Datasets:

GEM
/

wiki_auto_asset_turk

	import csv
	import json
	import os
	import datasets

	_CITATION = """\
	@inproceedings{jiang-etal-2020-neural,
	title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
	author = "Jiang, Chao and
	Maddela, Mounica and
	Lan, Wuwei and
	Zhong, Yang and
	Xu, Wei",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.709",
	doi = "10.18653/v1/2020.acl-main.709",
	pages = "7943--7960",
	}
	"""

	_DESCRIPTION = """\
	WikiAuto provides a set of aligned sentences from English Wikipedia and Simple
	English Wikipedia as a resource to train sentence simplification systems.

	The authors first crowd-sourced a set of manual alignments between sentences in
	a subset of the Simple English Wikipedia and their corresponding versions in
	English Wikipedia (this corresponds to the manual config in this version of the
	dataset), then trained a neural CRF system to predict these alignments.

	The trained alignment prediction model was then applied to the other articles in
	Simple English Wikipedia with an English counterpart to create a larger corpus
	of aligned sentences (corresponding to the auto and auto_acl configs here).
	"""

	_URLs = {
	"train": "train.tsv",
	"validation": "valid.tsv",
	"test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip",
	"test_contract": "benchmarks/contract-benchmark.tsv",
	"test_wiki": "benchmarks/wiki-benchmark.tsv",
	}

	# Add Asset files.
	_URLs[
	"test_asset_orig"
	] = "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.orig"
	for i in range(10):
	_URLs[
	f"test_asset_{i}"
	] = f"https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.simp.{i}"


	class WikiAuto(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	DEFAULT_CONFIG_NAME = "wiki_auto_asset_turk"

	def _info(self):
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"source": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=datasets.info.SupervisedKeysData(
	input="source", output="target"
	),
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs)
	challenge_sets = [
	(
	"challenge_train_sample",
	"train_wiki_auto_asset_turk_RandomSample500.json",
	),
	(
	"challenge_validation_sample",
	"validation_wiki_auto_asset_turk_RandomSample500.json",
	),
	(
	"challenge_test_asset_backtranslation",
	"test_asset_wiki_auto_asset_turk_BackTranslation.json",
	),
	(
	"challenge_test_asset_bfp02",
	"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
	),
	(
	"challenge_test_asset_bfp05",
	"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
	),
	(
	"challenge_test_asset_nopunc",
	"test_asset_wiki_auto_asset_turk_WithoutPunctuation.json",
	),
	(
	"challenge_test_turk_backtranslation",
	"detok_test_turk_wiki_auto_asset_turk_BackTranslation.json",
	),
	(
	"challenge_test_turk_bfp02",
	"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json",
	),
	(
	"challenge_test_turk_bfp05",
	"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json",
	),
	(
	"challenge_test_turk_nopunc",
	"detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json",
	),
	]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": dl_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": dl_dir["validation"],
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name="test_asset",
	gen_kwargs={
	"filepath": "",
	"split": "test_asset",
	"filepaths": [dl_dir["test_asset_orig"]]
	+ [dl_dir[f"test_asset_{i}"] for i in range(10)],
	},
	),
	datasets.SplitGenerator(
	name="test_turk",
	gen_kwargs={
	"filepath": dl_dir["test_turk"],
	"split": "test_turk",
	},
	),
	datasets.SplitGenerator(
	name="test_contract",
	gen_kwargs={
	"filepath": dl_dir["test_contract"],
	"split": "test_contract",
	},
	),
	datasets.SplitGenerator(
	name="test_wiki",
	gen_kwargs={
	"filepath": dl_dir["test_wiki"],
	"split": "test_wiki",
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(
	dl_dir["challenge_set"], "wiki_auto_asset_turk", filename
	),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None, lang=None):
	"""Yields examples."""
	if split in ["train", "validation"]:
	keys = [
	"source",
	"target",
	]
	with open(filepath, encoding="utf-8") as f:
	for id_, line in enumerate(f):
	values = line.strip().split("\t")
	assert (
	len(values) == 2
	), f"Not enough fields in ---- {line} --- {values}"
	example = dict([(k, val) for k, val in zip(keys, values)])
	example["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
	example["gem_parent_id"] = example["gem_id"]
	example["references"] = (
	[] if split == "train" else [example["target"]]
	)
	yield id_, example
	elif split == "test_turk":
	examples = json.load(open(filepath, encoding="utf-8"))
	for id_, example in enumerate(examples):
	example["gem_parent_id"] = example["gem_id"]
	for k in ["source_id", "target_id"]:
	if k in example:
	del example[k]
	yield id_, example
	elif split == "test_asset":
	files = [open(f_name, encoding="utf-8") for f_name in filepaths]
	for id_, lines in enumerate(zip(*files)):
	yield id_, {
	"gem_id": f"wiki_auto_asset_turk-{split}-{id_}",
	"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}",
	"target": lines[1].strip(),
	"source": lines[0].strip(),
	"references": [line.strip() for line in lines[1:]],
	}
	elif split == "test_wiki" or split == "test_contract":
	with open(filepath, 'r') as f:
	reader = csv.DictReader(f, delimiter="\t")
	for id_, entry in enumerate(reader):
	yield id_, {
	"gem_id": f"wiki_auto_asset_turk-{split}-{id_}",
	"gem_parent_id": f"wiki_auto_asset_turk-{split}-{id_}",
	"target": entry["simple"],
	"source": entry["complex"],
	"references": [entry["simple"]],
	}
	else:
	exples = json.load(open(filepath, encoding="utf-8"))
	if isinstance(exples, dict):
	assert len(exples) == 1, "multiple entries found"
	exples = list(exples.values())[0]
	for id_, exple in enumerate(exples):
	exple["gem_parent_id"] = exple["gem_id"]
	exple["gem_id"] = f"wiki_auto_asset_turk-{split}-{id_}"
	for k in ["source_id", "target_id"]:
	if k in exple:
	del exple[k]
	yield id_, exple