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lumees
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ms-marco-tr-hard-negatives

Tasks:
Text Retrieval
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Text
Formats:
json
Languages:
Turkish
Size:
100K - 1M
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search
information-retrieval
sentence-transformers
msmarco
hard-negatives
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metadata
language:
  - tr
tags:
  - search
  - information-retrieval
  - sentence-transformers
  - msmarco
  - hard-negatives
license: mit
task_categories:
  - text-retrieval
source_datasets:
  - parsak/msmarco-tr
size_categories:
  - 100K<n<1M

MS MARCO TR - Hard Negatives Dataset

Dataset Description

  • Source Repository: Derived from parsak/msmarco-tr.
  • Language: Turkish (tr)
  • Task: Semantic Search, Dense Retrieval, Embedding Training
  • Size: ~500k - 1M Training Triplets (Depending on processed queries)
  • Authors: Lumees AI, Hasan Kurşun, Kerem Berkay Yanık
  • Year: 2025
  • Website: lumees.io

Dataset Summary

This dataset contains Hard Negatives specifically mined for the Turkish MS MARCO dataset. It is designed for training or fine-tuning sentence embedding models (e.g., SBERT) for Turkish Information Retrieval tasks.

[Image of vector space diagram showing query positive hard negative and random negative]

Unlike standard random negatives, these "hard" negatives are passages that share high semantic similarity (high vector similarity) with the query but are not the correct answer. Training on this data forces the model to learn subtle semantic distinctions, significantly improving retrieval performance.

Creation Process

  1. Source Data: Training queries and passages were taken from the parsak/msmarco-tr dataset (Machine translated MS MARCO).
  2. Mining Model: The emrecan/bert-base-turkish-cased-mean-nli-stsb-tr model was used.
  3. Method:
    • Encoding: All queries and passages were converted into dense vectors using the mining model.
    • Retrieval: For each query, the top 100 similar passages were retrieved using Faiss (Inner Product).
    • Filtering: The true positive (correct answer) was removed from the results.
    • Safety Threshold: Passages with a similarity score higher than 0.98 were discarded to prevent "False Negatives" (correct answers accidentally labeled as negative).
    • Selection: From the remaining candidates, the top 10 passages with the highest scores were selected as Hard Negatives.

Data Structure

Data Examples

Each line in the .jsonl file is a valid JSON object representing a training example.

{
  "query": "Manhattan projesinin başarısının hemen etkisi neydi?",
  "pos": "Manhattan Projesi ve atom bombası, İkinci Dünya Savaşı'nın sona ermesine yardımcı oldu...",
  "negatives": [
    "Manhattan Projesi, II. Dünya Savaşı sırasında ilk atom bombasını geliştirmek için...",
    "Proje, nükleer silah üretimi üzerine odaklanmıştı...",
    "..."
  ],
  "scores": [
    0.874,
    0.852,
    "..."
  ]
}

Data Fields

  • query (string): The search query.
  • pos (string): The true positive passage (correct answer).
  • negatives (list of strings): A list of 10 passages that are semantically close to the query but incorrect. Sorted by similarity (highest to lowest).
  • scores (list of floats): Cosine similarity scores corresponding to the passages in the negatives list. Useful for margin-based filtering or weighted loss functions during training.

Usage Guide

Loading the Dataset (Python)

You can load this dataset using the Hugging Face datasets library or standard JSON line reading methods.

from datasets import load_dataset

# If uploaded to Hugging Face
ds = load_dataset("lumees/msmarco-tr-hard-negatives", split="train")

# If loading from a local file
ds = load_dataset("json", data_files="msmarco_tr_hard_negatives_final.jsonl", split="train")

print(ds[0])

Training with Sentence Transformers

This dataset is optimized for loss functions like MultipleNegativesRankingLoss or InfoNCE.

from sentence_transformers import InputExample

train_examples = []
for row in ds:
    # Structure: [Query, Positive, Negative1, Negative2, ...]
    texts = [row['query'], row['pos']] + row['negatives']
    train_examples.append(InputExample(texts=texts))

# Note: Ensure the Loss function you are using supports multiple negatives per example.

Limitations & Bias

  1. Translation Errors: The original parsak/msmarco-tr dataset was created via machine translation from English. Therefore, some Turkish expressions may not be natural or may contain translation errors.
  2. False Negatives: Despite the 0.98 similarity filter, there is a possibility that some passages selected as "negatives" are actually correct answers that were not labeled in the original dataset.
  3. Model Bias: The negatives were mined using the emrecan/bert-base-turkish model. The dataset naturally reflects the biases and semantic understanding of this base model.

Citation

If you use this dataset, please cite Lumees AI, the original MS MARCO authors, and the Turkish translation source as follows:

@misc{lumees_msmarco_hn_2025,
  author = {Lumees AI and Kurşun, Hasan and Yanık, Kerem Berkay},
  title = {MS MARCO TR - Hard Negatives Dataset},
  year = {2025},
  howpublished = {\url{[https://lumees.io](https://lumees.io)}},
}

@article{bajaj2016ms,
  title={MS MARCO: A Human Generated Machine Reading Comprehension Dataset},
  author={Bajaj, Payal and Campos, Daniel and Craswell, Nick and Deng, Li and Gao, Jianfeng and Liu, Xiaodong and Majumder, Rangan and McNamara, Andrew and Mitra, Bhaskar and Nguyen, Tri and others},
  journal={arXiv preprint arXiv:1611.09268},
  year={2016}
}

@misc{parsak_msmarco_tr,
  author = {Parsak},
  title = {MS MARCO Turkish Translation},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{[https://huggingface.co/datasets/parsak/msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr)}}
}