File size: 7,317 Bytes
6d41ecf 284d037 6d41ecf 9505249 6d41ecf 9505249 db55aee 6d41ecf 9505249 db55aee 6d41ecf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
import json
import os
import datasets
_CITATION = """\
@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared,
title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)},
author={Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia},
booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)},
pages = "55--76",
year = 2020,
address = {Dublin, Ireland (Virtual)},
publisher = {Association for Computational Linguistics}}
"""
_DESCRIPTION = """\
WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets
and short texts that cover about 450 different DBpedia properties. The WebNLG data
was originally created to promote the development of RDF verbalisers able to
generate short text and to handle micro-planning (i.e., sentence segmentation and
ordering, referring expression generation, aggregation); the goal of the task is
to generate texts starting from 1 to 7 input triples which have entities in common
(so the input is actually a connected Knowledge Graph). The dataset contains about
17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets
and 19,000 crowdsourced texts in Russian. A challenging test set section with
entities and/or properties that have not been seen at training time is available.
"""
_LANG = ["en", "ru"]
_URLs = {
"en": {
"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_train.json",
"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_val.json",
"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_test.json",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_en.zip",
},
"ru": {
"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_train.json",
"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_val.json",
"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_test.json",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_ru.zip",
},
}
class WebNLG(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=lang,
version=datasets.Version("1.0.0"),
description="",
)
for lang in _LANG
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"input": [datasets.Value("string")],
"target": datasets.Value("string"), # single target for train
"references": [datasets.Value("string")],
"category": datasets.Value("string"),
"webnlg_id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://webnlg-challenge.loria.fr/challenge_2020/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
lang = str(self.config.name)
challenge_sets = [
("challenge_train_sample", f"train_web_nlg_{lang}_RandomSample500.json"),
(
"challenge_validation_sample",
f"validation_web_nlg_{lang}_RandomSample500.json",
),
(
"challenge_test_scramble",
f"test_web_nlg_{lang}_ScrambleInputStructure500.json",
),
]
if lang == "en":
challenge_sets += [
(
"challenge_test_numbers",
f"test_web_nlg_{lang}_replace_numbers_500.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"], f"web_nlg_{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"web_nlg_{self.config.name}-{split}-{id_}"
yield id_, exple
else:
with open(filepath, encoding="utf-8") as f:
examples = json.load(f)
id_ = -1
for example in examples["values"]:
if split == "train":
for target in example["target"]:
id_ += 1
yield id_, {
"gem_id": f"web_nlg_{self.config.name}-{split}-{id_}",
"gem_parent_id": f"web_nlg_{self.config.name}-{split}-{id_}",
"input": example["input"],
"target": target,
"references": []
if split == "train"
else example["target"],
"category": example["category"],
"webnlg_id": example["webnlg-id"],
}
else:
id_ += 1
yield id_, {
"gem_id": f"web_nlg_{self.config.name}-{split}-{id_}",
"gem_parent_id": f"web_nlg_{self.config.name}-{split}-{id_}",
"input": example["input"],
"target": example["target"][0]
if len(example["target"]) > 0
else "",
"references": example["target"],
"category": example["category"],
"webnlg_id": example["webnlg-id"],
}
|