toneli/rt-retrieving-and-thinking 的主要功能有哪些？

toneli/rt-retrieving-and-thinking 的主要功能包括：In Context Learning, Information Extraction Frameworks, Named Entity Recognition。

toneli/rt-retrieving-and-thinking 有哪些开源替代品？

toneli/rt-retrieving-and-thinking 的开源替代品包括： 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…

RT Retrieving And Thinking

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.

RT Retrieving And Thinking 的开源替代方案

相似的开源项目，按与 RT Retrieving And Thinking 的功能重合度排序。

zjunlp/deepke
zjunlp/DeepKE
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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
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lfoppiano/matsci-lumen
lfoppiano/MatSci-LumEn
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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
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allenai/beacon
allenai/beacon
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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
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emma1066/self-improve-zero-shot-ner
Emma1066/Self-Improve-Zero-Shot-NER
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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.
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查看 RT Retrieving And Thinking 的所有 30 个替代方案

常见问题解答

toneli/rt-retrieving-and-thinking 是做什么的？

ToneLiRT-Retrieving-and-Thinking