ما هي الميزات الرئيسية لـ toneli/rt-retrieving-and-thinking؟

الميزات الرئيسية لـ toneli/rt-retrieving-and-thinking هي: In Context Learning, Information Extraction Frameworks, Named Entity Recognition.

ما هي البدائل مفتوحة المصدر لـ toneli/rt-retrieving-and-thinking؟

تشمل البدائل مفتوحة المصدر لـ toneli/rt-retrieving-and-thinking: 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.

RT Retrieving And Thinking by ToneLi

Name: toneli/rt-retrieving-and-thinking
Author: ToneLi

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…

Features

In Context Learning - Retrieval-based framework for few-shot medical entity recognition.
Information Extraction Frameworks - Retrieval-augmented framework for few-shot medical entity recognition.
Named Entity Recognition - Retrieval and chain-of-thought framework for medical entity recognition.

Features

In Context Learning - Retrieval-based framework for few-shot medical entity recognition.
Information Extraction Frameworks - Retrieval-augmented framework for few-shot medical entity recognition.
Named Entity Recognition - Retrieval and chain-of-thought framework for medical entity recognition.

zjunlp/DeepKE

4,433عرض على GitHub

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

lfoppiano/MatSci-LumEn

10عرض على GitHub

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

allenai/beacon

14عرض على GitHub

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

Emma1066/Self-Improve-Zero-Shot-NER

53عرض على GitHub

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.

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

Emma1066/Self-Improve-Zero-Shot-NER

53عرض على GitHub

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.

ToneLiRT-Retrieving-and-Thinking