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    "title": "Hugging Face Models Scraper",
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    "seoTitle": "Hugging Face Models Scraper · Model Metadata & Trends",
    "seoDescription": "Scrape Hugging Face models by keyword, author, task, and library. Export downloads, likes, tags, licenses, update dates, model URLs, and metadata.",
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    "readmeSummary": "## Hugging Face Models Scraper\n\nHugging Face Models Scraper collects public model catalog metadata from Hugging Face, enabling repeatable monitoring and cataloging of models by performing keyword search and applying filters for owner/organization, task pipeline, and framework/library; it supports sorting by popularity or recency, pagination, and optional model‑card metadata enrichment. The Actor produces normalized, one-record-per-model structured metadata records that include model identifiers and owner information, canonical names and public URLs, popularity metrics (likes, download counts, trending score), tags and pipeline/task classification, primary library/framework, parsed license information, creation and modification timestamps, access flags, repository commit identifiers and file counts, and optional expanded model-card metadata useful for ecosystem analysis, trend monitoring, compliance checks, and catalog enrichment.\n\n## Use cases\n\n- AI teams tracking model ecosystems and popular checkpoints for research and product decisions.  \n- Market researchers measuring adoption and popularity of open-source models.  \n- MLOps teams monitoring competitor model updates and release activity.  \n- Open-source intelligence teams watching license labels, tags, and download movement for compliance and discovery.  \n- Analysts building reports about AI model trends and popularity over time.  \n- Periodic model popularity tracking by comparing download, like, and trending metrics across runs.  \n- Competitive intelligence: monitor models from specific organizations and observe releases, tags, and license changes.  \n- License monitoring and compliance: extract license and tag metadata for policy review.  \n- AI catalog enrichment: augment internal catalogs with model identifiers, URLs, task/pipeline classification, and library/framework metadata.  \n- Scheduled snapshots for trend dashboards and model-change monitoring.",
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