282 citations · 377 across the 8 of their papers we have counts for
7 papers · 1 filter
Datasets: A Community Library for Natural Language Processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite +29
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community…
Learning from others' mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov +1
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying…
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Victor Sanh, Thomas Wolf, Alexander M. Rush
Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become stand…
TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation
Shaojie Jiang, Thomas Wolf, Christof Monz +1
Natural Language Generation (NLG) models are prone to generating repetitive utterances. In this work, we study the repetition problem for encoder-decoder models, using both recurre…
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond +1
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under const…
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Thomas Wolf, Victor Sanh, Julien Chaumond +1
We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme an…