README.md

---
language: ceb
language_name: Cebuano
language_family: austronesian_philippine_central
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - feature-extraction
  - sentence-similarity
  - tokenization
  - n-grams
  - markov-chain
  - text-mining
  - fasttext
  - babelvec
  - vocabulous
  - vocabulary
  - monolingual
  - family-austronesian_philippine_central
license: mit
library_name: wikilangs
pipeline_tag: text-generation
datasets:
  - omarkamali/wikipedia-monthly
dataset_info:
  name: wikipedia-monthly
  description: Monthly snapshots of Wikipedia articles across 300+ languages
metrics:
  - name: best_compression_ratio
    type: compression
    value: 4.164
  - name: best_isotropy
    type: isotropy
    value: 0.8551
  - name: best_alignment_r10
    type: alignment
    value: 0.5920
  - name: vocabulary_size
    type: vocab
    value: 208251
generated: 2026-03-04
---

# Cebuano — Wikilangs Models

Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **Cebuano** Wikipedia by [Wikilangs](https://wikilangs.org).

🌐 [Language Page](https://wikilangs.org/languages/ceb/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=ceb) · 📊 [Full Research Report](RESEARCH_REPORT.md)

## Language Samples

Example sentences drawn from the Cebuano Wikipedia corpus:

> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong mayor sa lalawigan sa Sugbo. Alkalde sa Lalawigan sa Sugbo Alkalde

> Ang sekswalidad puyde mopasabot sa: Sekswalidad sa tawo Sekswalidad sa tanom Sekswalidad (oryentasyon) Sekswalidad sa mananap

> Katawhan ug Kultura Ekonomiya Heyograpiya Politikal Mga lungsod Dakbayan Mga dakbayan Pisikal Kaagi Mga sumpay sa gawas

> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong gobernador sa lalawigan sa Samar. Mga Gobernador Antonio Bolastig Milagrosa T. Tan Gobernador Gobernador sa Samar

> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong gobernador sa lalawigan sa Biliran. Mga Gobernador (gikan Wayne Jaro Rogelio J. Espina Gobernador Gobernador sa Biliran

## Quick Start

### Load the Tokenizer

```python
import sentencepiece as spm

sp = spm.SentencePieceProcessor()
sp.Load("ceb_tokenizer_32k.model")

text = "Ang (MDCCL) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka tuig"
tokens = sp.EncodeAsPieces(text)
ids    = sp.EncodeAsIds(text)

print(tokens)  # subword pieces
print(ids)     # integer ids

# Decode back
print(sp.DecodeIds(ids))
```

<details>
<summary><b>Tokenization examples (click to expand)</b></summary>

**Sample 1:** `Ang (MDCCL) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka tuig…`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+27 more)` | 37 |
| 16k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+24 more)` | 34 |
| 32k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+22 more)` | 32 |
| 64k | `▁ang ▁( md c cl ) ▁mao ▁ang ▁usa ▁ka … (+21 more)` | 31 |

**Sample 2:** `Vilnius - Ulohan, Lyetuwanya. lungsod ug dakbayan sa Uropa`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁v il n ius ▁- ▁ulo han , ▁ly et … (+9 more)` | 19 |
| 16k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an … (+7 more)` | 17 |
| 32k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an … (+7 more)` | 17 |
| 64k | `▁vil n ius ▁- ▁ulohan , ▁lyetuwanya . ▁lungsod ▁ug … (+3 more)` | 13 |

**Sample 3:** `Ang manunuwat usa ka tawo nga naay propesyon sa pagsulat.`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na … (+9 more)` | 19 |
| 16k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na … (+8 more)` | 18 |
| 32k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁naay … (+6 more)` | 16 |
| 64k | `▁ang ▁man un uwat ▁usa ▁ka ▁tawo ▁nga ▁naay ▁propes … (+4 more)` | 14 |

</details>

### Load Word Embeddings

```python
from gensim.models import KeyedVectors

# Aligned embeddings (cross-lingual, mapped to English vector space)
wv = KeyedVectors.load("ceb_embeddings_128d_aligned.kv")

similar = wv.most_similar("word", topn=5)
for word, score in similar:
    print(f"  {word}: {score:.3f}")
```

