Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
no-annotation
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""WMT: Translate dataset.""" | |
import codecs | |
import functools | |
import glob | |
import gzip | |
import itertools | |
import os | |
import re | |
import xml.etree.cElementTree as ElementTree | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_DESCRIPTION = """\ | |
Translation dataset based on the data from statmt.org. | |
Versions exist for different years using a combination of data | |
sources. The base `wmt` allows you to create a custom dataset by choosing | |
your own data/language pair. This can be done as follows: | |
```python | |
from datasets import inspect_dataset, load_dataset_builder | |
inspect_dataset("wmt16", "path/to/scripts") | |
builder = load_dataset_builder( | |
"path/to/scripts/wmt_utils.py", | |
language_pair=("fr", "de"), | |
subsets={ | |
datasets.Split.TRAIN: ["commoncrawl_frde"], | |
datasets.Split.VALIDATION: ["euelections_dev2019"], | |
}, | |
) | |
# Standard version | |
builder.download_and_prepare() | |
ds = builder.as_dataset() | |
# Streamable version | |
ds = builder.as_streaming_dataset() | |
``` | |
""" | |
CWMT_SUBSET_NAMES = ["casia2015", "casict2011", "casict2015", "datum2015", "datum2017", "neu2017"] | |
class SubDataset: | |
"""Class to keep track of information on a sub-dataset of WMT.""" | |
def __init__(self, name, target, sources, url, path, manual_dl_files=None): | |
"""Sub-dataset of WMT. | |
Args: | |
name: `string`, a unique dataset identifier. | |
target: `string`, the target language code. | |
sources: `set<string>`, the set of source language codes. | |
url: `string` or `(string, string)`, URL(s) or URL template(s) specifying | |
where to download the raw data from. If two strings are provided, the | |
first is used for the source language and the second for the target. | |
Template strings can either contain '{src}' placeholders that will be | |
filled in with the source language code, '{0}' and '{1}' placeholders | |
that will be filled in with the source and target language codes in | |
alphabetical order, or all 3. | |
path: `string` or `(string, string)`, path(s) or path template(s) | |
specifing the path to the raw data relative to the root of the | |
downloaded archive. If two strings are provided, the dataset is assumed | |
to be made up of parallel text files, the first being the source and the | |
second the target. If one string is provided, both languages are assumed | |
to be stored within the same file and the extension is used to determine | |
how to parse it. Template strings should be formatted the same as in | |
`url`. | |
manual_dl_files: `<list>(string)` (optional), the list of files that must | |
be manually downloaded to the data directory. | |
""" | |
self._paths = (path,) if isinstance(path, str) else path | |
self._urls = (url,) if isinstance(url, str) else url | |
self._manual_dl_files = manual_dl_files if manual_dl_files else [] | |
self.name = name | |
self.target = target | |
self.sources = set(sources) | |
def _inject_language(self, src, strings): | |
"""Injects languages into (potentially) template strings.""" | |
if src not in self.sources: | |
raise ValueError(f"Invalid source for '{self.name}': {src}") | |
def _format_string(s): | |
if "{0}" in s and "{1}" and "{src}" in s: | |
return s.format(*sorted([src, self.target]), src=src) | |
elif "{0}" in s and "{1}" in s: | |
return s.format(*sorted([src, self.target])) | |
elif "{src}" in s: | |
return s.format(src=src) | |
else: | |
return s | |
return [_format_string(s) for s in strings] | |
def get_url(self, src): | |
return self._inject_language(src, self._urls) | |
def get_manual_dl_files(self, src): | |
return self._inject_language(src, self._manual_dl_files) | |
def get_path(self, src): | |
return self._inject_language(src, self._paths) | |
# Subsets used in the training sets for various years of WMT. | |
_TRAIN_SUBSETS = [ | |
# pylint:disable=line-too-long | |
SubDataset( | |
name="commoncrawl", | |
target="en", # fr-de pair in commoncrawl_frde | |
