U4RASD/Masrad

---
dataset_info:
features:
- name: semantic
dtype: float64
- name: semantic_rank
dtype: float64
- name: semantic_diff
dtype: float64
- name: lexical1
dtype: float64
- name: lexical1_rank
dtype: float64
- name: lexical1_diff
dtype: float64
- name: lexical2
dtype: float64
- name: lexical2_rank
dtype: float64
- name: lexical2_diff
dtype: float64
- name: entity
dtype: string
- name: src_entity
dtype: string
- name: phonetic
dtype: bool
- name: pos
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_examples: 19405
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
license: mit
task_categories:
- text-classification
language:
- ar
size_categories:
- 10K<n<100K
---

# Masrad Dataset

## Dataset Description

The **Masrad** dataset contains 19,405 examples with features related to semantic similarity, lexical similarity, entity recognition, phonetic matching, and part-of-speech tagging for Arabic text.

## Features

| Feature | Type | Description |
|---------|------|-------------|
| `semantic` | float | Semantic similarity score |
| `semantic_rank` | float | Rank based on semantic similarity |
| `semantic_diff` | float | Difference from top semantic score |
| `lexical1` | float | First lexical similarity score |
| `lexical1_rank` | float | Rank based on first lexical score |
| `lexical1_diff` | float | Difference from top first lexical score |
| `lexical2` | float | Second lexical similarity score |
| `lexical2_rank` | float | Rank based on second lexical score |
| `lexical2_diff` | float | Difference from top second lexical score |
| `entity` | string | Detected entity type (PER, ORG, LOC, none) |
| `src_entity` | string | Source entity type |
| `phonetic` | bool | Whether phonetic match exists |
| `pos` | string | Part of speech tag |
| `label` | bool | Target label |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("U4RASD/Masrad")
```