Named Entity Extraction with Open Source Large Language Models
During the Spring of 2024, ReLU delivered a model that could extract key entities from a body of text. Before the introduction of the model, human analysts would have to read through long articles, finding the key people, countries and projects manually. This is now automated by the model, resulting in many hours saved.
Our exploration included cutting-edge models like Llama-2, Llama-3, Gemma and GPT-4, aiming to enhance text summarization of news articles, a task previously done by humans. By constructing specialized evaluation metrics, we estimated and compared the quality of different models, tailored for Rystad Energy’s use case.
Our delivery consisted of the code used to produce and test new models, as well as a report on our findings, containing details on performance, quality, and economic viability.
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Rystad Energy is a leading global energy research and business intelligence firm, renowned for its vast databases and in-depth analysis across the oil, gas, and renewable energy sectors. Since its beginning 20 years ago, Rystad has delivered consulting and analytics services to a wide array of entities and is present all over the world, with offices in Oslo, New York, London, Singapore, Rio De Janeiro, Beijing and Sydney.