Datasets / Instruction

Alpaca-Cleaned (Swahili)

Swahili instruction-tuning data adapted from the cleaned Alpaca dataset.

  • Swahili
  • Instruction
  • Hugging Face
Overview

The data, in context

Swahili instruction-tuning data adapted from the cleaned Alpaca dataset.

CommonMind uses it to train and evaluate Swahili-gemma-1B-it.

DATASET INFORMATION

Language
Swahili
Data type
Instruction
Domain
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Format
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Size
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Samples used
14,844 examples
Version
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License
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Availability
Hugging Face ↗
Data

Explore what's in the dataset

Understand the language, content, and structure represented within the corpus.

DATA PREVIEW

IDLanguageTypeDomain
00001SwahiliInstruction–
00002SwahiliInstruction–
00003SwahiliInstruction–
00004SwahiliInstruction–

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
Swahili
Data Type
Instruction
Source
Hugging Face
Version
–
Methodology

How the dataset was built

Learn how the data was collected, processed, structured, reviewed, and prepared for civic language research.

  1. 01 — CollectionHow the source language data was gathered.
  2. 02 — ProcessingHow speech and related resources were prepared and structured.
  3. 03 — AnnotationHow language and civic information were organised within the dataset.
  4. 04 — ReviewHow the resulting data was assessed and prepared for use.
Usage

Use Alpaca-Cleaned (Swahili)

Access the dataset for research, model development, evaluation, and civic language experimentation.

load.py
from datasets import load_dataset

# Datasets with several configurations take one as a second argument.
ds = load_dataset("iamshnoo/alpaca-cleaned-swahili")
print(ds)

Dataset Details

Format
–
Version
–
License
–
Download Size
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Download DatasetRead Documentation
Connected Models

Models connected to this dataset

Explore models and language tools built with Alpaca-Cleaned (Swahili).

Limitations

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.
Versions

Dataset history

Track changes, additions, and improvements across releases.

VersionReleasedStatusNotes
––In use14,844 examples
View Release Notes