paradocs.py

# 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/jhu-clsp/paradocs"

_LICENSE = "cc-by-sa-4.0"

_URL = "https://huggingface.co/datasets/jhu-clsp/paradocs"

# Load the file paths for all the splits (per language currently)

file_list_url = "https://huggingface.co/datasets/jhu-clsp/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"),
                    "duplication_count": datasets.Value("int64"),
                    "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="train",
                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 gzip.open(filepath, "rt", 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",
                                                    "duplication_count",
                                                    "src_docid",
                                                    "tgt_docid"
                                                ],
                                                quoting=csv.QUOTE_NONE
                    )
                    for example in rstream:
                        yield id_, example
                        id_ += 1
            except Exception as e:
                print(e, filepath)
                print("Error reading file:", filepath)