Datasets:

shiyue
/

chr_en

	# 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.
	"""ChrEn: Cherokee-English Machine Translation data"""


	import openpyxl # noqa: requires this pandas optional dependency for reading xlsx files
	import pandas as pd

	import datasets


	_CITATION = """\
	@inproceedings{zhang2020chren,
	title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
	author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
	booktitle={EMNLP2020},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
	ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
	ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
	"""

	_HOMEPAGE = "https://github.com/ZhangShiyue/ChrEn"

	_LICENSE = ""

	_URLs = {
	"monolingual_raw": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/raw/monolingual_data.xlsx",
	"parallel_raw": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/raw/parallel_data.xlsx",
	"monolingual_chr": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/chr",
	"monolingual_en5000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en5000",
	"monolingual_en10000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en10000",
	"monolingual_en20000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en20000",
	"monolingual_en50000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en50000",
	"monolingual_en100000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en100000",
	"parallel_train.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/train.chr",
	"parallel_train.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/train.en",
	"parallel_dev.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/dev.chr",
	"parallel_dev.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/dev.en",
	"parallel_out_dev.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_dev.chr",
	"parallel_out_dev.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_dev.en",
	"parallel_test.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/test.chr",
	"parallel_test.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/test.en",
	"parallel_out_test.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_test.chr",
	"parallel_out_test.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_test.en",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ChrEn(datasets.GeneratorBasedBuilder):
	"""ChrEn: Cherokee-English Machine Translation data."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="monolingual_raw", version=VERSION, description="Monolingual data with metadata"),
	datasets.BuilderConfig(name="parallel_raw", version=VERSION, description="Parallel data with metadata"),
	datasets.BuilderConfig(name="monolingual", version=VERSION, description="Monolingual data text only"),
	datasets.BuilderConfig(
	name="parallel", version=VERSION, description="Parallel data text pairs only with default split"
	),
	]

	DEFAULT_CONFIG_NAME = (
	"parallel" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	if (
	self.config.name == "monolingual_raw"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"text_sentence": datasets.Value("string"),
	"text_title": datasets.Value("string"),
	"speaker": datasets.Value("string"),
	"date": datasets.Value("int32"),
	"type": datasets.Value("string"),
	"dialect": datasets.Value("string"),
	}
	)
	elif (
	self.config.name == "parallel_raw"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"line_number": datasets.Value("string"), # doesn't always map to a number
	"sentence_pair": datasets.Translation(languages=["en", "chr"]),
	"text_title": datasets.Value("string"),
	"speaker": datasets.Value("string"),
	"date": datasets.Value("int32"),
	"type": datasets.Value("string"),
	"dialect": datasets.Value("string"),
	}
	)
	elif (
	self.config.name == "parallel"
	): # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"sentence_pair": datasets.Translation(languages=["en", "chr"]),
	}
	)
	elif (
	self.config.name == "monolingual"
	): # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"sentence": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features, # Here we define them above because they are different between the two configurations
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download(_URLs)
	if self.config.name in [
	"monolingual_raw",
	"parallel_raw",
	]: # This is the name of the configuration selected in BUILDER_CONFIGS above
	return [
	datasets.SplitGenerator(
	name="full",
	gen_kwargs={
	"filepaths": data_dir,
	"split": "full",
	},
	)
	]
	elif self.config.name == "monolingual":
	return [
	datasets.SplitGenerator(
	name=spl,
	gen_kwargs={
	"filepaths": data_dir,
	"split": spl,
	},
	)
	for spl in ["chr", "en5000", "en10000", "en20000", "en50000", "en100000"]
	]
	else:
	return [
	datasets.SplitGenerator(
	name=spl,
	gen_kwargs={
	"filepaths": data_dir,
	"split": spl,
	},
	)
	for spl in ["train", "dev", "out_dev", "test", "out_test"]
	]

	def _generate_examples(self, filepaths, split):
	if self.config.name == "monolingual_raw":
	keys = ["text_sentence", "text_title", "speaker", "date", "type", "dialect"]
	with open(filepaths["monolingual_raw"], "rb") as f:
	monolingual = pd.read_excel(f, engine="openpyxl")
	for id_, row in enumerate(monolingual.itertuples()):
	yield id_, dict(zip(keys, row[1:]))
	elif self.config.name == "parallel_raw":
	keys = ["line_number", "en_sent", "chr_sent", "text_title", "speaker", "date", "type", "dialect"]
	with open(filepaths["parallel_raw"], "rb") as f:
	parallel = pd.read_excel(f, engine="openpyxl")
	for id_, row in enumerate(parallel.itertuples()):
	res = dict(zip(keys, row[1:]))
	res["sentence_pair"] = {"en": res["en_sent"], "chr": res["chr_sent"]}
	res["line_number"] = str(res["line_number"])
	del res["en_sent"]
	del res["chr_sent"]
	yield id_, res
	elif self.config.name == "monolingual":
	f = open(filepaths[f"monolingual_{split}"], encoding="utf-8")
	for id_, line in enumerate(f):
	yield id_, {"sentence": line.strip()}
	elif self.config.name == "parallel":
	fi = open(filepaths[f"parallel_{split}.en"], encoding="utf-8")
	fo = open(filepaths[f"parallel_{split}.chr"], encoding="utf-8")
	for id_, (line_en, line_chr) in enumerate(zip(fi, fo)):
	yield id_, {"sentence_pair": {"en": line_en.strip(), "chr": line_chr.strip()}}