How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
This is the source code of the model RT (Retrieving and Thinking). For the full project, please check the file RTBC5CDR/3RT and RTNCBI/3RT, the implementation of GPT-NER and PromptNER is in the BC5CDR.zip and NCBI.zip. we refer to the source of code of GPT-NER and paper of GPT-NER in our project…
The main features of toneli/rt-retrieving-and-thinking are: In Context Learning, Information Extraction Frameworks, Named Entity Recognition.
Open-source alternatives to toneli/rt-retrieving-and-thinking include: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… emma1066/self-improve-zero-shot-ner — This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot… allenai/beacon — This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper:… lfoppiano/matsci-lumen — Code, data, and results described in the paper "Mining experimental data from materials science literature with large… tangxuemei1995/chisiec — CHisIEC: An Information Extraction Corpus for Ancient Chinese History. eulring/vaner — unikg.
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
Code, data, and results described in the paper "Mining experimental data from materials science literature with large language models: an evaluation study", https://www.tandfonline.com/doi/full/10.1080/27660400.2024.2356506
This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper: On-the-fly Definition Augmentation of LLMs for Biomedical NER
This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models.