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 codebase of the paper: ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis.
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.
Data and code for ACL 2023 Findings: Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors.
https://aclanthology.org/2023.emnlp-main.745.pdf
Las características principales de qingwang-isu/augure son: Anotación de datos, Relation Extraction, Zero-Shot Prompting.
Las alternativas de código abierto para qingwang-isu/augure incluyen: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… ridiculouz/llmaaa — openai 0.27.4. komeijiforce/metaie — This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint… oceanntwt/era-cot — This is the codebase of the paper: ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis. osu-nlp-group/qa4re — Data and code for ACL 2023 Findings: Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation… sefeoglu/rag4re — -->.