ciral.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
from collections import defaultdict


_CITATION = ""

languages = ['hausa', 'yoruba']

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset consists of the queries and relevance judgements in the CIRAL test collection. 
"""

_HOMEPAGE = ""

_LICENSE = ""

_URLS = {
    lang: {
        'train': [
            f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/topics/topics.ciral-{lang}-train.tsv',
            f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/qrels/qrels.ciral-{lang}-train.tsv'
        ],
        'test':[]
    } for lang in languages
}


def load_queries(_file):
    if _file is None:
        return None

    queries = {}
    with open(_file, encoding="utf-8") as query_file:
        for line in query_file:
            id, query = line.strip().split('\t')
            queries[id] = query
    return queries

def load_qrels(_file):
    if _file is None:
        return None

    qrels = defaultdict(dict)
    with open(_file, encoding="utf-8") as qrel_file:
        for line in qrel_file:
            qid, _, docid, rel = line.strip().split('\t')
            qrels[qid][docid] = int(rel)
    return qrels



class CIRAL(datasets.GeneratorBasedBuilder):
    #VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang,
            version=datasets.Version("1.1.0"), 
            description=f"CIRAL data for {lang}.")  for lang in languages
    ]

    def _info(self):
        features = datasets.Features(
            {
                "query id": datasets.Value("string"),
                "query": datasets.Value("string"),
                "positive passages": [{
                    "docid": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "text": datasets.Value("string")}],
                "negative passages": datasets.features.Sequence({
                    "docid": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "text": datasets.Value("string")})
            }
        )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        
        lang = self.config.name
        downloaded_files = dl_manager.download_and_extract(_URLS[lang])
        return [
            datasets.SplitGenerator(
                name='train',
                gen_kwargs={
                    'filepaths': downloaded_files['train'],
                },
            ),
            datasets.SplitGenerator(
                name='test',
                gen_kwargs={
                    'filepaths': downloaded_files['test'],
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepaths):
        lang = self.config.name
        corpus = datasets.load_dataset('ciral/ciral-corpus', lang)['train']
        docid2doc = {doc['docid']: doc['text'] for doc in corpus}

        query_file, qrel_file = (filepaths) if len(filepaths) == 2 else (filepaths[0], None)
        queries = load_queries(query_file)
        qrels = load_qrels(qrel_file)
        for query_id in queries:
            data = {}
            data['Query Id'] = query_id
            data['Query'] = queries[query]
            data['Judgements'] = [{
                'docid': docid,
                'Judgement': judgement,
                'text': docid2doc[docid]
            } for docid, judgement in qrels[query_id].items()]

            yield query_id, data