347 citations · 1k across the 30 of their papers we have counts for
8 papers · 1 filter
Measuring Data
Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert +7
We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data…
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat
Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the pre…
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…
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…
Bugs in the Data: How ImageNet Misrepresents Biodiversity
Alexandra Sasha Luccioni, David Rolnick
ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object detection. Wild animals make up 27% of I…
Measuring the Carbon Intensity of AI in Cloud Instances
Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes +7
By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which…