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    "title": "Hugging Face Models Scraper",
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    "readmeSummary": "## Hugging Face Models Scraper\n\nA metadata-focused scraper for Hugging Face model catalog that collects and normalizes public model records into one-row-per-model outputs for repeatable monitoring and analysis. Keywords: Hugging Face, model catalog, model metadata, popularity metrics, likes, downloads, trending score, tags, licenses, update signals, model-card enrichment, owner/organization, task/pipeline classification, library/framework, timestamps, commit SHA, repository file counts, pagination, search and filter by keyword/author/task/library, sort by popularity or recency, normalized dataset, automated scheduled snapshots, monitoring and trend analysis.\n\n## Use cases\n\n- AI teams tracking model ecosystems and popular checkpoints over time  \n- Market researchers measuring adoption and popularity of open-source models  \n- MLOps teams monitoring competitor model updates and releases  \n- Open-source intelligence teams monitoring licenses, tags, and download movement  \n- Analysts building reports about AI model trends and change histories  \n- Model popularity tracking via recurring runs to compare likes, downloads, and trending metrics  \n- Competitive intelligence focused on models from specific organizations or namespaces  \n- License compliance monitoring through exported license and tag metadata  \n- AI catalog enrichment using model identifiers, URLs, task labels, and framework metadata",
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