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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 AdapTable-cluster26 dataset."""
import json
import os
import pandas as pd
import datasets
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {TODO: UPDATE TITLE HERE},
author={TODO: BUT AUTHORS HERE},
year={2020}
}
"""
# TODO: Update description
_DESCRIPTION = """\
The AdapTable dataset consists of tables that naturally occur on the web, that are 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.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
_LICENSE = "Apache 2.0"
_URL = "https://huggingface.co/datasets/MicPie/adaptable_cluster26/resolve/main/data/adaptable_cluster26.jsonl"
logger = datasets.logging.get_logger(__name__)
class AdapTableCluster(datasets.GeneratorBasedBuilder):
# TODO: Update docs:
"""
The AdapTable dataset consists of tables that naturally occur on the web, that are 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"],
} |