Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
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File size: 3,567 Bytes
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This loads the UnpredicTable-sporcle-com dataset."""
import json
import os
import pandas as pd
import datasets
_CITATION = """\
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
"""
_DESCRIPTION = """\
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
"""
_HOMEPAGE = "https://ethanperez.net/unpredictable"
_LICENSE = "Apache 2.0"
_URL = "https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com/resolve/main/data/unpredictable_sporcle-com.jsonl"
logger = datasets.logging.get_logger(__name__)
class UnpredicTable(datasets.GeneratorBasedBuilder):
"""
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"task": datasets.Value("string"),
"input": datasets.Value("string"),
"output": datasets.Value("string"),
"options": datasets.Sequence([datasets.Value("string")]),
"pageTitle": datasets.Value("string"),
"outputColName": datasets.Value("string"),
"url": datasets.Value("string"),
"wdcFile": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for i, row in enumerate(f):
data = json.loads(row)
key = f"{data['task']}_{i}"
yield key, {
"task": data["task"],
"input": data["input"],
"output": data["output"],
"options": data["options"],
"pageTitle": data["pageTitle"],
"outputColName": data["outputColName"],
"url": data["url"],
"wdcFile": data["wdcFile"],
}
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