activity
20182024
most citedHuggingFace's Transformers: State-of-the-art Natural Language Processing

3.1k citations · 3.4k across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.AI2023★ 11 cited

The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…

cs.CL2021★ 1 cited

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…

cs.CL2019★ 3.1k cited

HuggingFace's Transformers: State-of-the-art Natural Language Processing

Thomas Wolf, Lysandre Debut, Victor Sanh +19

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building hig…

cs.CL2019★ 282 cited

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…

cs.CL2018

Continuous Learning in a Hierarchical Multiscale Neural Network

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. We propose a hierarchical mu…

cs.CL2018

Meta-Learning a Dynamical Language Model

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…