parlaMintSI.py

# 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.

import csv
import json
import os

import datasets

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

_DESCRIPTION = """\
ParlaMint 3.0 is a multilingual set of 26 comparable corpora containing parliamentary debates mostly starting in 2015 and extending to mid-2022.

The corpora have extensive metadata, including aspects of the parliament; the speakers (name, gender, MP status, party affiliation, party coalition/opposition);
are structured into time-stamped terms, sessions and meetings; and with speeches being marked by the speaker and their role (e.g. chair, regular speaker).
The speeches also contain marked-up transcriber comments, such as gaps in the transcription, interruptions, applause, etc.
Note that some corpora have further information, e.g. the year of birth of the speakers, links to their Wikipedia articles, their membership in various committees, etc.
The corpora are also marked to the subcorpus they belong to ("reference", until 2020-01-30, "covid", from 2020-01-31, and "war", from 2022-02-24).

The corpora are encoded according to the Parla-CLARIN TEI recommendation (https://clarin-eric.github.io/parla-clarin/), but have been encoded against the compatible,
but much stricter ParlaMint encoding guidelines (https://clarin-eric.github.io/ParlaMint/) and schemas (included in this distribution.

This entry contains the ParlaMint TEI-encoded corpora with the derived plain text versions of the corpora along with TSV metadata of the speeches.
Also included is the 3.0 release of the data and scripts available at the GitHub repository of the ParlaMint project.

This dataset contains only Slovenian parliamentary debates.
"""

_HOMEPAGE = "http://hdl.handle.net/11356/1486"

_LICENSE = "Creative Commons - Attribution 4.0 International (CC BY 4.0)"

_URLS = {
    "parlamint": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1486/ParlaMint-SI.tgz?sequence=24&isAllowed=y",
}


class ParlaMintSI(datasets.GeneratorBasedBuilder):
    """This dataset contains transcriptions of Slovenian parliamentary debates and relevant metadata."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "ID": datasets.Value("string"),
                "Title": datasets.Value("string"),
                "Date": datasets.Value("string"),
                "Body": datasets.Value("string"),
                "Term": datasets.Value("string"),
                "Session": datasets.Value("string"),
                "Meeting": datasets.Value("int32"),
                "Sitting": datasets.Value("string"),
                "Agenda": datasets.Value("string"),
                "Subcorpus": datasets.Value("string"),
                "Speaker_role": datasets.Value("string"),
                "Speaker_MP": datasets.Value("string"),
                "Speaker_Minister": datasets.Value("string"),
                "Speaker_party": datasets.Value("string"),
                "Speaker_party_name": datasets.Value("string"),
                "Party_status": datasets.Value("string"),
                "Speaker_name": datasets.Value("string"),
                "Speaker_gender": datasets.Value("string"),
                "Speaker_birth": datasets.Value("string"),
                "text": datasets.Value("string")
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS["parlamint"]
        download_path = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": download_path,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        filepath = os.path.join(filepath, "ParlaMint-SI.txt")

        for year_dir in os.listdir(filepath):
            year_path = os.path.join(filepath, year_dir)
            if os.path.isdir(year_path):
                tsv_files = [f for f in os.listdir(year_path) if f.endswith(".tsv")]
                for tsv_file in tsv_files:
                    tsv_path = os.path.join(year_path, tsv_file)
                    txt_path = os.path.join(year_path, tsv_file.replace("-meta.tsv", ".txt"))

                    with open(tsv_path, "r", encoding="utf-8") as tsv, open(txt_path, "r", encoding="utf-8") as txt:
                        tsv_reader = csv.DictReader(tsv, delimiter="\t")
                        txt_content = txt.readlines()

                        for row in tsv_reader:
                            id_ = row.get("ID", "")
                            text = next((line.split("\t")[1] for line in txt_content if line.startswith(id_)), "")
                            example = {key: row.get(key, "") for key in row}
                            example["text"] = text
                            yield id_, example