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Datasets:
CIRAL
/
ciral

Tasks:
Text Retrieval
Languages:
Hausa
Somali
Swahili
License:
Dataset card Files Community
ciral / ciral.py
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# 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 = {'yoruba':'yo',
'hausa':'ha',
'swahili':'sw',
'somali':'so'}
# 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: {
'dev': [
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/topics/topics.ciral-v1.0-{lang_code}-dev.tsv',
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/qrels/qrels.ciral-v1.0-{lang_code}-dev.tsv'
],
'testA':[
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/topics/topics.ciral-v1.0-{lang_code}-test-a.tsv',
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/qrels/qrels.ciral-v1.0-{lang_code}-test-a.tsv',
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/qrels/qrels.ciral-v1.0-{lang_code}-test-a-pools.tsv',
],
'testB':[
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/topics/topics.ciral-v1.0-{lang_code}-test-b.tsv',
f'https://huggingface.co/datasets/CIRAL/ciral/resolve/main/ciral-{lang}/qrels/qrels.ciral-v1.0-{lang_code}-test-b.tsv',
]
} for lang, lang_code in languages.items()
}
def load_queries(_file):
if _file is None:
return []
queries = {}
with open(_file, encoding="utf-8") as query_file:
for line in query_file:
line = line.strip()
id, query = (line.split('\t')) if len(line.split('\t')) == 2 else ("", "")
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:
line = line.strip()
qid, _, docid, rel = (line.split('\t')) if len(line.split('\t')) == 4 else ("", "", "",False)
qrels[qid][docid] = int(rel)
#print(qrels)
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.keys()
]
def _info(self):
features = datasets.Features(
{
"query_id": datasets.Value("string"),
"query": datasets.Value("string"),
# "judgements": [{
# "docid": datasets.Value("string"),
# "judgement": datasets.Value("string"),
# "text": datasets.Value("string")}]
"positive_passages": [{
'docid': datasets.Value("string"),
'text': datasets.Value("string"),
}],
"negative_passages": [{
"docid": datasets.Value("string"),
"text": datasets.Value("string")
}],
"pools_positive_passages": [{
'docid': datasets.Value("string"),
'text': datasets.Value("string"),
}],
"pools_negative_passages": [{
"docid": 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='dev',
gen_kwargs={
'filepaths': downloaded_files['dev'],
},
),
datasets.SplitGenerator(
name='testA',
gen_kwargs={
'filepaths': downloaded_files['testA'],
},
),
datasets.SplitGenerator(
name='testB',
gen_kwargs={
'filepaths': downloaded_files['testB'],
},
),
]
# 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, pools_file = (filepaths) if len(filepaths) == 3 else (filepaths[0], filepaths[1], None)
queries = load_queries(query_file)
shallow_qrels = load_qrels(qrel_file)
pools_qrels = load_qrels(pools_file)
for query_id in queries:
positive_docids = [docid for docid, judgement in shallow_qrels[query_id].items() if judgement==1]
negative_docids = [docid for docid, judgement in shallow_qrels[query_id].items() if judgement==0]
pools_positive_docids = [docid for docid, judgement in pools_qrels[query_id].items() if judgement==1] if pools_qrels is not None else []
pools_negative_docids = [docid for docid, judgement in pools_qrels[query_id].items() if judgement==0] if pools_qrels is not None else []
data = {}
data['query_id'] = query_id
data['query'] = queries[query_id]
data['positive_passages'] = [{
'docid': docid,
'text': docid2doc[docid]
} for docid in positive_docids if docid in docid2doc]
data['negative_passages'] = [{
'docid': docid,
'text': docid2doc[docid]
} for docid in negative_docids if docid in docid2doc]
data['pools_positive_passages'] = [{
'docid': docid,
'text': docid2doc[docid]
} for docid in pools_positive_docids if docid in docid2doc]
data['pools_negative_passages'] = [{
'docid': docid,
'text': docid2doc[docid]
} for docid in pools_negative_docids if docid in docid2doc]
yield query_id, data