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Found 7 papers, 3 papers with code
Datasets: A Community Library for Natural Language Processing
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML.
RAFT: A Real-World Few-Shot Text Classification Benchmark
Will models soon solve classification tasks that have so far been reserved for human research assistants?
Ranked #2 on
Few-Shot Text Classification
on RAFT
AutoCompete: A Framework for Machine Learning Competition
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions.
AutoCompete: A Framework for Machine Learning Competitions
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present 'UltraMNIST dataset', a simple yet representative benchmark dataset for this task.
