Fast, accurate tokenization for Kinyarwanda.
Production-grade tokenization designed for reliability and throughput—so teams can ship NLP pipelines with confidence.
Overview
Tokenizer quality is where model quality starts.
ALTA Tokenizer turns raw Kinyarwanda text into stable, machine-ready tokens. It’s designed for production reliability—so teams can ship preprocessing and training pipelines with confidence.
Tokenization bridges the gap between human language and the structured numerical inputs models require. In enterprise workflows, it’s not a “pre-step”—it’s part of your reliability surface.
Compression rate (quick intuition)
A simple metric to compare tokenizers:compression = characters / tokens. Higher is usually better—up to the point where semantic boundaries are preserved.
Where it fits
- Data ingestion & cleaning
- Model training & evaluation
- RAG indexing / search pipelines
- Low-latency inference services
Kinyarwanda-first accuracy
Built with local language structure in mind—so downstream NLP gets cleaner, more faithful tokens.
Optimized token boundaries for Kinyarwanda
Fast & deterministic
Consistent output every time. Low-latency performance that fits production pipelines and batch jobs.
Throughput-ready for large corpora
Unicode-safe
Handles real-world text: diacritics, punctuation, mixed scripts, and noisy inputs without breaking.
Edge-case resilient
Developer-first API
Clean interfaces for encode/decode and training—easy to integrate in Python apps and services.
Simple primitives, composable usage
Offline-ready
Run locally where data can’t leave the environment—ideal for regulated or bandwidth-limited settings.
Works in private networks
Pipeline compatible
Drop-in building block for ingestion, preprocessing, and training workflows—without extra glue.
Plays well with modern stacks
Workflow
From raw text to model-ready tokens
A straightforward flow you can adopt in minutes—then scale across datasets and environments.
Encode raw text
Convert unstructured Kinyarwanda text into stable token IDs—the format models train on and reason with.
- Deterministic outputs for reproducible training
- Streaming-friendly batch encoding
Measure compression
Track compression rate as a practical proxy for how efficiently your vocabulary represents language.
- Compression = characters / token count
- Useful for comparing tokenizers across datasets
Decode for inspection
Debug and validate end-to-end pipelines by decoding token IDs back to text whenever you need.
- Human-readable verification
- Safer QA during integration
Train a custom tokenizer
Need a different domain or language? Train a custom BPE tokenizer with your own dataset and constraints.
- Tailor the vocabulary to your corpora
- Keep the same API surface
Ready to integrate ALTA Tokenizer?
Try the playground, install via PyPI, or talk to us about offline deployment and production support.
Custom Training
Easy to use python package with option to train your own tokenizer. By supplying a custom dataset and using the provided training function, users can retrain with on a custom dataset to improve it for specific use case.
Language Optimization
Built on the Byte-Pair Encoding algorithm, it efficiently processes texts and preserving their meaning. it is designed specifically for Kinyarwanda, it leverages its unique linguistic patterns to outperform generic tokenizers on kinyarwanda.
Easy Integration
With its Python package and clear documentation, alta-tokenizer can be integrated into existing NLP pipelines, making it a versatile tool for developers and researchers.
Language Flexibility
Although optimized for Kinyarwanda, alta-tokenizer can be applied to other languages ideal for multilingual projects or cases where you want a custom solution that fits your specific language data.
Enterprise readiness
Security & deployment
A pragmatic foundation designed for privacy, governance, and controlled rollout—without sacrificing velocity.
Privacy by design
Keep sensitive text controlled with strict access, redaction workflows, and clear data boundaries.
On‑prem / offline ready
Run close to your data—self-hosted deployments supported for regulated environments.
Audit & governance
Traceability, role-based controls, and policy-aligned practices for enterprise compliance.
Security posture
Defense-in-depth fundamentals: least privilege, secure defaults, and measurable controls.
Secure integrations
Connect safely to storage and pipelines with scoped credentials and strong boundaries.
Data residency aware
Architect deployments that respect regional requirements and internal governance rules.