# 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