Open-source alternatives to MatSci LumEn

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…

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.

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,…

This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.

This repo contains the code and datasets for paper "ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models".

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

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

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

Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking

An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition

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).

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition

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.

unikg

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

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

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

Source code and data for ACL 2023 main conference paper DICE: Data-Efficient Clinical Event Extraction with Generative Models.

This repository contains the code and resources accompanying the paper titled "Advancing Entity Recognition in Biomedicine via Instruction Tuning of Large Language Models." The paper explores the use of instruction-based learning paradigm to transform biomedical Named Entity Recognition (NER)…

Code and Data for arXiv paper: PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature.

This is the code for our ACL 2024 paper Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction.

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 …

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