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18 search results found

Argilla ⭐ 3,097

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach' (In Proc. of NAACL-HLT 2021).

Self-training with Weak Supervision (NAACL 2021)

Weakly Supervised End-to-End Learning (NeurIPS 2021)

BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision

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.

Amazon Weak Ner Needle ⭐ 92

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Weakly supervised medical named entity classification

Zeroshot_topics ⭐ 47

Topic Inference with Zeroshot models

Concept Based Xai ⭐ 37

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Coarse3d ⭐ 35

COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation (BMVC 2022)

Learning From Rules ⭐ 35

Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)

Chmm Alt ⭐ 27

Code for "BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition"

Wefend Aaai20 ⭐ 23

Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.

Spacy Partial Tagger ⭐ 17

A simple library for training named entity recognition model from partially annotated data

Neural Hidden Crf ⭐ 7

Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler

[NAACL 2022] This is the code repo for our paper `ACTUNE: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models'.

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