Open-source alternatives to LLM IE

Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024).

openai 0.27.4

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

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

Official repo for paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language.

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…

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

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

Please save your dataset in data folder. Note that CoNLL2003 and WNUT2017 are open-source datasets, ACE2004 and ACE2005 are not free. We keep our CoNLL2003 and WNUT2017 train and test JSON files in data folder.

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 repo contains code for the paper GPT-NER: Named Entity Recognition via Large LanguageModels. ``latex @article{wang2023gpt, title={GPT-NER: Named Entity Recognition via Large Language Models}, author={Wang, Shuhe and Sun, Xiaofei and Li, Xiaoya and Ouyang, Rongbin and Wu, Fei and Zhang,…

Updates | Datasets | Models | Environment | Running | Results | Website | Paper

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.

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

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

Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl

Flair is a transformer-based natural language processing framework used to build and train models for text classification and sequence tagging. It provides a specialized library for generating contextual text embeddings and performing linguistic analysis. The framework includes dedicated tools for named entity recognition, including the identification of specialized biomedical entities across multiple languages. It further supports entity linking to map identified text mentions to unique entries within general or biomedical knowledge bases. The project covers a broad range of language analys

BioGPT is a biomedical large language model and domain-specific transformer designed for processing and creating specialized medical text. It functions as a generative tool and knowledge extraction engine trained on large-scale scientific literature to produce human-like scientific prose and factual responses to queries. The project provides specialized capabilities for biomedical named entity recognition and the extraction of complex relations from unstructured medical corpora. It is designed to identify and classify biological entities through data mining and relation extraction to support

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

The codes and dataset will be released soon!

Zhao Zhang 3   Ziwei Liu &#x2709,1 1 S-Lab, Nanyang Technological University  2 Shanghai Jiao Tong University  3 SenseTime Research  4 Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China   Equal Contribution  † Project Lead …

This repository contains the code for our ACL 2023 paper Event Extraction as Question Generation and Answering .

Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

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