← Search

Text · dataset · 2026

Natively Extended FinQA

Listed in Hugging Face Datasets

Description

Natively Extended FinQA Natively Extended FinQA is a long-context derivative of FinQA for numerical reasoning over financial data. It preserves the FinQA task while increasing the amount of financial context surrounding each question. Average context increased from approximately: 611 words per question to: 5,629 words per question Splits Hugging Face Split File Records train long_train.json 6,251 validation long_dev.json 883 test long_test.json 1… See the full description on the dataset page: huggingface.co/datasets/Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset.

Links

Topics

Stated by source
question answering · text
Provenance · 1 source records, 10 field assertions
SourceKeyLast seenRaw
Hugging Face DatasetsMr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset15 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:textsource · Hugging Faceconnector:huggingface@1.0.0
concepts[task].hf_task:question-answeringsource · 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