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# coding=utf-8
# Source: https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
"""ELRC-Medical-V2 : European parallel corpus for healthcare machine translation"""
import os
import csv
import datasets
from tqdm import tqdm
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@inproceedings{losch-etal-2018-european,
title = "European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management",
author = {L{\"o}sch, Andrea and
Mapelli, Val{\'e}rie and
Piperidis, Stelios and
Vasi{\c{l}}jevs, Andrejs and
Smal, Lilli and
Declerck, Thierry and
Schnur, Eileen and
Choukri, Khalid and
van Genabith, Josef},
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1213",
}
"""
_LANGUAGE_PAIRS = ["en-" + lang for lang in ["bg", "cs", "da", "de", "el", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"]]
_LICENSE = """
This work is licensed under a <a rel="license" href="https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf">Attribution 4.0 International (CC BY 4.0) License</a>.
"""
_DESCRIPTION = "No description"
_URLS = {
"ELRC-Medical-V2": "https://huggingface.co/datasets/qanastek/ELRC-Medical-V2/resolve/main/ELRC_Medical_V2.zip"
}
class ELRC_Medical_V2(datasets.GeneratorBasedBuilder):
"""ELRC-Medical-V2 dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=name, version=datasets.Version("2.0.0"), description="The ELRC-Medical-V2 corpora") for name in _LANGUAGE_PAIRS
]
DEFAULT_CONFIG_NAME = "en-fr"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"id": datasets.Value("string"),
"lang": datasets.Value("string"),
"source_text": datasets.Value("string"),
"target_text": datasets.Value("string"),
}),
supervised_keys=None,
homepage="https://github.com/qanastek/ELRC-Medical-V2/",
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
TRAIN_PATH = 'train.conllu'
DEV_PATH = 'dev.conllu'
TEST_PATH = 'test.conllu'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, TRAIN_PATH),
"split": "train",
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, DEV_PATH),
"split": "dev",
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, TEST_PATH),
"split": "test",
}
),
]
def _generate_examples(self, filepath, split):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
for row in csv.reader(f, delimiter=','):
print(row)
yield guid, {
"id": str(guid),
"lang": "en-fr",
"source_text": "hi",
"target_text": "salut"
}
guid += 1
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