sources={"cs", "de", "es", "fr", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-commoncrawl.zip", | |
path=("commoncrawl.{src}-en.{src}", "commoncrawl.{src}-en.en"), | |
), | |
SubDataset( | |
name="commoncrawl_frde", | |
target="de", | |
sources={"fr"}, | |
url=( | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/commoncrawl.fr.gz", | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/commoncrawl.de.gz", | |
), | |
path=("", ""), | |
), | |
SubDataset( | |
name="czeng_10", | |
target="en", | |
sources={"cs"}, | |
url="http://ufal.mff.cuni.cz/czeng/czeng10", | |
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)], | |
# Each tar contains multiple files, which we process specially in | |
# _parse_czeng. | |
path=("data.plaintext-format/??train.gz",) * 10, | |
), | |
SubDataset( | |
name="czeng_16pre", | |
target="en", | |
sources={"cs"}, | |
url="http://ufal.mff.cuni.cz/czeng/czeng16pre", | |
manual_dl_files=["czeng16pre.deduped-ignoring-sections.txt.gz"], | |
path="", | |
), | |
SubDataset( | |
name="czeng_16", | |
target="en", | |
sources={"cs"}, | |
url="http://ufal.mff.cuni.cz/czeng", | |
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)], | |
# Each tar contains multiple files, which we process specially in | |
# _parse_czeng. | |
path=("data.plaintext-format/??train.gz",) * 10, | |
), | |
SubDataset( | |
# This dataset differs from the above in the filtering that is applied | |
# during parsing. | |
name="czeng_17", | |
target="en", | |
sources={"cs"}, | |
url="http://ufal.mff.cuni.cz/czeng", | |
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)], | |
# Each tar contains multiple files, which we process specially in | |
# _parse_czeng. | |
path=("data.plaintext-format/??train.gz",) * 10, | |
), | |
SubDataset( | |
name="dcep_v1", | |
target="en", | |
sources={"lv"}, | |
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/dcep.lv-en.v1.zip", | |
path=("dcep.en-lv/dcep.lv", "dcep.en-lv/dcep.en"), | |
), | |
SubDataset( | |
name="europarl_v7", | |
target="en", | |
sources={"cs", "de", "es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip", | |
path=("training/europarl-v7.{src}-en.{src}", "training/europarl-v7.{src}-en.en"), | |
), | |
SubDataset( | |
name="europarl_v7_frde", | |
target="de", | |
sources={"fr"}, | |
url=( | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/europarl-v7.fr.gz", | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/europarl-v7.de.gz", | |
), | |
path=("", ""), | |
), | |
SubDataset( | |
name="europarl_v8_18", | |
target="en", | |
sources={"et", "fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-ep-v8.zip", | |
path=("training/europarl-v8.{src}-en.{src}", "training/europarl-v8.{src}-en.en"), | |
), | |
SubDataset( | |
name="europarl_v8_16", | |
target="en", | |
sources={"fi", "ro"}, | |
url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-ep-v8.zip", | |
path=("training-parallel-ep-v8/europarl-v8.{src}-en.{src}", "training-parallel-ep-v8/europarl-v8.{src}-en.en"), | |
), | |
SubDataset( | |
name="europarl_v9", | |
target="en", | |
sources={"cs", "de", "fi", "lt"}, | |
url="https://huggingface.co/datasets/wmt/europarl/resolve/main/v9/training/europarl-v9.{src}-en.tsv.gz", | |
path="", | |
), | |
SubDataset( | |
name="gigafren", | |
target="en", | |
sources={"fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt10/resolve/main-zip/training-giga-fren.zip", | |
path=("giga-fren.release2.fixed.fr.gz", "giga-fren.release2.fixed.en.gz"), | |
), | |
SubDataset( | |
name="hindencorp_01", | |
target="en", | |
sources={"hi"}, | |
url="http://ufallab.ms.mff.cuni.cz/~bojar/hindencorp", | |
manual_dl_files=["hindencorp0.1.gz"], | |
path="", | |
), | |
SubDataset( | |
name="leta_v1", | |
target="en", | |
sources={"lv"}, | |
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/leta.v1.zip", | |
path=("LETA-lv-en/leta.lv", "LETA-lv-en/leta.en"), | |
), | |
SubDataset( | |
name="multiun", | |
target="en", | |
sources={"es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-un.zip", | |
path=("un/undoc.2000.{src}-en.{src}", "un/undoc.2000.{src}-en.en"), | |
), | |
SubDataset( | |
name="newscommentary_v9", | |
target="en", | |
