---
license: apache-2.0
task_categories:
  - text-classification
language:
  - yo   # Yoruba
  - hau  # Hausa
  - ibo  # Igbo
  - wol  # Wolof
  - swh  # Swahili
  - amh  # Amharic
  - zul  # Zulu
  - xho  # Xhosa
  - sna  # Shona
  - kin  # Kinyarwanda
  - lug  # Luganda
  - aka  # Akan/Twi
  - lin  # Lingala
  - gaz  # Oromo
  - pcm  # Nigerian Pidgin
  - sot  # Sesotho
  - bam  # Bambara
  - nqo  # N'Ko
  - tir  # Tigrinya
  - afr  # Afrikaans
  - eng  # English
  - fr   # French
pretty_name: "afri-fertility: African Language Tokenization Measurement Results"
size_categories:
  - n<1K
tags:
  - tokenization
  - african-languages
  - nlp
  - subword-fertility
  - llm
  - multilingual
  - ai-equity
---

# afri-fertility: African Language Tokenization Results

Measurement dataset for **[The African Language Tax](https://arxiv.org/abs/2606.24460)** — 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`](https://github.com/CipherSenseAI/afri-fertility) |
| Paper | [arXiv 2606.24460](https://arxiv.org/abs/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

```python
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:

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

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

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

## Citation

```bibtex
@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).