Datasets:

LocalDoc
/

msmarco-az-reranked

language:
  - az
  - en
license: ms-pl
task_categories:
  - text-retrieval
tags:
  - retrieval
  - azerbaijani
  - information-retrieval
  - hard-negatives
  - reranker
  - ms-marco
  - dense-retrieval
  - colbert
  - bi-encoder
  - translated
size_categories:
  - 1M<n<10M
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/train-*
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*
  - config_name: triplets
    data_files:
      - split: train
        path: triplets/train-*

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

Sampling: 3.2M triplets were sampled from the MS MARCO examples.json using reservoir sampling, with 31 negatives selected per query
Translation: All queries and passages were translated from English to Azerbaijani
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)
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].