File size: 2,891 Bytes
73cc8d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | 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
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