---
dataset_info:
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: positive_passages
    list:
    - name: docid
      dtype: string
    - name: text
      dtype: string
    - name: title
      dtype: string
  - name: negative_passages
    list:
    - name: docid
      dtype: string
    - name: text
      dtype: string
    - name: title
      dtype: string
  - name: subset
    dtype: string
  splits:
  - name: train
    num_bytes: 1747136221
    num_examples: 93581
  download_size: 1009882538
  dataset_size: 1747136221
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
pretty_name: RLHN-100K
size_categories:
- 10K<n<100K
---

# Dataset Card for RLHN-100K

## Dataset Description
[Repository](https://github.com/castorini/rlhn) |
[Paper](https://huggingface.co/papers/2505.16967) |
[ArXiv](https://arxiv.org/abs/2505.16967)

RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.

This Tevatron dataset (100K training pairs) contains the queries, positives + relabeled hard negatives, remaining hard negatives for 7 datasets in the BGE training collection. 

This repository contains the training pairs that can be used to fine-tune embedding, ColBERT or multi-vector, and reranker models. 

The original dataset (bad quality; containing false negatives) can be found at [rlhn/default-100K](https://huggingface.co/datasets/rlhn/default-100K/).

> Note: RLHN datasets are not **new** training datasets, but rather existing BGE collection training datasets with hard negatives cleaned!

## Dataset Structure

To access the data using HuggingFace `datasets`:
```python
rlhn = datasets.load_dataset('rlhn/rlhn-100K')

# training set:
for data in freshstack['train']:
    query_id = data["query_id"]                            # md5 hash of the query_id
    query = data["query"]                                  # query text
    subset = data["subset"]                                # training dataset, e.g., fiqa or msmarco_passage

    # positive passages
    for positive_passage in data["positive_passages"]:
        doc_id = positive_passage["docid"]
        title = positive_passage["title"]                  # title is usually empty, added in text
        text = positive_passage["text"]                    # contains both the title & text

    # hard negative passages
    for negative_passage in data["negative_passages"]:
        doc_id = negative_passage["docid"]
        title = negative_passage["title"]                  # title is usually empty, added in text
        text = negative_passage["text"]                    # contains both the title & text
```


## Original Dataset Statistics 
The following table contains the number of training pairs for each training dataset included in RLHN. These numbers are for the default setting.  

| Dataset           | 100K splits | 250K splits | 400K splits | 680K splits  |
|-------------------|-------------|-------------|-------------|------------- |
| arguana           | 4,065       | 4,065       | 4,065       | 4,065        |
| fever             | 28,755      | 28,755      | 28,755      | 28,755       |
| fiqa              | 5,500       | 5,500       | 5,500       | 5,500        |
| hotpotqa          | 10,250      | 30,000      | 84,516      | 84,516       |
| msmarco_passage   | 49,571      | 145,000     | 210,000     | 485,823      |
| nq                | 6,110       | 30,000      | 58,568      | 58,568       |
| scidocsrr         | 12,654      | 12,654      | 12,654      | 12,654       |
| **total**         | **96,167**  | **255,974** | **404,058** | **679,881**  |


## License
The RLHN dataset is made available with the CC-BY-SA 4.0 license.

## Hashing & IDs

We generate the md5 hash as the unique identifier (ID) for both the query \& documents, using the code below:

```python
import hashlib

def get_md5_hash(text):
  """Calculates the MD5 hash of a given string.
  Args:
    text: The string to hash.
  Returns:
    The MD5 hash of the string as a hexadecimal string.
  """
  text_bytes = text.encode('utf-8')  # Encode the string to bytes
  md5_hash = hashlib.md5(text_bytes).hexdigest()
  return md5_hash
```

## Citation
```
@misc{thakur2025relabel,
      title={Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval}, 
      author={Nandan Thakur and Crystina Zhang and Xueguang Ma and Jimmy Lin},
      year={2025},
      eprint={2505.16967},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2505.16967}, 
}
```