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Embrace Control with Custom Models
Exploring the emergence of custom machine learning models like Rebel and Reliq reveals exciting advancements in extracting relational data from unstructured text. These models are designed to yield more controllable outputs compared to large language models (LLMs), enhancing their usability for specific tasks such as graph creation. While these models require fine-tuning and a solid understanding of their outputs, they offer scalability for processing large data sets. Libraries like spaCy enhance natural language processing capabilities, but they lack direct tools for relationship extraction, highlighting the necessity of integration with specialized models for comprehensive application.