Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Russian
Size:
10K - 100K
ArXiv:
License:
Upload data files directly to the HF repository
Browse filesSince the download links from sberquad.py are currently unavailable, I have uploaded the files directly to the repository.
- data/dev_v1.0.json.gz +3 -0
- data/origin_test.json.gz +3 -0
- data/train_v1.0.json.gz +3 -0
- sberquad.py +2 -1
data/dev_v1.0.json.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:a9145c18e890e84fd070272fcaa607a688efd61debf36b89bc2b96654ac77d18
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size 1927670
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data/origin_test.json.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d24ee67e71ea95163342b72d8fb36914fa7d0b261b3552ed5824f355fe76963
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size 2725921
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data/train_v1.0.json.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a05221fe35eb1b4fcd17b8acca1d957e12d098783d8e8a2ebb2348f6c437b0a
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size 5838123
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sberquad.py
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# coding=utf-8
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"""SberQUAD: Sber Question Answering Dataset."""
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import json
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import datasets
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@@ -33,7 +34,7 @@ from the corresponding reading passage, or the question might be unanswerable. \
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Russian original analogue presented in Sberbank Data Science Journey 2017.
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"""
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_URLS = {"train": "
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class Sberquad(datasets.GeneratorBasedBuilder):
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# coding=utf-8
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"""SberQUAD: Sber Question Answering Dataset."""
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import os
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import json
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import datasets
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Russian original analogue presented in Sberbank Data Science Journey 2017.
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"""
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_URLS = {"train": os.path.join("data", "train_v1.0.json.gz"), "dev": os.path.join("data", "dev_v1.0.json.gz"), "test": os.path.join("data", "origin_test.json.gz")}
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class Sberquad(datasets.GeneratorBasedBuilder):
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