Papers by Abhishek Thakur
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements (2022.emnlp-demos)
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Leandro Von Werra, Lewis Tunstall, Abhishek Thakur, Sasha Luccioni, Tristan Thrush, Aleksandra Piktus, Felix Marty, Nazneen Rajani, Victor Mustar, Helen Ngo
| Challenge: | Evaluation is a key part of machine learning, yet there is neo-tooling to support it . auxiliary techniques such as testing for significance, measuring statistical power, and auxiliary methods are not available in ML. |
| Approach: | They propose a set of tools to facilitate the evaluation of models and datasets in machine learning . they propose 'evaluation on the Hub' platform that enables large-scale evaluation of over 75,000 models . |
| Outcome: | The proposed tools can be used to evaluate models and datasets on the Hugging Face Hub. |
Datasets: A Community Library for Natural Language Processing (2021.emnlp-demo)
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Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, Thomas Wolf
| Challenge: | Contemporary NLP systems use many different datasets at significantly varying scale and level of annotation. |
| Approach: | a community library for contemporary NLP is available at https://github.com/datasets . the library includes more than 650 unique datasets and has more than 250 contributors a year after its initial development . |
| Outcome: | the library includes more than 650 unique datasets and has more than 250 contributors . it supports a variety of cross-dataset research projects and shared tasks . |
AutoTrain: No-code training for state-of-the-art models (2024.emnlp-demo)
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| Challenge: | AutoTrain is an open-source, no code tool/library which can be used to train models on custom datasets. |
| Approach: | They propose an open-source, no-code tool/library to train models on custom datasets. |
| Outcome: | The open-source, no-code tool/library can be used to train models on custom datasets. |