LocalDoc/msmarco-az-reranked

MS MARCO Azerbaijani — Reranked Retrieval Training Dataset

A large-scale passage retrieval training dataset in Azerbaijani, built by translating a 3.2M subset of the MS MARCO passage ranking dataset and rescoring all query-passage pairs with a multilingual cross-encoder reranker.

Overview

	Count
Passages	8,473,865
Queries	~800,000
Triplets	~3,200,000
Negatives per triplet	up to 31
Total pairs scored	41,746,530

Dataset Configs

The dataset consists of three configs:

corpus

The full translated passage collection.

Column	Type	Description
pid	int	Passage ID (original MS MARCO pid)
passage	string	Passage text translated to Azerbaijani

queries

Translated queries.

Column	Type	Description
qid	int	Query ID (original MS MARCO qid)
query	string	Query text translated to Azerbaijani

triplets

Training triplets with both original MS MARCO scores and reranker scores computed on the Azerbaijani translations.

Column	Type	Description
qid	int	Query ID (links to queries)
pos_pid	int	Positive passage ID (links to corpus)
pos_score_original	float	Original MS MARCO cross-encoder score (English)
pos_score_reranker	float	Reranker score on Azerbaijani translation
neg_count	int	Number of valid negatives for this triplet
neg_{k}_pid	int	Passage ID of the k-th hard negative
neg_{k}_score_original	float	Original MS MARCO score of the k-th negative
neg_{k}_score_reranker	float	Reranker score of the k-th negative (Azerbaijani)

Negatives are sorted by score_reranker descending (hardest first). Columns run from neg_1_* to neg_31_*.

Construction Pipeline

1. Sampling: 3.2M triplets were sampled from the MS MARCO examples.json using reservoir sampling, with 31 negatives selected per query
2. Translation: All queries and passages were translated from English to Azerbaijani
3. Reranking: Every query-passage pair (positive + all negatives) was scored with BAAI/bge-reranker-v2-m3 on the Azerbaijani translations (~14 hours, 41.7M pairs scored)
4. Output: Triplets with dual scores (original English + Azerbaijani reranker) to enable flexible filtering during training

Why Reranker Scores?

The original MS MARCO scores were computed on English text. After translation, semantic relationships between queries and passages can shift — some negatives become closer to the positive, and some positives become weaker. The reranker scores on Azerbaijani text reflect what the model will actually see during training.

This also enables false negative filtering: negatives with score_reranker > threshold * pos_score_reranker are likely correct answers that MS MARCO did not annotate. These can be filtered out during training to avoid noisy supervision signals.

Usage

from datasets import load_dataset

corpus = load_dataset("LocalDoc/msmarco-az-reranked", "corpus")["train"]
queries = load_dataset("LocalDoc/msmarco-az-reranked", "queries")["train"]
triplets = load_dataset("LocalDoc/msmarco-az-reranked", "triplets")["train"]

# Build lookups
passage_lookup = {row["pid"]: row["passage"] for row in corpus}
query_lookup = {row["qid"]: row["query"] for row in queries}

# Inspect a triplet
t = triplets[0]
print(f"Query: {query_lookup[t['qid']]}")
print(f"Positive [reranker={t['pos_score_reranker']:.4f}]: {passage_lookup[t['pos_pid']][:200]}")

for k in range(1, 4):
    neg_pid = t[f"neg_{k}_pid"]
    neg_score = t[f"neg_{k}_score_reranker"]
    if neg_pid:
        print(f"Neg-{k} [reranker={neg_score:.4f}]: {passage_lookup[neg_pid][:200]}")

Training with False Negative Filtering

# Filter out false negatives where negative score > 95% of positive score
FN_THRESHOLD = 0.95

t = triplets[0]
pos_score = t["pos_score_reranker"]
cutoff = FN_THRESHOLD * pos_score

clean_negs = []
for k in range(1, 32):
    neg_pid = t[f"neg_{k}_pid"]
    neg_score = t[f"neg_{k}_score_reranker"]
    if neg_pid and neg_score < cutoff:
        clean_negs.append((neg_pid, neg_score))

print(f"Original negatives: {t['neg_count']}")
print(f"After FN filtering: {len(clean_negs)}")

Example Output

Query: Dişi aslanlar nə qədər doğurur

Positive [original=10.41, reranker=5.64]:
Dişi şir normalda hər 18-26 aydan bir doğur. Təxminən 100-119 günlük
hamiləlik dövründən sonra bir-altı bala doğur. Lakin, balaların sayı
adətən üç və ya dörd olur və hər birinin çəkisi təxminən 3 funt olur.

Neg-1 [original=9.26, reranker=7.41]:  ← false negative (reranker > positive)
Dişi aslanlar adətən hər iki ildən bir bala doğurlar. Dişilər hamilə
və ya əmizdirən deyillərsə, ildə bir neçə dəfə cütləşməyə hazırdırlar.

Neg-2 [original=9.35, reranker=5.41]:
Pride-ın dişi hissəsi bütün yetkinlik həyatlarını birlikdə yaşayır,
lakin erkəklər gəlib-gedir. Dişi aslanın hamiləliyi təxminən dörd ay
davam edir.

Neg-3 [original=3.27, reranker=2.77]:  ← true negative
At: Dişilərin hamiləliyi adətən 11-12 ay çəkir. Dəniz aslanı: Dəniz
şirləri də balalarını 11-12 aylıq hamiləlik dövründən sonra dünyaya
gətirirlər.

Limitations

• Passages and queries are machine-translated; translation artifacts (lexical mismatch, semantic drift) may affect quality
• Reranker scores are from a multilingual model that may underperform on Azerbaijani compared to English
• Original MS MARCO annotations are incomplete — some "negatives" are actually relevant (false negatives)

Contact

For questions or issues, please contact LocalDoc at [v.resad.89@gmail.com].