README.md

---
language: shi
language_name: Tachelhit
language_family: berber
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-berber
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: 3.819
  - name: best_isotropy
    type: isotropy
    value: 0.6948
  - name: best_alignment_r10
    type: alignment
    value: 0.1780
  - name: vocabulary_size
    type: vocab
    value: 31623
generated: 2026-03-02
---

# Tachelhit — Wikilangs Models

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

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

## Language Samples

Example sentences drawn from the Tachelhit Wikipedia corpus:

> 11 yan d mraw ( s Taɛrabt احدى عشر ) ( s Tafṛensist onze ) iga yan izwl Msmun awal n SGSM : Msmun awal amatay asnmalay n tmaziɣt (MMSM) tisaɣulin

> 12 sin d mraw ( s Taɛrabt اثنى عشرة ) ( s Tafṛensist douze ) iga yan izwl Msmun awal n SGSM : Msmun awal amatay asnmalay n tmaziɣt (MMSM) tisaɣulin

> 13 kṛaḍ d merraw ( s Taɛrabt ثلاثة عشرة ) ( s Tafṛensist treize ) iga yan izwl Msmun awal n SGSM : Msmun awal amatay asnmalay n tmaziɣt (MMSM) tisaɣulin

> Acfud iga yat tasklut mẓẓin, ilan isnnann. Tiwlafin Assaɣ Tasnalɣa (morphologie) Anzwi Tisaɣulin

> Acnyal nɣ Aknyal agdudan aṣbnyuli, ɣ tgzzumt tiss 4.1 n tmnḍawt taṣbnyulit yuma kṛaḍ ikʷlan: aẓggaɣ d uwraɣ d uẓggaɣ daɣ. Tisaɣulin

## Quick Start

### Load the Tokenizer

```python
import sentencepiece as spm

sp = spm.SentencePieceProcessor()
sp.Load("shi_tokenizer_32k.model")

text = "Sstekk iga yan ugḍiḍ imẓẓin. Assaɣ Tuzduɣt Tasnalɣa (morphologie) Tisaɣulin Msmu"
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:** `Sstekk iga yan ugḍiḍ imẓẓin. Assaɣ Tuzduɣt Tasnalɣa (morphologie) Tisaɣulin Msmu…`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁s ste kk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt … (+19 more)` | 29 |
| 16k | `▁s ste kk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt … (+19 more)` | 29 |
| 32k | `▁s stekk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ▁tasnalɣa … (+18 more)` | 28 |
| 64k | `▁sstekk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ▁tasnalɣa ▁( … (+17 more)` | 27 |

**Sample 2:** `Asimwas iga ass wiss Smmus g ussan n imalass. Tisaɣulin`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁as im was ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n … (+3 more)` | 13 |
| 16k | `▁as imwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass … (+2 more)` | 12 |
| 32k | `▁asimwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass . … (+1 more)` | 11 |
| 64k | `▁asimwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass . … (+1 more)` | 11 |

**Sample 3:** `Turdut (S turdut: اردو ) tga tutlayt nna s sawaln ayt Bakistan d Lhnd. Isuɣal`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁tur dut ▁( s ▁tur dut : ▁ا ر دو … (+14 more)` | 24 |
| 16k | `▁tur dut ▁( s ▁tur dut : ▁ار دو ▁) … (+13 more)` | 23 |
| 32k | `▁turdut ▁( s ▁turdut : ▁اردو ▁) ▁tga ▁tutlayt ▁nna … (+9 more)` | 19 |
| 64k | `▁turdut ▁( s ▁turdut : ▁اردو ▁) ▁tga ▁tutlayt ▁nna … (+8 more)` | 18 |

</details>

### Load Word Embeddings

```python
from gensim.models import KeyedVectors

# Aligned embeddings (cross-lingual, mapped to English vector space)
wv = KeyedVectors.load("shi_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("shi_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 | 3.02x |
| Tokenizer | 16k BPE | Compression | 3.30x |
| Tokenizer | 32k BPE | Compression | 3.56x |
| Tokenizer | 64k BPE | Compression | 3.82x 🏆 |
| N-gram | 2-gram (subword) | Perplexity | 255 🏆 |
| N-gram | 2-gram (word) | Perplexity | 1,027 |
| N-gram | 3-gram (subword) | Perplexity | 1,284 |
| N-gram | 3-gram (word) | Perplexity | 1,698 |
| N-gram | 4-gram (subword) | Perplexity | 3,345 |
| N-gram | 4-gram (word) | Perplexity | 3,109 |
| N-gram | 5-gram (subword) | Perplexity | 5,689 |
| N-gram | 5-gram (word) | Perplexity | 3,900 |
| Markov | ctx-1 (subword) | Predictability | 0.0% |
| Markov | ctx-1 (word) | Predictability | 36.7% |
| Markov | ctx-2 (subword) | Predictability | 0.0% |
| Markov | ctx-2 (word) | Predictability | 74.0% |
| Markov | ctx-3 (subword) | Predictability | 17.0% |
| Markov | ctx-3 (word) | Predictability | 91.6% |
| Markov | ctx-4 (subword) | Predictability | 43.6% |
| Markov | ctx-4 (word) | Predictability | 95.2% 🏆 |
| Vocabulary | full | Size | 31,623 |
| Vocabulary | full | Zipf R² | 0.9880 |
| Embeddings | mono_32d | Isotropy | 0.6948 |
| Embeddings | mono_64d | Isotropy | 0.5226 |
| Embeddings | mono_128d | Isotropy | 0.2352 |
| Embeddings | aligned_32d | Isotropy | 0.6948 🏆 |
| Embeddings | aligned_64d | Isotropy | 0.5226 |
| Embeddings | aligned_128d | Isotropy | 0.2352 |
| Alignment | aligned_32d | R@1 / R@5 / R@10 | 0.6% / 2.0% / 5.4% |
| Alignment | aligned_64d | R@1 / R@5 / R@10 | 2.4% / 8.0% / 12.8% |
| Alignment | aligned_128d | R@1 / R@5 / R@10 | 3.6% / 11.2% / 17.8% 🏆 |

📊 **[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/shi/)
- 🎮 [Playground](https://wikilangs.org/playground/?lang=shi)
- 🤗 [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 · 2026-03-02 12:00:32*