65 citations · 105 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…