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README.md

@@ -1,81 +1,14 @@
 ---
-license: apache-2.0
-base_model:
-- facebook/roberta-base
-- meta-llama/Llama-3.1-8B-Instruct
-library_name: transformers.js
-datasets:
-- Intel/misinformation-guard
-- ucsbnlp/liar
-- Isotonic/human_assistant_conversation
-- fever/fever
-- Holmeister/Climate-Fever-TR
-- 34data/polyglotfake-real
-- talab-ai/pi5-agricultural-iot-32day
-- brl-xfact/Eye4AllMulti
-- BeIR/scifact
-language:
-- en
-- fr
-- es
-- ar
-- ha
-- tw
-metrics:
-- accuracy
-- recall
-- precision
-pipeline_tag: text-classification
+language: en
+license: mit
 tags:
 - fact-checking
-- misinformation
-- digital-literacy
-- fake-news-detection
-- nlp
-- news
-widget:
-- text: The government has announced a new tax on all social media users.
-  example_title: Policy News
-- text: Scientists have discovered a planet made entirely of diamond.
-  example_title: Science Claim
-new_version: deepseek-ai/DeepSeek-V4-Pro
+- social-media
+- politics
+- health
+- science
 ---
 
-# 1. Load the different "Subject Experts"
-# We take a sample of 5,000 from each to keep the model balanced
-global_news = load_dataset("jason1966/algozee_fake-news", split='train[:5000]')
-politics = load_dataset("ucsbnlp/liar", split='train[:5000]')
-science_health = load_dataset("Intel/misinformation-guard", split='train[:5000]')
-
-# 2. Label Harmonization
-# Different datasets use different numbers for "False". 
-# We force them all to use: 0 for False, 1 for True.
-def clean_labels(example):
-    # Example logic: if the label is 'fake' or 0, it stays 0
-    if str(example['label']).lower() in ['fake', 'false', '0']:
-        example['label'] = 0
-    else:
-        example['label'] = 1
-    return example
-
-# Apply the cleaning to all datasets
-global_news = global_news.map(clean_labels)
-politics = politics.map(clean_labels)
-science_health = science_health.map(clean_labels)
-
-# 3. Create the "Super Dataset"
-universal_data = concatenate_datasets([global_news, politics, science_health])
-
-# 4. Shuffle so the model learns all subjects at the same time
-universal_data = universal_data.shuffle(seed=42)
-
-print(f"Universal model is ready to train on {len(universal_data)} claims across all categories!")
----
-
-# Digital Literacy & Fact-Checker AI 🌍
-
-This AI helps verify news claims globally, with a specialized focus on digital literacy and misinformation trends in West Africa."
-
-## How it Works
-This model uses the RoBERTa architecture to classify news claims into four categories: reliable, misleading, false, or unverified.
-from datasets import load_dataset, concatenate_datasets, DatasetDict
+# Digital Literacy Fact Checker
+This model is designed to classify misinformation across social media, 
+politics, health, science, religion, and agriculture.