README.md

---
dataset_info:
- config_name: Aka_Gha
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 3710836
    num_examples: 4985
  - name: dev
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    num_examples: 1247
  download_size: 2320907
  dataset_size: 4639103
- config_name: Amh_Eth
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 772838
    num_examples: 1945
  - name: dev
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    num_examples: 487
  download_size: 464248
  dataset_size: 966345
- config_name: Eng_Eth
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
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    num_examples: 4391
  - name: dev
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- config_name: Eng_Gha
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
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  - name: dev
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- config_name: Eng_Ken
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
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  - name: dev
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- config_name: Eng_Uga
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
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  - name: dev
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- config_name: Lug_Uga
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
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  - name: train
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    num_examples: 3801
  - name: dev
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- config_name: Swa_Ken
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
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  - name: train
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  - name: dev
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    num_examples: 585
  download_size: 495626
  dataset_size: 1795327
configs:
- config_name: Aka_Gha
  data_files:
  - split: train
    path: Aka/Aka_Gha/train-*
  - split: dev
    path: Aka/Aka_Gha/dev-*
- config_name: Amh_Eth
  data_files:
  - split: train
    path: Amh/Amh_Eth/train-*
  - split: dev
    path: Amh/Amh_Eth/dev-*
- config_name: Eng_Eth
  data_files:
  - split: train
    path: Eng/Eng_Eth/train-*
  - split: dev
    path: Eng/Eng_Eth/dev-*
- config_name: Eng_Gha
  data_files:
  - split: train
    path: Eng/Eng_Gha/train-*
  - split: dev
    path: Eng/Eng_Gha/dev-*
- config_name: Eng_Ken
  data_files:
  - split: train
    path: Eng/Eng_Ken/train-*
  - split: dev
    path: Eng/Eng_Ken/dev-*
- config_name: Eng_Uga
  data_files:
  - split: train
    path: Eng/Eng_Uga/train-*
  - split: dev
    path: Eng/Eng_Uga/dev-*
- config_name: Lug_Uga
  data_files:
  - split: train
    path: Lug/Lug_Uga/train-*
  - split: dev
    path: Lug/Lug_Uga/dev-*
- config_name: Swa_Ken
  data_files:
  - split: train
    path: Swa/Swa_Ken/train-*
  - split: dev
    path: Swa/Swa_Ken/dev-*
---
language:
- am
- en
- sw
- lg
- ak
---
# ZINDI_HASH_DATASET

**Multilingual Sexual and Reproductive Health Dataset (ZINDI_HASH_DATASET)**

![Hugging Face](https://huggingface.co/front/assets/huggingface_logo.svg)

## Dataset Summary
ZINDI_HASH_DATASET is a multilingual dataset for text-based sexual and reproductive health (SRH) content. It contains aligned text pairs across nine language-country configurations, designed to support research in natural language processing (NLP), translation, and text understanding for African languages.  

The dataset is split into training and validation (dev) for each language pair. It is suitable for sequence-to-sequence tasks such as translation, paraphrasing, or text generation in the SRH domain.

---

## Languages

The dataset covers the following languages:

| Language | Code |
|----------|------|
| Amharic | `am` |
| English | `en` |
| Luganda | `lg` |
| Akan | `ak` |
| Swahili | `sw` |

Each language is paired with a specific country context:

- Aka → Ghana (`aka_gha`)  
- Amh → Ethiopia (`amh_eth`)  
- Eng → Ethiopia, Ghana, Kenya, Uganda (`eng_eth`, `eng_gha`, `eng_ken`, `eng_uga`)  
- Lug → Uganda (`lug_uga`)  
- Swa → Kenya (`swa_ken`)  

---
language:
- am
- en
- sw
- lg
- ak
---

## Dataset Structure

The dataset is organized into language-first folders:

# Dataset Structure
```
aka/
└── aka_gha/
    ├── train-*
    └── dev-*
eng/
├── eng_eth/
│   ├── train-*
│   └── dev-*
├── eng_gha/
│   ├── train-*
│   └── dev-*
├── eng_ken/
│   ├── train-*
│   └── dev-*
└── eng_uga/
    ├── train-*
    └── dev-*
lug/
└── lug_uga/
    ├── train-*
    └── dev-*
swa/
└─── swa_ken/
    ├── train-*
    └── dev-*

```

Each split contains files with two columns:

- `input`: original SRH text  
- `output`: target text (translated, paraphrased, or processed)  

---

## Dataset Details

| Config | Train | Dev | 
|--------|-------|-----|
| Aka_Gha | 4985 | 1247 | 
| Amh_Eth | 1945 | 487 | 
| Eng_Eth | 4391 | 1098 | 
| Eng_Gha | 4986 | 1247 | 
| Eng_Ken | 2340 | 586 |
| Eng_Uga | 8848 | 2212 | 
| Lug_Uga | 3801 | 951 | 
| Swa_Ken | 2339 | 585 | 

---

## Use Cases

ZINDI_HASH_DATASET can be used for:

- Machine translation and multilingual NLP research  
- Sequence-to-sequence models in the SRH domain  
- Text classification or paraphrasing  
- Evaluating model performance across African languages  

---

## Loading the Dataset

```python
from datasets import load_dataset

# Example: load English-Ghana split
dataset = load_dataset("AiHub4MSRH-Hash/ZINDI_HASH_DATASET", "Eng_Gha")
print(dataset["train"][0])

@dataset{aihub4msrh-zindi_hash_dataset,
  title={ZINDI_HASH_DATASET},
  author={HASH / AiHub4MSRH},
  year={2026},
  url={https://huggingface.co/datasets/AiHub4MSRH-Hash/ZINDI_HASH_DATASET}