AH-Translit-Bench 2.1.1
Arabic to Hindi transliteration benchmark dataset (2k per domain)

# **AH-Translit_Benchmark**

**Arabic → Hindi Transliteration Benchmark Dataset**

[![PyPI version](https://badge.fury.io/py/AH-Translit_Bench.svg)](https://pypi.org/project/AH-Translit-Bench/)
[![GitHub license](https://img.shields.io/github/license/aclresearch/AH-Translit-Benchmark)](https://github.com/aclresearch/AH-Translit-Benchmark/blob/main/LICENSE)


---

## Description

**AH-Translit_Bench** is a **6,000-sample Arabic-to-Hindi transliteration benchmark dataset**, curated for **systematic evaluation and comparison** of transliteration models across **three linguistically distinct domains**:

* **Quranic Arabic**
* **Modern Standard Arabic (Daily Use)**
* **Modern Standard Arabic (Bibliographic)**

Each domain contributes **2,000 carefully selected sentence pairs**, ensuring **balanced, fair, and domain-aware evaluation** of cross-script transliteration systems.

This benchmark is designed **strictly for testing and reporting results**, and is complementary to the full AH-Translit training dataset.

---

## Dataset Usage

Designed for **benchmarking and evaluating Arabic-to-Hindi phonetic transliteration models** under domain shifts and varying sequence-length conditions.

---

## Content Type

Text — sentence-level **Arabic source text** paired with **Hindi (Devanagari) phonetic transliteration**.

---

## File Type

CSV (Comma-Separated Values)

---

## Dataset Structure

```
AH-Translit-Benchmark-Dataset
├── quranic_benchmark_2000.csv
├── msa_dailyuse_benchmark_2000.csv
├── msa_bibliographic_benchmark_2000.csv
├── all_domain_mix_benchmark_6000.csv
└── README.md
```

---

## File Descriptions

* **quranic_benchmark_2000.csv**
  2,000 sentence pairs from **Quranic Arabic**, characterized by long sequences and rich morphology.

* **msa_dailyuse_benchmark_2000.csv**
  2,000 sentence pairs from **daily-use Modern Standard Arabic**, representing short and conversational inputs.

* **msa_bibliographic_benchmark_2000.csv**
  2,000 sentence pairs from **formal and bibliographic MSA**, featuring higher lexical diversity.

* **all_domain_mix_benchmark_6000.csv**
  Combined benchmark file containing all **6,000 samples**, evenly distributed across domains.

Each CSV contains exactly **two columns**:

```
Arabic , Hindi
```

Hindi text is **phonetic transliteration**, not translation.

---

## Benchmark Scale Summary

| Domain            |   Samples |
| ----------------- | --------: |
| Quranic           |     2,000 |
| MSA Daily Use     |     2,000 |
| MSA Bibliographic |     2,000 |
| **Total**         | **6,000** |

This strict balance ensures **macro-averaged, unbiased evaluation** across domains.

---

## How to Use This Dataset

Here is a **clean, minimal, copy-paste–ready Python sample** that you can put directly in your **README.md** or use for quick sanity testing after installing the package.

This matches your **current AH-Translit-Bench v2.0.0 API**.

---

## Python Usage Example

### 1️⃣ Install the Package

```bash
pip install AH-Translit-Bench
```

---

### 2️⃣ Load Domain-wise Benchmark Datasets

```python
from AH_Translit_Bench import load_dataset, get_available_domains

# List all available domains
domains = get_available_domains()
print("Available domains:", domains)
```

**Expected output**

```text
Available domains: ['quranic', 'msa_dailyuse', 'msa_bibliographic', 'all']
```

---

### 3️⃣ Load Individual Domains

#### Quranic Benchmark (2000 samples)

```python
quranic_df = load_dataset("quranic")
print(quranic_df.head())
print("Quranic shape:", quranic_df.shape)
```

Expected:

```text
Quranic shape: (2000, 2)
```

---

#### MSA Daily Use Benchmark (2000 samples)

```python
daily_df = load_dataset("msa_dailyuse")
print(daily_df.head())
print("MSA Daily Use shape:", daily_df.shape)
```

Expected:

```text
MSA Daily Use shape: (2000, 2)
```

---

#### MSA Bibliographic Benchmark (2000 samples)

```python
biblo_df = load_dataset("msa_bibliographic")
print(biblo_df.head())
print("MSA Bibliographic shape:", biblo_df.shape)
```

Expected:

```text
MSA Bibliographic shape: (2000, 2)
```

---

### 4️⃣ Load the Full Mixed Benchmark (All Domains)

```python
all_df = load_dataset("all")
print(all_df.head())
print("All-domain benchmark shape:", all_df.shape)
```

Expected:

```text
All-domain benchmark shape: (6000, 2)
```

---

### 5️⃣ Access Arabic–Hindi Pairs

```python
arabic_text = quranic_df.iloc[0]["Arabic"]
hindi_translit = quranic_df.iloc[0]["Hindi"]

print("Arabic:", arabic_text)
print("Hindi :", hindi_translit)
```
---

## Example Data Snippet

```csv
Arabic,Hindi
المطبعة الحيدرية،,"अल-मतबअह अल-हैदरियह,"
```

---

## Version Overview

### **Version 2.0 (Current)**

**Version Description:**
Expanded benchmark release with **2,000 samples per domain**, covering Quranic Arabic, MSA Daily Use, and MSA Bibliographic text, along with a **combined 6,000-sample mixed-domain benchmark file** for standardized evaluation.

---

## Important Notes

* This is a **benchmark-only dataset**
* Not intended for model training
* No overlap with AH-Translit training splits
* Focuses on **phonetic fidelity**, not semantic translation

---

## License

* **Code:** MIT License
* **Dataset:** Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)

Permitted for **research and educational use only** with proper attribution.

Full license:
[https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)

---