---
license: cc-by-sa-4.0
language:
- en
tags:
- information-extraction
- relation-extraction
pretty_name: REBEL Processed Relation Dataset
dataset_info:
- config_name: full
  features:
  - name: title
    dtype: string
  - name: text
    dtype: string
  - name: entity1
    dtype: string
  - name: entity2
    dtype: string
  - name: relation
    dtype: string
  splits:
  - name: train
    num_bytes: 891747117
    num_examples: 3976707
  - name: validation
    num_bytes: 49122425
    num_examples: 219340
  - name: test
    num_bytes: 49440537
    num_examples: 220895
  download_size: 398758689
  dataset_size: 990310079
- config_name: subsampled
  features:
  - name: title
    dtype: string
  - name: text
    dtype: string
  - name: entity1
    dtype: string
  - name: entity2
    dtype: string
  - name: relation
    dtype: string
  - name: prompt_0_shot
    dtype: string
  - name: prompt_2_shot
    dtype: string
  - name: prompt_5_shot
    dtype: string
  splits:
  - name: train
    num_bytes: 458353403
    num_examples: 135049
  - name: validation
    num_bytes: 24851994
    num_examples: 7448
  - name: test
    num_bytes: 25459380
    num_examples: 7503
  download_size: 239589978
  dataset_size: 508664777
configs:
- config_name: full
  data_files:
  - split: train
    path: full/train-*
  - split: validation
    path: full/validation-*
  - split: test
    path: full/test-*
- config_name: subsampled
  data_files:
  - split: train
    path: subsampled/train-*
  - split: validation
    path: subsampled/validation-*
  - split: test
    path: subsampled/test-*
task_categories:
- text-classification
- text-generation
size_categories:
- 1M<n<10M
---

# REBEL Processed Relation Dataset

## Summary

This dataset is a processed version of the [Babelscape/rebel-dataset](https://huggingface.co/datasets/Babelscape/rebel-dataset). The original dataset contains Wikipedia abstracts (`context`), their titles (`title`), and a string (`triplets`) representing multiple knowledge graph relations found in the text.

This version transforms the original dataset by expanding the `triplets` string so that **each row corresponds to a single relation triplet**. This format is more convenient for tasks focusing on individual relation facts, such as relation extraction training or knowledge graph population.

This copy is packaged for the paper **"Sub-Billion, Super-Frontier: Fine-Tuned Small Language
Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction"**
(Christou & Tsoumakas, 2026) [arXiv:2606.22606](https://arxiv.org/abs/2606.22606). The
`subsampled` config additionally carries ready-to-use instruction prompts
(`prompt_0_shot`, `prompt_2_shot`, `prompt_5_shot`) for prompt-conditioned fine-tuning and
evaluation of (small) language models.

## Configs

* **`full`** — the complete expanded triplet dataset (~4.4M rows), fields: `title`, `text`, `entity1`, `entity2`, `relation`.
* **`subsampled`** — a smaller subset (~150K rows) used in the paper, with the same fields **plus** the `prompt_0_shot` / `prompt_2_shot` / `prompt_5_shot` instruction-prompt columns.

```python
from datasets import load_dataset

full = load_dataset("Despina/rebel-dataset", "full")
subsampled = load_dataset("Despina/rebel-dataset", "subsampled")
```

## Data Source and Processing

* **Source Dataset:** [Babelscape/rebel-dataset](https://huggingface.co/datasets/Babelscape/rebel-dataset)
* **Processing Steps:**
    1.  The original dataset was loaded using the Hugging Face `datasets` library.
    2.  The `triplets` feature string in each original row was parsed. The parsing assumed the format `<triplet> head <subj> relation <obj> tail [...]` based on the example provided in the original REBEL work (note: the exact delimiter tokens might vary slightly, but the `head <subj> relation <obj> tail` structure was targeted).
    3.  For each successfully parsed triplet (`head`, `relation`, `tail`) within a `triplets` string, a new row was created in the processed dataset.
    4.  The original `context` feature was retained and renamed to `text`.
    5.  The original `title` feature was retained.
    6.  The extracted `head` was stored as `entity1`.
    7.  The extracted `tail` was stored as `entity2`.
    8.  The extracted `relation` name was stored as `relation`.

## Dataset Structure

The dataset contains the standard `train`, `validation`, and `test` splits.

### Data Fields

Each row in the dataset has the following fields:

* `text` (string): The original Wikipedia abstract context.
* `title` (string): The title of the Wikipedia abstract.
* `entity1` (string): The head entity of the relation triplet.
* `entity2` (string): The tail entity of the relation triplet.
* `relation` (string): The name/label of the relation connecting `entity1` and `entity2`.

The `subsampled` config additionally includes:

* `prompt_0_shot` (string): Zero-shot instruction prompt (task instructions + the input text).
* `prompt_2_shot` (string): The same prompt with 2 in-context demonstrations prepended.
* `prompt_5_shot` (string): The same prompt with 5 in-context demonstrations prepended.

The three `prompt_*` columns are alternative renderings of the **same** example at different shot
counts, so pick one shot setting per experiment rather than concatenating them.

### Example Row

```json
{
  "text": "Gustavus Adolphus College is a private liberal arts college in St. Peter, Minnesota. It was founded in 1862 by Swedish Americans led by Eric Norelius and is affiliated with the Evangelical Lutheran Church in America. The college retains affiliations with the Lutheran Church and the Church of Sweden.",
  "title": "Gustavus Adolphus College",
  "entity1": "Gustavus Adolphus College",
  "entity2": "Evangelical Lutheran Church in America",
  "relation": "religion"
}
```
## Usage
You can load the dataset using the `datasets` library:
```python
from datasets import load_dataset

ds = load_dataset("Despina/rebel-dataset", "subsampled")

# Access splits
train_data = ds['train']
validation_data = ds['validation']
test_data = ds['test']

# Example: Print the first example from the training set
print(train_data[0])
```

## Licensing Information

This dataset is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), consistent with the licensing of the original REBEL dataset.

## Citation

**If you use this dataset, please cite our paper along with the original REBEL paper:**

``` markdown
@article{christou2026subbillion,
    title = "Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction",
    author = "Christou, Despina and Tsoumakas, Grigorios",
    journal = "arXiv preprint arXiv:2606.22606",
    year = "2026",
    url = "https://arxiv.org/abs/2606.22606",
}

@inproceedings{huguet-cabot-navigli-2021-rebel,
    title = "{REBEL}: Relation Extraction By End-to-end Language generation",
    author = "Huguet Cabot, Pere Llu{\'\i}s  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "[https://aclanthology.org/2021.findings-emnlp.204](https://aclanthology.org/2021.findings-emnlp.204)",
    doi = "10.18653/v1/2021.findings-emnlp.204",
    pages = "2370--2381",
}
```