Datasets:

ought
/

raft

Tasks:

Text Classification

Sub-tasks:

multi-class-classification

Languages:

Multilinguality:

Size Categories:

Language Creators:

expert-generated

Annotations Creators:

expert-generated

Source Datasets:

extended|ade_corpus_v2

extended|banking77

ArXiv:

Tags:

License:

Dataset card Viewer Files Files and versions Community

lewtun HF staff commited on Oct 29, 2021

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 The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset.
-You can explore the dataset [here](https://huggingface.co/spaces/ought/raft-viewer?dataset=raft&config=ade_corpus_v2).
 ### Supported Tasks and Leaderboards
 - `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score.

 The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset.
 ### Supported Tasks and Leaderboards
 - `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score.