Papers by François Lagunas
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 . |
Block Pruning For Faster Transformers (2021.emnlp-main)
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| Challenge: | Pruning methods have proven to be effective at reducing model size, while distillation methods are proven for speeding up inference. |
| Approach: | They propose a block pruning approach that integrates structured pruning methods with the movement pruning paradigm for fine-tuning. |
| Outcome: | The proposed model is 2.4x faster, 74% smaller and faster than distilled models on classification and generation tasks. |