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tojpaj/science-misinfo-model
science-misinfo-model is a machine learning model from tojpaj. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
MuRIL fine-tuned to classify a Hindi/Punjabi health/science claim's verdict: accurate, misleading, false, unverifiable. AutoScientist Challenge Part 2, Science category.
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From the Hugging Face model README
MuRIL fine-tuned to classify a Hindi/Punjabi health/science claim's verdict: accurate, misleading, false, unverifiable. AutoScientist Challenge Part 2, Science category.
Deliberately excludes political/communal content. India's most prominent Hindi fact-check archives (Alt News, Vishvas News) are currently dominated by politically sensitive material (deepfakes of politicians, protest-related claims) that's legally murky to redistribute and inappropriate for a public training dataset. This model stays to health/science claims only, sourced from BOOM Hindi (confirmed via robots.txt to have no anthropic-ai block, unlike Vishvas News, which explicitly disallows it) plus IndicCorpV2 health-domain text.
Accuracy 56.0%, Macro-F1 0.298 (held-out 20% split, 50 rows).
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| unverifiable | 0.65 | 0.58 | 0.61 | 26 |
| accurate | 0.48 | 0.72 | 0.58 | 18 |
| misleading | 0.00 | 0.00 | 0.00 | 4 |
| false | 0.00 | 0.00 | 0.00 | 2 |
Known limitation, disclosed honestly: misleading and false have
too few examples in this dataset for reliable classification. See the
source project's PART2_SUBMISSION.md for the full data-sourcing
writeup, including why only 8 of 248 rows are real verified
fact-checks rather than model-synthesized labels.