bo1929/anelfop
Focusing on potential named entities during active label acquisition.
This project helps natural language processing practitioners efficiently identify specific entities like names, locations, or organizations within large amounts of unstructured text. It takes your raw text data and, with less manual labeling effort, produces an accurate model for recognizing these named entities. Data scientists or NLP engineers who need to build accurate domain-specific named entity recognition (NER) systems with limited labeled data would use this.
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Use this if you need to build a high-performing named entity recognition model for your specific industry or data, but want to minimize the time and cost associated with manually labeling large datasets.
Not ideal if you already have a sufficiently large, fully labeled dataset for your named entity recognition task, or if your needs are covered by off-the-shelf general-purpose NER models.
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10
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Language
Python
License
MIT
Category
Last pushed
Oct 08, 2024
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