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Delete loading script auxiliary file
Browse files- wmt_utils.py +0 -1025
wmt_utils.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""WMT: Translate dataset."""
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import codecs
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import functools
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import glob
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import gzip
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import itertools
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import os
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import re
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import xml.etree.cElementTree as ElementTree
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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Translation dataset based on the data from statmt.org.
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Versions exist for different years using a combination of data
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sources. The base `wmt` allows you to create a custom dataset by choosing
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your own data/language pair. This can be done as follows:
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```python
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from datasets import inspect_dataset, load_dataset_builder
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inspect_dataset("wmt17", "path/to/scripts")
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builder = load_dataset_builder(
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"path/to/scripts/wmt_utils.py",
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language_pair=("fr", "de"),
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subsets={
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datasets.Split.TRAIN: ["commoncrawl_frde"],
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datasets.Split.VALIDATION: ["euelections_dev2019"],
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},
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)
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# Standard version
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builder.download_and_prepare()
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ds = builder.as_dataset()
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# Streamable version
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ds = builder.as_streaming_dataset()
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```
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"""
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CWMT_SUBSET_NAMES = ["casia2015", "casict2011", "casict2015", "datum2015", "datum2017", "neu2017"]
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class SubDataset:
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"""Class to keep track of information on a sub-dataset of WMT."""
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def __init__(self, name, target, sources, url, path, manual_dl_files=None):
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"""Sub-dataset of WMT.
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Args:
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name: `string`, a unique dataset identifier.
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target: `string`, the target language code.
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sources: `set<string>`, the set of source language codes.
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url: `string` or `(string, string)`, URL(s) or URL template(s) specifying
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where to download the raw data from. If two strings are provided, the
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first is used for the source language and the second for the target.
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Template strings can either contain '{src}' placeholders that will be
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filled in with the source language code, '{0}' and '{1}' placeholders
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that will be filled in with the source and target language codes in
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alphabetical order, or all 3.
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path: `string` or `(string, string)`, path(s) or path template(s)
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specifing the path to the raw data relative to the root of the
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downloaded archive. If two strings are provided, the dataset is assumed
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to be made up of parallel text files, the first being the source and the
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second the target. If one string is provided, both languages are assumed
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to be stored within the same file and the extension is used to determine
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how to parse it. Template strings should be formatted the same as in
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`url`.
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manual_dl_files: `<list>(string)` (optional), the list of files that must
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be manually downloaded to the data directory.
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"""
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self._paths = (path,) if isinstance(path, str) else path
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self._urls = (url,) if isinstance(url, str) else url
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self._manual_dl_files = manual_dl_files if manual_dl_files else []
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self.name = name
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self.target = target
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self.sources = set(sources)
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def _inject_language(self, src, strings):
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"""Injects languages into (potentially) template strings."""
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if src not in self.sources:
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raise ValueError(f"Invalid source for '{self.name}': {src}")
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def _format_string(s):
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if "{0}" in s and "{1}" and "{src}" in s:
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return s.format(*sorted([src, self.target]), src=src)
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elif "{0}" in s and "{1}" in s:
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return s.format(*sorted([src, self.target]))
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elif "{src}" in s:
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return s.format(src=src)
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else:
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return s
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return [_format_string(s) for s in strings]
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def get_url(self, src):
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return self._inject_language(src, self._urls)
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def get_manual_dl_files(self, src):
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return self._inject_language(src, self._manual_dl_files)
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def get_path(self, src):
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return self._inject_language(src, self._paths)
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# Subsets used in the training sets for various years of WMT.
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_TRAIN_SUBSETS = [
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# pylint:disable=line-too-long
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SubDataset(
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name="commoncrawl",
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target="en", # fr-de pair in commoncrawl_frde
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sources={"cs", "de", "es", "fr", "ru"},
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url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-commoncrawl.zip",
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path=("commoncrawl.{src}-en.{src}", "commoncrawl.{src}-en.en"),
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),
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SubDataset(
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name="commoncrawl_frde",
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target="de",
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sources={"fr"},
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url=(
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"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/commoncrawl.fr.gz",
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"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/commoncrawl.de.gz",
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),
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path=("", ""),
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),
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SubDataset(
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name="czeng_10",
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target="en",
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sources={"cs"},
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url="http://ufal.mff.cuni.cz/czeng/czeng10",
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manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
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# Each tar contains multiple files, which we process specially in
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# _parse_czeng.
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path=("data.plaintext-format/??train.gz",) * 10,
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),
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SubDataset(
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name="czeng_16pre",
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target="en",
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sources={"cs"},
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url="http://ufal.mff.cuni.cz/czeng/czeng16pre",
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manual_dl_files=["czeng16pre.deduped-ignoring-sections.txt.gz"],
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path="",
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),
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SubDataset(
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name="czeng_16",
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target="en",
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sources={"cs"},
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url="http://ufal.mff.cuni.cz/czeng",
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manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
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# Each tar contains multiple files, which we process specially in
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# _parse_czeng.
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path=("data.plaintext-format/??train.gz",) * 10,
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),
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SubDataset(
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# This dataset differs from the above in the filtering that is applied
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# during parsing.
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name="czeng_17",
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target="en",
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sources={"cs"},
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url="http://ufal.mff.cuni.cz/czeng",
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manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
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# Each tar contains multiple files, which we process specially in
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# _parse_czeng.