sources={"cs", "de", "fr", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip", | |
path=("training/news-commentary-v9.{src}-en.{src}", "training/news-commentary-v9.{src}-en.en"), | |
), | |
SubDataset( | |
name="newscommentary_v10", | |
target="en", | |
sources={"cs", "de", "fr", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip", | |
path=("news-commentary-v10.{src}-en.{src}", "news-commentary-v10.{src}-en.en"), | |
), | |
SubDataset( | |
name="newscommentary_v11", | |
target="en", | |
sources={"cs", "de", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip", | |
path=( | |
"training-parallel-nc-v11/news-commentary-v11.{src}-en.{src}", | |
"training-parallel-nc-v11/news-commentary-v11.{src}-en.en", | |
), | |
), | |
SubDataset( | |
name="newscommentary_v12", | |
target="en", | |
sources={"cs", "de", "ru", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip", | |
path=("training/news-commentary-v12.{src}-en.{src}", "training/news-commentary-v12.{src}-en.en"), | |
), | |
SubDataset( | |
name="newscommentary_v13", | |
target="en", | |
sources={"cs", "de", "ru", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip", | |
path=( | |
"training-parallel-nc-v13/news-commentary-v13.{src}-en.{src}", | |
"training-parallel-nc-v13/news-commentary-v13.{src}-en.en", | |
), | |
), | |
SubDataset( | |
name="newscommentary_v14", | |
target="en", # fr-de pair in newscommentary_v14_frde | |
sources={"cs", "de", "kk", "ru", "zh"}, | |
url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.{0}-{1}.tsv.gz", | |
path="", | |
), | |
SubDataset( | |
name="newscommentary_v14_frde", | |
target="de", | |
sources={"fr"}, | |
url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.de-fr.tsv.gz", | |
path="", | |
), | |
SubDataset( | |
name="onlinebooks_v1", | |
target="en", | |
sources={"lv"}, | |
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/books.lv-en.v1.zip", | |
path=("farewell/farewell.lv", "farewell/farewell.en"), | |
), | |
SubDataset( | |
name="paracrawl_v1", | |
target="en", | |
sources={"cs", "de", "et", "fi", "ru"}, | |
url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-{src}.zipporah0-dedup-clean.tgz", # TODO(QL): use zip for streaming | |
path=( | |
"paracrawl-release1.en-{src}.zipporah0-dedup-clean.{src}", | |
"paracrawl-release1.en-{src}.zipporah0-dedup-clean.en", | |
), | |
), | |
SubDataset( | |
name="paracrawl_v1_ru", | |
target="en", | |
sources={"ru"}, | |
url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz", # TODO(QL): use zip for streaming | |
path=( | |
"paracrawl-release1.en-ru.zipporah0-dedup-clean.ru", | |
"paracrawl-release1.en-ru.zipporah0-dedup-clean.en", | |
), | |
), | |
SubDataset( | |
name="paracrawl_v3", | |
target="en", # fr-de pair in paracrawl_v3_frde | |
sources={"cs", "de", "fi", "lt"}, | |
url="https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-{src}.bicleaner07.tmx.gz", | |
path="", | |
), | |
SubDataset( | |
name="paracrawl_v3_frde", | |
target="de", | |
sources={"fr"}, | |
url=( | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/de-fr.bicleaner07.de.gz", | |
"https://huggingface.co/datasets/wmt/wmt19/resolve/main/translation-task/fr-de/bitexts/de-fr.bicleaner07.fr.gz", | |
), | |
path=("", ""), | |
), | |
SubDataset( | |
name="rapid_2016", | |
target="en", | |
sources={"de", "et", "fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip", | |
path=("rapid2016.{0}-{1}.{src}", "rapid2016.{0}-{1}.en"), | |
), | |
SubDataset( | |
name="rapid_2016_ltfi", | |
target="en", | |
sources={"fi", "lt"}, | |
url="https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-{src}.tmx.zip", | |
path="rapid2016.en-{src}.tmx", | |
), | |
SubDataset( | |
name="rapid_2019", | |
target="en", | |
sources={"de"}, | |
url="https://s3-eu-west-1.amazonaws.com/tilde-model/rapid2019.de-en.zip", | |
path=("rapid2019.de-en.de", "rapid2019.de-en.en"), | |
), | |
SubDataset( | |
name="setimes_2", | |
target="en", | |
sources={"ro", "tr"}, | |
url="https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-{src}.tmx.gz", | |
path="", | |
), | |
SubDataset( | |
name="uncorpus_v1", | |
target="en", | |
sources={"ru", "zh"}, | |
