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# coding=utf-8
# Copyright 2022 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.
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
# Keep blank; dataset has no associated paper
_CITATION = """\
@article{,
author = {},
title = {},
journal = {},
volume = {},
year = {},
url = {},
doi = {},
biburl = {},
bibsource = {}
}
"""
_LOCAL = False
_LANGUAGES = ["mya", "ceb", "gor", "hil", "ilo", "ind", "jav", "khm", "lao", "zlm", "nia", "tgl", "tha", "vie"]
_DATASETNAME = "mozilla_pontoon"
_DESCRIPTION = """
This dataset contains crowdsource translations of more than 200 languages for
different Mozilla open-source projects from Mozilla's Pontoon localization platform.
Source sentences are in English.
"""
_HOMEPAGE = "https://huggingface.co/datasets/ayymen/Pontoon-Translations"
_LICENSE = Licenses.BSD_3_CLAUSE.value
_URL = "https://huggingface.co/datasets/ayymen/Pontoon-Translations"
_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class MozillaPontoonDataset(datasets.GeneratorBasedBuilder):
"""Dataset of translations from Mozilla's Pontoon platform."""
# Two-letter ISO code is used when available
# otherwise 3-letter one is used
LANG_CODE_MAPPER = {"mya": "my", "ceb": "ceb", "gor": "gor", "hil": "hil", "ilo": "ilo", "ind": "id", "jav": "jv", "khm": "km", "lao": "lo", "zlm": "ms", "nia": "nia", "tgl": "tl", "tha": "th", "vie": "vi"}
# Config to load individual datasets per language
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_eng_{lang}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for {lang} language",
schema="source",
subset_id=f"{_DATASETNAME}_eng_{lang}",
)
for lang in _LANGUAGES
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_eng_{lang}_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for {lang} language",
schema="seacrowd_t2t",
subset_id=f"{_DATASETNAME}_eng_{lang}",
)
for lang in _LANGUAGES
]
# Config to load all datasets
BUILDER_CONFIGS.extend(
[
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for all languages",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for all languages",
schema="seacrowd_t2t",
subset_id=_DATASETNAME,
),
]
)
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"source_string": datasets.Value("string"),
"target_string": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_t2t":
features = schemas.text2text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# dl_manager not used since dataloader uses HF 'load_dataset'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split": "train"},
),
]
def _load_hf_data_from_remote(self, language: str) -> datasets.DatasetDict:
"""Load dataset from HuggingFace."""
hf_lang_code = self.LANG_CODE_MAPPER[language]
hf_remote_ref = "/".join(_URL.split("/")[-2:])
return datasets.load_dataset(hf_remote_ref, f"en-{hf_lang_code}", split="train")
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
languages = []
pontoon_datasets = []
lang = self.config.subset_id.split("_")[-1]
if lang in _LANGUAGES:
languages.append(lang)
pontoon_datasets.append(self._load_hf_data_from_remote(lang))
else:
for lang in _LANGUAGES:
languages.append(lang)
pontoon_datasets.append(self._load_hf_data_from_remote(lang))
index = 0
for lang, lang_subset in zip(languages, pontoon_datasets):
for row in lang_subset:
if self.config.schema == "source":
example = row
elif self.config.schema == "seacrowd_t2t":
example = {
"id": str(index),
"text_1": row["source_string"],
"text_2": row["target_string"],
"text_1_name": "eng",
"text_2_name": lang,
}
yield index, example
index += 1
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