---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': sarcastic
  - name: explanation
    dtype: string
  - name: is_ocr
    dtype: bool
  splits:
  - name: train
    num_bytes: 333867120
    num_examples: 2983
  - name: validation
    num_bytes: 19589769
    num_examples: 175
  - name: test
    num_bytes: 38841746
    num_examples: 352
  download_size: 390979603
  dataset_size: 392298635
language:
- en
tags:
- Sarcasm
- Multimodal
- Multimodal-sarcasm
- sarcasm-explanation
---

# MuSe: Multimodal Sarcasm Explanation (Reformatted)

This repository provides a Hugging Face-compatible version of the **MuSe (MORE)** dataset. 

## Dataset Description
- **Original Authors:** LCS2-IIITD (Shweta Desai, et al.)
- **Original Source:** [GitHub Repository](https://github.com/LCS2-IIITD/Multimodal-Sarcasm-Explanation-MuSE)
- **Paper:** [Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation (AAAI 2022)](https://ojs.aaai.org/index.php/AAAI/article/view/21311)

## Modifications in this version
To make the dataset easier to use with the `datasets` library, the following changes were made:
1. **Unified Schema:** Merged separate OCR and Non-OCR files into a single `test` split.
2. **Metadata Flags:** Added an `is_ocr` (boolean) column to distinguish between image types.
3. **Image Integration:** Converted image paths into a native Hugging Face `Image` feature for direct loading.
4. **Labeling:** Explicitly labeled all samples as "sarcastic" to match standard classification formats.

## Citation
If you use this dataset in your research, please cite the original work:


@inproceedings{desai2022nice,
  title={Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation},
  author={Desai, Shweta and Pawar, Tanmay and Shah, Parth and Mittal, Akshat and Das, Amitava and Shah, Rajiv Ratn and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}


## License
This dataset is distributed under the **MIT License**, as per the original source repository.