Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
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Updated
Jul 16, 2024 - Python
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
A system for prompted weak supervision.
Code accompanying the TOP paper "Predicting the Demographics of Twitter Users with Programmatic Weak Supervision".
Pretrained segmenter models for Portuguese legislative text.
A system for quickly generating training data with weak supervision
Flippers is a weak supervision library for creating high quality labels using your domain kownledge and weak supervision sources.
Implementing wide variety of transformers, fine tuning as well as trying architectural variants from various research papers and blogs.
❤️ GDCurer: An AI-assisted Drug Dosage Prediction System for Graves' Disease
An approach to curating naturally adversarial datasets.
SPEAR: Programmatically label and build training data quickly.
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Code associated to the article "Multi-annotator Deep Learning: A Probabilistic Framework for Classification"
Python Implementation of Weakly Supervised Clustering article
Labeling is boring. Use this tool to speed up your next object detection project!
A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.
Scaffolded Learning Regime for training maritime obstacle detection networks
A simple library for training named entity recognition model from partially annotated data
Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
skweak: A software toolkit for weak supervision applied to NLP tasks
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