url="https://huggingface.co/datasets/wmt/uncorpus/resolve/main-zip/UNv1.0.en-{src}.zip", | |
path=("en-{src}/UNv1.0.en-{src}.{src}", "en-{src}/UNv1.0.en-{src}.en"), | |
), | |
SubDataset( | |
name="wikiheadlines_fi", | |
target="en", | |
sources={"fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip", | |
path="wiki/fi-en/titles.fi-en", | |
), | |
SubDataset( | |
name="wikiheadlines_hi", | |
target="en", | |
sources={"hi"}, | |
url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/wiki-titles.zip", | |
path="wiki/hi-en/wiki-titles.hi-en", | |
), | |
SubDataset( | |
# Verified that wmt14 and wmt15 files are identical. | |
name="wikiheadlines_ru", | |
target="en", | |
sources={"ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip", | |
path="wiki/ru-en/wiki.ru-en", | |
), | |
SubDataset( | |
name="wikititles_v1", | |
target="en", | |
sources={"cs", "de", "fi", "gu", "kk", "lt", "ru", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wikititles/resolve/main/v1/wikititles-v1.{src}-en.tsv.gz", | |
path="", | |
), | |
SubDataset( | |
name="yandexcorpus", | |
target="en", | |
sources={"ru"}, | |
url="https://translate.yandex.ru/corpus?lang=en", | |
manual_dl_files=["1mcorpus.zip"], | |
path=("corpus.en_ru.1m.ru", "corpus.en_ru.1m.en"), | |
), | |
# pylint:enable=line-too-long | |
] + [ | |
SubDataset( # pylint:disable=g-complex-comprehension | |
name=ss, | |
target="en", | |
sources={"zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main/cwmt-wmt/%s.zip" % ss, | |
path=("%s/*_c[hn].txt" % ss, "%s/*_en.txt" % ss), | |
) | |
for ss in CWMT_SUBSET_NAMES | |
] | |
_DEV_SUBSETS = [ | |
SubDataset( | |
name="euelections_dev2019", | |
target="de", | |
sources={"fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/euelections_dev2019.fr-de.src.fr", "dev/euelections_dev2019.fr-de.tgt.de"), | |
), | |
SubDataset( | |
name="newsdev2014", | |
target="en", | |
sources={"hi"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2014.hi", "dev/newsdev2014.en"), | |
), | |
SubDataset( | |
name="newsdev2015", | |
target="en", | |
sources={"fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2015-fien-src.{src}.sgm", "dev/newsdev2015-fien-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdiscussdev2015", | |
target="en", | |
sources={"ro", "tr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdev2016", | |
target="en", | |
sources={"ro", "tr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2016-{src}en-src.{src}.sgm", "dev/newsdev2016-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdev2017", | |
target="en", | |
sources={"lv", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2017-{src}en-src.{src}.sgm", "dev/newsdev2017-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdev2018", | |
target="en", | |
sources={"et"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2018-{src}en-src.{src}.sgm", "dev/newsdev2018-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdev2019", | |
target="en", | |
sources={"gu", "kk", "lt"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdev2019-{src}en-src.{src}.sgm", "dev/newsdev2019-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdiscussdev2015", | |
target="en", | |
sources={"fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdiscusstest2015", | |
target="en", | |
sources={"fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newssyscomb2009", | |
target="en", | |
sources={"cs", "de", "es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newssyscomb2009.{src}", "dev/newssyscomb2009.en"), | |
), | |
SubDataset( | |
name="newstest2008", | |
target="en", | |
sources={"cs", "de", "es", "fr", "hu"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/news-test2008.{src}", "dev/news-test2008.en"), | |
), | |
SubDataset( | |
name="newstest2009", | |
target="en", | |
sources={"cs", "de", "es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2009.{src}", "dev/newstest2009.en"), | |
), | |
SubDataset( | |
name="newstest2010", | |
target="en", | |
sources={"cs", "de", "es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2010.{src}", "dev/newstest2010.en"), | |
), | |
SubDataset( | |
name="newstest2011", | |
target="en", | |
