File size: 4,447 Bytes
ce90d54 |
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 |
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=lang,
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, lang=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
|