ovbystrova/InstructionNER
Unofficial implementation of paper "InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER" (https://arxiv.org/pdf/2203.03903v1.pdf)
This tool helps data scientists and NLP engineers build or improve systems that extract specific information from text, even with limited examples. You provide raw text and a list of entity types you want to find (like names, locations, or organizations), and it identifies and labels those entities within the text. It's designed for those who work with natural language processing tasks requiring precise data extraction.
No commits in the last 6 months.
Use this if you need to extract specific categories of information from unstructured text, especially when you only have a small number of examples to teach the system what to look for.
Not ideal if you're looking for a no-code solution or a pre-trained model that covers all possible entity types without any customization or setup.
Stars
38
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2
Language
Python
License
MIT
Category
Last pushed
Feb 14, 2024
Commits (30d)
0
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