sources={"cs", "de", "es", "fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2011.{src}", "dev/newstest2011.en"), | |
), | |
SubDataset( | |
name="newstest2012", | |
target="en", | |
sources={"cs", "de", "es", "fr", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2012.{src}", "dev/newstest2012.en"), | |
), | |
SubDataset( | |
name="newstest2013", | |
target="en", | |
sources={"cs", "de", "es", "fr", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2013.{src}", "dev/newstest2013.en"), | |
), | |
SubDataset( | |
name="newstest2014", | |
target="en", | |
sources={"cs", "de", "es", "fr", "hi", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2014-{src}en-src.{src}.sgm", "dev/newstest2014-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newstest2015", | |
target="en", | |
sources={"cs", "de", "fi", "ru"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2015-{src}en-src.{src}.sgm", "dev/newstest2015-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newsdiscusstest2015", | |
target="en", | |
sources={"fr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newstest2016", | |
target="en", | |
sources={"cs", "de", "fi", "ro", "ru", "tr"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2016-{src}en-src.{src}.sgm", "dev/newstest2016-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newstestB2016", | |
target="en", | |
sources={"fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstestB2016-enfi-ref.{src}.sgm", "dev/newstestB2016-enfi-src.en.sgm"), | |
), | |
SubDataset( | |
name="newstest2017", | |
target="en", | |
sources={"cs", "de", "fi", "lv", "ru", "tr", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2017-{src}en-src.{src}.sgm", "dev/newstest2017-{src}en-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newstestB2017", | |
target="en", | |
sources={"fi"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstestB2017-fien-src.fi.sgm", "dev/newstestB2017-fien-ref.en.sgm"), | |
), | |
SubDataset( | |
name="newstest2018", | |
target="en", | |
sources={"cs", "de", "et", "fi", "ru", "tr", "zh"}, | |
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip", | |
path=("dev/newstest2018-{src}en-src.{src}.sgm", "dev/newstest2018-{src}en-ref.en.sgm"), | |
), | |
] | |
DATASET_MAP = {dataset.name: dataset for dataset in _TRAIN_SUBSETS + _DEV_SUBSETS} | |
_CZENG17_FILTER = SubDataset( | |
name="czeng17_filter", | |
target="en", | |
sources={"cs"}, | |
url="http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip", | |
path="convert_czeng16_to_17.pl", | |
) | |
class WmtConfig(datasets.BuilderConfig): | |
"""BuilderConfig for WMT.""" | |
def __init__(self, url=None, citation=None, description=None, language_pair=(None, None), subsets=None, **kwargs): | |
"""BuilderConfig for WMT. | |
Args: | |
url: The reference URL for the dataset. | |
citation: The paper citation for the dataset. | |
description: The description of the dataset. | |
language_pair: pair of languages that will be used for translation. Should | |
contain 2 letter coded strings. For example: ("en", "de"). | |
configuration for the `datasets.features.text.TextEncoder` used for the | |
`datasets.features.text.Translation` features. | |
subsets: Dict[split, list[str]]. List of the subset to use for each of the | |
split. Note that WMT subclasses overwrite this parameter. | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
name = "%s-%s" % (language_pair[0], language_pair[1]) | |
if "name" in kwargs: # Add name suffix for custom configs | |
name += "." + kwargs.pop("name") | |
super(WmtConfig, self).__init__(name=name, description=description, **kwargs) | |
self.url = url or "http://www.statmt.org" | |
self.citation = citation | |
self.language_pair = language_pair | |
self.subsets = subsets | |
# TODO(PVP): remove when manual dir works | |
# +++++++++++++++++++++ | |
if language_pair[1] in ["cs", "hi", "ru"]: | |
assert NotImplementedError(f"The dataset for {language_pair[1]}-en is currently not fully supported.") | |
# +++++++++++++++++++++ | |
class Wmt(datasets.GeneratorBasedBuilder): | |
"""WMT translation dataset.""" | |
BUILDER_CONFIG_CLASS = WmtConfig | |
