data/polyglotfake-real
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- talab-ai/pi5-agricultural-iot-32day
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- brl-xfact/Eye4AllMulti
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- BeIR/scifact
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language:
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- en
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- fr
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- nlp
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- news
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widget:
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- text: The government has announced a new tax on all social media users.
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example_title: Policy News
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- text: Scientists have discovered a planet made entirely of diamond.
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example_title: Science Claim
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new_version: deepseek-ai/DeepSeek-V4-Pro
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---
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# 1. Load the different "Subject Experts"
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