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.gitattributes

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 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
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+ende/wmt14-en-de-pre-processed-train-00000-of-00003.parquet filter=lfs diff=lfs merge=lfs -text
+ende/wmt14-en-de-pre-processed-train-00001-of-00003.parquet filter=lfs diff=lfs merge=lfs -text

README.md

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-# WMT14 English-German Translation Data w/ further preprocessing
-
-The original pre-processing script is [here](https://github.com/pytorch/fairseq/blob/master/examples/translation/prepare-wmt14en2de.sh).
-
-This pre-processed dataset was created by running:
-
-```
-git clone https://github.com/pytorch/fairseq
-cd fairseq
-cd examples/translation/
-./prepare-wmt14en2de.sh
-```
-
-It was originally used by  `transformers` [`finetune_trainer.py`](https://github.com/huggingface/transformers/blob/641f418e102218c4bf16fcd3124bfebed6217ef6/examples/seq2seq/finetune_trainer.py)
-
-The data itself resides at https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz

dataset_infos.json

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-{"ende": {"description": "WMT14 English-German Translation Data with further preprocessing", "citation": "@InProceedings{huggingface:dataset,\ntitle = {WMT14 English-German Translation Data with further preprocessing},\nauthors={},\nyear={2016}\n}\n", "homepage": "http://www.statmt.org/wmt16/", "license": "", "features": {"translation": {"languages": ["en", "de"], "id": null, "_type": "Translation"}}, "post_processed": null, "supervised_keys": {"input": "en", "output": "de"}, "builder_name": "wmt14_en_de_pre_processed", "config_name": "ende", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1373123263, "num_examples": 4548885, "dataset_name": "wmt14_en_de_pre_processed"}, "validation": {"name": "validation", "num_bytes": 522989, "num_examples": 2169, "dataset_name": "wmt14_en_de_pre_processed"}, "test": {"name": "test", "num_bytes": 735516, "num_examples": 2999, "dataset_name": "wmt14_en_de_pre_processed"}}, "download_checksums": {"https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz": {"num_bytes": 476503157, "checksum": "6011fdebebcd196040c77aa1853e7631adf47e078acbc5cbc7c7264019130c51"}}, "download_size": 476503157, "post_processing_size": null, "dataset_size": 1374381768, "size_in_bytes": 1850884925}}

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process.txt

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-
-# how this build script and dataset_infos.json were generated
-
-# this is translation, so let's adapt flores - it has an almost identical input format, just files are named differently:
-cp https://github.com/huggingface/datasets/blob/master/datasets/flores/flores.py wmt14-en-de-pre-processed.py
-perl -pi -e 's|Flores|wmt14-en-de-pre-processed|g' wmt14-en-de-pre-processed.py
-
-(good models for other tasks can be found here: https://huggingface.co/docs/datasets/add_dataset.html#dataset-scripts-of-reference)
-
-# then edit to change the language pairs, file template and the data url
-git add wmt14-en-de-pre-processed.py
-git commit -m "build script" wmt14-en-de-pre-processed.py
-git push
-
-# finally test
-datasets-cli test stas/wmt14-en-de-pre-processed --save_infos --all_configs
-
-# add push the generated config
-git add dataset_infos.json
-git commit -m "add dataset_infos.json" dataset_infos.json
-git push

wmt14-en-de-pre-processed.py

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" WMT16 English-Romanian Translation Data with further preprocessing """
-
-from __future__ import absolute_import, division, print_function
-
-import csv
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {WMT14 English-German Translation Data with further preprocessing},
-authors={},
-year={2016}
-}
-"""
-
-_DESCRIPTION = "WMT14 English-German Translation Data with further preprocessing"
-_HOMEPAGE = "http://www.statmt.org/wmt16/"
-_LICENSE = ""
-
-
-_DATA_URL = "https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz"
-
-
-class Wmt14EnDePreProcessedConfig(datasets.BuilderConfig):
-    """BuilderConfig for wmt16."""
-
-    def __init__(self, language_pair=(None, None), **kwargs):
-        """BuilderConfig for wmt16
-
-        Args:
-            for the `datasets.features.text.TextEncoder` used for the features feature.
-          language_pair: pair of languages that will be used for translation. Should
-            contain 2-letter coded strings. First will be used at source and second
-            as target in supervised mode. For example: ("se", "en").
-          **kwargs: keyword arguments forwarded to super.
-        """
-        name = "%s%s" % (language_pair[0], language_pair[1])
-
-        description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1])
-        super(Wmt14EnDePreProcessedConfig, self).__init__(
-            name=name,
-            description=description,
-            version=datasets.Version("1.1.0", ""),
-            **kwargs,
-        )
-
-        # Validate language pair.
-        assert "en" in language_pair, ("Config language pair must contain `en`, got: %s", language_pair)
-        source, target = language_pair
-        non_en = source if target == "en" else target
-        assert non_en in ["de"], ("Invalid non-en language in pair: %s", non_en)
-
-        self.language_pair = language_pair
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class Wmt14EnDePreProcessed(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        Wmt14EnDePreProcessedConfig(
-            language_pair=("en", "de"),
-        ),
-    ]
-
-    def _info(self):
-        source, target = self.config.language_pair
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
-            ),
-            supervised_keys=(source, target),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-
-        source, target = self.config.language_pair
-        non_en = source if target == "en" else target
-        path_tmpl = "{dl_dir}/wmt_en_de/{split}.{type}"
-
-        files = {}
-        for split in ("train", "val", "test"):
-            files[split] = {
-                "source_file": path_tmpl.format(dl_dir=dl_dir, split=split, type="source"),
-                "target_file": path_tmpl.format(dl_dir=dl_dir, split=split, type="target"),
-            }
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["val"]),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
-        ]
-
-    def _generate_examples(self, source_file, target_file):
-        """This function returns the examples in the raw (text) form."""
-        with open(source_file, mode="rb") as f:
-            source_sentences = f.read().decode("utf8").split("\n")
-        with open(target_file, mode="rb") as f:
-            target_sentences = f.read().decode("utf8").split("\n")
-
-        assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
-            len(source_sentences),
-            len(target_sentences),
-            source_file,
-            target_file,
-        )
-
-        source, target = self.config.language_pair
-        for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
-            result = {"translation": {source: l1, target: l2}}
-            # Make sure that both translations are non-empty.
-            if all(result.values()):
-                yield idx, result