CipherSenseAI/afri-fertility-results

afri-fertility: African Language Tokenization Results

Measurement dataset for The African Language Tax — the first systematic audit of the subword tokenization penalty imposed on African languages by frontier large language models.

Every row is one (language, tokenizer, corpus) triple, with fertility, English-relative premium, and confidence intervals computed from a parallel corpus using sum-then-divide aggregation.

Dataset summary

Property	Value
Rows	616
Languages	22 (20 African + English + French baselines)
Tokenizers	11
Corpora	3 (FLORES-200+, SIB-200, MAFAND-MT)
File size	~58 KB
Format	Parquet
Tool	afri-fertility
Paper	arXiv 2606.24460

Key findings

• Every African language carries a tokenization premium above English on every tokenizer — 209 FLORES-200+ pairs (19 languages × 11 tokenizers), zero exceptions
• Premiums range from 1.29× (Swahili on BLOOM) to 8.92× (N'Ko on o200k_base)
• Ethiopic-script languages (Amharic, Tigrinya) reach 6.8–9.3×; N'Ko reaches 8.9×
• Best available tokenizer for African languages: Gemma 4 (mean 2.38× vs 3.31× for cl100k_base)
• Premium is near-invariant across corpora (FLORES vs SIB-200: Pearson r = 0.9998)

Load the dataset

import pandas as pd
from datasets import load_dataset

# Via HuggingFace datasets
ds = load_dataset("CipherSenseAI/afri-fertility-results")
df = ds["train"].to_pandas()

# Or directly as parquet
df = pd.read_parquet("hf://datasets/CipherSenseAI/afri-fertility-results/data/results.parquet")

Column reference

Column	Type	Description
corpus	str	Source corpus: flores, sib200, or mafand
domain	str	Text domain (where applicable)
language	str	Language display name
iso639_3	str	ISO 639-3 code
script	str	Writing script: Latin, Ethiopic, N'Ko
family	str	Language family
tokenizer	str	Tokenizer identifier (e.g. openai/o200k_base)
vocab_size	int	Tokenizer vocabulary size
inspectable	bool	Whether subword segmentation is accessible
n_sentences	int	Number of parallel sentences in the corpus slice
n_words	int	Total word count (UAX-29 word boundaries)
n_tokens	int	Total subword token count
n_chars	int	Total character count (NFC)
n_bytes	int	Total UTF-8 byte count
fertility	float	n_tokens / n_words — subword tokens per word
premium	float	fertility(L,T) / fertility(English,T) — English-relative multiplier
cpt	float	Characters per token
bpt	float	Bytes per token
fertility_ci_low	float	Bootstrap 95% CI lower bound for fertility
fertility_ci_high	float	Bootstrap 95% CI upper bound for fertility
premium_ci_low	float	Bootstrap 95% CI lower bound for premium
premium_ci_high	float	Bootstrap 95% CI upper bound for premium

Languages covered

Language	ISO	Script	Tier
Yoruba	yor	Latin	Core
Hausa	hau	Latin	Core
Igbo	ibo	Latin	Core
Wolof	wol	Latin	Core
Swahili	swh	Latin	Core
Amharic	amh	Ethiopic	Core
Zulu	zul	Latin	Breadth
Xhosa	xho	Latin	Breadth
Shona	sna	Latin	Breadth
Kinyarwanda	kin	Latin	Breadth
Luganda	lug	Latin	Breadth
Akan/Twi	aka	Latin	Breadth
Lingala	lin	Latin	Breadth
Oromo	gaz	Latin	Breadth
Nigerian Pidgin	pcm	Latin	Breadth
Sesotho	sot	Latin	Breadth
Bambara	bam	Latin	Breadth
N'Ko	nqo	N'Ko	Non-Latin
Tigrinya	tir	Ethiopic	Non-Latin
Afrikaans	afr	Latin	Control
English	eng	Latin	Baseline
French	fra	Latin	Baseline

Tokenizers measured

Tokenizer ID	Model family	Vocab size
openai/o200k_base	GPT-5 / o-series	200k
openai/o200k_harmony	gpt-oss open weights	201k
openai/cl100k_base	GPT-4 (legacy)	100k
meta/llama-3.1	Llama 3.1	128k
meta/llama-4	Llama 4 Scout/Maverick	~200k
google/gemma-4	Gemma 4	262k
mistral/tekken	Mistral / Tekken	131k
deepseek/v3	DeepSeek V3	128k
bigscience/bloom	BLOOM	251k
cohere/aya-expanse	Aya Expanse	256k
qwen/qwen3	Qwen 3	152k

Reproduce

Results are fully reproducible using the open-source afri-fertility tool:

pip install afri-fertility
afri-fertility reproduce   # runs offline reference suite, prints headline numbers

Full study replication (requires HuggingFace token for gated models):

git clone https://github.com/CipherSenseAI/afri-fertility
cd afri-fertility
pip install -e ".[dev]"
afri-fertility run configs/study_main.yaml

Citation

@article{somide2026africanlanguagetax,
  title   = {The African Language Tax: Quantifying the Cost, Latency, and Context
             Penalty of Tokenizing African Languages in Frontier LLMs},
  author  = {Somide, Olaoye Anthony},
  year    = {2026},
  journal = {arXiv preprint arXiv:2606.24460},
  url     = {https://arxiv.org/abs/2606.24460}
}

License

Data: Apache 2.0. Source corpora: FLORES-200+ (CC-BY-SA-4.0), SIB-200 (CC-BY-SA-4.0), MAFAND-MT (per-dataset Masakhane licences).