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paramed.py
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# 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.
"""
NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website.
English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through
http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011.
The script loads dataset in bigbio schema (using schemas/text-to-text) AND/OR source (default) schema
"""
import os # useful for paths
from typing import Dict, Iterable, List
import datasets
from bigbio.utils import schemas
from bigbio.utils.configs import BigBioConfig
from bigbio.utils.constants import Tasks
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{,
author = {Liu, Boxiang and Huang, Liang},
title = {ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain},
journal = {BMC Medical Informatics and Decision Making},
volume = {21},
year = {2021},
url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01621-8},
doi = {10.1186/s12911-021-01621-8}
}
"""
_DATASETNAME = "paramed"
_DESCRIPTION = """\
NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website.
English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through
http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011.
"""
_HOMEPAGE = "https://github.com/boxiangliu/ParaMed"
_LICENSE = "Creative Commons Attribution 4.0 International"
_URLs = {
"source": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true",
"bigbio_t2t": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true",
}
_SUPPORTED_TASKS = [Tasks.TRANSLATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
_DATA_DIR = "./processed_data/open_access/open_access"
class ParamedDataset(datasets.GeneratorBasedBuilder):
"""Write a short docstring documenting what this dataset is"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="paramed_source",
version=SOURCE_VERSION,
description="Paramed source schema",
schema="source",
subset_id="paramed",
),
BigBioConfig(
name="paramed_bigbio_t2t",
version=BIGBIO_VERSION,
description="Paramed BigBio schema",
schema="bigbio_t2t",
subset_id="paramed",
),
]
DEFAULT_CONFIG_NAME = "paramed_source"
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"document_id": datasets.Value("string"),
"text_1": datasets.Value("string"),
"text_2": datasets.Value("string"),
"text_1_name": datasets.Value("string"),
"text_2_name": datasets.Value("string"),
}
)
elif self.config.schema == "bigbio_t2t":
features = schemas.text2text_features
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
my_urls = _URLs[self.config.schema]
data_dir = os.path.join(dl_manager.download_and_extract(my_urls), _DATA_DIR)
print(data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
"zh_file": os.path.join(data_dir, "nejm.train.zh"),
"en_file": os.path.join(data_dir, "nejm.train.en"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir,
"zh_file": os.path.join(data_dir, "nejm.dev.zh"),
"en_file": os.path.join(data_dir, "nejm.dev.en"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir,
"zh_file": os.path.join(data_dir, "nejm.test.zh"),
"en_file": os.path.join(data_dir, "nejm.test.en"),
"split": "test",
},
),
]
def _generate_examples(self, filepath, zh_file, en_file, split):
logger.info("generating examples from = %s", filepath)
zh_file = open(zh_file, "r")
en_file = open(en_file, "r")
zh_file.seek(0)
en_file.seek(0)
zh_lines = zh_file.readlines()
en_lines = en_file.readlines()
assert len(en_lines) == len(zh_lines), "Line mismatch"
if self.config.schema == "source":
for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)):
yield key, {
"document_id": str(key),
"text_1": zh_line,
"text_2": en_line,
"text_1_name": "zh",
"text_2_name": "en",
}
zh_file.close()
en_file.close()
elif self.config.schema == "bigbio_t2t":
uid = 0
for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)):
uid += 1
yield key, {
"id": str(uid),
"document_id": str(key),
"text_1": zh_line,
"text_2": en_line,
"text_1_name": "zh",
"text_2_name": "en",
}
zh_file.close()
en_file.close()