Datasets:
metadata
dataset_info:
- config_name: corpus
features:
- name: passage_id
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 36755279
num_examples: 38741
download_size: 20357329
dataset_size: 36755279
- config_name: hard_negatives
features:
- name: passage_id
dtype: string
- name: question
dtype: string
- name: pos_score
dtype: float64
- name: neg_1_id
dtype: string
- name: neg_1_score
dtype: float64
- name: neg_2_id
dtype: string
- name: neg_2_score
dtype: float64
- name: neg_3_id
dtype: string
- name: neg_3_score
dtype: float64
- name: neg_4_id
dtype: string
- name: neg_4_score
dtype: float64
- name: neg_5_id
dtype: string
- name: neg_5_score
dtype: float64
- name: neg_6_id
dtype: string
- name: neg_6_score
dtype: float64
- name: neg_7_id
dtype: string
- name: neg_7_score
dtype: float64
- name: neg_8_id
dtype: string
- name: neg_8_score
dtype: float64
- name: neg_9_id
dtype: string
- name: neg_9_score
dtype: float64
- name: neg_10_id
dtype: string
- name: neg_10_score
dtype: float64
splits:
- name: train
num_bytes: 123482169
num_examples: 329990
download_size: 77478214
dataset_size: 123482169
- config_name: queries
features:
- name: passage_id
dtype: string
- name: question
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 36635279
num_examples: 329990
download_size: 12596688
dataset_size: 36635279
configs:
- config_name: corpus
data_files:
- split: train
path: corpus/train-*
- config_name: hard_negatives
data_files:
- split: train
path: hard_negatives/train-*
- config_name: queries
data_files:
- split: train
path: queries/train-*
license: cc-by-4.0
task_categories:
- sentence-similarity
language:
- az
tags:
- retrieval
- lquad
- azerbaijani
pretty_name: LDQuAd v2 Retrieval Dataset
size_categories:
- 100K<n<1M
LDQuAd v2 Retrieval Dataset
A retrieval dataset built from LocalDoc/LDQuAd_v2 — a question-answer dataset over Azerbaijani-language Wikipedia content. Designed for training and evaluating information retrieval, semantic search, and RAG pipelines in Azerbaijani.
Dataset Configs
The dataset consists of three configs that can be joined via passage_id:
corpus
The passage collection — one row per unique content passage.
| Column | Description |
|---|---|
passage_id |
Unique identifier of the passage (SHA-256 prefix) |
title |
Wikipedia article title |
content |
The text passage |
queries
One question per passage, each as a separate row.
| Column | Description |
|---|---|
passage_id |
Links to the relevant passage in corpus |
title |
Wikipedia article title |
question |
The question in Azerbaijani |
hard_negatives
BM25-mined hard negatives scored by a cross-encoder reranker (BAAI/bge-reranker-v2-m3). Each row contains up to 10 hard negative passage IDs with their reranker scores.
| Column | Description |
|---|---|
passage_id |
Positive passage ID (links to corpus) |
question |
The question text in Azerbaijani |
pos_score |
Reranker score of the positive passage |
neg_{k}_id |
passage_id of the k-th hard negative |
neg_{k}_score |
Reranker score of the k-th hard negative |
Source Dataset
Based on LocalDoc/LDQuAd_v2 which contains 351,000 question-answer pairs derived from Azerbaijani-language content. Passages were filtered by content length (200–10,000 characters) and deduplicated before building the retrieval corpus.
Hard Negative Mining Pipeline
- Unique passages were extracted and deduplicated by content
- For each question, top-100 candidates were retrieved using BM25
- The positive passage was excluded from candidates
- Each candidate was scored with a cross-encoder reranker (BAAI/bge-reranker-v2-m3)
- Candidates with scores above 95% of the positive score were filtered out as likely false negatives
- Top-10 remaining negatives were kept, sorted by score (hardest first)
Example
from datasets import load_dataset
corpus = load_dataset("LocalDoc/ldquad_v2_retrieval", "corpus")["train"]
queries = load_dataset("LocalDoc/ldquad_v2_retrieval", "queries")["train"]
hard_negs = load_dataset("LocalDoc/ldquad_v2_retrieval", "hard_negatives")["train"]
# Build lookups
passage_lookup = {row["passage_id"]: row for row in corpus}
neg_lookup = {row["passage_id"]: row for row in hard_negs}
# Pick a query
q = queries[0]
print(f"Question: {q['question']}")
# Positive passage
pos = passage_lookup[q["passage_id"]]
print(f"Positive: {pos['content'][:200]}...")
# Hard negatives
hn = neg_lookup[q["passage_id"]]
print(f"Positive score: {hn['pos_score']:.4f}")
for k in range(1, 4):
nid = hn[f"neg_{k}_id"]
nscore = hn[f"neg_{k}_score"]
if nid:
neg = passage_lookup[nid]
print(f"Neg-{k} [score={nscore:.4f}]: {neg['content'][:200]}...")
Example Output
Question: 2006/2007-ci il Azərbaycan kubokunda "Xəzər Lənkəran" hansı mərhələdə yarışa qoşuldu?
✅ Positive [score=6.3750]:
2006/2007-ci il Azərbaycan kubokuna "Xəzər Lənkəran" 1/8 final mərhələsində qoşuldu.
Lənkəran təmsilçisi "Bakılı" klubunu 4:0 və 3:0 məğlub edərək növbəti mərhələyə keçdi.
1/4 final mərhələsində Lənkəran təmsilçisinin rəqibi "Bakı FK" oldu...
❌ Neg-1 [score=5.9414]:
Daha dəqiq olan Lənkəran təmsilçisi 3:5 hesablı qələbə qazandı və növbəti mərhələyə
keçdi. 1/4 final mərhələsində rəqib Bakının "Rəvan" klubu oldu. "Xəzər Lənkəran"
hər iki oyunda qalib gəldi (1:2 və 4:1) və növbəti mərhələyə keçdi...
❌ Neg-2 [score=3.2168]:
Rəqib Gəncənin "Kəpəz" klubu oldu. Reqlamentə əsasən cütlüyün taleyi 1 oyunda həll
olundu. 1:0 hesablı qələbə qazanan "Xəzər Lənkəran" növbəti mərhələyə keçdi...
❌ Neg-3 [score=2.6895]:
Ölkə birinciliyində Yakuba Bamba və Edmond Ntiamoah 5, Rəşad Abdullayev və Mario
Serjio Souza 4, Emin Quliyev, Nadir Nəbiyev və Junior Osvaldo 3, Elmar Baxşıyev 2...
Contact
For more information, questions, or issues, please contact LocalDoc at [v.resad.89@gmail.com].