def __init__(self, *args, **kwargs): | |
super(Wmt, self).__init__(*args, **kwargs) | |
def _subsets(self): | |
"""Subsets that make up each split of the dataset.""" | |
raise NotImplementedError("This is a abstract method") | |
def subsets(self): | |
"""Subsets that make up each split of the dataset for the language pair.""" | |
source, target = self.config.language_pair | |
filtered_subsets = {} | |
subsets = self._subsets if self.config.subsets is None else self.config.subsets | |
for split, ss_names in subsets.items(): | |
filtered_subsets[split] = [] | |
for ss_name in ss_names: | |
dataset = DATASET_MAP[ss_name] | |
if dataset.target != target or source not in dataset.sources: | |
logger.info("Skipping sub-dataset that does not include language pair: %s", ss_name) | |
else: | |
filtered_subsets[split].append(ss_name) | |
logger.info("Using sub-datasets: %s", filtered_subsets) | |
return filtered_subsets | |
def _info(self): | |
src, target = self.config.language_pair | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{"translation": datasets.features.Translation(languages=self.config.language_pair)} | |
), | |
supervised_keys=(src, target), | |
homepage=self.config.url, | |
citation=self.config.citation, | |
) | |
def _vocab_text_gen(self, split_subsets, extraction_map, language): | |
for _, ex in self._generate_examples(split_subsets, extraction_map, with_translation=False): | |
yield ex[language] | |
def _split_generators(self, dl_manager): | |
source, _ = self.config.language_pair | |
manual_paths_dict = {} | |
urls_to_download = {} | |
for ss_name in itertools.chain.from_iterable(self.subsets.values()): | |
if ss_name == "czeng_17": | |
# CzEng1.7 is CzEng1.6 with some blocks filtered out. We must download | |
# the filtering script so we can parse out which blocks need to be | |
# removed. | |
urls_to_download[_CZENG17_FILTER.name] = _CZENG17_FILTER.get_url(source) | |
# get dataset | |
dataset = DATASET_MAP[ss_name] | |
if dataset.get_manual_dl_files(source): | |
# TODO(PVP): following two lines skip configs that are incomplete for now | |
# +++++++++++++++++++++ | |
logger.info("Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}") | |
continue | |
# +++++++++++++++++++++ | |
manual_dl_files = dataset.get_manual_dl_files(source) | |
manual_paths = [ | |
os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), fname) | |
for fname in manual_dl_files | |
] | |
assert all( | |
os.path.exists(path) for path in manual_paths | |
), f"For {dataset.name}, you must manually download the following file(s) from {dataset.get_url(source)} and place them in {dl_manager.manual_dir}: {', '.join(manual_dl_files)}" | |
# set manual path for correct subset | |
manual_paths_dict[ss_name] = manual_paths | |
else: | |
urls_to_download[ss_name] = dataset.get_url(source) | |
# Download and extract files from URLs. | |
downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
# Extract manually downloaded files. | |
manual_files = dl_manager.extract(manual_paths_dict) | |
extraction_map = dict(downloaded_files, **manual_files) | |
for language in self.config.language_pair: | |
self._vocab_text_gen(self.subsets[datasets.Split.TRAIN], extraction_map, language) | |
return [ | |
datasets.SplitGenerator( # pylint:disable=g-complex-comprehension | |
name=split, gen_kwargs={"split_subsets": split_subsets, "extraction_map": extraction_map} | |
) | |
for split, split_subsets in self.subsets.items() | |
] | |
def _generate_examples(self, split_subsets, extraction_map, with_translation=True): | |
"""Returns the examples in the raw (text) form.""" | |
source, _ = self.config.language_pair | |
def _get_local_paths(dataset, extract_dirs): | |
rel_paths = dataset.get_path(source) | |
if len(extract_dirs) == 1: | |
extract_dirs = extract_dirs * len(rel_paths) | |
return [ | |
os.path.join(ex_dir, rel_path) if rel_path else ex_dir | |
for ex_dir, rel_path in zip(extract_dirs, rel_paths) | |
] | |
def _get_filenames(dataset): | |
rel_paths = dataset.get_path(source) | |
urls = dataset.get_url(source) | |
if len(urls) == 1: | |
urls = urls * len(rel_paths) | |
