README.md

---
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](https://huggingface.co/datasets/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](https://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](https://huggingface.co/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.

```json
{
  "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.

```python
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`.

```python
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:

```bibtex
@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)}}
}
```