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RAFT Dataset
- paperswithcode.com
Updated Nov 15, 2022
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EmailClick to copy linkLink copiedCiteNeel Alex; Eli Lifland; Lewis Tunstall; Abhishek Thakur; Pegah Maham; C. Jess Riedel; Emmie Hine; Carolyn Ashurst; Paul Sedille; Alexis Carlier; Michael Noetel; Andreas Stuhlmüller (2022). RAFT Dataset [Dataset]. https://paperswithcode.com/dataset/raftDataset updatedNov 15, 2022AuthorsNeel Alex; Eli Lifland; Lewis Tunstall; Abhishek Thakur; Pegah Maham; C. Jess Riedel; Emmie Hine; Carolyn Ashurst; Paul Sedille; Alexis Carlier; Michael Noetel; Andreas StuhlmüllerDescriptionThe RAFT benchmark (Realworld Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment.
RAFT is a few-shot classification benchmark that tests language models:
across multiple domains (lit reviews, medical data, tweets, customer interaction, etc.) on economically valuable classification tasks (someone inherently cares about the task) with evaluation that mirrors deployment (50 labeled examples per task, info retrieval allowed, hidden test set)
Description from: https://raft.elicit.org/
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RAFT (Realworld Annotated Few-shot Tasks)
- opendatalab.com
zipUpdated Mar 22, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteUniversity of Oxford (2023). RAFT (Realworld Annotated Few-shot Tasks) [Dataset]. https://opendatalab.com/OpenDataLab/RAFTzipAvailable download formatsDataset updatedMar 22, 2023Dataset provided byAlan Turing Institute
University of Oxford
Australian Catholic UniversityDescriptionThe RAFT benchmark (Realworld Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. RAFT is a few-shot classification benchmark that tests language models: across multiple domains (lit reviews, medical data, tweets, customer interaction, etc.) on economically valuable classification tasks (someone inherently cares about the task) with evaluation that mirrors deployment (50 labeled examples per task, info retrieval allowed, hidden test set) Description from: https://raft.elicit.org/
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FacebookTwitterEmailThe RAFT benchmark (Realworld Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment.
RAFT is a few-shot classification benchmark that tests language models:
across multiple domains (lit reviews, medical data, tweets, customer interaction, etc.) on economically valuable classification tasks (someone inherently cares about the task) with evaluation that mirrors deployment (50 labeled examples per task, info retrieval allowed, hidden test set)
Description from: https://raft.elicit.org/