return [rel_path if rel_path else os.path.basename(url) for url, rel_path in zip(urls, rel_paths)] | |
for ss_name in split_subsets: | |
# TODO(PVP) remove following five lines when manual data works | |
# +++++++++++++++++++++ | |
dataset = DATASET_MAP[ss_name] | |
source, _ = self.config.language_pair | |
if dataset.get_manual_dl_files(source): | |
logger.info(f"Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}") | |
continue | |
# +++++++++++++++++++++ | |
logger.info("Generating examples from: %s", ss_name) | |
dataset = DATASET_MAP[ss_name] | |
extract_dirs = extraction_map[ss_name] | |
files = _get_local_paths(dataset, extract_dirs) | |
filenames = _get_filenames(dataset) | |
sub_generator_args = tuple(files) | |
if ss_name.startswith("czeng"): | |
if ss_name.endswith("16pre"): | |
sub_generator = functools.partial(_parse_tsv, language_pair=("en", "cs")) | |
sub_generator_args += tuple(filenames) | |
elif ss_name.endswith("17"): | |
filter_path = _get_local_paths(_CZENG17_FILTER, extraction_map[_CZENG17_FILTER.name])[0] | |
sub_generator = functools.partial(_parse_czeng, filter_path=filter_path) | |
else: | |
sub_generator = _parse_czeng | |
elif ss_name == "hindencorp_01": | |
sub_generator = _parse_hindencorp | |
elif len(files) == 2: | |
if ss_name.endswith("_frde"): | |
sub_generator = _parse_frde_bitext | |
else: | |
sub_generator = _parse_parallel_sentences | |
sub_generator_args += tuple(filenames) | |
elif len(files) == 1: | |
fname = filenames[0] | |
# Note: Due to formatting used by `download_manager`, the file | |
# extension may not be at the end of the file path. | |
if ".tsv" in fname: | |
sub_generator = _parse_tsv | |
sub_generator_args += tuple(filenames) | |
elif ( | |
ss_name.startswith("newscommentary_v14") | |
or ss_name.startswith("europarl_v9") | |
or ss_name.startswith("wikititles_v1") | |
): | |
sub_generator = functools.partial(_parse_tsv, language_pair=self.config.language_pair) | |
sub_generator_args += tuple(filenames) | |
elif "tmx" in fname or ss_name.startswith("paracrawl_v3"): | |
sub_generator = _parse_tmx | |
elif ss_name.startswith("wikiheadlines"): | |
sub_generator = _parse_wikiheadlines | |
else: | |
raise ValueError("Unsupported file format: %s" % fname) | |
else: | |
raise ValueError("Invalid number of files: %d" % len(files)) | |
for sub_key, ex in sub_generator(*sub_generator_args): | |
if not all(ex.values()): | |
continue | |
# TODO(adarob): Add subset feature. | |
# ex["subset"] = subset | |
key = f"{ss_name}/{sub_key}" | |
if with_translation is True: | |
ex = {"translation": ex} | |
yield key, ex | |
def _parse_parallel_sentences(f1, f2, filename1, filename2): | |
"""Returns examples from parallel SGML or text files, which may be gzipped.""" | |
def _parse_text(path, original_filename): | |
"""Returns the sentences from a single text file, which may be gzipped.""" | |
split_path = original_filename.split(".") | |
if split_path[-1] == "gz": | |
lang = split_path[-2] | |
def gen(): | |
with open(path, "rb") as f, gzip.GzipFile(fileobj=f) as g: | |
for line in g: | |
yield line.decode("utf-8").rstrip() | |
return gen(), lang | |
if split_path[-1] == "txt": | |
# CWMT | |
lang = split_path[-2].split("_")[-1] | |
lang = "zh" if lang in ("ch", "cn") else lang | |
else: | |
lang = split_path[-1] | |
def gen(): | |
with open(path, "rb") as f: | |
for line in f: | |
yield line.decode("utf-8").rstrip() | |
return gen(), lang | |
def _parse_sgm(path, original_filename): | |
"""Returns sentences from a single SGML file.""" | |
lang = original_filename.split(".")[-2] | |
# Note: We can't use the XML parser since some of the files are badly | |
# formatted. | |
seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>") | |
def gen(): | |
with open(path, encoding="utf-8") as f: | |
for line in f: | |
seg_match = re.match(seg_re, line) | |
if seg_match: | |
assert len(seg_match.groups()) == 1 | |
yield seg_match.groups()[0] | |
return gen(), lang | |
parse_file = _parse_sgm if os.path.basename(f1).endswith(".sgm") else _parse_text | |
# Some datasets (e.g., CWMT) contain multiple parallel files specified with | |
# a wildcard. We sort both sets to align them and parse them one by one. | |
f1_files = sorted(glob.glob(f1)) | |
