Datasets:

LocalDoc
/

azerbaijani_books_retriever_corpus-reranked

dataset_info:
  - config_name: corpus
    features:
      - name: passage_id
        dtype: large_string
      - name: content
        dtype: large_string
    splits:
      - name: train
        num_bytes: 1214277260
        num_examples: 570573
    download_size: 646910354
    dataset_size: 1214277260
  - config_name: hard_negatives
    features:
      - name: query_id
        dtype: string
      - name: passage_id
        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: 512278813
        num_examples: 1616877
    download_size: 288801845
    dataset_size: 512278813
  - config_name: queries
    features:
      - name: query_id
        dtype: string
      - name: passage_id
        dtype: string
      - name: query
        dtype: string
      - name: query_type
        dtype: string
    splits:
      - name: train
        num_bytes: 190703388
        num_examples: 1616877
    download_size: 67508957
    dataset_size: 190703388
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
  - books
  - azerbaijani
pretty_name: Azerbaijani Books Retrieval Dataset (Reranked)
size_categories:
  - 1M<n<10M

Azerbaijani Books Retrieval Dataset (Reranked)

A large-scale retrieval dataset built from LocalDoc/books_dataset — a collection of 2,804 Azerbaijani-language books with 7.8M sentences spanning politics, history, literature, science, and more. 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 and query_id:

`corpus`

The passage collection — one row per unique content passage.

Column	Description
`passage_id`	Unique identifier of the passage (SHA-256 prefix)
`content`	The text passage (up to ~2000 characters)

`queries`

Three queries per passage (question, statement, keyword), each as a separate row.

Column	Description
`query_id`	Unique query identifier (row index)
`passage_id`	Links to the relevant passage in `corpus`
`query`	The query text in Azerbaijani
`query_type`	One of: `question`, `statement`, `keyword`

`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
`query_id`	Links to the query in `queries`
`passage_id`	Positive passage ID (links to `corpus`)
`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/books_dataset which contains 7.8M sentences from 2,804 Azerbaijani-language books. The original dataset provides sentence-level text with metadata (title, author, year, publisher, category). Sentences were reassembled by book ID and chunked into passages of up to ~2000 characters.

Query Generation

For each passage chunk, three types of search queries were generated using an LLM:

question — a natural question in Azerbaijani (e.g., "Hansı ölkələr enerji sahəsində əməkdaşlıq edir?")
statement — a declarative statement describing the passage topic (e.g., "Azərbaycan-Türkiyə enerji əməkdaşlığı")
keyword — a short keyword-style search query, 2–5 words (e.g., "enerji əməkdaşlıq TANAP qaz")

Hard Negative Mining Pipeline

Book sentences were reassembled by ID and chunked into passages (~2000 chars max)
Unique passages were deduplicated by content
For each query, 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)

Statistics

Config	Rows
`corpus`	570,573 passages
`queries`	1,616,877 queries
`hard_negatives`	1,616,877 rows × 10 negatives

Example

from datasets import load_dataset

corpus = load_dataset("LocalDoc/azerbaijani_books_retriever_corpus-reranked", "corpus")["train"]
queries = load_dataset("LocalDoc/azerbaijani_books_retriever_corpus-reranked", "queries")["train"]
hard_negs = load_dataset("LocalDoc/azerbaijani_books_retriever_corpus-reranked", "hard_negatives")["train"]

# Build lookups
passage_lookup = {row["passage_id"]: row for row in corpus}
neg_lookup = {row["query_id"]: row for row in hard_negs}

# Pick a query
q = queries[0]
print(f"Query [{q['query_type']}]: {q['query']}")

# Positive passage
pos = passage_lookup[q["passage_id"]]
print(f"Positive: {pos['content'][:200]}...")

# Hard negatives
hn = neg_lookup[q["query_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

Query [question]: Türkiyə-Yunanıstan qaz kəməri və Şahdəniz-2 layihəsi Avropanın enerji təhlükəsizliyinə necə təsir göstərir?

✅ Positive [score=5.3242]:
Hazırda Yunanıstan Avropa İttifaqının üzvü olaenerji təhlükəsizliyinin təmin edilməsində
önəmli rol oyna- raq, enerji sahəsində Azərbaycanla birbaşa əməkdaşlıq edir və yacaqdır.
onun az miqdarda olsa da ixrac qazını alır...

❌ Neg-1 [score=4.4609]:
Bu iki tarixi layihə bizi bir-birimizə çox sıx şəkildə bağlamaqdadır. Bu mənada bir
məqamı xüsusi vurğulamaq istəyirəm: Biz Xəzər hövzəsi və Orta Asiya təbii qazının
ölkəmizin ərazisindən alternativ marşrutlarla Avropaya nəqlini nəzərdə tutan...

❌ Neg-2 [score=4.2500]:
«Şahdəniz» yatağında qaz ehtiyatları 1 trilyon kubmetrdən çoxdur. Ümumiyyətlə,
Azərbaycanın digər yataqları ilə birlikdə qaz ehtiyatları 2.6 trilyon kubmetr təşkil edir.
İkinci layihə Azərbaycanı Gürcüstanla birləşdirən...

❌ Neg-3 [score=4.2109]:
TANAP Azərbaycan xalqının böyük lideri, mənim dostum İlham Əliyevin rəhbərliyi ilə
Azərbaycanın enerji təhlükəsizliyi, türk xalqının böyük lideri, Ukraynanın dostu və
mənim dostum Türkiyə Prezidenti Rəcəb Tayyib Ərdoğanın rəhbərliyi ilə...

Contact

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