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The main features of ej0cl6/textee are: Evaluation and Benchmarking, Event Extraction, In Context Learning.
Projects with overlapping indexed features include: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… xingyaoww/code4struct — Official repo for paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language. zhaoyuesun/phee-with-chatgpt — Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024). mayubo2333/llm-ie — This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information… recommenders-team/recommenders — This project is a recommendation system framework designed for building, evaluating, and operationalizing personalized… alirezadir/machine-learning-interviews — This project is a comprehensive machine learning interview guide and technical study resource designed for individuals…
Official repo for paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language.
Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024).
This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!. EMNLP'2023 (Findings).
DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured knowledge graphs. It provides a pipeline for identifying and classifying named entities, semantic relations, and events, converting raw datasets into structured triples. The project utilizes large language models as tool callers through a standardized context protocol to drive automated data extraction processes. It supports schema-driven extraction across multiple domains and bilingual text, employing joint entity and relation extraction to identify components in a single structured