| from __future__ import annotations |
|
|
| import datasets |
|
|
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
| from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
|
|
|
|
| class GermanDPR(AbsTaskRetrieval): |
| _EVAL_SPLIT = "test" |
|
|
| metadata = TaskMetadata( |
| name="GermanDPR", |
| description="GermanDPR is a German Question Answering dataset for open-domain QA. It associates questions with a textual context containing the answer", |
| reference="https://huggingface.co/datasets/deepset/germandpr", |
| dataset={ |
| "path": "deepset/germandpr", |
| "revision": "5129d02422a66be600ac89cd3e8531b4f97d347d", |
| }, |
| type="Retrieval", |
| category="s2p", |
| eval_splits=[_EVAL_SPLIT], |
| eval_langs=["deu-Latn"], |
| main_score="ndcg_at_10", |
| date=None, |
| form=None, |
| domains=None, |
| task_subtypes=None, |
| license=None, |
| socioeconomic_status=None, |
| annotations_creators=None, |
| dialect=None, |
| text_creation=None, |
| bibtex_citation=None, |
| n_samples=None, |
| avg_character_length=None, |
| ) |
|
|
| @staticmethod |
| def _format_documents(docs, id_prefix="", existing_docs=None): |
| if existing_docs is None: |
| existing_docs = dict() |
| result = {} |
| for i, (title, content) in enumerate(zip(docs["title"], docs["text"])): |
| formatted_content = content.split("==\n")[-1].replace("\n", " ").lstrip() |
| if formatted_content in existing_docs: |
| id_value = existing_docs[formatted_content] |
| else: |
| id_value = f"{id_prefix}{i}" |
| existing_docs[formatted_content] = id_value |
| result[id_value] = {"title": title, "text": formatted_content} |
| return result |
|
|
| def load_data(self, **kwargs): |
| if self.data_loaded: |
| return |
|
|
| data = datasets.load_dataset( |
| split=self._EVAL_SPLIT, |
| **self.metadata_dict["dataset"], |
| ) |
| corpus = dict() |
| queries = dict() |
| relevant_docs = dict() |
| all_docs = dict() |
| for i, row in enumerate(data): |
| q_id = f"q_{i}" |
| queries[q_id] = row["question"] |
| pos_docs = self._format_documents( |
| row["positive_ctxs"], id_prefix=f"doc_{i}_p_", existing_docs=all_docs |
| ) |
| corpus.update(pos_docs) |
| neg_docs = self._format_documents( |
| row["hard_negative_ctxs"], |
| id_prefix=f"doc_{i}_n_", |
| existing_docs=all_docs, |
| ) |
| corpus.update(neg_docs) |
| relevant_docs[q_id] = {k: 1 for k in pos_docs} |
| self.queries = {self._EVAL_SPLIT: queries} |
| self.corpus = {self._EVAL_SPLIT: corpus} |
| self.relevant_docs = {self._EVAL_SPLIT: relevant_docs} |
|
|
| self.data_loaded = True |
|
|
