Datasets:

GEM
/

totto

	import json
	import os

	import datasets

	_CITATION = """\@inproceedings{parikh2020totto,
	title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
	author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
	booktitle={Proceedings of EMNLP},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
	"""

	_URLs = {
	"totto": {
	"data": "https://storage.googleapis.com/totto-public/totto_data.zip",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip",
	},
	}


	class Mlsum(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="totto",
	version=datasets.Version("1.0.0"),
	description=f"GEM benchmark: struct2text task",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"totto_id": datasets.Value("int32"),
	"table_page_title": datasets.Value("string"),
	"table_webpage_url": datasets.Value("string"),
	"table_section_title": datasets.Value("string"),
	"table_section_text": datasets.Value("string"),
	"table": [
	[
	{
	"column_span": datasets.Value("int32"),
	"is_header": datasets.Value("bool"),
	"row_span": datasets.Value("int32"),
	"value": datasets.Value("string"),
	}
	]
	],
	"highlighted_cells": [[datasets.Value("int32")]],
	"example_id": datasets.Value("string"),
	"sentence_annotations": [
	{
	"original_sentence": datasets.Value("string"),
	"sentence_after_deletion": datasets.Value("string"),
	"sentence_after_ambiguity": datasets.Value("string"),
	"final_sentence": datasets.Value("string"),
	}
	],
	"overlap_subset": datasets.Value("string"),
	"target": datasets.Value("string"), # single target for train
	"references": [datasets.Value("string")],
	},
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
	challenge_sets = [
	("challenge_train_sample", "train_totto_RandomSample500.json"),
	("challenge_validation_sample", "validation_totto_RandomSample500.json"),
	("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"),
	]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"),
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"),
	"split": "test",
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None, lang=None):
	"""Yields examples."""
	if "challenge" in split:
	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):
	if len(exple) == 0:
	continue
	exple["gem_parent_id"] = exple["gem_id"]
	exple["gem_id"] = f"{self.config.name}-{split}-{id_}"
	yield id_, exple
	else:
	with open(filepath, "r", encoding="utf-8") as json_file:
	json_list = list(json_file)
	id_ = -1
	i = -1
	for json_str in json_list:
	result = json.loads(json_str)
	if split == "train":
	i += 1
	for sentence in result["sentence_annotations"]:
	id_ += 1
	response = {
	"gem_id": f"{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"{self.config.name}-{split}-{id_}",
	"totto_id": i,
	"table_page_title": result["table_page_title"],
	"table_webpage_url": result["table_webpage_url"],
	"table_section_title": result["table_section_title"],
	"table_section_text": result["table_section_text"],
	"table": result["table"],
	"highlighted_cells": result["highlighted_cells"],
	"example_id": str(result["example_id"]),
	"overlap_subset": "none",
	"sentence_annotations": [sentence],
	"references": [],
	"target": sentence["final_sentence"],
	}
	yield id_, response
	else:
	id_ += 1
	response = {
	"gem_id": f"{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"{self.config.name}-{split}-{id_}",
	"totto_id": id_,
	"table_page_title": result["table_page_title"],
	"table_webpage_url": result["table_webpage_url"],
	"table_section_title": result["table_section_title"],
	"table_section_text": result["table_section_text"],
	"table": result["table"],
	"highlighted_cells": result["highlighted_cells"],
	"example_id": str(result["example_id"]),
	"overlap_subset": str(result["overlap_subset"]),
	}
	response["sentence_annotations"] = [] if split == "test" else result["sentence_annotations"]
	response["references"] = [
	sentence["final_sentence"] for sentence in response["sentence_annotations"]
	]
	response["target"] = response["references"][0] if len(response["references"]) > 0 else ""
	yield id_, response