The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
May 22, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
A system for quickly generating training data with weak supervision
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
skweak: A software toolkit for weak supervision applied to NLP tasks
BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.
Implementation of CRAFT Text Detection
Manga&Comic text detection
Self-training with Weak Supervision (NAACL 2021)
Weakly Supervised End-to-End Learning (NeurIPS 2021)
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.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
Weakly supervised medical named entity classification
Topic Inference with Zeroshot models
Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation (BMVC 2022)
A system for prompted weak supervision.
PyTorch implementation of AAAI 2021 paper: A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
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