| from __future__ import annotations |
|
|
| from datasets import load_dataset |
|
|
| from mteb.abstasks import AbsTaskRetrieval, TaskMetadata |
|
|
|
|
| class JaQuADRetrieval(AbsTaskRetrieval): |
| metadata = TaskMetadata( |
| name="JaQuADRetrieval", |
| dataset={ |
| "path": "SkelterLabsInc/JaQuAD", |
| "revision": "05600ff310a0970823e70f82f428893b85c71ffe", |
| }, |
| description="Human-annotated question-answer pairs for Japanese wikipedia pages.", |
| reference="https://arxiv.org/abs/2202.01764", |
| type="Retrieval", |
| category="p2p", |
| eval_splits=["validation"], |
| eval_langs=["jpn-Jpan"], |
| main_score="ndcg_at_10", |
| date=("2022-01-01", "2022-12-31"), |
| form=["written"], |
| domains=["Encyclopaedic", "Non-fiction"], |
| task_subtypes=["Question answering"], |
| license="CC-BY-SA-3.0", |
| socioeconomic_status="high", |
| annotations_creators="human-annotated", |
| dialect=None, |
| text_creation="found", |
| bibtex_citation="""@misc{so2022jaquad, |
| title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}}, |
| author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho}, |
| year={2022}, |
| eprint={2202.01764}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| }""", |
| n_samples={"validation": 2048}, |
| avg_character_length={"validation": 400.75}, |
| ) |
|
|
| def load_data(self, **kwargs): |
| if self.data_loaded: |
| return |
|
|
| split = self.metadata_dict["eval_splits"][0] |
| ds = load_dataset(**self.metadata_dict["dataset"], split=split) |
| ds = ds.shuffle(seed=42) |
| max_samples = min(2048, len(ds)) |
| ds = ds.select( |
| range(max_samples) |
| ) |
| title = ds["title"] |
| question = ds["question"] |
| context = ds["context"] |
| answer = [a["text"][0] for a in ds["answers"]] |
|
|
| self.corpus = {split: {}} |
| self.relevant_docs = {split: {}} |
| self.queries = {split: {}} |
|
|
| text2id = {} |
| n = 0 |
| for t, q, cont, ans in zip(title, question, context, answer): |
| self.queries[split][str(n)] = q |
| q_n = n |
| n += 1 |
| if cont not in text2id: |
| text2id[cont] = n |
| self.corpus[split][str(n)] = {"title": t, "text": cont} |
| n += 1 |
| if ans not in text2id: |
| text2id[ans] = n |
| self.corpus[split][str(n)] = {"title": t, "text": ans} |
| n += 1 |
|
|
| self.relevant_docs[split][str(q_n)] = { |
| str(text2id[ans]): 1, |
| str(text2id[cont]): 1, |
| } |
|
|
| self.data_loaded = True |
|
|
