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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
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).
This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.
This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.
The main features of komeijiforce/metaie are: Data Annotation, Event Extraction, Named Entity Recognition, Relation Extraction.
Projects with overlapping indexed features include: mayubo2333/llm-ie — This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information… zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… ridiculouz/llmaaa — openai 0.27.4. arkhn/bio-nlp2023 — This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3… lbnlp/nerre-llama — If you are just looking to download the LoRA weights directly, use this url:… pluslabnlp/tempgen — Source code for the EMNLP' 21 paper Document-level Entity-based Extraction as Template Generation.
