---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: query
    dtype: string
  - name: docs
    sequence: string
  - name: scores
    sequence: float64
  splits:
  - name: train
    num_bytes: 957899062
    num_examples: 502939
  download_size: 917834227
  dataset_size: 957899062
---

# Dataset Card for **MS MARCO Hard Negatives LLM Scores (OpenSearch)**

## Dataset Summary

This dataset is derived from the **MS MARCO** train split([Hugging Face](https://huggingface.co/datasets/mteb/msmarco)) and provides **hard-negative mining** annotations to train retrieval systems. For each query from the source split, we retrieve the **top-100 candidate documents** using the [opensearch-project/opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) and attach **re-ranking scores** from bi-encoder teachers and cross-encoder teachers: [opensearch-project/opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1), [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5), [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl), [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2), [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise), and [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight).

> ⚠️ **Licensing/Usage:** Because this dataset is derived from MS MARCO, please review Microsoft’s terms before using this dataset. ([Microsoft GitHub](https://microsoft.github.io/msmarco/Datasets.html), [GitHub](https://github.com/microsoft/msmarco))

---

## How to Load

```python
import datasets
ds = datasets.load_dataset("opensearch-project/msmarco-hard-negatives-llm-scores", split="train")
```

---

## Training example

Related training example: **opensearch-sparse-model-tuning-sample**. ([GitHub](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample))

To convert the dataset to text-only format for sample repo training:
```python
import datasets

# 1) Load datasets
msmarco_hard_negatives = datasets.load_dataset(
    "opensearch-project/msmarco-hard-negatives-llm-scores", split="train"
)
msmarco_queries = datasets.load_dataset("BeIR/msmarco", "queries")["queries"]
msmarco_corpus = datasets.load_dataset("BeIR/msmarco", "corpus")["corpus"]

# 2) fix occasional text encoding issues
def transform_str(s):
    try:
        s = s.encode("latin1").decode("utf-8")
        return s
    except Exception:
        return s

msmarco_corpus = msmarco_corpus.map(
    lambda x: {"text": transform_str(x["text"])}, num_proc=30
)

# 3) Build convenient lookup tables
id_to_text = {_id: text for _id, text in zip(msmarco_corpus["_id"], msmarco_corpus["text"])}
qid_to_text = {_id: text for _id, text in zip(msmarco_queries["_id"], msmarco_queries["text"])}

# 4) Replace IDs with raw texts to get a text-only dataset
msmarco_hard_negatives = msmarco_hard_negatives.map(
    lambda x: {
        "query": qid_to_text[x["query"]],
        "docs": [id_to_text[doc] for doc in x["docs"]],
    },
    num_proc=30,
)

# 5) Save to disk (directory will contain the text-only view)
msmarco_hard_negatives.save_to_disk("data/msmarco_ft_llm_scores")
```

---

## Citation

If you use this dataset, **please cite**:
[Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers](https://arxiv.org/abs/2411.04403)
```
@misc{geng2024competitivesearchrelevanceinferencefree,
      title={Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers}, 
      author={Zhichao Geng and Dongyu Ru and Yang Yang},
      year={2024},
      eprint={2411.04403},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2411.04403}, 
}
```

## Related Papers
- [Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers ](https://arxiv.org/abs/2504.14839)

## License

This project is licensed under the [Apache v2.0 License](https://github.com/opensearch-project/neural-search/blob/main/LICENSE).

---


## Copyright

Copyright OpenSearch Contributors. See [NOTICE](https://github.com/opensearch-project/neural-search/blob/main/NOTICE) for details.