File size: 3,282 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | from __future__ import annotations
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import MultilingualTask
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
_EVAL_SPLIT = "test"
_LANGS = {"de": ["deu-Latn"], "es": ["spa-Latn"]}
def _load_miracl_data(
path: str, langs: list, split: str, cache_dir: str = None, revision: str = None
):
queries = {lang: {split: {}} for lang in langs}
corpus = {lang: {split: {}} for lang in langs}
relevant_docs = {lang: {split: {}} for lang in langs}
for lang in langs:
data = datasets.load_dataset(
path,
lang,
split=split,
cache_dir=cache_dir,
revision=revision,
)
# Generate unique IDs for queries and documents
query_id_counter = 1
document_id_counter = 1
for row in data:
query_text = row["query"]
positive_texts = row["positive"]
negative_texts = row["negative"]
# Assign unique ID to the query
query_id = f"Q{query_id_counter}"
queries[lang][split][query_id] = query_text
query_id_counter += 1
# Add positive and negative texts to corpus with unique IDs
for text in positive_texts + negative_texts:
doc_id = f"D{document_id_counter}"
corpus[lang][split][doc_id] = {"text": text}
document_id_counter += 1
# Add relevant document information to relevant_docs for positive texts only
if text in positive_texts:
if query_id not in relevant_docs[lang][split]:
relevant_docs[lang][split][query_id] = {}
relevant_docs[lang][split][query_id][doc_id] = 1
corpus = datasets.DatasetDict(corpus)
queries = datasets.DatasetDict(queries)
relevant_docs = datasets.DatasetDict(relevant_docs)
return corpus, queries, relevant_docs
class MIRACLRetrieval(MultilingualTask, AbsTaskRetrieval):
metadata = TaskMetadata(
name="MIRACLRetrieval",
description="MIRACLRetrieval",
reference=None,
dataset={
"path": "jinaai/miracl",
"revision": "d28a029f35c4ff7f616df47b0edf54e6882395e6",
},
type="Retrieval",
category="s2p",
eval_splits=[_EVAL_SPLIT],
eval_langs=_LANGS,
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,
)
def load_data(self, **kwargs):
if self.data_loaded:
return
self.corpus, self.queries, self.relevant_docs = _load_miracl_data(
path=self.metadata_dict["dataset"]["path"],
langs=self.hf_subsets,
split=self.metadata_dict["eval_splits"][0],
cache_dir=kwargs.get("cache_dir", None),
revision=self.metadata_dict["dataset"]["revision"],
)
self.data_loaded = True
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