Search Results for author: Thomas Wolf

Found 20 papers, 12 papers with code

Overview of the SustaiNLP 2020 Shared Task

no code implementations EMNLP (sustainlp) 2020 Alex Wang, Thomas Wolf

We describe the SustaiNLP 2020 shared task: efficient inference on the SuperGLUE benchmark (Wang et al., 2019).

VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

1 code implementation21 Jun 2021 Hao Tan, Jie Lei, Thomas Wolf, Mohit Bansal

Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e. g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations.

Action Classification Action Recognition +2

Evolution of Helimagnetic Correlations when approaching the Quantum Critical Point of Mn$_{1-x}$Fe$_x$Si

no code implementations9 Dec 2020 Catherine Pappas, Andrey O. Leonov, Lars J. Bannenberg, Peter Fouquet, Thomas Wolf, Frank Weber

We argue that this effect explains both the expansion of the precursor phase with increasing $x$ and the abrupt disappearance of long range helimagnetic periodicity at $x^*$.

Strongly Correlated Electrons Mesoscale and Nanoscale Physics

Learning from others' mistakes: Avoiding dataset biases without modeling them

no code implementations ICLR 2021 Victor Sanh, Thomas Wolf, Yonatan Belinkov, Alexander M. Rush

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 task.

The Amazing World of Neural Language Generation

no code implementations EMNLP 2020 Yangfeng Ji, Antoine Bosselut, Thomas Wolf, Asli Celikyilmaz

Neural Language Generation (NLG) {--} using neural network models to generate coherent text {--} is among the most promising methods for automated text creation.

Language Modelling Text Generation +1

Movement Pruning: Adaptive Sparsity by Fine-Tuning

3 code implementations NeurIPS 2020 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 standard for state-of-the-art natural language processing applications.

Network Pruning Transfer Learning

TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation

1 code implementation26 Mar 2020 Shaojie Jiang, Thomas Wolf, Christof Monz, Maarten de Rijke

We hypothesize that the deeper reason is that in the training corpora, there are hard tokens that are more difficult for a generative model to learn than others and, once learning has finished, hard tokens are still under-learned, so that repetitive generations are more likely to happen.

Text Generation

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

22 code implementations NeurIPS 2019 Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf

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 constrained computational training or inference budgets remains challenging.

Hate Speech Detection Knowledge Distillation +7

Large-Scale Transfer Learning for Natural Language Generation

1 code implementation ACL 2019 Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alex Tselousov, er, Thomas Wolf

Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.

Open-Domain Dialog Text Generation +1

Transfer Learning in Natural Language Processing

no code implementations NAACL 2019 Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, Thomas Wolf

The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset.

Transfer Learning Word Embeddings

TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents

18 code implementations23 Jan 2019 Thomas Wolf, Victor Sanh, Julien Chaumond, Clement Delangue

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 and a high-capacity Transformer model.

Dialogue Generation Information Retrieval +1

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

1 code implementation14 Nov 2018 Victor Sanh, Thomas Wolf, Sebastian Ruder

The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.

Ranked #9 on Relation Extraction on ACE 2005 (using extra training data)

Multi-Task Learning Named Entity Recognition +1

Continuous Learning in a Hierarchical Multiscale Neural Network

no code implementations ACL 2018 Thomas Wolf, Julien Chaumond, Clement Delangue

We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework.

Language Modelling Meta-Learning

Meta-Learning a Dynamical Language Model

no code implementations28 Mar 2018 Thomas Wolf, Julien Chaumond, Clement Delangue

We consider the task of word-level language modeling and study the possibility of combining hidden-states-based short-term representations with medium-term representations encoded in dynamical weights of a language model.

Language Modelling Meta-Learning

Studying Invariances of Trained Convolutional Neural Networks

no code implementations15 Mar 2018 Charlotte Bunne, Lukas Rahmann, Thomas Wolf

Convolutional Neural Networks (CNNs) define an exceptionally powerful class of models for image classification, but the theoretical background and the understanding of how invariances to certain transformations are learned is limited.

General Classification Image Classification

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