README.md

---
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- relation-extraction
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - 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: 18464886
    num_examples: 7200
  - name: validation
    num_bytes: 2032939
    num_examples: 800
  - name: test
    num_bytes: 6968890
    num_examples: 2717
  download_size: 11578042
  dataset_size: 27466715
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---
---
# Dataset Card for Transformed SemEval 2010 Task 8

## Dataset Description

### Dataset Summary

This dataset is released as part of 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 dataset is a transformed version of the SemEval 2010 Task 8 dataset (`SemEvalWorkshop/sem_eval_2010_task_8`). The original dataset is a standard benchmark for relation extraction and classification between nominal pairs.

This version preprocesses the original data into a more readily usable format with distinct features for entities and cleaned sentence text. Specifically:
* Entity text spans (<e1>, <e2>) are extracted into separate fields.
* Special entity tags are removed from the main sentence text.
* Relation labels are provided as their full text names (e.g., "Cause-Effect(e1,e2)") instead of numerical IDs.
* Placeholder fields for entity types are included (set to "none").

The dataset contains training and testing splits as provided in the original SemEval task.

### Supported Tasks and Leaderboards

* **Relation Extraction:** The primary task is to classify the semantic relationship between the two marked entities (`entity1`, `entity2`) within the given `text`.
* **Few-Shot Learning:** The dataset structure is suitable for N-shot evaluation protocols.
* **Zero-Shot Learning:** The dataset can be used for zero-shot evaluation, typically by leveraging the relation name strings as semantic information (requires further setup to define seen/unseen classes and map inputs to relation name embeddings).


### Languages

The text in the dataset is in English (BCP-47: `en`).

## Dataset Structure

### Data Instances

A typical example from the dataset looks like this:

```python
{
  'entity1': 'television',
  'entity2': 'programmes',
  'entity1Type': 'none',
  'entity2Type': 'none',
  'relation': 'Product-Producer(e2,e1)',
  'text': 'Most programmes have commercial breaks, but the quality of the television programmes depends on the channel.'
}
```

### Data Fields

The dataset contains the following fields:

* `entity1`: (string) The text content of the first marked entity (head entity, originally marked with `<e1>`).
* `entity2`: (string) The text content of the second marked entity (tail entity, originally marked with `<e2>`).
* `entity1Type`: (string) Placeholder for the type of the first entity. Always set to `"none"` in this version as the original dataset does not provide explicit types.
* `entity2Type`: (string) Placeholder for the type of the second entity. Always set to `"none"` in this version.
* `relation`: (string) The text label representing the semantic relation between `entity1` and `entity2`. Examples include `"Cause-Effect(e1,e2)"`, `"Entity-Destination(e1,e2)"`, `"Product-Producer(e2,e1)"`, `"Other"`. Note the directionality indicated in the label.
* `text`: (string) The full sentence text with the special entity marker tags (`<e1>`, `</e1>`, `<e2>`, `</e2>`) removed.

## How to use
You can load the dataset using the Hugging Face datasets library:
```
from datasets import load_dataset

# Replace with the actual path on the Hugging Face Hub
dataset_name = "Despina/semeval2010_task8"
dataset = load_dataset(dataset_name)

# Access splits and features
print(dataset['train'][0])
# Expected Output (example):
# {
#  'entity1': 'television',
#  'entity2': 'programmes',
#  'entity1Type': 'none',
#  'entity2Type': 'none',
#  'relation': 'Product-Producer(e2,e1)',
#  'text': 'Most programmes have commercial breaks, but the quality of the television programmes depends on the channel.'
# }
```

## Additional Information

### Dataset Curators
This transformed version was generated based on user request using the Hugging Face `datasets` library. The original dataset was curated by the SemEval-2010 Task 8 organizers.

### Licensing Information
The licensing terms for this transformed dataset are inherited from the original `SemEvalWorkshop/sem_eval_2010_task_8` dataset. Please refer to the original dataset card or SemEval guidelines for specific licensing information (e.g., CC BY-SA or similar, but confirmation is recommended).

### Citation Information
If you use this dataset in your work, please cite the original SemEval 2010 Task 8 paper, as also our paper:

```bibtex
@inproceedings{hendrickx-etal-2010-semeval,
    title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals",
    author = "Hendrickx, Iris  and
      Kim, Su Nam  and
      Kozareva, Zornitsa  and
      Nakov, Preslav  and
      {'O} S{'e}aghdha, Diarmuid  and
      Pad{'o}, Sebastian  and
      Pennacchiotti, Marco  and
      Romano, Lorenza  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation",
    month = jul,
    year = "2010",
    address = "Uppsala, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S10-1006",
    pages = "33--38",
}

@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}
}
```



## Contributions
Thanks to the original SemEval 2010 Task 8 organizers and contributors, and the Hugging Face team for hosting the original dataset.