File size: 5,348 Bytes
ce90d54 13d678c ce90d54 13d678c ce90d54 ee69481 ce90d54 a985f6d |
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 |
import json
import os
import datasets
_CITATION = """\
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
"""
_DESCRIPTION = """\
This is the XSUM subset of the GEM benchmark.
"""
_URLs = {
"xsum": {
"data": "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz",
"splits": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_xsum_confidence_0.8.json",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/xsum.zip",
},
}
_XSUM_REMOVE_LINES = set(
[
"Share this with\n",
"Email\n",
"Facebook\n",
"Messenger\n",
"Twitter\n",
"Pinterest\n",
"WhatsApp\n",
"Linkedin\n",
"LinkedIn\n",
"Copy this link\n",
"These are external links and will open in a new window\n",
]
)
class Xsum(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="xsum",
version=datasets.Version("1.0.0"),
description="",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features = datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"xsum_id": datasets.Value("string"),
"document": datasets.Value("string"),
"target": datasets.Value("string"),
"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_xsum_RandomSample500.json"),
("challenge_validation_sample", "validation_xsum_RandomSample500.json"),
("challenge_test_backtranslation", "test_xsum_BackTranslation500.json"),
("challenge_test_bfp_02", "test_xsum_ButterFingersPerturbation_p=0.02_500.json"),
("challenge_test_bfp_05", "test_xsum_ButterFingersPerturbation_p=0.05_500.json"),
("challenge_test_nopunc", "test_xsum_WithoutPunctuation500.json"),
("challenge_test_covid", f"en_test_covid19.jsonl"),
]
return [
datasets.SplitGenerator(
name=challenge_split,
gen_kwargs={
"filepath": os.path.join(dl_dir["challenge_set"], "xsum", filename),
"split": challenge_split,
},
)
for challenge_split, filename in challenge_sets
]
def _generate_examples(self, filepath, split, filepaths=None):
"""Yields examples."""
if "challenge" in split:
if "covid" in split:
with open(filepath, encoding="utf-8") as f:
id_ = -1
for line in f:
data = json.loads(line)
id_ += 1
yield id_, {
"gem_id": f"{self.config.name}-{split}-{id_}",
"gem_parent_id": f"{self.config.name}-{split}-{id_}",
"xsum_id": data["url"],
"document": data["text"],
"target": data["summary"],
"references": [] if split == "train" else [data["summary"]],
}
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"{self.config.name}-{split}-{id_}"
yield id_, exple
else:
with open(filepath, "r", encoding="utf-8") as f:
split_ids = json.load(f)
for id_, i in enumerate(split_ids[split]):
with open(os.path.join(filepaths, i + ".summary"), "r", encoding="utf-8") as f:
text = "".join(
[line for line in f.readlines() if line not in _XSUM_REMOVE_LINES and line.strip()]
)
segs = text.split("[SN]")
yield id_, {
"gem_id": f"{self.config.name}-{split}-{id_}",
"gem_parent_id": f"{self.config.name}-{split}-{id_}",
"xsum_id": i,
"document": segs[8].strip(),
"target": segs[6].strip(),
"references": [] if split == "train" else [segs[6].strip()],
}
|