|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""This loads the AdapTable-en.wikipedia.org dataset.""" |
|
|
|
import json |
|
import os |
|
import pandas as pd |
|
|
|
import datasets |
|
|
|
_CITATION = """\ |
|
@misc{https://ethanperez.net/adaptable, |
|
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, |
|
title = {Exploring Few-Shot Adaptation of Language Models with Tables}, |
|
publisher = {arXiv}, |
|
year = {2022}, |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
The AdapTable 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/adaptable" |
|
|
|
_LICENSE = "Apache 2.0" |
|
|
|
_URL = "https://huggingface.co/datasets/MicPie/adaptable_en.wikipedia.org/resolve/main/data/adaptable_en.wikipedia.org.jsonl" |
|
|
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
|
|
class AdapTableSite(datasets.GeneratorBasedBuilder): |
|
""" |
|
The AdapTable 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"], |
|
} |