f2_files = sorted(glob.glob(f2)) | |
assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2) | |
assert len(f1_files) == len(f2_files), "Number of files do not match: %d vs %d for %s vs %s." % ( | |
len(f1_files), | |
len(f2_files), | |
f1, | |
f2, | |
) | |
for f_id, (f1_i, f2_i) in enumerate(zip(sorted(f1_files), sorted(f2_files))): | |
l1_sentences, l1 = parse_file(f1_i, filename1) | |
l2_sentences, l2 = parse_file(f2_i, filename2) | |
for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)): | |
key = f"{f_id}/{line_id}" | |
yield key, {l1: s1, l2: s2} | |
def _parse_frde_bitext(fr_path, de_path): | |
with open(fr_path, encoding="utf-8") as fr_f: | |
with open(de_path, encoding="utf-8") as de_f: | |
for line_id, (s1, s2) in enumerate(zip(fr_f, de_f)): | |
yield line_id, {"fr": s1.rstrip(), "de": s2.rstrip()} | |
def _parse_tmx(path): | |
"""Generates examples from TMX file.""" | |
def _get_tuv_lang(tuv): | |
for k, v in tuv.items(): | |
if k.endswith("}lang"): | |
return v | |
raise AssertionError("Language not found in `tuv` attributes.") | |
def _get_tuv_seg(tuv): | |
segs = tuv.findall("seg") | |
assert len(segs) == 1, "Invalid number of segments: %d" % len(segs) | |
return segs[0].text | |
with open(path, "rb") as f: | |
# Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563 | |
utf_f = codecs.getreader("utf-8")(f) | |
for line_id, (_, elem) in enumerate(ElementTree.iterparse(utf_f)): | |
if elem.tag == "tu": | |
yield line_id, {_get_tuv_lang(tuv): _get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")} | |
elem.clear() | |
def _parse_tsv(path, filename, language_pair=None): | |
"""Generates examples from TSV file.""" | |
if language_pair is None: | |
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", filename) | |
assert lang_match is not None, "Invalid TSV filename: %s" % filename | |
l1, l2 = lang_match.groups() | |
else: | |
l1, l2 = language_pair | |
with open(path, encoding="utf-8") as f: | |
for j, line in enumerate(f): | |
cols = line.split("\t") | |
if len(cols) != 2: | |
logger.warning("Skipping line %d in TSV (%s) with %d != 2 columns.", j, path, len(cols)) | |
continue | |
s1, s2 = cols | |
yield j, {l1: s1.strip(), l2: s2.strip()} | |
def _parse_wikiheadlines(path): | |
"""Generates examples from Wikiheadlines dataset file.""" | |
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path) | |
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path | |
l1, l2 = lang_match.groups() | |
with open(path, encoding="utf-8") as f: | |
for line_id, line in enumerate(f): | |
s1, s2 = line.split("|||") | |
yield line_id, {l1: s1.strip(), l2: s2.strip()} | |
def _parse_czeng(*paths, **kwargs): | |
"""Generates examples from CzEng v1.6, with optional filtering for v1.7.""" | |
filter_path = kwargs.get("filter_path", None) | |
if filter_path: | |
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]") | |
with open(filter_path, encoding="utf-8") as f: | |
bad_blocks = {blk for blk in re.search(r"qw{([\s\d]*)}", f.read()).groups()[0].split()} | |
logger.info("Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.", len(bad_blocks)) | |
for path in paths: | |
for gz_path in sorted(glob.glob(path)): | |
with open(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f: | |
filename = os.path.basename(gz_path) | |
for line_id, line in enumerate(f): | |
line = line.decode("utf-8") # required for py3 | |
if not line.strip(): | |
continue | |
id_, unused_score, cs, en = line.split("\t") | |
if filter_path: | |
block_match = re.match(re_block, id_) | |
if block_match and block_match.groups()[0] in bad_blocks: | |
continue | |
sub_key = f"{filename}/{line_id}" | |
yield sub_key, { | |
"cs": cs.strip(), | |
"en": en.strip(), | |
} | |
def _parse_hindencorp(path): | |
with open(path, encoding="utf-8") as f: | |
for line_id, line in enumerate(f): | |
split_line = line.split("\t") | |
if len(split_line) != 5: | |
logger.warning("Skipping invalid HindEnCorp line: %s", line) | |
continue | |
yield line_id, {"translation": {"en": split_line[3].strip(), "hi": split_line[4].strip()}} | |