An AI model expands its database of short, impactful text by gathering new content from many sources. The AI model processes each new source through a set pipeline. This pipeline includes scraping, selecting, cleaning, enriching, and deduplicating the text.
By Pawan Nayar · from the LLOS AI Learning record
An AI model expands its database of short, impactful text by gathering new content from many sources.
The AI model processes each new source through a set pipeline. This pipeline includes scraping, selecting, cleaning, enriching, and deduplicating the text.
An AI model collects text from many online places. These include social media, song lyrics, and product text. Each new source of text goes through a set process. This process makes sure the text is clean, useful, and unique. The AI model adds new text to its main collection without changing old text.
A developer wants to add text from popular social media posts. The developer scrapes posts from a social media site. The developer then filters these posts for short, impactful phrases. The phrases are cleaned and enriched with tags by an AI model. Finally, the developer checks for duplicate phrases and adds new ones to the main database.
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An AI model uses a 'stem engine' to build quiz questions. This engine takes parts of sentences and turns them into question ideas.
A developer can build a Popular Social Text Archive (POSM). This archive collects many short, common phrases.
The AI model uses a blocklist to identify names. This blocklist is part of the existing moderation system.
An AI model looks for ideas that appear in many different data sets. It uses math to find where these ideas overlap.
An AI model expands its database of short, impactful text by gathering new content from many sources. The AI model processes each new source through a set pipeline. This pipeline includes scraping, selecting, cleaning, enriching, and deduplicating the text.