metadata
license: mit
language:
- en
task_categories:
- question-answering
- image-to-text
- text-to-image
tags:
- dataset
- commonsense-reasoning
- VCR
size_categories:
- n<1K
Dataset Name: Atomic-EgMM
Contributors
- Mohamed Gamil
- Abdelrahman Elsayed
- Abdelrahman Lila
- Ahmed Anwar Gad
- Hesham Abdelgawad
- Mohamed Aref
Overview
Atomic-EgMM is a commonsense event dataset specific to Egyptian culture, covering everyday life, food, celebrations, religious occasions, and cultural practices. Each event captures actions, effects, intentions, needs, and reactions for both the actor (PersonX) and others (O).
It is suitable for tasks like:
- Commonsense reasoning
- Event understanding
- Natural language processing (NLP)
Dataset Splits
The dataset is divided into three splits:
| Split | Approx. Rows | Description |
|---|---|---|
train |
~70% | For training models |
validation |
~20% | For tuning and validation |
test |
~10% | For final evaluation |
Each split is available in CSV format.
Columns
| Column | Description |
|---|---|
event |
Description of the event. |
oEffect |
Effects on others (O) as a comma-separated list. |
oReact |
Reactions of others (O) as a comma-separated list. |
oWant |
What others (O) want to do as a result, comma-separated. |
xAttr |
Attributes of the actor (PersonX), comma-separated. |
xEffect |
Effects on the actor (PersonX), comma-separated. |
xIntent |
Intentions of the actor (PersonX), comma-separated. |
xNeed |
Needs of the actor (PersonX) to perform the action, comma-separated. |
xReact |
Reactions of the actor (PersonX), comma-separated. |
xWant |
Desires of the actor (PersonX) as a result, comma-separated. |
Usage Example
Using Hugging Face datasets:
from datasets import load_dataset
dataset = load_dataset(
"csv",
data_files={
"train": "train.csv",
"validation": "validation.csv",
"test": "test.csv"
}
)
print(dataset)
Notes
- List-like columns are stored as comma-separated strings; you can split them into Python lists for modeling.
- All events are specific to Egyptian contexts, covering food, holidays, religious events, and celebrations.