---
language: shn
language_name: Shan
language_family: taikadai_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-taikadai_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: 4.905
  - name: best_isotropy
    type: isotropy
    value: 0.7537
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10
---

# Shan - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Shan** 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** | 3.964x | 3.97 | 1.0788% | 1,015,636 |
| **16k** | 4.402x | 4.40 | 1.1980% | 914,601 |
| **32k** | 4.651x | 4.65 | 1.2658% | 865,595 |
| **64k** | 4.905x 🏆 | 4.91 | 1.3350% | 820,755 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `တႃႈႁိူဝ်းမိၼ် မိူင်းတူၼ် ၼႆႉ ပဵၼ်တႃႈႁိူဝ်းမိၼ် ဢၼ်မီးတီႈ ဝဵင်းမိူင်းတူၼ်၊ မိူင်း...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁တႃႈႁိူဝ်းမိၼ် ▁မ ိူင်းတူၼ် ▁ၼႆႉ ▁ပဵၼ် တႃႈႁိူဝ်းမိၼ် ▁ဢၼ်မီးတီႈ ▁ဝဵင်းမ ိူင်းတူၼ် ၊ ... (+7 more)` | 17 |
| 16k | `▁တႃႈႁိူဝ်းမိၼ် ▁မိူင်းတူၼ် ▁ၼႆႉ ▁ပဵၼ် တႃႈႁိူဝ်းမိၼ် ▁ဢၼ်မီးတီႈ ▁ဝဵင်းမ ိူင်းတူၼ်၊ ▁မိူင်းတႆး၊ ▁မိူင်းမျၢၼ်ႇမႃႇ ... (+4 more)` | 14 |
| 32k | `▁တႃႈႁိူဝ်းမိၼ် ▁မိူင်းတူၼ် ▁ၼႆႉ ▁ပဵၼ် တႃႈႁိူဝ်းမိၼ် ▁ဢၼ်မီးတီႈ ▁ဝဵင်းမ ိူင်းတူၼ်၊ ▁မိူင်းတႆး၊ ▁မိူင်းမျၢၼ်ႇမႃႇ ... (+4 more)` | 14 |
| 64k | `▁တႃႈႁိူဝ်းမိၼ် ▁မိူင်းတူၼ် ▁ၼႆႉ ▁ပဵၼ် တႃႈႁိူဝ်းမိၼ် ▁ဢၼ်မီးတီႈ ▁ဝဵင်းမ ိူင်းတူၼ်၊ ▁မိူင်းတႆး၊ ▁မိူင်းမျၢၼ်ႇမႃႇ ... (+4 more)` | 14 |

**Sample 2:** `ၶႂ်ႈမၢႆထိုင်ဝႃႈ - တူဝ်ၼပ်ႉ 30 ၸိူဝ်းပဵၼ်ပီ ဢေႇတီႇ 30,`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 3 0 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 3 ... (+2 more)` | 12 |
| 16k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 3 0 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 3 ... (+2 more)` | 12 |
| 32k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 3 0 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 3 ... (+2 more)` | 12 |
| 64k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 3 0 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 3 ... (+2 more)` | 12 |

**Sample 3:** `ၶႂ်ႈမၢႆထိုင်ဝႃႈ - တူဝ်ၼပ်ႉ 47 ၸိူဝ်းပဵၼ်ပီ ဢေႇတီႇ 47,`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 4 7 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 4 ... (+2 more)` | 12 |
| 16k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 4 7 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 4 ... (+2 more)` | 12 |
| 32k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 4 7 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 4 ... (+2 more)` | 12 |
| 64k | `▁ၶႂ်ႈမၢႆထိုင်ဝႃႈ ▁- ▁တူဝ်ၼပ်ႉ ▁ 4 7 ▁ၸိူဝ်းပဵၼ်ပီ ▁ဢေႇတီႇ ▁ 4 ... (+2 more)` | 12 |


