Luganda LLM Data
A prepared English–Luganda parallel corpus with training, validation, and test splits for translation model development.
- Luganda
- Parallel
- Kaggle
The data, in context
A prepared English–Luganda parallel corpus with training, validation, and test splits for translation model development.
CommonMind uses it to train and evaluate Luganda-gemma-1B-it.
DATASET INFORMATION
- Language
- Luganda
- Data type
- Parallel
- Domain
- Agriculture, Health, Society
- Format
- –
- Size
- –
- Samples used
- 94,542 examples, split 90/5/5
- Version
- –
- License
- –
- Availability
- Kaggle ↗
Explore what's in the dataset
Understand the language, content, and structure represented within the corpus.
DATA PREVIEW
| ID | Language | Type | Domain |
|---|---|---|---|
| 00001 | Luganda | Parallel | Agriculture |
| 00002 | Luganda | Parallel | Health |
| 00003 | Luganda | Parallel | Society |
| 00004 | Luganda | Parallel | Agriculture |
Sample rows will appear here once the preview is published.
What's represented
Understand the languages, data types, and contexts covered by this dataset.
- Language
- Luganda
- Data Type
- Parallel
- Source
- Kaggle
- Version
- –
- Agriculture
- Health
- Society
How the dataset was built
Learn how the data was collected, processed, structured, reviewed, and prepared for civic language research.
- 01 — CollectionHow the source language data was gathered.
- 02 — ProcessingHow speech and related resources were prepared and structured.
- 03 — AnnotationHow language and civic information were organised within the dataset.
- 04 — ReviewHow the resulting data was assessed and prepared for use.
Use Luganda LLM Data
Access the dataset for research, model development, evaluation, and civic language experimentation.
import kagglehub
path = kagglehub.dataset_download("gracenoble/luganda-llm-data")
print(path)Models connected to this dataset
Explore models and language tools built with Luganda LLM Data.
Understand the boundaries of the data
Review known limitations, representation gaps, intended uses, and areas where additional language data or research may be needed.
- Known Limitations
- Document known gaps and constraints within the dataset.
- Intended Use
- Describe the research and development contexts the dataset is designed to support.
- Out-of-Scope Use
- Clarify applications or interpretations the dataset was not designed to support.
- Representation
- Document relevant considerations around language and contextual coverage.
Dataset history
Track changes, additions, and improvements across releases.
| Version | Released | Status | Notes |
|---|---|---|---|
| – | – | In use | 94,542 examples, split 90/5/5 |