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path=("data.plaintext-format/??train.gz",) * 10,
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),
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SubDataset(
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name="dcep_v1",
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target="en",
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sources={"lv"},
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url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/dcep.lv-en.v1.zip",
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path=("dcep.en-lv/dcep.lv", "dcep.en-lv/dcep.en"),
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),
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SubDataset(
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name="europarl_v7",
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target="en",
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sources={"cs", "de", "es", "fr"},
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url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip",
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path=("training/europarl-v7.{src}-en.{src}", "training/europarl-v7.{src}-en.en"),
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),
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SubDataset(
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name="europarl_v7_frde",
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target="de",
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sources={"fr"},
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url=(
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"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/europarl-v7.fr.gz",
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"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/europarl-v7.de.gz",
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),
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path=("", ""),
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),
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SubDataset(
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name="europarl_v8_18",
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target="en",
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sources={"et", "fi"},
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url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-ep-v8.zip",
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path=("training/europarl-v8.{src}-en.{src}", "training/europarl-v8.{src}-en.en"),
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),
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SubDataset(
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name="europarl_v8_16",
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target="en",
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sources={"fi", "ro"},
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url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-ep-v8.zip",
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path=("training-parallel-ep-v8/europarl-v8.{src}-en.{src}", "training-parallel-ep-v8/europarl-v8.{src}-en.en"),
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),
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SubDataset(
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name="europarl_v9",
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target="en",
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sources={"cs", "de", "fi", "lt"},
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url="https://huggingface.co/datasets/wmt/europarl/resolve/main/v9/training/europarl-v9.{src}-en.tsv.gz",
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path="",
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),
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SubDataset(
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name="gigafren",
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target="en",
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sources={"fr"},
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url="https://huggingface.co/datasets/wmt/wmt10/resolve/main-zip/training-giga-fren.zip",
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path=("giga-fren.release2.fixed.fr.gz", "giga-fren.release2.fixed.en.gz"),
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),
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SubDataset(
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name="hindencorp_01",
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target="en",
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sources={"hi"},
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url="http://ufallab.ms.mff.cuni.cz/~bojar/hindencorp",
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manual_dl_files=["hindencorp0.1.gz"],
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path="",
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),
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SubDataset(
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name="leta_v1",
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target="en",
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sources={"lv"},
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url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/leta.v1.zip",
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path=("LETA-lv-en/leta.lv", "LETA-lv-en/leta.en"),
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),
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SubDataset(
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name="multiun",
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target="en",
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sources={"es", "fr"},
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url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-un.zip",
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path=("un/undoc.2000.{src}-en.{src}", "un/undoc.2000.{src}-en.en"),
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),
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SubDataset(
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name="newscommentary_v9",
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target="en",
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sources={"cs", "de", "fr", "ru"},
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url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip",
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path=("training/news-commentary-v9.{src}-en.{src}", "training/news-commentary-v9.{src}-en.en"),
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),
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SubDataset(
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name="newscommentary_v10",
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target="en",
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sources={"cs", "de", "fr", "ru"},
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url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip",
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path=("news-commentary-v10.{src}-en.{src}", "news-commentary-v10.{src}-en.en"),
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),
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SubDataset(
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name="newscommentary_v11",
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target="en",
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sources={"cs", "de", "ru"},
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url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip",
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path=(
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"training-parallel-nc-v11/news-commentary-v11.{src}-en.{src}",
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"training-parallel-nc-v11/news-commentary-v11.{src}-en.en",
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),
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),
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SubDataset(
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name="newscommentary_v12",
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target="en",
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sources={"cs", "de", "ru", "zh"},
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url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip",
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path=("training/news-commentary-v12.{src}-en.{src}", "training/news-commentary-v12.{src}-en.en"),
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),
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SubDataset(
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name="newscommentary_v13",
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target="en",
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sources={"cs", "de", "ru", "zh"},
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url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip",
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path=(
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"training-parallel-nc-v13/news-commentary-v13.{src}-en.{src}",
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"training-parallel-nc-v13/news-commentary-v13.{src}-en.en",
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),
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),
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SubDataset(
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name="newscommentary_v14",
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target="en", # fr-de pair in newscommentary_v14_frde
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sources={"cs", "de", "kk", "ru", "zh"},
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url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.{0}-{1}.tsv.gz",
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path="",
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),
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SubDataset(
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name="newscommentary_v14_frde",
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target="de",
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sources={"fr"},
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url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.de-fr.tsv.gz",
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path="",
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),
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SubDataset(
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name="onlinebooks_v1",
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target="en",
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sources={"lv"},
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url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/books.lv-en.v1.zip",
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path=("farewell/farewell.lv", "farewell/farewell.en"),
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),
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SubDataset(
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name="paracrawl_v1",
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target="en",
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sources={"cs", "de", "et", "fi", "ru"},
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url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-{src}.zipporah0-dedup-clean.tgz", # TODO(QL): use gzip for streaming
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path=(
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"paracrawl-release1.en-{src}.zipporah0-dedup-clean.{src}",
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"paracrawl-release1.en-{src}.zipporah0-dedup-clean.en",
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),
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),
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SubDataset(
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name="paracrawl_v1_ru",
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target="en",
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sources={"ru"},
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url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz", # TODO(QL): use gzip for streaming
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path=(
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"paracrawl-release1.en-ru.zipporah0-dedup-clean.ru",
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"paracrawl-release1.en-ru.zipporah0-dedup-clean.en",
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),
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),
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SubDataset(
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name="paracrawl_v3",
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target="en", # fr-de pair in paracrawl_v3_frde
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sources={"cs", "de", "fi", "lt"},
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351 |
-
url="https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-{src}.bicleaner07.tmx.gz",
|
352 |
-
path="",
|
353 |
-
),
|
354 |
-
SubDataset(
|
355 |
-
name="paracrawl_v3_frde",
|
356 |
-
target="de",
|
357 |
-
sources={"fr"},
|
358 |
-
url=(
|
359 |
-
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/de-fr.bicleaner07.de.gz",
|
360 |
-
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/de-fr.bicleaner07.fr.gz",
|
361 |
-
),
|
362 |
-
path=("", ""),
|
363 |
-
),
|
364 |
-
SubDataset(
|
365 |
-
name="rapid_2016",
|
366 |
-
target="en",
|
367 |
-
sources={"de", "et", "fi"},
|
368 |
-
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip",
|
369 |
-
path=("rapid2016.{0}-{1}.{src}", "rapid2016.{0}-{1}.en"),
|
370 |
-
),
|
371 |
-
SubDataset(
|
372 |
-
name="rapid_2016_ltfi",
|
373 |
-
target="en",
|
374 |
-
sources={"fi", "lt"},
|
375 |
-
url="https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-{src}.tmx.zip",
|
376 |
-
path="rapid2016.en-{src}.tmx",
|
377 |
-
),
|
378 |
-
SubDataset(
|
379 |
-
name="rapid_2019",
|
380 |
-
target="en",
|
381 |
-
sources={"de"},
|
382 |
-
url="https://s3-eu-west-1.amazonaws.com/tilde-model/rapid2019.de-en.zip",
|
383 |
-
path=("rapid2019.de-en.de", "rapid2019.de-en.en"),
|
384 |
-
),
|
385 |
-
SubDataset(
|
386 |
-
name="setimes_2",
|
387 |
-
target="en",
|
388 |
-
sources={"ro", "tr"},
|
389 |
-
url="https://object.pouta.csc.fi/OPUS-SETIMES/v2/tmx/en-{src}.tmx.gz",
|
390 |
-
path="",
|
391 |
-
),
|
392 |
-
SubDataset(
|
393 |
-
name="uncorpus_v1",
|
394 |
-
target="en",
|
395 |
-
sources={"ru", "zh"},
|
396 |
-
url="https://huggingface.co/datasets/wmt/uncorpus/resolve/main-zip/UNv1.0.en-{src}.zip",
|
397 |
-
path=("en-{src}/UNv1.0.en-{src}.{src}", "en-{src}/UNv1.0.en-{src}.en"),
|
398 |
-
),
|
399 |
-
SubDataset(
|
400 |
-
name="wikiheadlines_fi",
|
401 |
-
target="en",
|
402 |
-
sources={"fi"},
|
403 |
-
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip",
|
404 |
-
path="wiki/fi-en/titles.fi-en",
|
405 |
-
),
|
406 |
-
SubDataset(
|
407 |
-
name="wikiheadlines_hi",
|
408 |
-
target="en",
|
409 |
-
sources={"hi"},
|
410 |
-
url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/wiki-titles.zip",
|
411 |
-
path="wiki/hi-en/wiki-titles.hi-en",
|
412 |
-
),
|
413 |
-
SubDataset(
|
414 |
-
# Verified that wmt14 and wmt15 files are identical.
|
415 |
-
name="wikiheadlines_ru",
|
416 |
-
target="en",
|
417 |
-
sources={"ru"},
|
418 |
-
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip",
|
419 |
-
path="wiki/ru-en/wiki.ru-en",
|
420 |
-
),
|
421 |
-
SubDataset(
|
422 |
-
name="wikititles_v1",
|
423 |
-
target="en",
|
424 |
-
sources={"cs", "de", "fi", "gu", "kk", "lt", "ru", "zh"},
|
425 |
-
url="https://huggingface.co/datasets/wmt/wikititles/resolve/main/v1/wikititles-v1.{src}-en.tsv.gz",
|
426 |
-
path="",
|
427 |
-
),
|
428 |
-
SubDataset(
|
429 |
-
name="yandexcorpus",
|
430 |
-
target="en",
|
431 |
-
sources={"ru"},
|
432 |
-
url="https://translate.yandex.ru/corpus?lang=en",
|
433 |
-
manual_dl_files=["1mcorpus.zip"],
|
434 |
-
path=("corpus.en_ru.1m.ru", "corpus.en_ru.1m.en"),
|
435 |
-
),
|
436 |
-
# pylint:enable=line-too-long
|
437 |
-
] + [
|
438 |
-
SubDataset( # pylint:disable=g-complex-comprehension
|
439 |
-
name=ss,
|
440 |
-
target="en",
|
441 |
-
sources={"zh"},
|
442 |
-
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/cwmt-wmt/%s.zip" % ss,
|
443 |
-
path=("%s/*_c[hn].txt" % ss, "%s/*_en.txt" % ss),
|
444 |
-
)
|
445 |
-
for ss in CWMT_SUBSET_NAMES
|
446 |
-
]
|
447 |
-
|
448 |
-
_DEV_SUBSETS = [
|
449 |
-
SubDataset(
|
450 |
-
name="euelections_dev2019",
|
451 |
-
target="de",
|
452 |
-
sources={"fr"},
|
453 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
454 |
-
path=("dev/euelections_dev2019.fr-de.src.fr", "dev/euelections_dev2019.fr-de.tgt.de"),
|
455 |
-
),
|
456 |
-
SubDataset(
|
457 |
-
name="newsdev2014",
|
458 |
-
target="en",
|
459 |
-
sources={"hi"},
|
460 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
461 |
-
path=("dev/newsdev2014.hi", "dev/newsdev2014.en"),
|
462 |
-
),
|
463 |
-
SubDataset(
|
464 |
-
name="newsdev2015",
|
465 |
-
target="en",
|
466 |
-
sources={"fi"},
|
467 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
468 |
-
path=("dev/newsdev2015-fien-src.{src}.sgm", "dev/newsdev2015-fien-ref.en.sgm"),
|
469 |
-
),
|
470 |
-
SubDataset(
|
471 |
-
name="newsdiscussdev2015",
|
472 |
-
target="en",
|
473 |
-
sources={"ro", "tr"},
|
474 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
475 |
-
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"),
|
476 |
-
),
|
477 |
-
SubDataset(
|
478 |
-
name="newsdev2016",
|
479 |
-
target="en",
|
480 |
-
sources={"ro", "tr"},
|
481 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
482 |
-
path=("dev/newsdev2016-{src}en-src.{src}.sgm", "dev/newsdev2016-{src}en-ref.en.sgm"),
|
483 |
-
),
|
484 |
-
SubDataset(
|
485 |
-
name="newsdev2017",
|
486 |
-
target="en",
|
487 |
-
sources={"lv", "zh"},
|
488 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
489 |
-
path=("dev/newsdev2017-{src}en-src.{src}.sgm", "dev/newsdev2017-{src}en-ref.en.sgm"),
|
490 |
-
),
|
491 |
-
SubDataset(
|
492 |
-
name="newsdev2018",
|
493 |
-
target="en",
|
494 |
-
sources={"et"},
|
495 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
496 |
-
path=("dev/newsdev2018-{src}en-src.{src}.sgm", "dev/newsdev2018-{src}en-ref.en.sgm"),
|
497 |
-
),
|
498 |
-
SubDataset(
|
499 |
-
name="newsdev2019",
|
500 |
-
target="en",
|
501 |
-
sources={"gu", "kk", "lt"},
|
502 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
503 |
-
path=("dev/newsdev2019-{src}en-src.{src}.sgm", "dev/newsdev2019-{src}en-ref.en.sgm"),
|
504 |
-
),
|
505 |
-
SubDataset(
|
506 |
-
name="newsdiscussdev2015",
|
507 |
-
target="en",
|
508 |
-
sources={"fr"},
|
509 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
510 |
-
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"),
|
511 |
-
),
|
512 |
-
SubDataset(
|
513 |
-
name="newsdiscusstest2015",
|
514 |
-
target="en",
|
515 |
-
sources={"fr"},
|
516 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
517 |
-
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"),
|
518 |
-
),
|
519 |
-
SubDataset(
|
520 |
-
name="newssyscomb2009",
|
521 |
-
target="en",
|
522 |
-
sources={"cs", "de", "es", "fr"},
|
523 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
524 |
-
path=("dev/newssyscomb2009.{src}", "dev/newssyscomb2009.en"),
|
525 |
-
),
|
526 |
-
SubDataset(
|
527 |
-
name="newstest2008",
|
528 |
-
target="en",
|
529 |
-
sources={"cs", "de", "es", "fr", "hu"},
|
530 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
531 |
-
path=("dev/news-test2008.{src}", "dev/news-test2008.en"),
|
532 |
-
),
|
533 |
-
SubDataset(
|
534 |
-
name="newstest2009",
|
535 |
-
target="en",
|
536 |
-
sources={"cs", "de", "es", "fr"},
|
537 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
538 |
-
path=("dev/newstest2009.{src}", "dev/newstest2009.en"),
|
539 |
-
),
|
540 |
-
SubDataset(
|
541 |
-
name="newstest2010",
|
542 |
-
target="en",
|
543 |
-
sources={"cs", "de", "es", "fr"},
|
544 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
545 |
-
path=("dev/newstest2010.{src}", "dev/newstest2010.en"),
|
546 |
-
),
|
547 |
-
SubDataset(
|
548 |
-
name="newstest2011",
|
549 |
-
target="en",
|
550 |
-
sources={"cs", "de", "es", "fr"},
|
551 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
552 |
-
path=("dev/newstest2011.{src}", "dev/newstest2011.en"),
|
553 |
-
),
|
554 |
-
SubDataset(
|
555 |
-
name="newstest2012",
|
556 |
-
target="en",
|
557 |
-
sources={"cs", "de", "es", "fr", "ru"},
|
558 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
559 |
-
path=("dev/newstest2012.{src}", "dev/newstest2012.en"),
|
560 |
-
),
|
561 |
-
SubDataset(
|
562 |
-
name="newstest2013",
|
563 |
-
target="en",
|
564 |
-
sources={"cs", "de", "es", "fr", "ru"},
|
565 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
566 |
-
path=("dev/newstest2013.{src}", "dev/newstest2013.en"),
|
567 |
-
),
|
568 |
-
SubDataset(
|
569 |
-
name="newstest2014",
|
570 |
-
target="en",
|
571 |
-
sources={"cs", "de", "es", "fr", "hi", "ru"},
|
572 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
573 |
-
path=("dev/newstest2014-{src}en-src.{src}.sgm", "dev/newstest2014-{src}en-ref.en.sgm"),
|
574 |
-
),
|
575 |
-
SubDataset(
|
576 |
-
name="newstest2015",
|
577 |
-
target="en",
|
578 |
-
sources={"cs", "de", "fi", "ru"},
|
579 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
580 |
-
path=("dev/newstest2015-{src}en-src.{src}.sgm", "dev/newstest2015-{src}en-ref.en.sgm"),
|
581 |
-
),
|
582 |
-
SubDataset(
|
583 |
-
name="newsdiscusstest2015",
|
584 |
-
target="en",
|
585 |
-
sources={"fr"},
|
586 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
587 |
-
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"),
|
588 |
-
),
|
589 |
-
SubDataset(
|
590 |
-
name="newstest2016",
|
591 |
-
target="en",
|
592 |
-
sources={"cs", "de", "fi", "ro", "ru", "tr"},
|
593 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
594 |
-
path=("dev/newstest2016-{src}en-src.{src}.sgm", "dev/newstest2016-{src}en-ref.en.sgm"),
|
595 |
-
),
|
596 |
-
SubDataset(
|
597 |
-
name="newstestB2016",
|
598 |
-
target="en",
|
599 |
-
sources={"fi"},
|
600 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
601 |
-
path=("dev/newstestB2016-enfi-ref.{src}.sgm", "dev/newstestB2016-enfi-src.en.sgm"),
|
602 |
-
),
|
603 |
-
SubDataset(
|
604 |
-
name="newstest2017",
|
605 |
-
target="en",
|
606 |
-
sources={"cs", "de", "fi", "lv", "ru", "tr", "zh"},
|
607 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
608 |
-
path=("dev/newstest2017-{src}en-src.{src}.sgm", "dev/newstest2017-{src}en-ref.en.sgm"),
|
609 |
-
),
|
610 |
-
SubDataset(
|
611 |
-
name="newstestB2017",
|
612 |
-
target="en",
|
613 |
-
sources={"fi"},
|
614 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
615 |
-
path=("dev/newstestB2017-fien-src.fi.sgm", "dev/newstestB2017-fien-ref.en.sgm"),
|
616 |
-
),
|
617 |
-
SubDataset(
|
618 |
-
name="newstest2018",
|
619 |
-
target="en",
|
620 |
-
sources={"cs", "de", "et", "fi", "ru", "tr", "zh"},
|
621 |
-
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
|
622 |
-
path=("dev/newstest2018-{src}en-src.{src}.sgm", "dev/newstest2018-{src}en-ref.en.sgm"),
|
623 |
-
),
|
624 |
-
]
|
625 |
-
|
626 |
-
DATASET_MAP = {dataset.name: dataset for dataset in _TRAIN_SUBSETS + _DEV_SUBSETS}
|
627 |
-
|
628 |
-
_CZENG17_FILTER = SubDataset(
|
629 |
-
name="czeng17_filter",
|
630 |
-
target="en",
|
631 |
-
sources={"cs"},
|
632 |
-
url="http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip",
|
633 |
-
path="convert_czeng16_to_17.pl",
|
634 |
-
)
|
635 |
-
|
636 |
-
|
637 |
-
class WmtConfig(datasets.BuilderConfig):
|
638 |
-
"""BuilderConfig for WMT."""
|
639 |
-
|
640 |
-
def __init__(self, url=None, citation=None, description=None, language_pair=(None, None), subsets=None, **kwargs):
|
641 |
-
"""BuilderConfig for WMT.
|
642 |
-
|
643 |
-
Args:
|
644 |
-
url: The reference URL for the dataset.
|
645 |
-
citation: The paper citation for the dataset.
|
646 |
-
description: The description of the dataset.
|
647 |
-
language_pair: pair of languages that will be used for translation. Should
|
648 |
-
contain 2 letter coded strings. For example: ("en", "de").
|
649 |
-
configuration for the `datasets.features.text.TextEncoder` used for the
|
650 |
-
`datasets.features.text.Translation` features.
|
651 |
-
subsets: Dict[split, list[str]]. List of the subset to use for each of the
|
652 |
-
split. Note that WMT subclasses overwrite this parameter.
|
653 |
-
**kwargs: keyword arguments forwarded to super.
|
654 |
-
"""
|
655 |
-
name = "%s-%s" % (language_pair[0], language_pair[1])
|
656 |
-
if "name" in kwargs: # Add name suffix for custom configs
|
657 |
-
name += "." + kwargs.pop("name")
|
658 |
-
|
659 |
-
super(WmtConfig, self).__init__(name=name, description=description, **kwargs)
|
660 |
-
|
661 |
-
self.url = url or "http://www.statmt.org"
|
662 |
-
self.citation = citation
|
663 |
-
self.language_pair = language_pair
|
664 |
-
self.subsets = subsets
|
665 |
-
|
666 |
-
# TODO(PVP): remove when manual dir works
|
667 |
-
# +++++++++++++++++++++
|
668 |
-
if language_pair[1] in ["cs", "hi", "ru"]:
|
669 |
-
assert NotImplementedError(f"The dataset for {language_pair[1]}-en is currently not fully supported.")
|
670 |
-
# +++++++++++++++++++++
|
671 |
-
|
672 |
-
|
673 |
-
class Wmt(datasets.GeneratorBasedBuilder):
|
674 |
-
"""WMT translation dataset."""
|
675 |
-
|
676 |
-
BUILDER_CONFIG_CLASS = WmtConfig
|
677 |
-
|
678 |
-
def __init__(self, *args, **kwargs):
|
679 |
-
super(Wmt, self).__init__(*args, **kwargs)
|
680 |
-
|
681 |
-
@property
|
682 |
-
def _subsets(self):
|
683 |
-
"""Subsets that make up each split of the dataset."""
|
684 |
-
raise NotImplementedError("This is a abstract method")
|
685 |
-
|
686 |
-
@property
|
687 |
-
def subsets(self):
|
688 |
-
"""Subsets that make up each split of the dataset for the language pair."""
|
689 |
-
source, target = self.config.language_pair
|
690 |
-
filtered_subsets = {}
|
691 |
-
subsets = self._subsets if self.config.subsets is None else self.config.subsets
|
692 |
-
for split, ss_names in subsets.items():
|
693 |
-
filtered_subsets[split] = []
|
694 |
-
for ss_name in ss_names:
|
695 |
-
dataset = DATASET_MAP[ss_name]
|
696 |
-
if dataset.target != target or source not in dataset.sources:
|
697 |
-
logger.info("Skipping sub-dataset that does not include language pair: %s", ss_name)
|
698 |
-
else:
|
699 |
-
filtered_subsets[split].append(ss_name)
|
700 |
-
logger.info("Using sub-datasets: %s", filtered_subsets)
|
701 |
-
return filtered_subsets
|
702 |
-
|
703 |
-
def _info(self):
|
704 |
-
src, target = self.config.language_pair
|
705 |
-
return datasets.DatasetInfo(
|
706 |
-
description=_DESCRIPTION,
|
707 |
-
features=datasets.Features(
|
708 |
-
{"translation": datasets.features.Translation(languages=self.config.language_pair)}
|
709 |
-
),
|
710 |
-
supervised_keys=(src, target),
|
711 |
-
homepage=self.config.url,
|
712 |
-
citation=self.config.citation,
|
713 |
-
)
|
714 |
-
|
715 |
-
def _vocab_text_gen(self, split_subsets, extraction_map, language):
|
716 |
-
for _, ex in self._generate_examples(split_subsets, extraction_map, with_translation=False):
|
717 |
-
yield ex[language]
|
718 |
-
|
719 |
-
def _split_generators(self, dl_manager):
|
720 |
-
source, _ = self.config.language_pair
|
721 |
-
manual_paths_dict = {}
|
722 |
-
urls_to_download = {}
|
723 |
-
for ss_name in itertools.chain.from_iterable(self.subsets.values()):
|
724 |
-
if ss_name == "czeng_17":
|
725 |
-
# CzEng1.7 is CzEng1.6 with some blocks filtered out. We must download
|
726 |
-
# the filtering script so we can parse out which blocks need to be
|
727 |
-
# removed.
|
728 |
-
urls_to_download[_CZENG17_FILTER.name] = _CZENG17_FILTER.get_url(source)
|
729 |
-
|
730 |
-
# get dataset
|
731 |
-
dataset = DATASET_MAP[ss_name]
|
732 |
-
if dataset.get_manual_dl_files(source):
|
733 |
-
# TODO(PVP): following two lines skip configs that are incomplete for now
|
734 |
-
# +++++++++++++++++++++
|
735 |
-
logger.info("Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
|
736 |
-
continue
|
737 |
-
# +++++++++++++++++++++
|
738 |
-
|
739 |
-
manual_dl_files = dataset.get_manual_dl_files(source)
|
740 |
-
manual_paths = [
|
741 |
-
os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), fname)
|
742 |
-
for fname in manual_dl_files
|
743 |
-
]
|
744 |
-
assert all(
|
745 |
-
os.path.exists(path) for path in manual_paths
|
746 |
-
), 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)}"
|
747 |
-
|
748 |
-
# set manual path for correct subset
|
749 |
-
manual_paths_dict[ss_name] = manual_paths
|
750 |
-
else:
|
751 |
-
urls_to_download[ss_name] = dataset.get_url(source)
|
752 |
-
|
753 |
-
# Download and extract files from URLs.
|
754 |
-
downloaded_files = dl_manager.download_and_extract(urls_to_download)
|
755 |
-
# Extract manually downloaded files.
|
756 |
-
manual_files = dl_manager.extract(manual_paths_dict)
|
757 |
-
extraction_map = dict(downloaded_files, **manual_files)
|
758 |
-
|
759 |
-
for language in self.config.language_pair:
|
760 |
-
self._vocab_text_gen(self.subsets[datasets.Split.TRAIN], extraction_map, language)
|
761 |
-
|
762 |
-
return [
|
763 |
-
datasets.SplitGenerator( # pylint:disable=g-complex-comprehension
|
764 |
-
name=split, gen_kwargs={"split_subsets": split_subsets, "extraction_map": extraction_map}
|
765 |
-
)
|
766 |
-
for split, split_subsets in self.subsets.items()
|
767 |
-
]
|
768 |
-
|
769 |
-
def _generate_examples(self, split_subsets, extraction_map, with_translation=True):
|
770 |
-
"""Returns the examples in the raw (text) form."""
|
771 |
-
source, _ = self.config.language_pair
|
772 |
-
|
773 |
-
def _get_local_paths(dataset, extract_dirs):
|
774 |
-
rel_paths = dataset.get_path(source)
|
775 |
-
if len(extract_dirs) == 1:
|
776 |
-
extract_dirs = extract_dirs * len(rel_paths)
|
777 |
-
return [
|
778 |
-
os.path.join(ex_dir, rel_path) if rel_path else ex_dir
|
779 |
-
for ex_dir, rel_path in zip(extract_dirs, rel_paths)
|
780 |
-
]
|
781 |
-
|
782 |
-
def _get_filenames(dataset):
|
783 |
-
rel_paths = dataset.get_path(source)
|
784 |
-
urls = dataset.get_url(source)
|
785 |
-
if len(urls) == 1:
|
786 |
-
urls = urls * len(rel_paths)
|
787 |
-
return [rel_path if rel_path else os.path.basename(url) for url, rel_path in zip(urls, rel_paths)]
|
788 |
-
|
789 |
-
for ss_name in split_subsets:
|
790 |
-
# TODO(PVP) remove following five lines when manual data works
|
791 |
-
# +++++++++++++++++++++
|
792 |
-
dataset = DATASET_MAP[ss_name]
|
793 |
-
source, _ = self.config.language_pair
|
794 |
-
if dataset.get_manual_dl_files(source):
|
795 |
-
logger.info(f"Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
|
796 |
-
continue
|
797 |
-
# +++++++++++++++++++++
|
798 |
-
|
799 |
-
logger.info("Generating examples from: %s", ss_name)
|
800 |
-
dataset = DATASET_MAP[ss_name]
|
801 |
-
extract_dirs = extraction_map[ss_name]
|
802 |
-
files = _get_local_paths(dataset, extract_dirs)
|
803 |
-
filenames = _get_filenames(dataset)
|
804 |
-
|
805 |
-
sub_generator_args = tuple(files)
|
806 |
-
|
807 |
-
if ss_name.startswith("czeng"):
|
808 |
-
if ss_name.endswith("16pre"):
|
809 |
-
sub_generator = functools.partial(_parse_tsv, language_pair=("en", "cs"))
|
810 |
-
sub_generator_args += tuple(filenames)
|
811 |
-
elif ss_name.endswith("17"):
|
812 |
-
filter_path = _get_local_paths(_CZENG17_FILTER, extraction_map[_CZENG17_FILTER.name])[0]
|
813 |
-
sub_generator = functools.partial(_parse_czeng, filter_path=filter_path)
|
814 |
-
else:
|
815 |
-
sub_generator = _parse_czeng
|
816 |
-
elif ss_name == "hindencorp_01":
|
817 |
-
sub_generator = _parse_hindencorp
|
818 |
-
elif len(files) == 2:
|
819 |
-
if ss_name.endswith("_frde"):
|
820 |
-
sub_generator = _parse_frde_bitext
|
821 |
-
else:
|
822 |
-
sub_generator = _parse_parallel_sentences
|
823 |
-
sub_generator_args += tuple(filenames)
|
824 |
-
elif len(files) == 1:
|
825 |
-
fname = filenames[0]
