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".
Die Hauptfunktionen von bigai-nlco/docgnre sind: Datenannotation.
Open-Source-Alternativen zu bigai-nlco/docgnre sind unter anderem: voxel51/fiftyone — FiftyOne is a visual tool for curating, analyzing, and managing image and video datasets for machine learning model… bmw-innovationlab/bmw-labeltool-lite — This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes.… heartexlabs/label-studio — Label Studio is a multi-type data labeling tool and data annotation workspace designed to prepare datasets for machine… hitachi-automotive-and-industry-lab/semantic-segmentation-editor — Web labeling tool for bitmap images and point clouds. komeijiforce/metaie — This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint… arkhn/bio-nlp2023 — This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3…
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
This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes. It supports Auto-Labeling.
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
This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.