sent_debias VS PaddleHelix

Compare sent_debias vs PaddleHelix and see what are their differences.

sent_debias

[ACL 2020] Towards Debiasing Sentence Representations (by pliang279)
Machine Learning Natural Language Processing fairness-ai representation-learning
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PaddleHelix

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集 (by PaddlePaddle)
biocomputing Machine Learning Deeplearning rna-structure-prediction dti representation-learning graph-networks protein-structure-prediction self-supervised-learning ppi molecule-design protein-folding ddi protein-docking protein-design
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sent_debias PaddleHelix
1 1
55 792
- 1.5%
0.0 5.4
over 1 year ago 2 months ago
Python Python
MIT License Apache License 2.0
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sent_debias

Posts with mentions or reviews of sent_debias. We have used some of these posts to build our list of alternatives and similar projects.
  • academic ethics issues in NLP
    1 project | /r/LanguageTechnology | 30 Jan 2022
    following on from the above, to what extent should we trust big models and the built in biases that they learn from huge scraped datasets? Many current SOTA trends for doing few shot learning on nlp tasks involve fine tuning existing large language models. There are lots of interesting research is going on around understanding and removing these biases like this paper from Liang and Li @ ACL2020. A related point is explainability - again some interesting work going on around things like rationale generation this now somewhat old paper by Lei et al 2016 gives some good context

PaddleHelix

Posts with mentions or reviews of PaddleHelix. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing sent_debias and PaddleHelix you can also consider the following projects:

transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

dipy - DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB

DeBERTa - The implementation of DeBERTa

eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21

Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

PaddleHelix vs transferlearning PaddleHelix vs dipy PaddleHelix vs typedb-ml PaddleHelix vs DeBERTa PaddleHelix vs eirli PaddleHelix vs Unsupervised-Classification