### Load N-gram Model

```python
import pyarrow.parquet as pq

df = pq.read_table("ceb_3gram_word.parquet").to_pandas()
print(df.head())
```

## Models Overview

![Performance Dashboard](visualizations/performance_dashboard.png)

| Category | Assets |
|----------|--------|
| Tokenizers | BPE at 8k, 16k, 32k, 64k vocab sizes |
| N-gram models | 2 / 3 / 4 / 5-gram (word & subword) |
| Markov chains | Context 1–5 (word & subword) |
| Embeddings | 32d, 64d, 128d — mono & aligned |
| Vocabulary | Full frequency list + Zipf analysis |
| Statistics | Corpus & model statistics JSON |

## Metrics Summary

| Component | Model | Key Metric | Value |
|-----------|-------|------------|-------|
| Tokenizer | 8k BPE | Compression | 3.20x |
| Tokenizer | 16k BPE | Compression | 3.59x |
| Tokenizer | 32k BPE | Compression | 3.89x |
| Tokenizer | 64k BPE | Compression | 4.16x 🏆 |
| N-gram | 2-gram (subword) | Perplexity | 244 🏆 |
| N-gram | 2-gram (word) | Perplexity | 1,490 |
| N-gram | 3-gram (subword) | Perplexity | 1,343 |
| N-gram | 3-gram (word) | Perplexity | 2,538 |
| N-gram | 4-gram (subword) | Perplexity | 3,750 |
| N-gram | 4-gram (word) | Perplexity | 4,059 |
| N-gram | 5-gram (subword) | Perplexity | 6,751 |
| N-gram | 5-gram (word) | Perplexity | 5,049 |
| Markov | ctx-1 (subword) | Predictability | 13.0% |
| Markov | ctx-1 (word) | Predictability | 0.0% |
| Markov | ctx-2 (subword) | Predictability | 32.8% |
| Markov | ctx-2 (word) | Predictability | 66.0% |
| Markov | ctx-3 (subword) | Predictability | 28.5% |
| Markov | ctx-3 (word) | Predictability | 83.0% |
| Markov | ctx-4 (subword) | Predictability | 31.1% |
| Markov | ctx-4 (word) | Predictability | 94.4% 🏆 |
| Vocabulary | full | Size | 208,251 |
| Vocabulary | full | Zipf R² | 0.9938 |
| Embeddings | mono_32d | Isotropy | 0.8551 |
| Embeddings | mono_64d | Isotropy | 0.8254 |
| Embeddings | mono_128d | Isotropy | 0.7631 |
| Embeddings | aligned_32d | Isotropy | 0.8551 🏆 |
| Embeddings | aligned_64d | Isotropy | 0.8254 |
| Embeddings | aligned_128d | Isotropy | 0.7631 |
| Alignment | aligned_32d | R@1 / R@5 / R@10 | 5.8% / 18.8% / 31.4% |
| Alignment | aligned_64d | R@1 / R@5 / R@10 | 11.2% / 32.6% / 46.4% |
| Alignment | aligned_128d | R@1 / R@5 / R@10 | 23.8% / 47.0% / 59.2% 🏆 |

📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**

---

## About

Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.

A project by **[Wikilangs](https://wikilangs.org)** · Maintainer: [Omar Kamali](https://omarkamali.com) · [Omneity Labs](https://omneitylabs.com)

### Citation

```bibtex
@misc{wikilangs2025,
  author    = {Kamali, Omar},
  title     = {Wikilangs: Open NLP Models for Wikipedia Languages},
  year      = {2025},
  doi       = {10.5281/zenodo.18073153},
  publisher = {Zenodo},
  url       = {https://huggingface.co/wikilangs},
  institution = {Omneity Labs}
}
```

### Links

- 🌐 [wikilangs.org](https://wikilangs.org)
- 🌍 [Language page](https://wikilangs.org/languages/ceb/)
- 🎮 [Playground](https://wikilangs.org/playground/?lang=ceb)
- 🤗 [HuggingFace models](https://huggingface.co/wikilangs)
- 📊 [wikipedia-monthly dataset](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
- 👤 [Omar Kamali](https://huggingface.co/omarkamali)
- 🤝 Sponsor: [Featherless AI](https://featherless.ai)

**License:** MIT — free for academic and commercial use.

---
*Generated by Wikilangs Pipeline · 2026-03-04 08:49:55*