---
license: cc-by-4.0
task_categories:
- text2text-generation
- translation
- text-generation
language:
- ar
- en
tags:
- text-image pairs
- proverbs
- culture
- heritage
- generative
- prompt
size_categories:
- n < 1K
dataset_info:
  features:
  - name: tunisan_proverb
    dtype: string
  - name: proverb_arabic_explaination
    dtype: string
  - name: context
    dtype: string
  - name: caption
    dtype: string
  - name: caption_formal
    dtype: string
  - name: dynamic
    dtype: string
  - name: prompt
    dtype: string
  - name: image_path_1
    dtype: image
  - name: image_path_2
    dtype: image
  - name: image_path_3
    dtype: image
  - name: image_path_4
    dtype: image
  - name: clip_scores
    dtype: float32
configs:
- config_name: default
  data_files:
  - dataset.csv
  description: >
    This configuration contains Tunisian proverbs with corresponding textual
    explanations and up to four AI-generated image associations per entry,
    covering cultural and linguistic insight.
 
---
<iframe src="https://huggingface.co/datasets/HabibaAbderrahim/Tunisian-Proverbs-with-Image-Associations-A-Cultural-and-Linguistic-Dataset/embed/sql-console/PrKUOGi" frameborder="0" width="100%" height="560px"></iframe>

<h1>Tunisian Proverbs with Image Associations: A Cultural and Linguistic Dataset </h1>

## Description
This dataset explores the rich oral tradition of Tunisian proverbs mapped into text format, pairing each with contextual explanations, English translations both word-to-word and it's equivalent Target Language dynamic, Automated prompt and AI-generated visual interpretations.
It bridges linguistic, cultural, and visual modalities making it valuable for tasks in cross-cultural NLP, generative art, and multi-modal learning for low-resourced Language such as the Arabic Tunisian Dialect.

## Some Selections

<table>
  <tr>
    <td align="center">
      <img src="images/text_image_dataset_000/proverb_418_image_1.png" width="500"/><br/>
      <b>ظل راجل ولا ظل حيط</b><br/>
    </td>
    <td align="center">
      <img src="images/text_image_dataset_0000000/proverb_1230_image_0.png" width="500"/><br/>
      <b>الملح و الصحبة</b><br/>
    </td>
    <td align="center">
      <img src="images/text_image_dataset_00000/proverb_605_image_0.png" width="500"/><br/>
      <b>كل قرده في عين امه غزال</b><br/>
    </td>
    <td align="center">
      <img src="images/text_image_dataset_0/proverb_55_image_0.png" width="500"/><br/>
      <b>قلبه أبيض كالحليب</b><br/>
    </td>
     <td align="center">
      <img src="images/text_image_dataset_0/proverb_202_image_1.png" width="500"/><br/>
      <b>اضرب القطوسة تتأدب العروسة</b><br/>
    </td>
    <td align="center">
      <img src="images/text_image_dataset_0/proverb_209_image_0.png" width="500"/><br/>
      <b>اللي وجهها يفجعها مال بوها ينفعها</b><br/>
    </td>
  </tr>
</table>

## Objectives
<ul>
<li>Preserve and promote intangible cultural heritage from underrepresented languages.</li>

<li>Create an open-access, FAIR-compliant resource to support Generative AI, NLP, and multimodal ML in low-resource languages like Tunisian Arabic.</li>

<li>Provide a dataset suitable for translation, text and image generation, proverb understanding, visual grounding, and educational tools.</li>
</ul>

<h2>Dataset Structure</h2>
<ul>
<li>A <strong>Tunisian proverb</strong> in dialectal Arabic.</li>
<li>An <strong>explanation</strong> of its meaning.</li>
<li><strong>Contextual tags</strong>.</li>
<li><strong>English translations</strong> in different styles:
<ul>
<li>Informal translation</li>
<li>Formal interpretation</li>
</ul>
</li>
<li>A <strong>text-to-image automated prompt</strong> used to generate 4 unique images.</li>
<li>Four associated <strong>image paths</strong>.</li>
<li>A <strong>CLIP score</strong> indicating relevance of the images to the proverb.</li>
</ul>

## Language & Cultural Focus:
<ul>
<li>Dialect: Tunisian Arabic (Derja or Tounsi)</li>

<li>Languag variety: Tunisian Arabic and English</li>
</ul>

## How to Use

To load the dataset in Google Colab, you can use the datasets library from Hugging Face:

```python
from datasets import load_dataset
import cv2
from google.colab.patches import cv2_imshow

# Load the dataset
dataset = load_dataset("Heubub/Tunisian-Proverbs-with-Image-Associations-A-Cultural-and-Linguistic-Dataset")

# Get the first sample from the 'train' split
sample = dataset["train"][0]

# Extract proverb and prompt and images e.g the first image
proverb = sample["tunisan_proverb"]
prompt = sample["prompt"]
image_path_1 = sample["image_path_1"]

print(f"Proverb: {proverb}")
print(f"Prompt: {prompt}")

img_bgr = np.array(image_path_1)[:, :, ::-1]
cv2_imshow(img_bgr)

##Citation

If you use this dataset, please cite it as follows:

@misc{abderrahim_habiba_2025,
	author       = { Abderrahim Habiba and Hedi Ayadi and Fadoua Ouamani },
	title        = { Tunisian-Proverbs-with-Image-Associations-A-Cultural-and-Linguistic-Dataset (Revision c524d31) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/HabibaAbderrahim/Tunisian-Proverbs-with-Image-Associations-A-Cultural-and-Linguistic-Dataset },
	doi          = { 10.57967/hf/5189 },
	publisher    = { Hugging Face }
}