--- dataset_info: - config_name: Aka_Gha features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3317998.598901099 num_examples: 4455 - name: dev num_bytes: 829685.844932845 num_examples: 1114 download_size: 2112700 dataset_size: 4147684.443833944 - config_name: Amh_Eth features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 744767.7850967477 num_examples: 1845 - name: dev num_bytes: 186494.69740634007 num_examples: 462 download_size: 455333 dataset_size: 931262.4825030877 - config_name: Eng_Eth features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 890291.6692546584 num_examples: 3915 - name: dev num_bytes: 222629.76863354037 num_examples: 979 download_size: 528816 dataset_size: 1112921.4378881988 - config_name: Eng_Gha features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3136577.832440704 num_examples: 4443 - name: dev num_bytes: 784320.947972456 num_examples: 1111 download_size: 2026934 dataset_size: 3920898.78041316 - config_name: Eng_Ken features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1206904.2012414243 num_examples: 2080 - name: dev num_bytes: 302306.2927147991 num_examples: 521 download_size: 614502 dataset_size: 1509210.4939562234 - config_name: Eng_Uga features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5206956.087034064 num_examples: 7624 - name: dev num_bytes: 1302421.9908150525 num_examples: 1907 download_size: 1915907 dataset_size: 6509378.077849117 - config_name: Lug_Uga features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2542238.1573553053 num_examples: 3383 - name: dev num_bytes: 635747.4079581994 num_examples: 846 download_size: 1021986 dataset_size: 3177985.5653135045 - config_name: Swa_Ken features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1274525.8522167488 num_examples: 2070 - name: dev num_bytes: 318939.31954022986 num_examples: 518 download_size: 603351 dataset_size: 1593465.1717569786 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 | 4455 | 1114 | | Amh_Eth | 1845 | 462 | | Eng_Eth | 3915 | 979 | | Eng_Gha | 4443 | 1111 | | Eng_Ken | 2080 | 521 | | Eng_Uga | 7624 | 1907 | | Lug_Uga | 3383 | 846 | | Swa_Ken | 2070 | 518 | | Swa_Uga | 678 | 170 | --- ## 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}