activity
20192026
most citedHuggingFace's Transformers: State-of-the-art Natural Language Processing

3.1k citations · 3.2k across the 10 of their papers we have counts for

collaborators

11 papers

cs.RO2026

LeRobot: An Open-Source Library for End-to-End Robot Learning

Remi Cadene, Simon Aliberts, Francesco Capuano +14

Robotics is undergoing a significant transformation powered by advances in high-level control techniques based on machine learning, giving rise to the field of robot learning. Rece…

cs.LG2024★ 18 cited

Croissant: A Metadata Format for ML-Ready Datasets

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…

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.CL2023★ 1 cited

AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages

Chris Chinenye Emezue, Sanchit Gandhi, Lewis Tunstall +10

The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora. To address thi…

cs.CL2023★ 65 cited

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Hugo Laurençon, Lucile Saulnier, Thomas Wang +51

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…

cs.CL2022

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

BigScience Workshop, :, Teven Le Scao +391

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…

cs.LG2022★ 5 cited

Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements

Leandro von Werra, Lewis Tunstall, Abhishek Thakur +16

Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation o…

cs.LG2022

Training Transformers Together

Alexander Borzunov, Max Ryabinin, Tim Dettmers +5

The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and instituti…

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.LG2021★ 4 cited

Distributed Deep Learning in Open Collaborations

Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin +13

Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High…

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…