---
license: cc-by-nc-4.0
language:
- ro
dataset_info:
  features:
  - name: id
    dtype: string
  - name: images
    sequence: image
  - name: instruction
    dtype: string
  - name: caption
    dtype: string
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  splits:
  - name: train
    num_bytes: 12604079941.908
    num_examples: 558127
  download_size: 12422974428
  dataset_size: 12604079941.908
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
[Laion](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) is a subset of LAION/CC/SBU dataset, filtered with a more balanced concept coverage distribution. Captions are also associated with BLIP synthetic caption for reference. It is constructed for the pretraining stage for feature alignment in visual instruction tuning.


Here we provide the Romanian translation of the Laion dataset, translated with [Seed-X-PPO](ByteDance-Seed/Seed-X-PPO-7B).
This dataset is part of the instruction finetune protocol for Romanian VLMs proposed in *"Înțelegi românește?" A Recipe for Romanian Vision-Language Models* ([Masala et al., 2026](https://arxiv.org/abs/2605.31401)).


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

```bibtex
@article{liu2023visual,
  title={Visual instruction tuning},
  author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
  journal={Advances in neural information processing systems},
  volume={36},
  pages={34892--34916},
  year={2023}
}
```

```bibtext
@misc{masala2026intelegi,
      title={``\^{I}n\c{t}elegi Rom\^{a}ne\c{s}te?'' A Recipe for Romanian Vision-Language Models},
      author={Mihai Masala and Marius Leordeanu and Mihai Dascalu and Traian Rebedea},
      year={2026},
      eprint={2605.31401},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.31401},
}
```