30 open-source projects similar to komeijiforce/metaie, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best MetaIE alternative.
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
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).
CHisIEC: An Information Extraction Corpus for Ancient Chinese History
This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.
Source code for the EMNLP' 21 paper Document-level Entity-based Extraction as Template Generation.
Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024).
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.
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
FiftyOne is a visual tool for curating, analyzing, and managing image and video datasets for machine learning model training. It serves as a platform for identifying annotation errors, refining ground truth labels, and evaluating vision model performance by comparing predictions against ground truth to identify failure modes. The system functions as a containerized data platform that supports team collaboration on large-scale visual datasets in a cloud environment. It includes specialized capabilities for exploring high-dimensional embeddings to discover data clusters and retrieve correspondi
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
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
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
Source code and data for ACL 2023 main conference paper DICE: Data-Efficient Clinical Event Extraction with Generative Models.
Read this in English. 本仓库基于LLaMA-Factory代码,实现了基于大语言模型的文档级关系抽取系统AutoRE。使用的抽取范式为RHF(论文链接)。 目前基于Re-DocRED数据集进行实验,能够抽取文档级文本中的96个关系的三元组事实。
This repository contains the code for our ACL 2023 paper Event Extraction as Question Generation and Answering .
Label Studio is a multi-type data labeling tool and data annotation workspace designed to prepare datasets for machine learning training. It functions as a cloud-integrated data pipeline that imports raw data from storage, manages the annotation process, and exports labels into standardized formats. The platform features a machine learning model integration framework that connects to external model servers. This enables model-assisted annotation and active learning, allowing the system to perform pre-labeling and refine predictions based on human feedback. The software provides project manag
Dependencies are managed using Poetry. Use poetry install to install dependencies. You can then use poetry shell to obtain a shell with the created virtual environment activated.
This repo contains the code used for the EMNLP 2023 paper "Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models".
Universal Information Extraction, codes for the NeurIPS-2022 paper: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model.
Web labeling tool for bitmap images and point clouds
This repository contains the code for our paper:
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.
Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
The code for EMNLP24 paper "Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition"