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translate_enfr.py
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translate_enfr.py
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# coding=utf-8
# Copyright 2023 The Tensor2Tensor 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.
"""Data generators for translation data-sets."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_encoder
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators import translate
from tensor2tensor.data_generators import wiki_lm
from tensor2tensor.utils import registry
# End-of-sentence marker.
EOS = text_encoder.EOS_ID
_ENFR_TRAIN_SMALL_DATA = [
[
"https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
("baseline-1M-enfr/baseline-1M_train.en",
"baseline-1M-enfr/baseline-1M_train.fr")
],
]
_ENFR_TEST_SMALL_DATA = [
[
"https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
("baseline-1M-enfr/baseline-1M_valid.en",
"baseline-1M-enfr/baseline-1M_valid.fr")
],
]
_ENFR_TRAIN_LARGE_DATA = [
[
"http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz",
("commoncrawl.fr-en.en", "commoncrawl.fr-en.fr")
],
[
"http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz",
("training/europarl-v7.fr-en.en", "training/europarl-v7.fr-en.fr")
],
[
"http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz",
("training/news-commentary-v9.fr-en.en",
"training/news-commentary-v9.fr-en.fr")
],
[
"http://www.statmt.org/wmt10/training-giga-fren.tar",
("giga-fren.release2.fixed.en.gz",
"giga-fren.release2.fixed.fr.gz")
],
[
"http://www.statmt.org/wmt13/training-parallel-un.tgz",
("un/undoc.2000.fr-en.en", "un/undoc.2000.fr-en.fr")
],
]
_ENFR_TEST_LARGE_DATA = [
[
"http://data.statmt.org/wmt17/translation-task/dev.tgz",
("dev/newstest2013.en", "dev/newstest2013.fr")
],
]
@registry.register_problem
class TranslateEnfrWmtSmall8k(translate.TranslateProblem):
"""Problem spec for WMT En-Fr translation."""
@property
def approx_vocab_size(self):
return 2**13 # 8192
@property
def use_small_dataset(self):
return True
def source_data_files(self, dataset_split):
train = dataset_split == problem.DatasetSplit.TRAIN
if self.use_small_dataset:
datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
else:
datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
return datasets
def vocab_data_files(self):
return (_ENFR_TRAIN_SMALL_DATA if self.use_small_dataset
else _ENFR_TRAIN_LARGE_DATA)
@registry.register_problem
class TranslateEnfrWmtSmall32k(TranslateEnfrWmtSmall8k):
@property
def approx_vocab_size(self):
return 2**15 # 32768
@registry.register_problem
class TranslateEnfrWmt8k(TranslateEnfrWmtSmall8k):
@property
def use_small_dataset(self):
return False
@registry.register_problem
class TranslateEnfrWmt32k(TranslateEnfrWmtSmall32k):
@property
def use_small_dataset(self):
return False
@registry.register_problem
class TranslateEnfrWmt32kPacked(TranslateEnfrWmt32k):
@property
def packed_length(self):
return 256
@property
def use_vocab_from_other_problem(self):
return TranslateEnfrWmt32k()
@registry.register_problem
class TranslateEnfrWmt32kWithBacktranslateFr(TranslateEnfrWmt32k):
"""En-Fr translation with added French data, back-translated."""
@property
def use_vocab_from_other_problem(self):
return TranslateEnfrWmt32k()
@property
def already_shuffled(self):
return True
@property
def skip_random_fraction_when_training(self):
return False
@property
def backtranslate_data_filenames(self):
"""List of pairs of files with matched back-translated data."""
# Files must be placed in tmp_dir, each similar size to authentic data.
return [("fr_mono_en.txt", "fr_mono_fr.txt")]
@property
def dataset_splits(self):
"""Splits of data to produce and number of output shards for each."""
return [{
"split": problem.DatasetSplit.TRAIN,
"shards": 1, # Use just 1 shard so as to not mix data.
}, {
"split": problem.DatasetSplit.EVAL,
"shards": 1,
}]
def generate_samples(self, data_dir, tmp_dir, dataset_split):
datasets = self.source_data_files(dataset_split)
tag = "train" if dataset_split == problem.DatasetSplit.TRAIN else "dev"
data_path = translate.compile_data(
tmp_dir, datasets, "%s-compiled-%s" % (self.name, tag))
# For eval, use authentic data.
if dataset_split != problem.DatasetSplit.TRAIN:
for example in text_problems.text2text_txt_iterator(
data_path + ".lang1", data_path + ".lang2"):
yield example
else: # For training, mix synthetic and authentic data as follows.
for (file1, file2) in self.backtranslate_data_filenames:
path1 = os.path.join(tmp_dir, file1)
path2 = os.path.join(tmp_dir, file2)
# Synthetic data first.
for example in text_problems.text2text_txt_iterator(path1, path2):
yield example
# Now authentic data.
for example in text_problems.text2text_txt_iterator(
data_path + ".lang1", data_path + ".lang2"):
yield example
@registry.register_problem
class TranslateEnfrWmt32kWithBacktranslateEn(
TranslateEnfrWmt32kWithBacktranslateFr):
"""En-Fr translation with added English data, back-translated."""
@property
def backtranslate_data_filenames(self):
"""List of pairs of files with matched back-translated data."""
# Files must be placed in tmp_dir, each similar size to authentic data.
return [("en_mono_en.txt%d" % i, "en_mono_fr.txt%d" % i) for i in [0, 1, 2]]
@registry.register_problem
class TranslateEnfrWmtSmallCharacters(translate.TranslateProblem):
"""Problem spec for WMT En-Fr translation."""
@property
def vocab_type(self):
return text_problems.VocabType.CHARACTER
@property
def use_small_dataset(self):
return True
def source_data_files(self, dataset_split):
train = dataset_split == problem.DatasetSplit.TRAIN
if self.use_small_dataset:
datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
else:
datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
return datasets
@registry.register_problem
class TranslateEnfrWmtCharacters(TranslateEnfrWmtSmallCharacters):
@property
def use_small_dataset(self):
return False
@registry.register_problem
class TranslateEnfrWmtMulti64k(TranslateEnfrWmtSmall32k):
"""Translation with muli-lingual vocabulary."""
@property
def use_small_dataset(self):
return False
@property
def use_vocab_from_other_problem(self):
return wiki_lm.LanguagemodelDeEnFrRoWiki64k()
@registry.register_problem
class TranslateEnfrWmtMulti64kPacked1k(TranslateEnfrWmtMulti64k):
"""Translation with muli-lingual vocabulary."""
@property
def packed_length(self):
return 1024
@property
def num_training_examples(self):
return 1760600
@property
def inputs_prefix(self):
return "translate English French "
@property
def targets_prefix(self):
return "translate French English "