|
826 |
-
# Note: Due to formatting used by `download_manager`, the file
|
827 |
-
# extension may not be at the end of the file path.
|
828 |
-
if ".tsv" in fname:
|
829 |
-
sub_generator = _parse_tsv
|
830 |
-
sub_generator_args += tuple(filenames)
|
831 |
-
elif (
|
832 |
-
ss_name.startswith("newscommentary_v14")
|
833 |
-
or ss_name.startswith("europarl_v9")
|
834 |
-
or ss_name.startswith("wikititles_v1")
|
835 |
-
):
|
836 |
-
sub_generator = functools.partial(_parse_tsv, language_pair=self.config.language_pair)
|
837 |
-
sub_generator_args += tuple(filenames)
|
838 |
-
elif "tmx" in fname or ss_name.startswith("paracrawl_v3"):
|
839 |
-
sub_generator = _parse_tmx
|
840 |
-
elif ss_name.startswith("wikiheadlines"):
|
841 |
-
sub_generator = _parse_wikiheadlines
|
842 |
-
else:
|
843 |
-
raise ValueError("Unsupported file format: %s" % fname)
|
844 |
-
else:
|
845 |
-
raise ValueError("Invalid number of files: %d" % len(files))
|
846 |
-
|
847 |
-
for sub_key, ex in sub_generator(*sub_generator_args):
|
848 |
-
if not all(ex.values()):
|
849 |
-
continue
|
850 |
-
# TODO(adarob): Add subset feature.
|
851 |
-
# ex["subset"] = subset
|
852 |
-
key = f"{ss_name}/{sub_key}"
|
853 |
-
if with_translation is True:
|
854 |
-
ex = {"translation": ex}
|
855 |
-
yield key, ex
|
856 |
-
|
857 |
-
|
858 |
-
def _parse_parallel_sentences(f1, f2, filename1, filename2):
|
859 |
-
"""Returns examples from parallel SGML or text files, which may be gzipped."""
|
860 |
-
|
861 |
-
def _parse_text(path, original_filename):
|
862 |
-
"""Returns the sentences from a single text file, which may be gzipped."""
|
863 |
-
split_path = original_filename.split(".")
|
864 |
-
|
865 |
-
if split_path[-1] == "gz":
|
866 |
-
lang = split_path[-2]
|
867 |
-
|
868 |
-
def gen():
|
869 |
-
with open(path, "rb") as f, gzip.GzipFile(fileobj=f) as g:
|
870 |
-
for line in g:
|
871 |
-
yield line.decode("utf-8").rstrip()
|
872 |
-
|
873 |
-
return gen(), lang
|
874 |
-
|
875 |
-
if split_path[-1] == "txt":
|
876 |
-
# CWMT
|
877 |
-
lang = split_path[-2].split("_")[-1]
|
878 |
-
lang = "zh" if lang in ("ch", "cn", "c[hn]") else lang
|
879 |
-
else:
|
880 |
-
lang = split_path[-1]
|
881 |
-
|
882 |
-
def gen():
|
883 |
-
with open(path, "rb") as f:
|
884 |
-
for line in f:
|
885 |
-
yield line.decode("utf-8").rstrip()
|
886 |
-
|
887 |
-
return gen(), lang
|
888 |
-
|
889 |
-
def _parse_sgm(path, original_filename):
|
890 |
-
"""Returns sentences from a single SGML file."""
|
891 |
-
lang = original_filename.split(".")[-2]
|
892 |
-
# Note: We can't use the XML parser since some of the files are badly
|
893 |
-
# formatted.
|
894 |
-
seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
|
895 |
-
|
896 |
-
def gen():
|
897 |
-
with open(path, encoding="utf-8") as f:
|
898 |
-
for line in f:
|
899 |
-
seg_match = re.match(seg_re, line)
|
900 |
-
if seg_match:
|
901 |
-
assert len(seg_match.groups()) == 1
|
902 |
-
yield seg_match.groups()[0]
|
903 |
-
|
904 |
-
return gen(), lang
|
905 |
-
|
906 |
-
parse_file = _parse_sgm if os.path.basename(f1).endswith(".sgm") else _parse_text
