| from __future__ import annotations |
|
|
| import datasets |
|
|
| from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
|
|
| class SlovakSumRetrieval(AbsTaskRetrieval): |
| metadata = TaskMetadata( |
| name="SlovakSumRetrieval", |
| description=""" |
| SlovakSum, a Slovak news summarization dataset consisting of over 200 thousand |
| news articles with titles and short abstracts obtained from multiple Slovak newspapers. |
| |
| Originally intended as a summarization task, but since no human annotations were provided |
| here reformulated to a retrieval task. |
| """, |
| reference="https://huggingface.co/datasets/NaiveNeuron/slovaksum", |
| dataset={ |
| "path": "NaiveNeuron/slovaksum", |
| "revision": "85d6b32f2762313714618171b9d1a65eb7408835", |
| }, |
| type="Retrieval", |
| category="s2s", |
| eval_splits=["test"], |
| eval_langs=["slk-Latn"], |
| main_score="ndcg_at_10", |
| date=("2015-04-26", "2022-01-11"), |
| form=["written"], |
| domains=["News", "Social", "Web"], |
| task_subtypes=["Article retrieval"], |
| license="openrail", |
| socioeconomic_status="mixed", |
| annotations_creators="derived", |
| dialect=[], |
| text_creation="found", |
| bibtex_citation=""" |
| @inproceedings{OndrejowaSlovakSum24, |
| title = {SlovakSum: A Large Scale Slovak Summarization Dataset}, |
| booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation}, |
| author = {Ondrejová, Viktória and Šuppa, Marek}, |
| date = {2024}, |
| } |
| """, |
| n_samples={"test": 600}, |
| avg_character_length={"test": 238.44}, |
| ) |
|
|
| def load_data(self, **kwargs): |
| if self.data_loaded: |
| return |
| self.corpus, self.queries, self.relevant_docs = {}, {}, {} |
| dataset_path = self.metadata_dict["dataset"]["path"] |
| n_sample = self.metadata_dict["n_samples"]["test"] |
|
|
| for split in kwargs.get("eval_splits", self.metadata_dict["eval_splits"]): |
| split_ds = datasets.load_dataset( |
| dataset_path, split=f"{split}[:{n_sample}]" |
| ) |
| |
| queries = {f"q{e+1}": x["sum"] for e, x in enumerate(split_ds)} |
| corpus = { |
| f"d{e+1}": {"title": x["title"], "text": x["text"]} |
| for e, x in enumerate(split_ds) |
| } |
| qrels = {f"q{i+1}": {f"d{i+1}": 1} for i in range(split_ds.shape[0])} |
| self.corpus[split], self.queries[split], self.relevant_docs[split] = ( |
| corpus, |
| queries, |
| qrels, |
| ) |
| self.data_loaded = True |
|
|
