Create miracl-reranking.py
Browse files- miracl-reranking.py +155 -0
miracl-reranking.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the 'License');
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an 'AS IS' BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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import json
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import datasets
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from collections import defaultdict
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from dataclasses import dataclass
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_CITATION = '''
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'''
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surprise_languages = ['de', 'yo']
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new_languages = ['es', 'fa', 'fr', 'hi', 'zh'] + surprise_languages
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languages = ['ar', 'bn', 'en', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th', 'zh'] + surprise_languages
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_DESCRIPTION = 'dataset load script for MIRACL'
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_DATASET_URLS = {
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lang: {
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'dev': [
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-dev.tsv',
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/qrels/qrels.miracl-v1.0-{lang}-dev.tsv',
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],
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'testB': [
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-test-b.tsv',
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],
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} for lang in languages
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}
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for lang in languages:
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if lang in surprise_languages:
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continue
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_DATASET_URLS[lang]['train'] = [
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-train.tsv',
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/qrels/qrels.miracl-v1.0-{lang}-train.tsv',
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]
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for lang in languages:
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if lang in new_languages:
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continue
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_DATASET_URLS[lang]['testA'] = [
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f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-test-a.tsv',
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]
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def load_topic(fn):
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qid2topic = {}
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with open(fn, encoding="utf-8") as f:
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for line in f:
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qid, topic = line.strip().split('\t')
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qid2topic[qid] = topic
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return qid2topic
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def load_qrels(fn):
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if fn is None:
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return None
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qrels = defaultdict(dict)
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with open(fn, encoding="utf-8") as f:
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for line in f:
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qid, _, docid, rel = line.strip().split('\t')
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qrels[qid][docid] = int(rel)
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return qrels
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class MIRACLReranking(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [datasets.BuilderConfig(
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version=datasets.Version('1.0.0'),
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name=lang, description=f'MIRACL Reranking in language {lang}.'
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) for lang in languages
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]
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def _info(self):
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features = datasets.Features({
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'query_id': datasets.Value('string'),
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'query': datasets.Value('string'),
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'positive_passages': [{
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'docid': datasets.Value('string'),
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'text': datasets.Value('string'), 'title': datasets.Value('string')
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}],
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'negative_passages': [{
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'docid': datasets.Value('string'),
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'text': datasets.Value('string'), 'title': datasets.Value('string'),
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}],
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})
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage='https://project-miracl.github.io',
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# License for the dataset if available
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license='',
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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lang = self.config.name
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downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[lang])
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splits = [
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datasets.SplitGenerator(
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name='dev',
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gen_kwargs={
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'filepaths': downloaded_files['dev'],
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},
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),
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]
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return splits
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def _generate_examples(self, filepaths):
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def formulate_doc(title, text):
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return f"{title} {text}"
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lang = self.config.name
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miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
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docid2doc = {doc['docid']: (doc['title'], doc['text']) for doc in miracl_corpus}
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topic_fn, qrel_fn = (filepaths) if len(filepaths) == 2 else (filepaths[0], None)
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qid2topic = load_topic(topic_fn)
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qrels = load_qrels(qrel_fn)
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for qid in qid2topic:
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data = {}
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data['query'] = qid2topic[qid]
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pos_docids = [docid for docid, rel in qrels[qid].items() if rel == 1] if qrels is not None else []
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neg_docids = [docid for docid, rel in qrels[qid].items() if rel == 0] if qrels is not None else []
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data['positive'] = [formulate_doc(
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docid2doc[docid]['title'], docid2doc[docid]['text'],
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) for docid in pos_docids]
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data['negative'] = [(
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docid2doc[docid]['title'], docid2doc[docid]['text'],
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) for docid in neg_docids]
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yield qid, data
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