|
907 |
-
|
908 |
-
# Some datasets (e.g., CWMT) contain multiple parallel files specified with
|
909 |
-
# a wildcard. We sort both sets to align them and parse them one by one.
|
910 |
-
f1_files = sorted(glob.glob(f1))
|
911 |
-
f2_files = sorted(glob.glob(f2))
|
912 |
-
|
913 |
-
assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
|
914 |
-
assert len(f1_files) == len(f2_files), "Number of files do not match: %d vs %d for %s vs %s." % (
|
915 |
-
len(f1_files),
|
916 |
-
len(f2_files),
|
917 |
-
f1,
|
918 |
-
f2,
|
919 |
-
)
|
920 |
-
|
921 |
-
for f_id, (f1_i, f2_i) in enumerate(zip(sorted(f1_files), sorted(f2_files))):
|
922 |
-
l1_sentences, l1 = parse_file(f1_i, filename1)
|
923 |
-
l2_sentences, l2 = parse_file(f2_i, filename2)
|
924 |
-
|
925 |
-
for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
|
926 |
-
key = f"{f_id}/{line_id}"
|
927 |
-
yield key, {l1: s1, l2: s2}
|
928 |
-
|
929 |
-
|
930 |
-
def _parse_frde_bitext(fr_path, de_path):
|
931 |
-
with open(fr_path, encoding="utf-8") as fr_f:
|
932 |
-
with open(de_path, encoding="utf-8") as de_f:
|
933 |
-
for line_id, (s1, s2) in enumerate(zip(fr_f, de_f)):
|
934 |
-
yield line_id, {"fr": s1.rstrip(), "de": s2.rstrip()}
|
935 |
-
|
936 |
-
|
937 |
-
def _parse_tmx(path):
|
938 |
-
"""Generates examples from TMX file."""
|
939 |
-
|
940 |
-
def _get_tuv_lang(tuv):
|
941 |
-
for k, v in tuv.items():
|
942 |
-
if k.endswith("}lang"):
|
943 |
-
return v
|
944 |
-
raise AssertionError("Language not found in `tuv` attributes.")
|
945 |
-
|
946 |
-
def _get_tuv_seg(tuv):
|
947 |
-
segs = tuv.findall("seg")
|
948 |
-
assert len(segs) == 1, "Invalid number of segments: %d" % len(segs)
|
949 |
-
return segs[0].text
|
950 |
-
|
951 |
-
with open(path, "rb") as f:
|
952 |
-
# Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
|
953 |
-
utf_f = codecs.getreader("utf-8")(f)
|
954 |
-
for line_id, (_, elem) in enumerate(ElementTree.iterparse(utf_f)):
|
955 |
-
if elem.tag == "tu":
|
956 |
-
yield line_id, {_get_tuv_lang(tuv): _get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")}
|
957 |
-
elem.clear()
|
958 |
-
|
959 |
-
|
960 |
-
def _parse_tsv(path, filename, language_pair=None):
|
961 |
-
"""Generates examples from TSV file."""
|
962 |
-
if language_pair is None:
|
963 |
-
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", filename)
|
964 |
-
assert lang_match is not None, "Invalid TSV filename: %s" % filename
|
965 |
-
l1, l2 = lang_match.groups()
|
966 |
-
else:
|
967 |
-
l1, l2 = language_pair
|
968 |
-
with open(path, encoding="utf-8") as f:
|
969 |
-
for j, line in enumerate(f):
|
970 |
-
cols = line.split("\t")
|
971 |
-
if len(cols) != 2:
|
972 |
-
logger.warning("Skipping line %d in TSV (%s) with %d != 2 columns.", j, path, len(cols))
|
973 |
-
continue
|
974 |
-
s1, s2 = cols
|
975 |
-
yield j, {l1: s1.strip(), l2: s2.strip()}
|
976 |
-
|
977 |
-
|
978 |
-
def _parse_wikiheadlines(path):
|
979 |
-
"""Generates examples from Wikiheadlines dataset file."""
|
980 |
-
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
|
981 |
-
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
|
982 |
-
l1, l2 = lang_match.groups()
|
983 |
-
with open(path, encoding="utf-8") as f:
|
984 |
-
for line_id, line in enumerate(f):
|
985 |
-
s1, s2 = line.split("|||")
|
986 |
-
yield line_id, {l1: s1.strip(), l2: s2.strip()}
|
987 |
-
|
988 |
-
|
989 |
-
def _parse_czeng(*paths, **kwargs):
|
990 |
-
"""Generates examples from CzEng v1.6, with optional filtering for v1.7."""
|
991 |
-
filter_path = kwargs.get("filter_path", None)
|
992 |
-
if filter_path:
|
993 |
-
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
|
994 |
-
with open(filter_path, encoding="utf-8") as f:
|
995 |
-
bad_blocks = {blk for blk in re.search(r"qw{([\s\d]*)}", f.read()).groups()[0].split()}
|
996 |
-
logger.info("Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.", len(bad_blocks))
|
997 |
-
|
998 |
-
for path in paths:
|
999 |
-
for gz_path in sorted(glob.glob(path)):
|
1000 |
-
with open(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
|
1001 |
-
filename = os.path.basename(gz_path)
|
1002 |
-
for line_id, line in enumerate(f):
|
1003 |
-
line = line.decode("utf-8") # required for py3
|
1004 |
-
if not line.strip():
|
1005 |
-
continue
|
1006 |
-
id_, unused_score, cs, en = line.split("\t")
|
1007 |
-
if filter_path:
|
1008 |
-
block_match = re.match(re_block, id_)
|
1009 |
-
if block_match and block_match.groups()[0] in bad_blocks:
|
1010 |
-
continue
|
1011 |
-
sub_key = f"{filename}/{line_id}"
|
1012 |
-
yield sub_key, {
|
1013 |
-
"cs": cs.strip(),
|
1014 |
-
"en": en.strip(),
|
1015 |
-
}
|
1016 |
-
|
1017 |
-
|
1018 |
-
def _parse_hindencorp(path):
|
1019 |
-
with open(path, encoding="utf-8") as f:
|
1020 |
-
for line_id, line in enumerate(f):
|
1021 |
-
split_line = line.split("\t")
|
1022 |
-
if len(split_line) != 5:
|
1023 |
-
logger.warning("Skipping invalid HindEnCorp line: %s", line)
|
1024 |
-
continue
|
1025 |
-
yield line_id, {"translation": {"en": split_line[3].strip(), "hi": split_line[4].strip()}}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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