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
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# Digital Literacy Fact Checker
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This model is designed to classify misinformation across social media,
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politics, health, science, religion, and agriculture.
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# Digital Literacy Fact Checker
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This model is designed to classify misinformation across social media,
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politics, health, science, religion, and agriculture.
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# NOTICE
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Digital Literacy & Fact-Checker AI (Ghana Edition)
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Copyright 2026 George Asomaning Peprah
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This project is licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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### ATTRIBUTION
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This model was developed by George Asomaning Peprah as part of a mission to
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improve digital literacy and combat misinformation in Ghana and West Africa.
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The model incorporates weights and/or data inspired by or derived from:
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- LIAR Dataset
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- FEVER Dataset
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- Misinformation-Guard
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- Custom Ghanaian Digital Literacy benchmarks
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Any redistribution of this model or derivative works MUST include this
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NOTICE file in its entirety.
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