An AI model needs a large, well-organized collection of text to learn from. This collection must be carefully built and maintained. The quality of the text data directly affects the AI model's performance.
By Pawan Nayar · from the LLOS AI Learning record
An AI model needs a large, well-organized collection of text to learn from.
This collection must be carefully built and maintained. The quality of the text data directly affects the AI model's performance.
A developer can build a Popular Social Text Archive (POSM). This archive collects many short, common phrases. The phrases come from social media, song lyrics, and other public sources. Each phrase is enriched with extra information. This helps the AI model understand the text better. The archive uses a process to clean and organize the data. This process includes steps like scraping, selecting, adding metadata, cleaning, enriching, and removing duplicates. The goal is to have a large, useful dataset for the AI model.
A developer wants to train an AI model to write natural-sounding social media posts. The developer creates a POSM by collecting popular phrases from Twitter and Reddit. The developer adds tags to each phrase, like 'happy' or 'sad'. This helps the AI model learn to use the right words for different feelings. Without this organized archive, the AI model would struggle to create relevant and engaging posts.
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An AI model collects text from many online places. These include social media, song lyrics, and product text.
Sometimes a computer program finds words that are not useful. These can be parts of a website like "submit" or "close".
An AI system can use a 'Word Object Architecture'. This means each word is treated like an object.
An AI model checks user text for certain words. It uses a special list for this check.
An AI model needs a large, well-organized collection of text to learn from. This collection must be carefully built and maintained. The quality of the text data directly affects the AI model's performance.