active-learning

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Refine high-quality datasets and visual AI models

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

A modular active learning framework for Python

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Active Learning for Text Classification in Python

Open source no-code system for text annotation and building of text classifiers

PyTorch library for Active Fine-Tuning

Use Large Language Models like OpenAI's GPT-3.5 for data annotation and model enhancement. This framework combines human expertise with LLMs, employs Iterative Active Learning for continuous improvement, and integrates CleanLab (Confident Learning) to ensure high-quality datasets and better model performance

Building a model to recognize incentives for landscape restoration in environmental policies from Latin America, the US and India. Bringing NLP to the world of policy analysis through an extensible framework that includes scraping, preprocessing, active learning and text analysis pipelines.

[ACL 2023] The code for our ACL'23 paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

QDrant-NLP

EduMIND — Multimodal Bilingual Lecture Assistant with Anti-Forget RAG, Whisper ASR, VietMix NMT (CMI-aware), and Human-in-the-Loop Active Learning via Label Studio. Built for code-mixed Vietnamese–English academic environments.