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This repository contains the code and models of the paper "AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation"
The main features of salesforce/augtriever are: Data Curation, Information Retrieval and Ranking.
Open-source alternatives to salesforce/augtriever include: zetaalphavector/inpars — Inquisitive Parrots for Search A toolkit for end-to-end synthetic data generation using LLMs for IR. openlm-research/open_llama — Open Llama is an open source large language model and pre-trained transformer designed as a permissively licensed… humansignal/labelimg — labelImg is a computer vision labeling tool and image bounding box annotator used to create training datasets for… maiot-io/zenml — ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data… jiacheng-ye/zerogen — This repository contains the code for our paper “ZeroGen: Efficient Zero-shot Learning via Dataset Generation”. Our… devsinghsachan/unsupervised-passage-reranking — Setup - Input Data Format - Downloading Data - Usage - Results - Issues - Citation.
Inquisitive Parrots for Search A toolkit for end-to-end synthetic data generation using LLMs for IR
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labelImg is a computer vision labeling tool and image bounding box annotator used to create training datasets for machine learning models. It functions as a desktop utility for drawing rectangular labels on images and saving object coordinates and class names in common machine learning formats. The tool is specifically designed to generate and edit PascalVOC formatted XML files and create image labels in the text-based format required by YOLO object detection pipelines. The software covers object detection annotation and training data preparation, including the ability to manage label catego
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself