---
language: nqo
language_name: N’Ko
language_family: constructed_other
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-constructed_other
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: 4.453
  - name: best_isotropy
    type: isotropy
    value: 0.8251
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10
---

# N’Ko - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **N’Ko** Wikipedia data.
We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.

## 📋 Repository Contents

### Models & Assets

- Tokenizers (8k, 16k, 32k, 64k)
- N-gram models (2, 3, 4, 5-gram)
- Markov chains (context of 1, 2, 3, 4 and 5)
- Subword N-gram and Markov chains
- Embeddings in various sizes and dimensions (aligned and unaligned)
- Language Vocabulary
- Language Statistics

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

### Analysis and Evaluation

- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
- [4. Vocabulary Analysis](#4-vocabulary-analysis)
- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
- [7. Summary & Recommendations](#7-summary--recommendations)
- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
- [Visualizations Index](#visualizations-index)

---
## 1. Tokenizer Evaluation

![Tokenizer Compression](visualizations/tokenizer_compression.png)

![Tokenizer Fertility](visualizations/tokenizer_fertility.png)

![Tokenizer OOV](visualizations/tokenizer_oov.png)

![Total Tokens](visualizations/tokenizer_total_tokens.png)

### Results

| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
|------------|-------------|---------------|----------|--------------|
| **8k** | 4.044x | 4.05 | 0.1822% | 749,607 |
| **16k** | 4.267x | 4.27 | 0.1923% | 710,416 |
| **32k** | 4.453x 🏆 | 4.45 | 0.2007% | 680,695 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `ߘߊ߲ߘߊߟߌ ߓߏߟߏ߲ ߡߍ߲ ߣߊ߬ߕߊ ߦߋ߫ ߘߊ߲ߝߋ߲ ߞߍ߲ߘߍ ߥߟߴߊ߬ ߛߎ߭ ߟߎ߬ ߝߊ߬ߘߌ߬ ߛߓߏ ߓߣߊ߬ߦߊ߬ߣߍ߲ ߠߎ߬...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߣߊ߬ ߕߊ ▁ߦߋ߫ ▁ߘߊ߲ߝߋ߲ ▁ߞߍ߲ߘߍ ▁ߥߟߴߊ߬ ▁ߛߎ߭ ... (+10 more)` | 20 |
| 16k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߣߊ߬ߕߊ ▁ߦߋ߫ ▁ߘߊ߲ߝߋ߲ ▁ߞߍ߲ߘߍ ▁ߥߟߴߊ߬ ▁ߛߎ߭ ▁ߟߎ߬ ... (+9 more)` | 19 |
| 32k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߣߊ߬ߕߊ ▁ߦߋ߫ ▁ߘߊ߲ߝߋ߲ ▁ߞߍ߲ߘߍ ▁ߥߟߴߊ߬ ▁ߛߎ߭ ▁ߟߎ߬ ... (+7 more)` | 17 |

**Sample 2:** `ߞߍ߲ߘߍߘߐߦߊ ߓߏߟߏ߲ ߡߍ߲ ߦߋ߫ ߞߏ߫ ߟߎ߫ ߞߊ߬ߙߊ߲߬ ߠߊ߫ ߸ ߡߍ߲ ߠߎ߬ ߦߋ߫ ߕߊ߬ ߟߊ߫ ߗߍ ߘߐ߫ ߓߐ߲ߛߐ߲ߢ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ߞߍ߲ߘߍߘߐߦߊ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߞߏ߫ ▁ߟߎ߫ ▁ߞߊ߬ߙߊ߲߬ ▁ߠߊ߫ ▁߸ ▁ߡߍ߲ ... (+11 more)` | 21 |
| 16k | `▁ߞߍ߲ߘߍߘߐߦߊ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߞߏ߫ ▁ߟߎ߫ ▁ߞߊ߬ߙߊ߲߬ ▁ߠߊ߫ ▁߸ ▁ߡߍ߲ ... (+11 more)` | 21 |
| 32k | `▁ߞߍ߲ߘߍߘߐߦߊ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߞߏ߫ ▁ߟߎ߫ ▁ߞߊ߬ߙߊ߲߬ ▁ߠߊ߫ ▁߸ ▁ߡߍ߲ ... (+10 more)` | 20 |

**Sample 3:** `ߘߊ߲ߘߊߟߌ ߓߏߟߏ߲ ߡߍ߲ ߦߋ߫ ߝߘߏ߬ߓߊ߬ ߓߣߊ߬ ߞߟߊߞߟߊߕߊ ߟߎ߬ ߕߌߙߌ߲߫ ߠߊ߫.`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߝߘߏ߬ߓߊ߬ ▁ߓߣߊ߬ ▁ߞߟߊߞߟߊ ߕߊ ▁ߟߎ߬ ▁ߕߌߙߌ߲߫ ... (+2 more)` | 12 |
| 16k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߝߘߏ߬ߓߊ߬ ▁ߓߣߊ߬ ▁ߞߟߊߞߟߊ ߕߊ ▁ߟߎ߬ ▁ߕߌߙߌ߲߫ ... (+2 more)` | 12 |
| 32k | `▁ߘߊ߲ߘߊߟߌ ▁ߓߏߟߏ߲ ▁ߡߍ߲ ▁ߦߋ߫ ▁ߝߘߏ߬ߓߊ߬ ▁ߓߣߊ߬ ▁ߞߟߊߞߟߊߕߊ ▁ߟߎ߬ ▁ߕߌߙߌ߲߫ ▁ߠߊ߫ ... (+1 more)` | 11 |


### Key Findings

- **Best Compression:** 32k achieves 4.453x compression
- **Lowest UNK Rate:** 8k with 0.1822% unknown tokens
- **Trade-off:** Larger vocabularies improve compression but increase model size
- **Recommendation:** 32k vocabulary provides optimal balance for production use

---
## 2. N-gram Model Evaluation

![N-gram Perplexity](visualizations/ngram_perplexity.png)

![N-gram Unique](visualizations/ngram_unique.png)

![N-gram Coverage](visualizations/ngram_coverage.png)

### Results

| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
|--------|---------|------------|---------|----------------|------------------|-------------------|
| **2-gram** | Word | 5,637 | 12.46 | 18,788 | 22.7% | 49.1% |
| **2-gram** | Subword | 492 🏆 | 8.94 | 5,832 | 56.4% | 93.0% |
| **3-gram** | Word | 14,726 | 13.85 | 27,596 | 10.9% | 29.7% |
| **3-gram** | Subword | 3,539 | 11.79 | 36,188 | 26.7% | 62.8% |
| **4-gram** | Word | 46,049 | 15.49 | 58,306 | 4.0% | 12.6% |
| **4-gram** | Subword | 16,382 | 14.00 | 132,351 | 14.2% | 37.7% |
| **5-gram** | Word | 40,435 | 15.30 | 45,104 | 2.8% | 9.5% |
| **5-gram** | Subword | 47,115 | 15.52 | 243,605 | 7.6% | 24.8% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ߊ߬ ߣߌ߫` | 4,822 |
| 2 | `ߟߋ߬ ߘߌ߫` | 4,660 |
| 3 | `ߕߘߍ߬ ߦߋ߫` | 3,060 |
| 4 | `ߏ߬ ߟߋ` | 2,522 |
| 5 | `ߟߎ߬ ߟߊ߫` | 2,496 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ߘߏ߫ ߟߋ߬ ߘߌ߫` | 1,073 |
| 2 | `ߟߋ߬ ߘߌ߫ ߡߍ߲` | 752 |
| 3 | `ߟߋ߬ ߘߌ߫ ߊ߬` | 656 |
| 4 | `ߊ߬ ߣߌ߫ ߞߊ߬` | 633 |
| 5 | `ߘߐ߫ ߊ߬ ߣߌ߫` | 615 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ߘߏ߫ ߟߋ߬ ߘߌ߫ ߡߍ߲` | 257 |
| 2 | `ߟߋ߬ ߘߌ߫ ߊ߬ ߣߌ߫` | 165 |
| 3 | `ߏ߬ ߟߋ ߞߍ߫ ߘߊ߫` | 160 |
| 4 | `ߘߏ߫ ߟߋ߬ ߘߌ߫ ߊ߬` | 159 |
| 5 | `ߏ߬ ߡߍ߲ ߕߘߍ߬ ߦߋ߫` | 145 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ߦߋ߫ ߝߊ߬ߙߊ߲߬ߛߌ ߥߞߌߔߋߘߌߦߊ ߟߋ߬ ߡߊ߬` | 123 |
| 2 | `ߘߟߊߡߌ߬ߘߊ߬ߣߍ߲߫ ߦߋ߫ ߝߊ߬ߙߊ߲߬ߛߌ ߥߞߌߔߋߘߌߦߊ ߟߋ߬` | 118 |
| 3 | `ߣߌ߲߬ ߘߟߊߡߌ߬ߘߊ߬ߣߍ߲߫ ߦߋ߫ ߝߊ߬ߙߊ߲߬ߛߌ ߥߞߌߔߋߘߌߦߊ` | 111 |
| 4 | `ߘߏ߫ ߟߋ߬ ߘߌ߫ ߡߍ߲ ߦߋ߫` | 67 |
| 5 | `ߛߏ ߣߴߊ߬ ߡߙߊ߬ߘߊ߬ߘߎ߯ߟߊ ߘߏ߫ ߟߋ߬` | 65 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ߞ` | 120,074 |
| 2 | `_ ߟ` | 100,993 |
| 3 | `_ ߘ` | 87,888 |
| 4 | `ߊ߬ _` | 83,535 |
| 5 | `ߊ߫ _` | 73,226 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ߟ ߊ߫` | 32,190 |
| 2 | `ߟ ߊ߫ _` | 29,535 |
| 3 | `ߟ ߎ߬ _` | 23,162 |
| 4 | `_ ߞ ߊ߬` | 22,371 |
| 5 | `_ ߊ߬ _` | 21,289 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ߟ ߊ߫ _` | 24,007 |
| 2 | `_ ߦ ߋ߫ _` | 19,822 |
| 3 | `_ ߟ ߎ߬ _` | 18,435 |
| 4 | `_ ߣ ߌ߫ _` | 17,034 |
| 5 | `_ ߟ ߋ߬ _` | 15,241 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ߊ _ ߟ ߎ߬ _` | 6,974 |
| 2 | `_ ߞ ߵ ߊ߬ _` | 6,885 |
| 3 | `_ ߕ ߘ ߍ߬ _` | 6,060 |
| 4 | `_ ߟ ߋ߬ _ ߘ` | 5,988 |
| 5 | `_ ߟ ߊ߫ _ ߞ` | 5,476 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 492
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~25% of corpus
- **Recommendation:** 4-gram or 5-gram for best predictive performance

---
## 3. Markov Chain Evaluation

![Markov Entropy](visualizations/markov_entropy.png)

![Markov Contexts](visualizations/markov_contexts.png)

![Markov Branching](visualizations/markov_branching.png)

### Results

| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
|---------|---------|-------------|------------|------------------|-----------------|----------------|
| **1** | Word | 0.7313 | 1.660 | 5.40 | 59,713 | 26.9% |
| **1** | Subword | 0.9951 | 1.993 | 10.03 | 1,379 | 0.5% |
| **2** | Word | 0.2921 | 1.224 | 1.79 | 321,747 | 70.8% |
| **2** | Subword | 0.9509 | 1.933 | 5.76 | 13,830 | 4.9% |
| **3** | Word | 0.1083 | 1.078 | 1.20 | 575,482 | 89.2% |
| **3** | Subword | 0.6832 | 1.606 | 3.28 | 79,681 | 31.7% |
| **4** | Word | 0.0356 🏆 | 1.025 | 1.05 | 689,204 | 96.4% |
| **4** | Subword | 0.4827 | 1.397 | 2.20 | 261,417 | 51.7% |

### Generated Text Samples (Word-based)

Below are text samples generated from each word-based Markov chain model:

**Context Size 1:**

1. `ߟߊ߫ ߓߌ߬ߟߊ߬ߢߐ߲߰ߠߊ ߃ߟߋ߬ ߘߐ߫ ߊ߬ ߕߌ߲߬ߞߎߘߎ߲ ߘߐ߫ ߣߴߊ߬ߟߎ߫ ߕߘߍ߬ ߘߊ߫ ߓߏ߲ ߠߊ߫ ߝߊ߬ߙߊ߲߬ߛߌ߫ ߟߊ߫ ߞߎߡߊߘߋ߲ ߘߏ߫`
2. `ߊ߬ ߖߘߍ߬ ߟߊ߫ ߊ߬ ߣߴߊ߬ ߥߟߎ߬ߥߟߎ ߟߎ߬ ߡߊ߫ ߡߊ߰ ߘߴߊ߬ߟߎ߫ ߡߊ߬ ߞߵߊ߬ ߘߊߡߌ߬ߣߊ߬ ߞߏ߫ ߌ ߝߣߊ߫`
3. `ߦߋ߫ ߜߟߊ߬ߜߟߊ߫ ߘߌ߫ ߟߊ߫ ߕߓߌߟߌ߫ ߕߙߏߞߏ ߟߎ߬ ߣߴߊ߬ ߘߟߊߡߌ߬ߘߊ߬ߣߍ߲ ߘߴߊ߬ ߦߋ߫ ߕߌ߲߬ߞߎ߬ߘߎ߲߬ ߇߲ ߞߊ߬ ߛߎ߲ߞߊߙߏ ߓߊ߲`

**Context Size 2:**

1. `ߊ߬ ߣߌ߫ ߞߐ ߟߊ߫ ߏ߬ ߞߵߊ߬ ߛߐ߫ ߟߌ߲߬ߖߌ߯ߟߌ ߟߊ߫ ߞߊ߲ߘߦߊ ߣߌ߫ ߟߊ߬ߟߌ߬ߟߌ ߟߋ߬ ߓߟߏ߫ ߓߊ ߏ߬ ߟߋ`
2. `ߟߋ߬ ߘߌ߫ ߟߊߓߋ߫ ߕߌ߲߬ߞߎߘߎ߲ ߘߐ߫ ߊ߬ ߟߊ߫ ߢߣߊߡߦߊ ߘߐ߫ ߛߔߑߙߌ߲ߜ߭ߛ ߔߊߦߑߣߌ߫ ߞߊ߲ߕߌ߮ ߟߌߓߋߙߌߦߞߊ`
3. `ߕߘߍ߬ ߦߋ߫ ߡߐ߰ ߟߎ߫ ߟߋ߬ ߞߘߊߡߊ߫ ߞߊ߬ ߝߊ߬ߛߏ߬ߟߊ߬ߞߊ ߘߐ߬ߕߊߡߌ߲ ߟߊ߬ߟߌ߰ߟߌ ߓߌ߬ߟߊ߬ ߘߊ߫ ߛߋ߲߬ߠߊ߫ ߕߎߟߊߝߌ߲ ߂߈ ߡߊ߬ ߕߙߍߛ...`

**Context Size 3:**

1. `ߘߏ߫ ߟߋ߬ ߘߌ߫ ߞߊ߬ ߝߘߊ߫ ߊߘߐߟߝ ߤߌߕߑߟߍߙ ߟߊ߫ ߘߊߘߐߥߛߊ ߞߛߊ߬ߓߌ ߡߊ߬ ߊ߬ ߞߵߊ߬ ߟߊ߫ ߞߟߏߞߕߏߦߊ ߏ߬ ߛߐ߬ߘߐ߲߬ ߋߙߐߔߎߞߊ`
2. `ߟߋ߬ ߘߌ߫ ߡߍ߲ ߣߌ߫ ߥߙߐ߬ߞߘߐ߫ ߝߊ߭ߡߘߎ߬ ߟߎ߫ ߘߍ߬ ߘߊ߫ ߞߊ߬ ߣߏߙߊߛߏߓߊ߫ ߘߟߊߛߌ߰ ߞߊ߬ ߟߊ߬ߥߛߊ߫ ߛߐ߬ߘߐ߲߫ ߞߊ߬ߙߊ߲߬ߕߏ߲߫ ߞߎ...`
3. `ߟߋ߬ ߘߌ߫ ߊ߬ ߥߟߏߘߊ ߟߋ߬ ߟߊ߫ ߏ߬ ߞߍ ߊ߬ ߓߐ߫ ߘߴߊ߬ ߟߐ߬ߘߎ߮ ߘߐ߫ ߊ߬ ߓߊ߯ߙߘߊ߫ ߘߊߺߊ߳ߑߥߟߊ߫ ߌߡߊ߰ߡߎ߲߫ ߣߌ߫`

**Context Size 4:**

1. `ߘߏ߫ ߟߋ߬ ߘߌ߫ ߡߍ߲ ߕߘߍ߬ ߦߋ߫ ߞߘߏߥߊߙߌ߫ ߕߟߋ߬ߓߋ ߥߙߏ߬ߘߎ߮ ߣߌ߫ ߕߐ߬ߙߐ߲߬ ߣߌ߫ ߝߏߟߏ߲ߣߍ߲߬ߜߍ߫ ߟߎ߫ ߣߴߊ߬ߟߎ߬ ߟߊߡߌߣߌ߲ ߞߊ...`
2. `ߟߋ߬ ߘߌ߫ ߊ߬ ߣߌ߫ ߞߴߏ߬ ߥߊ߯ߕߌ߫ ߞߋߟߋ߲ ߠߊ߫ ߞߏ߫ ߞߍߒߞߊ߲ߠߌ߲߫ ߘߍ߰ߜߍ ߘߏ߫ ߘߌ߫ ߓߌ߬ߟߊ߫ ߛߋ߲߬ߠߊ߫ ߥߙߏ߬ߘߎ߮ ߘߐ߫ ߏߔߋߙߊߛ߭...`
3. `ߏ߬ ߟߋ ߞߍ߫ ߘߊ߫ ߖߋ߬ߟߌ ߛߎ߯ߦߊ߫ ߞߎߘߊ߫ ߟߊߘߊ߲ߣߍ߲ ߘߌ߫ ߡߊ߲߬ߘߋ߲߫ ߛߊ߫ ߛߏ߲߬ߖߘߊ߫ ߛߌ߰ߣߍ߲ ߏ߬ ߞߍ߫ ߘߊ߫ ߡߊ߲߬ߘߋ߲߬ߞߊ ߟߎ߬...`


### Generated Text Samples (Subword-based)

Below are text samples generated from each subword-based Markov chain model:

**Context Size 1:**

1. `_ߟߎ߬_ߖߙߊ߲߬ߕߎ߲߬_ߟߐ߬ߘߏ߬ߘߌ߫`
2. `ߟߋߘߺߋ߬ߓߍߘߌ߫_ߟߌ_ߓߍߟ`
3. `ߞߵߊ߬_ߞߏ߬_ߊ߬_ߘߎ߲ߣߴߊ߬_ߛ`

**Context Size 2:**

1. `_ߞߛߐߟߊ_ߓߟߏ߫_ߣߌ߫_ߊ߬ߟߌ`
2. `_ߟߊ߫_߸_ߤߙߊ_ߕߊ_ߓߘߍ߬ߣ`
3. `_ߘߐ߫_ߞߊ߬ߦߊ_ߟߌ߲ߓߊ߫_ߡߴߊ߬`

**Context Size 3:**

1. `_ߟߊ߫_ߝߍ߫_ߦߋ߲߬_ߠߋ߬_ߦߋ߫_ߓߊ߯`
2. `ߟߊ߫_ߞߏ߫_ߡߐ߱_ߟߎ߬_ߡߐ߰_ߡߴߊ߬`
3. `ߟߎ߬_ߖߍ߬ߘߍ_ߛߌ߰_ߗߋߘߊ_ߣߌ߲߬`

**Context Size 4:**

1. `_ߟߊ߫_ߕߟߋ߬ߓߋ_ߘߐ߫߸_ߗߍ߭_ߡߛ`
2. `_ߦߋ߫_ߡߊ߬ߟߌ_ߞߐߛߊߦߌߡߊ_ߏ߬`
3. `_ߟߎ߬_ߟߊ߫_ߝߛߊߦߌ߫߸_ߓߎߙߎ߲ߘ`


### Key Findings

- **Best Predictability:** Context-4 (word) with 96.4% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (261,417 contexts)
- **Recommendation:** Context-3 or Context-4 for text generation

---
## 4. Vocabulary Analysis

![Zipf's Law](visualizations/zipf_law.png)

![Top Words](visualizations/top20_words.png)

![Coverage Curve](visualizations/vocab_coverage.png)

### Statistics

| Metric | Value |
|--------|-------|
| Vocabulary Size | 24,726 |
| Total Tokens | 758,182 |
| Mean Frequency | 30.66 |
| Median Frequency | 3 |
| Frequency Std Dev | 453.65 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ߟߊ߫ | 32,133 |
| 2 | ߊ߬ | 22,764 |
| 3 | ߦߋ߫ | 20,445 |
| 4 | ߘߌ߫ | 19,370 |
| 5 | ߟߎ߬ | 19,254 |
| 6 | ߘߐ߫ | 18,014 |
| 7 | ߣߌ߫ | 17,228 |
| 8 | ߏ߬ | 16,452 |
| 9 | ߟߋ߬ | 15,933 |
| 10 | ߞߊ߬ | 15,452 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ߛߌߦߋߙߊߟߋߦߐ߲߫ | 2 |
| 2 | ߡߊ߲߬ߜ߭ߊ߫ | 2 |
| 3 | ߛߏߟߌߡߊ߫ | 2 |
| 4 | ߦߊ߬ߟߎ߲߬ߞߊ߫ | 2 |
| 5 | ߞߏߦߌ߲ߘߎ߯ | 2 |
| 6 | ߞߊߦߌߟߊ߯ߤߎ߲߫ | 2 |
| 7 | ߥߙߏ߬ߘߜ߭ߎ | 2 |
| 8 | ߢߐ߲ߜ߭ߐ߲ | 2 |
| 9 | ep | 2 |
| 10 | ߣߊߣߌ | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.1458 |
| R² (Goodness of Fit) | 0.995876 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 53.3% |
| Top 1,000 | 76.5% |
| Top 5,000 | 90.4% |
| Top 10,000 | 95.0% |

### Key Findings

- **Zipf Compliance:** R²=0.9959 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 53.3% of corpus
- **Long Tail:** 14,726 words needed for remaining 5.0% coverage

---
## 5. Word Embeddings Evaluation

![Embedding Isotropy](visualizations/embedding_isotropy.png)

![Similarity Matrix](visualizations/embedding_similarity.png)

![t-SNE Words](visualizations/tsne_words.png)

![t-SNE Sentences](visualizations/tsne_sentences.png)


### 5.1 Cross-Lingual Alignment

![Alignment Quality](visualizations/embedding_alignment_quality.png)

![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)


### 5.2 Model Comparison

| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
|-------|-----------|----------|------------------|---------------|----------------|
| **mono_32d** | 32 | 0.8251 🏆 | 0.3375 | N/A | N/A |
| **mono_64d** | 64 | 0.6469 | 0.2857 | N/A | N/A |
| **mono_128d** | 128 | 0.1940 | 0.2840 | N/A | N/A |
| **aligned_32d** | 32 | 0.8251 | 0.3411 | 0.0347 | 0.2431 |
| **aligned_64d** | 64 | 0.6469 | 0.2880 | 0.0625 | 0.2708 |
| **aligned_128d** | 128 | 0.1940 | 0.2779 | 0.0764 | 0.2639 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8251 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3024. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 7.6% R@1 in cross-lingual retrieval.
- **Recommendation:** 128d aligned for best cross-lingual performance

---
## 6.  Morphological Analysis (Experimental)

This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.

### 6.1 Productivity & Complexity

| Metric | Value | Interpretation | Recommendation |
|--------|-------|----------------|----------------|
| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
| Idiomaticity Gap | **-0.615** | Low formulaic content | - |

### 6.2 Affix Inventory (Productive Units)

These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.

#### Productive Prefixes
| Prefix | Examples |
|--------|----------|
| `-ߞ` | ߞߏߔߣߌ߲߬, ߞߴߊ߬ߟߌ߬ߞߊ߰ߓߊ߬, ߞߐ߰ߖߌ߬ߘߟߊ߬ |
| `-ߛ` | ߛߍ߲ߕߊ߬, ߛߐ߲ߞߐ߫, ߛߊ߲ߡߊߝߋ߲ |
| `-ߟߊ` | ߟߊߛߴߊ߬, ߟߊ߬ߡߙߊ߬ߟߌ, ߟߊߡߐ߰ |
| `-ߡߊ` | ߡߊ߬ߣߌ߲߬ߝߐߛߐ߲, ߡߊ߬ߘߌ߬ߡߌ߲߬ߣߌ߲, ߡߊ߯ |
| `-ߓ` | ߓߍ߲߬ߓߊ߬ߟߌ߬ߦߊ߬, ߓߊߓߋ߬, ߓߛߌ߬ߞߌ߬ߟߌ |
| `-ߘ` | ߘߎ߰ߓߊ߫, ߘߐߜߍߕߊ, ߘߐ߲߬ߓߏ߲ |
| `-ߡ` | ߡߍ߲ߘߌߦߊ߫, ߡߊ߬ߣߌ߲߬ߝߐߛߐ߲, ߡߊ߬ߘߌ߬ߡߌ߲߬ߣߌ߲ |
| `-ߕ` | ߕߙߐߝߍ߬, ߕߎ߲߯ߣߍ߲߫, ߕߍ߬ߘߵߊ߬ߟߎ߬ |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ߊ` | ߝߊ߬ߘߌ߬ߜߊ, ߞߊ߬ߙߊ߲߬ߡߐ߰ߓߊ, ߖߊ߯ߓߊߟߌߦߊ |
| `-ߌ` | ߓߛߌ߬ߞߌ߬ߟߌ, ߜߏ߬ߞߌ, ߣߌ߬ߣߌ߬ߟߌ |
| `-ߦߊ` | ߖߊ߯ߓߊߟߌߦߊ, ߗߍ߬ߣߌ߫ߡߛߏ߬ߦߊ, ߣߝߊ߬ߢߐ߰ߦߊ |
| `-ߟߌ` | ߓߛߌ߬ߞߌ߬ߟߌ, ߣߌ߬ߣߌ߬ߟߌ, ߟߊ߬ߡߙߊ߬ߟߌ |
| `-ߟߊ` | ߛߋߟߊ, ߟߊߓߌ߬ߟߊ, ߥߎߟߊ |
| `-ߞߊ` | ߞߊ߲ߞߊ, ߓߌߋߟߏߙߎߛߌߞߊ, ߊߡߋߙߞߌߟߞߊ |
| `-ߏ` | ߟߊ߬ߖߊ߲ߞߏ, ߡߊ߬ߞߊߝߏ, ߦߙߏ |
| `-ߡߊ` | ߡߐ߬ߟߐ߲߬ߡߊ, ߖߛߐߡߊ, ߝߊߕߎߡߊ |

### 6.3 Bound Stems (Lexical Roots)

Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.

| Stem | Cohesion | Substitutability | Examples |
|------|----------|------------------|----------|
| `ߝߙߌߞ` | 2.30x | 19 contexts | ߊߝߙߌߞ, ߊߝߙߌߞߊ, ߊߝߙߌߞߊ߲ |
| `ߡߋߙߌ` | 2.17x | 14 contexts | ߊߡߋߙߌߞ, ߋߡߋߙߌߞ, ߊߡߋߙߌߞߌ |
| `ߞߎߡߘ` | 2.28x | 12 contexts | ߞߎߡߘߊ, ߞߎߡߘߊ߫, ߘߐ߫ߞߎߡߘߊ |
| `ߊߙߊߓ` | 2.14x | 14 contexts | ߊߙߊߓߎ, ߊߙߊߓߍߟ, ߊߙߊߓߎ߫ |
| `ߟߌߦߊ` | 1.67x | 30 contexts | ߦߟߌߦߊ, ߜߟߌߦߊ, ߞߊߟߌߦߊ |
| `ߞߏߟߊ` | 1.88x | 20 contexts | ߞߏߟߊ߫, ߞߏߟߊߕߍ, ߣߌߞߏߟߊ |
| `ߊߟߌߦ` | 1.85x | 14 contexts | ߞߊߟߌߦߊ, ߓߊߟߌߦߊ, ߥߊߟߌߦߊ |
| `ߟߌߡߊ` | 1.48x | 25 contexts | ߟߌߡߊ߫, ߦߟߌߡߊ, ߥߊߟߌߡߊ |
| `ߦߊߟߌ` | 1.72x | 15 contexts | ߖߏߦߊߟߌ, ߗߋߦߊߟߌ, ߗߋߦߊߟߌ߫ |
| `ߓߟߏߡ` | 1.64x | 16 contexts | ߓߟߏߡߊ, ߓߟߏߡߐ, ߓߟߏߡߐ߮ |
| `ߊߟߏߡ` | 2.36x | 6 contexts | ߊߟߏߡߊ߲, ߊߟߏߡߊ߲߫, ߊߟߏߡߊߦߌ߲ |
| `ߛߓߍߟ` | 1.65x | 11 contexts | ߛߓߍߟߌ, ߛߓߍߟߊ, ߛߓߍߟߊ߲ |

### 6.4 Affix Compatibility (Co-occurrence)

This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.

| Prefix | Suffix | Frequency | Examples |
|--------|--------|-----------|----------|
| `-ߞ` | `-ߊ` | 158 words | ߞߊ߲߬ߖߊ, ߞߐ߯ߟߕߊ |
| `-ߛ` | `-ߊ` | 102 words | ߛߦߊ, ߛߏ߯ߡߦߊ |
| `-ߘ` | `-ߊ` | 85 words | ߘߐ߲߬ߖߊ߬ߓߊ, ߘߐߜߟߌߦߊ |
| `-ߓ` | `-ߊ` | 73 words | ߓߏ߬ߢߊ, ߓߋߕߊ |
| `-ߝ` | `-ߊ` | 63 words | ߝߎߥߟߊ, ߝߘߏ߬ߓߊ߬ߦߊ |
| `-ߟߊ` | `-ߌ` | 53 words | ߟߊߕߊ߯ߟߌ, ߟߊ߬ߕߊ߲߬ߞߊ߬ߟߌ |
| `-ߞ` | `-ߦߊ` | 48 words | ߞߏ߲߬ߓߏ߬ߦߊ, ߞߌ߬ߣߊ߬ߦߊ |
| `-ߕ` | `-ߊ` | 43 words | ߕߊ߲ߓߊ߲ߞߕߐߦߊ, ߕߍߟߐߦߊ |
| `-ߞ` | `-ߌ` | 41 words | ߞߎ߬ߙߊ߬ߦߌ߬ߛߌ, ߞߊ߲ߠߊߓߌߟߊߟߌ |
| `-ߘ` | `-ߌ` | 40 words | ߘߝߐߟߌ, ߘߝߊߟߌ |

### 6.5 Recursive Morpheme Segmentation

Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).

| Word | Suggested Split | Confidence | Stem |
|------|-----------------|------------|------|
| ߛߊߡߊߞߎߟߎ߲ | **`ߛߊ-ߡߊ-ߞߎߟߎ߲`** | 7.5 | `ߞߎߟߎ߲` |
| ߖߙߊߘߛߌߕߙߊߦߊ | **`ߖߙߊߘߛߌߕߙ-ߊ-ߦߊ`** | 7.5 | `ߊ` |
| ߘߟߊߡߌ߬ߣߊ߬ | **`ߘ-ߟߊ-ߡߌ߬ߣߊ߬`** | 7.5 | `ߡߌ߬ߣߊ߬` |
| ߊߙߑߛ߭ߌߣߊߙ | **`ߊߙߑߛ߭ߌߣ-ߊ-ߙ`** | 7.5 | `ߊ` |
| ߘߊߟߞߊߟߌߦߊ | **`ߘߊ-ߟ-ߞߊߟߌߦߊ`** | 7.5 | `ߞߊߟߌߦߊ` |
| ߓߟߏߟߊߓߊ߯ߙߊ߫ | **`ߓߟߏ-ߟߊ-ߓߊ߯ߙߊ߫`** | 7.5 | `ߓߊ߯ߙߊ߫` |
| ߦߟߌߓߌߟߊߟߌ | **`ߦߟߌߓߌߟ-ߊ-ߟߌ`** | 7.5 | `ߊ` |
| ߓߟߏߡߊߕߌߢߍߣߍ߲ | **`ߓߟߏ-ߡߊ-ߕߌߢߍߣߍ߲`** | 7.5 | `ߕߌߢߍߣߍ߲` |
| ߣߊߡߎ߲ߘߐߞߏ | **`ߣߊߡߎ߲-ߘߐ-ߞߏ`** | 7.5 | `ߘߐ` |
| ߝߘߊߢߐ߲߯ߦߊ | **`ߝ-ߘߊ-ߢߐ߲߯ߦߊ`** | 7.5 | `ߢߐ߲߯ߦߊ` |
| ߦߟߍ߬ߡߊ߲߬ߓߊߟߌ | **`ߦߟߍ߬ߡߊ߲߬ߓ-ߊ-ߟߌ`** | 7.5 | `ߊ` |
| ߦߌߟߡߊߦߊߟߌ | **`ߦߌߟߡߊ-ߦߊ-ߟߌ`** | 6.0 | `ߦߌߟߡߊ` |
| ߝߘߎߓߊߟߌߦߊ | **`ߝ-ߘߎ-ߓߊߟߌߦߊ`** | 6.0 | `ߓߊߟߌߦߊ` |
| ߞߐ߲ߛߐ߲ߦߊߟߌ | **`ߞߐ߲ߛߐ߲-ߦߊ-ߟߌ`** | 6.0 | `ߞߐ߲ߛߐ߲` |
| ߛߏ߯ߙߏߟߌߟߊ | **`ߛߏ߯ߙߏ-ߟߌ-ߟߊ`** | 6.0 | `ߛߏ߯ߙߏ` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language N’Ko shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

---
## 7. Summary & Recommendations

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

### Production Recommendations

| Component | Recommended | Rationale |
|-----------|-------------|-----------|
| Tokenizer | **32k BPE** | Best compression (4.45x) |
| N-gram | **2-gram** | Lowest perplexity (492) |
| Markov | **Context-4** | Highest predictability (96.4%) |
| Embeddings | **100d** | Balanced semantic capture and isotropy |


---
## Appendix: Metrics Glossary & Interpretation Guide

This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.

### Tokenizer Metrics

**Compression Ratio**
> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
>
> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
>
> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.

**Average Token Length (Fertility)**
> *Definition:* Mean number of characters per token produced by the tokenizer.
>
> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
>
> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.

**Unknown Token Rate (OOV Rate)**
> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
>
> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
>
> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.

### N-gram Model Metrics

**Perplexity**
> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
>
> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
>
> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.

**Entropy**
> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
>
> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
>
> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.

**Coverage (Top-K)**
> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
>
> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
>
> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.

### Markov Chain Metrics

**Average Entropy**
> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
>
> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
>
> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.

**Branching Factor**
> *Definition:* Average number of unique next tokens observed for each context.
>
> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
>
> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.

**Predictability**
> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
>
> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
>
> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.

### Vocabulary & Zipf's Law Metrics

**Zipf's Coefficient**
> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
>
> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
>
> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.

**R² (Coefficient of Determination)**
> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
>
> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
>
> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.

**Vocabulary Coverage**
> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
>
> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
>
> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.

### Word Embedding Metrics

**Isotropy**
> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
>
> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
>
> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.

**Average Norm**
> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
>
> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
>
> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).

**Cosine Similarity**
> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
>
> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
>
> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.

**t-SNE Visualization**
> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
>
> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
>
> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.

### General Interpretation Guidelines

1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.


### Visualizations Index

| Visualization | Description |
|---------------|-------------|
| Tokenizer Compression | Compression ratios by vocabulary size |
| Tokenizer Fertility | Average token length by vocabulary |
| Tokenizer OOV | Unknown token rates |
| Tokenizer Total Tokens | Total tokens by vocabulary |
| N-gram Perplexity | Perplexity by n-gram size |
| N-gram Entropy | Entropy by n-gram size |
| N-gram Coverage | Top pattern coverage |
| N-gram Unique | Unique n-gram counts |
| Markov Entropy | Entropy by context size |
| Markov Branching | Branching factor by context |
| Markov Contexts | Unique context counts |
| Zipf's Law | Frequency-rank distribution with fit |
| Vocab Frequency | Word frequency distribution |
| Top 20 Words | Most frequent words |
| Vocab Coverage | Cumulative coverage curve |
| Embedding Isotropy | Vector space uniformity |
| Embedding Norms | Vector magnitude distribution |
| Embedding Similarity | Word similarity heatmap |
| Nearest Neighbors | Similar words for key terms |
| t-SNE Words | 2D word embedding visualization |
| t-SNE Sentences | 2D sentence embedding visualization |
| Position Encoding | Encoding method comparison |
| Model Sizes | Storage requirements |
| Performance Dashboard | Comprehensive performance overview |

---
## About This Project

### Data Source

Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.

### Project

A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.

### Maintainer

[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)

### Citation

If you use these models in your research, please cite:

```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}
}
```

### License

MIT License - Free for academic and commercial use.

### Links

- 🌐 Website: [wikilangs.org](https://wikilangs.org)
- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
---
*Generated by Wikilangs Models Pipeline*

*Report Date: 2026-01-10 15:59:19*