| from __future__ import annotations |
|
|
| import hashlib |
|
|
| import datasets |
|
|
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
| from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
|
|
| _EVAL_SPLIT = "test" |
|
|
|
|
| class GermanGovServiceRetrieval(AbsTaskRetrieval): |
| metadata = TaskMetadata( |
| name="GermanGovServiceRetrieval", |
| description="LHM-Dienstleistungen-QA is a German question answering dataset for government services of the Munich city administration. It associates questions with a textual context containing the answer", |
| reference="https://huggingface.co/datasets/it-at-m/LHM-Dienstleistungen-QA", |
| dataset={ |
| "path": "it-at-m/LHM-Dienstleistungen-QA", |
| "revision": "ed40131b56ce86ce3666f2942953595dd9d29608", |
| }, |
| type="Retrieval", |
| category="s2p", |
| eval_splits=[_EVAL_SPLIT], |
| eval_langs=["deu-Latn"], |
| main_score="ndcg_at_5", |
| date=("2022-11-01", "2022-11-30"), |
| form=["written"], |
| domains=["Government"], |
| task_subtypes=["Question answering"], |
| license="mit", |
| socioeconomic_status="medium", |
| annotations_creators="derived", |
| dialect=[], |
| bibtex_citation="""@software{lhm-dienstleistungen-qa, |
| author = {Schröder, Leon Marius and |
| Gutknecht, Clemens and |
| Alkiddeh, Oubada and |
| Susanne Weiß, |
| Lukas, Leon}, |
| title = {LHM-Dienstleistungen-QA - german public domain question-answering dataset}, |
| month = nov, |
| year = 2022, |
| publisher = {it@M}, |
| url = {https://huggingface.co/datasets/it-at-m/LHM-Dienstleistungen-QA} |
| }""", |
| text_creation="found", |
| n_samples={"test": 357}, |
| avg_character_length={"test": 1211.69}, |
| ) |
|
|
| @staticmethod |
| def get_hash(input_str) -> str: |
| return hashlib.md5(input_str.encode("utf-8")).hexdigest() |
|
|
| def load_data(self, **kwargs): |
| if self.data_loaded: |
| return |
|
|
| dataset = datasets.load_dataset( |
| path=self.metadata_dict["dataset"]["path"], |
| split=_EVAL_SPLIT, |
| cache_dir=kwargs.get("cache_dir", None), |
| revision=self.metadata_dict["dataset"]["revision"], |
| ) |
| corpus = {} |
| queries = {} |
| relevant_docs = {} |
|
|
| for row in dataset: |
| |
| d_id = "d_" + self.get_hash(row["title"] + row["context"]) |
| q_id = "q_" + self.get_hash(row["question"]) |
|
|
| corpus[d_id] = { |
| "_id": d_id, |
| "title": row["title"], |
| "text": row["context"], |
| } |
| queries[q_id] = row["question"] |
|
|
| if q_id not in relevant_docs: |
| relevant_docs[q_id] = {} |
|
|
| relevant_docs[q_id][d_id] = 1 |
|
|
| self.queries = {_EVAL_SPLIT: queries} |
| self.corpus = {_EVAL_SPLIT: corpus} |
| self.relevant_docs = {_EVAL_SPLIT: relevant_docs} |
|
|
| self.data_loaded = True |
|
|
