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content-categorization

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chigwell / text-parsematch

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text-parsematch processes text input with pattern matching and retries to ensure structured, validated output for data extraction and content categorization.

automation validation pattern-matching reliability error-handling data-extraction schema-validation text-processing api-integration structured-output retry-mechanism response-generation content-categorization formatting-consistency no-media-handling rulebased-parsing
  • Updated Dec 22, 2025
  • Python

chigwell / text-snippet-summarizer

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A new package facilitates extracting a concise, structured summary from user-provided news headlines or brief texts by utilizing pattern matching and LLM interactions. This tool aims to help researche

pattern-matching text-analysis information-extraction text-summarization structured-data-extraction data-structuring insight-generation content-categorization natural-language-processing-nlp key-point-extraction headline-analysis concise-summaries theme-identification large-language-models-llms domain-specific-issues
  • Updated Dec 21, 2025
  • Python

chigwell / text-structura

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A new package that takes user-provided text input and returns structured, validated output using pattern matching to ensure consistent formatting. It processes text extracted from various sources like

natural-language-processing text-formatting validation pattern-matching regex text-analysis text-extraction summarization content-analysis text-parsing report-generation machine-learning-model-management system-prompts content-categorization query-framing
  • Updated Dec 21, 2025
  • Python

chigwell / vidconcept-sum

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vidconcept-sum generates structured, factual summaries of scientific/educational concepts from video titles or descriptions using an LLM.

topic-extraction knowledge-distillation semantic-parsing educational-tool educational-content contextual-information media-analysis content-categorization ai-powered-summarization content-indexing video-summary structured-summary quick-insights llm-based-interpretation scientific-concept-extraction concise-explanation key-terms factual-accuracy non-speculative-response video-metadata-summarization
  • Updated Dec 21, 2025
  • Python

Niveditha-2003 / Netflix-Data-Analysis-Using-SQL

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This project analyzes Netflix's content library using SQL. It explores content type distribution, rating trends, country-wise content availability, and genre classification to extract meaningful insights from Netflix data for better analysis.

duration-analysis missing-data-handling content-categorization content-breakdown rating-analysis year-wise-trends top-countries recent-additions director-search tv-show-duration genre-analysis documentary-filtering actor-analysis top-actors-in-india
  • Updated Feb 21, 2025

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