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# dataset loading script for huggingface
import datasets
import json
try:
import lzma as xz
except ImportError:
import pylzma as xz
datasets.logging.set_verbosity_info()
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
"""
_HOMEPAGE = "https://skatinger.github.io/master_thesis/",
_LICENSE = ""
_CITATION = ""
_TYPES = ["original", "paraphrased"]
_SIZES = [4096, 512]
_URLS = {
"original_4096": "data/original_4096.jsonl.xz",
"original_512": "data/original_512.jsonl.xz",
"paraphrased_4096": "data/paraphrased_4096.jsonl.xz",
"paraphrased_512": "data/paraphrased_512.jsonl.xz"
}
class WikipediaForMaskFillingConfig(datasets.BuilderConfig):
"""BuilderConfig for WikipediaForMaskFilling.
features: *list[string]*, list of the features that will appear in the
feature dict. Should not include "label".
**kwargs: keyword arguments forwarded to super
"""
def __init__(self, type, size=4096, **kwargs):
"""BuilderConfig for WikipediaForMaskFilling.
Args:
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 1.0.0: first version
super().__init__(**kwargs)
self.size = size
self.type = type
class WikipediaForMaskFilling(datasets.GeneratorBasedBuilder):
"""WikipediaForMaskFilling dataset."""
BUILDER_CONFIGS = [
WikipediaForMaskFillingConfig(
name="original_4096",
version=datasets.Version("1.0.0"),
description="Part of the dataset with original texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
max_tokens=4096,
type="original"
),
WikipediaForMaskFillingConfig(
name="original_512",
version=datasets.Version("1.0.0"),
description="text chunks split into size of max 512 tokens (roberta).",
max_tokens=512,
type="original"
),
WikipediaForMaskFillingConfig(
name="paraphrased_4096",
version=datasets.Version("1.0.0"),
description="Part of the dataset with paraphrased texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
max_tokens=4096,
type="paraphrased"
),
WikipediaForMaskFillingConfig(
name="paraphrased_512",
version=datasets.Version("1.0.0"),
description="Paraphrased text chunks split into size of max 512 tokens (roberta).",
max_tokens=512,
type="paraphrased"
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"texts": datasets.Value("string"),
"masks": datasets.Sequence(datasets.Value("string")),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
type = self.config.type
size = self.config.size
urls_to_download = f"data/{type}_{size}.jsonl.xz"
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
]
def _generate_examples(self, filepath):
_id = 0
with open(filepath, encoding="utf-8") as f:
try:
with xz.open(filepath) as f:
for line in f:
data = json.loads(line)
yield _id, {
"texts": data["texts"],
"masks": data["masks"]
}
_id += 1
except Exception:
logger.exception("Error while processing file %s", filepath)
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