Datasets:
scb_mt_enth_2020

	import json
	import os

	import datasets


	_CITATION = """\
	@article{lowphansirikul2020scb,
	title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
	author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
	journal={arXiv preprint arXiv:2007.03541},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
	The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
	We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
	namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
	Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
	We train machine translation models based on this dataset. Our models' performance are comparable to that of
	Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
	included in the training data for both Thai-English and English-Thai translation.
	The dataset, pre-trained models, and source code to reproduce our work are available for public use.
	"""


	class ScbMtEnth2020Config(datasets.BuilderConfig):
	"""BuilderConfig for ScbMtEnth2020."""

	def __init__(self, language_pair=(None, None), **kwargs):
	"""BuilderConfig for ScbMtEnth2020.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ScbMtEnth2020Config, self).__init__(
	name=f"{language_pair[0]}{language_pair[1]}",
	description="Translate {language_pair[0]} to {language_pair[1]}",
	version=datasets.Version("1.0.0"),
	**kwargs,
	)
	self.language_pair = language_pair


	class ScbMtEnth2020(datasets.GeneratorBasedBuilder):
	"""scb-mt-en-th-2020: A Large English-Thai Parallel Corpus"""

	_DOWNLOAD_URL = "https://archive.org/download/scb_mt_enth_2020/data.zip"
	_TRAIN_FILE = "train.jsonl"
	_VAL_FILE = "valid.jsonl"
	_TEST_FILE = "test.jsonl"
	BUILDER_CONFIG_CLASS = ScbMtEnth2020Config
	BUILDER_CONFIGS = [
	ScbMtEnth2020Config(
	language_pair=("en", "th"),
	),
	ScbMtEnth2020Config(
	language_pair=("th", "en"),
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"translation": datasets.features.Translation(languages=self.config.language_pair),
	"subdataset": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://airesearch.in.th/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
	data_dir = os.path.join(arch_path, "data")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)}
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)}
	),
	]

	def _generate_examples(self, filepath):
	"""Generate examples."""
	source, target = self.config.language_pair
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	yield id_, {
	"translation": {source: data[source], target: data[target]},
	"subdataset": data["subdataset"],
	}