Datasets:
Tasks:
Table to Text
Modalities:
Text
Languages:
English
Size:
10K - 100K
Tags:
data-to-text
License:
File size: 4,242 Bytes
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import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{e2e_cleaned,
address = {Tokyo, Japan},
title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
url = {https://www.aclweb.org/anthology/W19-8652/},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena},
year = {2019},
pages = {421--426},
}
"""
_DESCRIPTION = """\
The E2E dataset is designed for a limited-domain data-to-text task --
generation of restaurant descriptions/recommendations based on up to 8 different
attributes (name, area, price range etc.).
"""
_URLs = {
"train": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/train-fixed.no-ol.csv",
"validation": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/devel-fixed.no-ol.csv",
"test": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/test-fixed.csv",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/e2e_nlg.zip",
}
class E2ENlg(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_CONFIG_NAME = "e2e_nlg"
def _info(self):
features = datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"meaning_representation": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=datasets.info.SupervisedKeysData(
input="meaning_representation", output="target"
),
homepage="http://www.macs.hw.ac.uk/InteractionLab/E2E/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs)
challenge_sets = [
("challenge_train_sample", "train_e2e_nlg_RandomSample500.json"),
("challenge_validation_sample", "validation_e2e_nlg_RandomSample500.json"),
("challenge_test_scramble", "test_e2e_nlg_ScrambleInputStructure500.json"),
]
return [
datasets.SplitGenerator(
name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}
)
for spl in ["train", "validation", "test"]
] + [
datasets.SplitGenerator(
name=challenge_split,
gen_kwargs={
"filepath": os.path.join(
dl_dir["challenge_set"], "e2e_nlg", 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.startswith("challenge"):
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"e2e_nlg-{split}-{id_}"
yield id_, exple
else:
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for id_, example in enumerate(reader):
yield id_, {
"gem_id": f"e2e_nlg-{split}-{id_}",
"gem_parent_id": f"e2e_nlg-{split}-{id_}",
"meaning_representation": example["mr"],
"target": example["ref"],
"references": [] if split == "train" else [example["ref"]],
}
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