Open-source alternatives to RELA

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.

If you are just looking to download the LoRA weights directly, use this url: https://figshare.com/ndownloader/files/43044994 and view the data entry on Figshare.

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 is the codebase of the paper: ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis.

The original implementation of the paper. You can cite the paper as below.

Data and code for ACL 2023 Findings: Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors.

Source code for the EMNLP' 21 paper Document-level Entity-based Extraction as Template Generation.

https://aclanthology.org/2023.emnlp-main.745.pdf

openai 0.27.4

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CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model

CHisIEC: An Information Extraction Corpus for Ancient Chinese History

Implementation of Document-level Relation Extraction with Knowledge Distillation and Adaptive Focal Loss - Findings of ACL 2022 (https://aclanthology.org/2022.findings-acl.132)

Official code for our paper "An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction" which will be published at AAAI 2024.

Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling.

This is the official repository for the paper "GPT-RE: In-context Learning for Relation Extraction using Large Language Models" (EMNLP 2023).

mREBEL is here. We present two new datasets for multilingual Relation Extraction and an array of mREBEL versions. Go to Section.

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