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# 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.
"""
This file provides a HuggingFace dataset loader implementation for
the ParaDocs dataset
ParaDocs is a multilingual machine translation dataset that has
labelled document annotations for ParaCrawl, NewsCommentary, and
Europarl data which can be used to create parallel document
datasets for training of context-aware machine translation models.
"""
# https://huggingface.co/docs/datasets/dataset_script
import csv
import json
import os
import re
import pathlib
from pathlib import Path
import yaml
from ast import literal_eval
import datasets
import gzip
try:
import lzma as xz
except ImportError:
import pylzma as xz
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
ParaDocs is a multilingual machine translation dataset that has
labelled document annotations for ParaCrawl, NewsCommentary, and
Europarl data which can be used to create parallel document
datasets for training of context-aware machine translation models.
"""
_HOMEPAGE = "https://huggingface.co/datasets/rewicks/paradocs"
_LICENSE = "cc-by-sa-4.0"
_URL = "https://huggingface.co/datasets/rewicks/paradocs"
# Load the file paths for all the splits (per language currently)
file_list_url = "https://huggingface.co/datasets/rewicks/paradocs/raw/main/files.yml"
import urllib.request
with urllib.request.urlopen(file_list_url) as f:
try:
fnames = yaml.safe_load(f)
except yaml.YAMLError as exc:
print("Error loading the file paths for the dataset splits. Aborting.")
exit(1)
_DATA_URL = fnames['fnames']
_VARIANTS = list(_DATA_URL.keys())
class ParaDocs(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"src": datasets.Value("string"),
"tgt": datasets.Value("string"),
"sim_score_one" : datasets.Value("float32"),
"sim_score_two": datasets.Value("float32"),
"collection": datasets.Value("string"),
"src_paragraph_id": datasets.Value("string"),
"tgt_paragraph_id": datasets.Value("string"),
"src_sentence_id": datasets.Value("string"),
"tgt_sentence_id": datasets.Value("string"),
"src_start_id": datasets.Value("string"),
"src_end_id": datasets.Value("string"),
"tgt_start_id": datasets.Value("string"),
"tgt_end_id": datasets.Value("string"),
"src_lid_prob": datasets.Value("float32"),
"tgt_lid_prob": datasets.Value("float32"),
"src_docid": datasets.Value("string"),
"tgt_docid": datasets.Value("string")
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_sources = {self.config.name: _DATA_URL[self.config.name]}
return [
datasets.SplitGenerator(
name=lang,
gen_kwargs={
"filepaths": dl_manager.download(data_sources[lang])
}
)
for lang
in data_sources
]
def _get_qa_pair_list_features(self, qa_pair, feature_name):
res = []
if feature_name in qa_pair:
if qa_pair[feature_name]:
return qa_pair[feature_name]
else:
if feature_name.startswith('en'):
feature_name = '_'.join(feature_name.split('_')[1:])
return self._get_qa_pair_list_features(qa_pair, feature_name)
return res
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_ = 0
for filepath in filepaths:
# logger.info("Generating examples from = %s", filepath)
try:
with open(filepath, "r", encoding="utf-8") as f:
rstream = csv.DictReader(f,
delimiter='\t',
fieldnames = [
"src",
"tgt",
"sim_score_one",
"sim_score_two",
"collection",
"src_paragraph_id",
"tgt_paragraph_id",
"src_sentence_id",
"tgt_sentence_id",
"src_start_id",
"src_end_id",
"tgt_start_id",
"tgt_end_id",
"src_lid_prob",
"tgt_lid_prob",
"src_docid",
"tgt_docid"
],
quoting=csv.QUOTE_NONE
)
for example in rstream:
yield id_, example
id_ += 1
except:
print("Error reading file:", filepath)
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