README.md

metadata

dataset_info:
  features:
    - name: relation
      dtype: string
    - name: prompt
      dtype: string
    - name: dataset
      dtype: string
  splits:
    - name: train_0
      num_bytes: 136028669
      num_examples: 397625
    - name: eval_0
      num_bytes: 18697180
      num_examples: 54099
    - name: test_0
      num_bytes: 18875338
      num_examples: 53123
    - name: train_2
      num_bytes: 432394197
      num_examples: 397625
    - name: eval_2
      num_bytes: 58205004
      num_examples: 54099
    - name: test_2
      num_bytes: 59493575
      num_examples: 53123
    - name: train_5
      num_bytes: 859491868
      num_examples: 397625
    - name: eval_5
      num_bytes: 115074295
      num_examples: 54099
    - name: test_5
      num_bytes: 118159632
      num_examples: 53123
  download_size: 856690372
  dataset_size: 1816419758
configs:
  - config_name: default
    data_files:
      - split: train_0
        path: data/train_0-*
      - split: eval_0
        path: data/eval_0-*
      - split: test_0
        path: data/test_0-*
      - split: train_2
        path: data/train_2-*
      - split: eval_2
        path: data/eval_2-*
      - split: test_2
        path: data/test_2-*
      - split: train_5
        path: data/train_5-*
      - split: eval_5
        path: data/eval_5-*
      - split: test_5
        path: data/test_5-*
task_categories:
  - text-classification
  - text-generation
tags:
  - relation-extraction
  - instruction-tuning
  - general-domain
  - dataset-mixture
pretty_name: GenTune (General-Domain RE Instruction Mixture)

GenTune (General-Domain RE Instruction Mixture)

GenTune is an aggregated instruction-tuning mixture for general-domain relation extraction (RE). It concatenates seven general-domain RE datasets into a single prompt/label corpus used to fine-tune small language models in 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.

It is one of the three training regimes in the paper:

• GenTune — general-domain only (this dataset)
• LitTune — literary only (Despina/re_littune)
• MixTune — a domain-balanced mix of the two (Despina/re_mixtune)

Component datasets

GenTune is the concatenation (shuffled) of the training/validation/test portions of:

Source	Repo
TACRED	Despina/tacred
SemEval-2010 Task 8	Despina/semeval2010_task8
CoNLL04	Despina/conll04
NYT11	Despina/nyt_11
GIDS	Despina/gids
Re-DocRED	Despina/re-docred
REBEL (subsampled)	Despina/rebel-dataset

Dataset structure

Each source example is reshaped into a unified instruction-tuning row. The mixture is materialized once per shot setting (0, 2, 5), and each shot setting has its own train / eval / test split:

Split	Shot
train_0, eval_0, test_0	0-shot prompts
train_2, eval_2, test_2	2-shot prompts
train_5, eval_5, test_5	5-shot prompts

Columns

Column	Description
prompt	The instruction prompt for the example at this shot count. For the 0-shot splits, an "\nAnswer: " cue is appended to the prompt.
relation	The gold relation label (the target completion).
dataset	The source dataset the row came from (e.g. conll04, tacred), so the mixture can be stratified by source.

Pick one shot setting per experiment (e.g. train on train_2 / evaluate on test_2) rather than mixing splits across shot counts.

Usage

from datasets import load_dataset

# a full DatasetDict with all shot splits
gentune = load_dataset("Despina/re_gentune")
print(gentune)  # train_0, eval_0, test_0, train_2, ... test_5

# or a single split
train2 = load_dataset("Despina/re_gentune", split="train_2")
print(train2[0]["prompt"], "->", train2[0]["relation"])

Licensing and redistribution

GenTune includes TACRED and NYT11, which are derived from Linguistic Data Consortium (LDC) corpora (LDC newswire / the NYT Annotated Corpus, LDC2008T19). Those components are not freely redistributable: using this mixture requires the appropriate LDC licenses, and this aggregate should not be redistributed publicly as-is. If you need a shareable general-domain mixture, rebuild GenTune with src/build_mixtures.py while excluding the LDC-restricted sources.

Each component also retains its own upstream license (CC BY-SA for DocRED/REBEL, etc.); see the individual dataset cards under the Despina/ namespace.

Citation

If you use this dataset, please cite our paper (and the underlying source datasets you rely on):

@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}
}