Datasets:
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from typing import Optional, Callable, List, Dict, Any, Tuple, Generator
from dataclasses import dataclass
import itertools
import os
import datasets
from .utils import Sample, list_keyby, parse_tmx, parse_sgm, parse_tsv, cleanup, normalize, dict_map, dict_filter_keys, dict_flatten
logger = datasets.logging.get_logger(
name=__name__
)
@dataclass(frozen=True)
class Candidate:
name: str
url: str
paths: Tuple[str, ...]
num_examples: int
parser: Callable[
[Tuple[str, ...]], Generator[Sample, None, None]
]
def download_paths(
self,
base_path: str
):
return tuple(
os.path.join(base_path, path)
for path in self.paths
)
@dataclass(frozen=True)
class Constraint:
start: Optional[int] = None
stop: Optional[int] = None
step: Optional[int] = None
_CANDIDATES = [
Candidate(
name='europarl_v10',
url='https://statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz',
paths=('.',),
num_examples=1828521,
parser=lambda filepaths: parse_tsv(
filepaths=filepaths,
columns={
'de': 0, 'en': 1
}
)
),
Candidate(
name='newscommentary_v17',
url='https://www.statmt.org/news-commentary/v17/training/news-commentary-v17.de-en.tsv.gz',
paths=('.',),
num_examples=418621,
parser=lambda filepaths: parse_tsv(
filepaths=filepaths,
columns={
'de': 0, 'en': 1
}
)
),
Candidate(
name='wikititles_v3',
url='https://object.pouta.csc.fi/OPUS-WikiTitles/v3/tmx/de-en.tmx.gz',
paths=('.',),
num_examples=1386770,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='ecb_2017',
url='https://s3-eu-west-1.amazonaws.com/tilde-model/ecb2017.de-en.tmx.zip',
paths=('ecb2017.UNIQUE.de-en.tmx',),
num_examples=4147,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='rapid_2019',
url='https://s3-eu-west-1.amazonaws.com/tilde-model/rapid2019.de-en.tmx.zip',
paths=('RAPID_2019.UNIQUE.de-en.tmx',),
num_examples=939808,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='eesc_2017',
url='https://s3-eu-west-1.amazonaws.com/tilde-model/EESC2017.de-en.tmx.zip',
paths=('EESC.de-en.tmx',),
num_examples=2857850,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='ema_2016',
url='https://s3-eu-west-1.amazonaws.com/tilde-model/EMA2016.de-en.tmx.zip',
paths=('EMEA2016.de-en.tmx',),
num_examples=347631,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='europat_v3',
url='https://web-language-models.s3.amazonaws.com/europat/release3/de-en.txt.gz',
paths=('.',),
num_examples=19734742,
parser=lambda filepaths: parse_tsv(
filepaths=filepaths,
columns={
'de': 0, 'en': 1
}
)
),
Candidate(
name='books_v1',
url='https://object.pouta.csc.fi/OPUS-Books/v1/tmx/de-en.tmx.gz',
paths=('.',),
num_examples=51106,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='ted2020_v1',
url='https://object.pouta.csc.fi/OPUS-TED2020/v1/tmx/de-en.tmx.gz',
paths=('.',),
num_examples=289374,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='qed_v2',
url='https://object.pouta.csc.fi/OPUS-QED/v2.0a/tmx/de-en.tmx.gz',
paths=('.',),
num_examples=492811,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='eubookshop_v2',
url='https://object.pouta.csc.fi/OPUS-EUbookshop/v2/tmx/de-en.tmx.gz',
paths=('.',),
num_examples=8312724,
parser=lambda filepaths: parse_tmx(
filepaths=filepaths,
attributes={
'de': 'xml:lang="de"', 'en': 'xml:lang="en"',
}
)
),
Candidate(
name='newstest2018',
url='https://data.statmt.org/wmt22/translation-task/dev.tgz',
paths=('dev/sgm/newstest2018-deen-src.de.sgm',
'dev/sgm/newstest2018-deen-ref.en.sgm'),
num_examples=2998,
parser=lambda filepaths: parse_sgm(
filepaths=filepaths,
files={
'de': 0, 'en': 1
}
)
),
Candidate(
name='newstest2019',
url='https://data.statmt.org/wmt22/translation-task/dev.tgz',
paths=('dev/sgm/newstest2019-deen-src.de.sgm',
'dev/sgm/newstest2019-deen-ref.en.sgm'),
num_examples=2000,
parser=lambda filepaths: parse_sgm(
filepaths=filepaths,
files={
'de': 0, 'en': 1
}
)
)
]
_CANDIDATES_BY_NAME = list_keyby(
input=_CANDIDATES,
key_fn=lambda candidate: candidate.name
)
class NordmannConfig(
datasets.BuilderConfig
):
def __init__(
self,
splits: Dict[datasets.NamedSplit, List[str]],
constraints: Dict[str, Constraint],
normalizer: Callable[[Sample], Sample],
filter: Callable[[Sample], bool],
**kwargs: Any
):
assert splits
datasets.BuilderConfig.__init__(
self, **kwargs
)
self.splits = dict_map(
input=splits, map_fn=lambda key, value: (
key, dict_filter_keys(
input=_CANDIDATES_BY_NAME, keys=value
)
)
)
self.constraints = constraints
self.normalizer = normalizer
self.filter = filter
class Nordmann(
datasets.GeneratorBasedBuilder
):
BUILDER_CONFIG_CLASS = NordmannConfig
BUILDER_CONFIGS = [
NordmannConfig(
name='balanced',
description='NORDMANN 2023 (balanced) translation task dataset.',
version=datasets.Version(
version_str='0.0.1'
),
splits={
datasets.Split.TRAIN: [
'europarl_v10',
'newscommentary_v17',
'wikititles_v3',
'europat_v3',
'books_v1',
'ted2020_v1',
'qed_v2',
'eubookshop_v2'
],
datasets.Split.VALIDATION: [
'newstest2018'
],
datasets.Split.TEST: [
'newstest2019'
]
},
constraints={
'europat_v3': Constraint(stop=1000000),
'eubookshop_v2': Constraint(stop=2000000)
},
normalizer=normalize(
strip_whitespaces=True,
clean_control_characters=True,
enforce_unicode_form='NFC'
),
filter=cleanup(
length_min=4,
length_max=4096,
length_ratio_max=1.33,
alpha_ratio_min=.5
)
)
]
def _info(
self
):
features = {
'translation': datasets.features.Translation(
languages=['de', 'en']
)
}
return datasets.DatasetInfo(
description='Translation dataset based on statmt.org',
features=datasets.Features(features)
)
def _split_generators(
self,
dl_manager: datasets.DownloadManager
):
self.config: NordmannConfig
urls = dict_map(
input=dict_flatten(
input=self.config.splits
),
map_fn=lambda key, value: (
key, value.url
)
)
base_paths: Dict[str, str]
base_paths = dl_manager.download_and_extract( # pyright: ignore
url_or_urls=urls
)
generators: List[datasets.SplitGenerator]
generators = list()
for split, split_candidates in self.config.splits.items():
generators.append(
datasets.SplitGenerator(
name=str(split),
gen_kwargs={
'candidates': split_candidates,
'base_paths': base_paths
}
)
)
return generators
def _generate_examples( # pyright: ignore
self,
candidates: Dict[str, Candidate],
base_paths: Dict[str, str]
):
self.config: NordmannConfig
for name, candidate in candidates.items():
constraint = (
self.config.constraints[name]
if name in self.config.constraints else Constraint()
)
samples = candidate.parser(
candidate.download_paths(
base_path=base_paths[name]
)
)
for sample_num, sample in enumerate(
itertools.islice(
samples,
constraint.start,
constraint.stop,
constraint.step
)
):
normalized_sample = self.config.normalizer(sample)
if not self.config.filter(normalized_sample):
continue
yield candidate.name + '_' + str(sample_num), normalized_sample
samples.close()
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