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Data · dataset · 2022

ZEST

Listed in Hugging Face Datasets

ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task.

Description

It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples.

ZEST's structure allows us to systematically test whether models can generalize in five different ways.

Links

Where it is published

Documentation and papers

Catalogue records · 1

Topics

Inferred from text
Artificial intelligence 72%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
Hugging Face Datasetsallenai/zest10 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].anzsrc:group:4602enrichment · Hugging Facetaxonomy-embedding@1.1.0title+keywords+description (72%)
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[task].hf_task:question-answeringsource · Hugging Faceconnector:huggingface@1.0.0/tags[task_categories:*]
concepts[task].hf_task:token-classificationsource · Hugging Faceconnector:huggingface@1.0.0/tags[task_categories:*]
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
licensesource · Hugging Faceconnector:huggingface@1.0.0/tags[license:*]
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0