Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
100K<n<1M
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""PMC Open Access Subset.""" | |
import datetime | |
import pandas as pd | |
import datasets | |
from datasets.tasks import LanguageModeling | |
# TODO: Add BibTeX citation | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@InProceedings{huggingface:dataset, | |
title = {A great new dataset}, | |
author={huggingface, Inc. | |
}, | |
year={2020} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under | |
license terms that allow reuse. | |
Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles | |
in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more | |
liberal redistribution and reuse than a traditional copyrighted work. | |
The PMC Open Access Subset is one part of the PMC Article Datasets | |
""" | |
_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/" | |
# TODO: Add the licence for the dataset here if you can find it | |
_LICENSE = "" | |
_URL = "https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/{subset}/txt/" | |
_SUBSETS = { | |
"commercial": "oa_comm", | |
"non_commercial": "oa_noncomm", | |
"other": "oa_other", | |
} | |
_BASELINE_DATE = "2021-12-17" | |
class OpenAccessConfig(datasets.BuilderConfig): | |
"""BuilderConfig for the PMC Open Access Subset.""" | |
def __init__(self, subsets=None, **kwargs): | |
"""BuilderConfig for the PMC Open Access Subset. | |
Args: | |
subsets (:obj:`List[str]`): List of subsets/groups to load. | |
**kwargs: Keyword arguments forwarded to super. | |
""" | |
subsets = [subsets] if isinstance(subsets, str) else subsets | |
super().__init__( | |
name="+".join(subsets), **kwargs, | |
) | |
self.subsets = subsets if self.name != "all" else list(_SUBSETS.keys()) | |
class OpenAccess(datasets.GeneratorBasedBuilder): | |
"""PMC Open Access Subset.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIG_CLASS = OpenAccessConfig | |
BUILDER_CONFIGS = [OpenAccessConfig(subsets="all")] + [OpenAccessConfig(subsets=subset) for subset in _SUBSETS] | |
DEFAULT_CONFIG_NAME = "all" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"pmid": datasets.Value("string"), | |
"accession_id": datasets.Value("string"), | |
"license": datasets.Value("string"), | |
"last_updated": datasets.Value("string"), | |
"retracted": datasets.Value("string"), | |
"citation": datasets.Value("string"), | |
} | |
), | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
task_templates=[LanguageModeling(text_column="text")], | |
) | |
def _split_generators(self, dl_manager): | |
incremental_paths = { | |
"incremental_file_lists": [], | |
"incremental_archives": [] | |
} | |
baseline_file_lists = [] | |
baseline_archives = [] | |
for subset in self.config.subsets: | |
url = _URL.format(subset=_SUBSETS[subset]) | |
basename = f"{_SUBSETS[subset]}_txt." | |
# Baselines | |
baselines = [f"PMC00{i}xxxxxx.baseline.{_BASELINE_DATE}" for i in range(9)] | |
# baseline_urls = { | |
# "baseline_file_lists": [f"{url}{basename}{baseline}.filelist.csv" for baseline in baselines], | |
# "baseline_archives": [f"{url}{basename}{baseline}.tar.gz" for baseline in baselines], | |
# } | |
# baseline_paths = dl_manager.download(baseline_urls) | |
for baseline in baselines: | |
baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv" | |
try: | |
baseline_file_list = dl_manager.download(baseline_file_list_url) | |
except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist | |
continue | |
baseline_archive_url = f"{url}{basename}{baseline}.tar.gz" | |
try: | |
baseline_archive = dl_manager.download(baseline_archive_url) | |
except FileNotFoundError: | |
continue | |
baseline_file_lists.append(baseline_file_list) | |
baseline_archives.append(baseline_archive) | |
# Incremental | |
date_delta = datetime.date.today() - datetime.date.fromisoformat(_BASELINE_DATE) | |
incremental_dates = [ | |
(datetime.date.fromisoformat(_BASELINE_DATE) + datetime.timedelta(days=i + 1)).isoformat() | |
for i in range(date_delta.days) | |
] | |
incrementals = [f"incr.{date}" for date in incremental_dates] | |
incremental_urls = { | |
"incremental_file_lists": [ | |
f"{url}{basename}{incremental}.filelist.csv" for incremental in incrementals | |
], | |
"incremental_archives": [f"{url}{basename}{incremental}.tar.gz" for incremental in incrementals], | |
} | |
paths = dl_manager.download(incremental_urls) | |
incremental_paths["incremental_file_lists"].extend(paths["incremental_file_lists"]) | |
incremental_paths["incremental_archives"].extend(paths["incremental_archives"]) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"baseline_file_lists": baseline_file_lists, | |
"baseline_archives": [dl_manager.iter_archive(archive) for archive in baseline_archives], | |
"incremental_file_lists": incremental_paths["incremental_file_lists"], | |
"incremental_archives": [ | |
dl_manager.iter_archive(archive) for archive in incremental_paths["incremental_archives"] | |
], | |
}, | |
), | |
] | |
def _generate_examples(self, baseline_file_lists, baseline_archives, incremental_file_lists, incremental_archives): | |
key = 0 | |
# Baselines | |
for baseline_file_list, baseline_archive in zip(baseline_file_lists, baseline_archives): | |
try: | |
baselines = pd.read_csv(baseline_file_list, index_col="Article File").to_dict(orient="index") | |
for path, file in baseline_archive: | |
data = baselines.pop(path) | |
content = file.read() | |
try: | |
text = content.decode("utf-8").strip() | |
except UnicodeDecodeError as e: | |
text = content.decode("latin-1").strip() | |
data = { | |
"text": text, | |
"pmid": data["PMID"], | |
"accession_id": data["AccessionID"], | |
"license": data["License"], | |
"last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"], | |
"retracted": data["Retracted"], | |
"citation": data["Article Citation"], | |
} | |
yield key, data | |
key += 1 | |
except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist | |
continue | |
# Incrementals | |
if incremental_file_lists: | |
for incremental_file_list, incremental_archive in zip(incremental_file_lists, incremental_archives): | |
import pdb | |
pdb.set_trace() | |
incrementals = pd.read_csv(incremental_file_list, index_col="Article File").to_dict(orient="index") | |
for path, file in incremental_archive: | |
data = incrementals.pop(path) | |
content = file.read() | |
try: | |
text = content.decode("utf-8").strip() | |
except UnicodeDecodeError as e: | |
text = content.decode("latin-1").strip() | |
data = { | |
"text": text, | |
"pmid": data["PMID"], | |
"accession_id": data["AccessionID"], | |
"license": data["License"], | |
"last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"], | |
"retracted": data["Retracted"], | |
"citation": data["Article Citation"], | |
} | |
yield key, data | |
key += 1 | |