### Key Findings

- **Best Compression:** 64k achieves 4.905x compression
- **Lowest UNK Rate:** 8k with 1.0788% 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 | 304 🏆 | 8.25 | 6,013 | 75.0% | 92.0% |
| **2-gram** | Subword | 774 | 9.60 | 13,675 | 49.8% | 86.7% |
| **3-gram** | Word | 430 | 8.75 | 11,217 | 69.6% | 89.1% |
| **3-gram** | Subword | 4,483 | 12.13 | 77,354 | 27.7% | 59.2% |
| **4-gram** | Word | 621 | 9.28 | 23,157 | 67.2% | 84.2% |
| **4-gram** | Subword | 15,378 | 13.91 | 268,593 | 20.2% | 44.3% |
| **5-gram** | Word | 620 | 9.28 | 22,270 | 68.2% | 83.7% |
| **5-gram** | Subword | 30,653 | 14.90 | 454,166 | 17.4% | 39.0% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `1 ဝၼ်း` | 30,342 |
| 2 | `ၼႆႉ မီးဝႆႉတီႈ` | 5,369 |
| 3 | `ပဵၼ် ယဝ်ႉ` | 5,138 |
| 4 | `ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း` | 4,826 |
| 5 | `သေ ႁူဝ်ၼပ်ႉၵူၼ်း` | 4,818 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ်` | 4,773 |
| 2 | `ၸႄႈတိူင်းတႃႈလိူဝ်ႇ ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ` | 4,773 |
| 3 | `သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း` | 4,741 |
| 4 | `သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ` | 4,740 |
| 5 | `ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ` | 4,740 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ၸႄႈတိူင်းတႃႈလိူဝ်ႇ ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ်` | 4,773 |
| 2 | `ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ` | 4,740 |
| 3 | `သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ` | 4,740 |
| 4 | `ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ` | 4,735 |
| 5 | `ၵေႃႉ သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း` | 4,595 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ` | 4,740 |
| 2 | `ၵေႃႉ သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ` | 4,595 |
| 3 | `ၸႄႈတိူင်းတႃႈလိူဝ်ႇ ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ် ယဝ်ႉ` | 4,586 |
| 4 | `ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ` | 4,549 |
| 5 | `ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ် ယဝ်ႉ ၸွမ်းလူၺ်ႈ` | 4,548 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ၼ် း` | 202,089 |
| 2 | `း _` | 191,313 |
| 3 | `) _` | 136,283 |
| 4 | `_ (` | 136,166 |
| 5 | `င် း` | 128,103 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ဝ ၼ် း` | 122,686 |
| 2 | `_ ဝ ၼ်` | 119,537 |
| 3 | `) _ ဝ` | 116,929 |
| 4 | `ၼ် း _` | 111,432 |
| 5 | `း _ (` | 90,718 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ဝ ၼ် း` | 119,517 |
| 2 | `) _ ဝ ၼ်` | 116,755 |
| 3 | `ဝ ၼ် း _` | 89,164 |
| 4 | `ၼ် း _ (` | 86,035 |
| 5 | `ယ ဝ် ႉ ။` | 44,872 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `) _ ဝ ၼ် း` | 116,755 |
| 2 | `_ ဝ ၼ် း _` | 88,818 |
| 3 | `ဝ ၼ် း _ (` | 85,077 |
| 4 | `ယ ဝ် ႉ ။ _` | 44,169 |
| 5 | `1 ) _ ဝ ၼ်` | 38,381 |


### Key Findings

- **Best Perplexity:** 2-gram (word) with 304
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~39% 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 | 0.2330 | 1.175 | 1.77 | 288,426 | 76.7% |
| **1** | Subword | 0.1366 | 1.099 | 3.35 | 23,531 | 86.3% |
| **2** | Word | 0.0481 | 1.034 | 1.09 | 510,797 | 95.2% |
| **2** | Subword | 0.3263 | 1.254 | 2.92 | 78,767 | 67.4% |
| **3** | Word | 0.0147 | 1.010 | 1.03 | 554,509 | 98.5% |
| **3** | Subword | 0.4202 | 1.338 | 2.67 | 229,767 | 58.0% |
| **4** | Word | 0.0059 🏆 | 1.004 | 1.01 | 566,193 | 99.4% |
| **4** | Subword | 0.3421 | 1.268 | 2.01 | 614,095 | 65.8% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `ဝၼ်း 27 ဝၼ်း 9 ၸုမ်းၼၼ်ႉ ဢွၼ်ၵၼ်ၶပ်ႉယႆပႆၸွမ်း သဵၼ်ႈတၢင်းပၢင်းပိတၵၢတ်ႈလႄႈ ၵူၼ်းသမ်ႉပေႃးတဵမ်ၵဵဝ်ႇတဵမ်တ...`
2. `1 ဝၼ်း လိူၼ်သႅပ်ႇထႅမ်ႇပႃႇ 1 ဝၼ်း 6 ဝၼ်း 7 ဝၼ်း 28 ဝၼ်း 19 ဝၼ်း 5 ၶိုၼ်းယဝ်ႉ မိၼ်းယႄးၵျေႃႇၸႂႃႇၵေႃႈ`
3. `ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ ပဵၼ်မႃး ငဝ်းမၢပ်ႈႁိူဝ်ႈလႄႈ တီႈလွၵ်းသီမၢပ်ႈႁိူဝ်ႈၼႆႉ တေလႆႈဢဝ်သီ...`

**Context Size 2:**

1. `1 ဝၼ်း လိူၼ်ၼူဝ်ႇဝႅမ်ႇပႃႇ 1 ဝၼ်း လိူၼ်ၾႅပ်ႇဝႃႇရီႇ 1 ဝၼ်း သႅပ်ႇထႅမ်ႇပႃႇ ၁ ဝၼ်း ၂ ဝၼ်း ၃ ဝၼ်း ၄ ဝၼ်း`
2. `ၼႆႉ မီးဝႆႉတီႈ ဢိူင်ႇသတဵင်ႇ ၸႄႈဝဵင်းꧤူၵ်ႉပျိၼ်း ၸႄႈတွၼ်ႈၵေႃႉတွင်း ၸႄႈတိူင်းတၼိၼ်းတႃႇယီႇ ယဝ်ႉ ၶူတ်ႉဢွင...`
3. `ပဵၼ် ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ ဢိူင်ႇဢၢႆႇတူဝ်ႇၼႆႉ မီးဝႆႉ ၸၢႆး ၵေႃႉ ယိင်း ၵေႃႉ သေ ႁူဝ...`

**Context Size 3:**

1. `ၸႄႈတိူင်းတႃႈလိူဝ်ႇ ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ် ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ ဢိူင်ႇတယႅတ်ႉ...`
2. `ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ် ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ ဢိူင်ႇတလၢႆးသျေႇ ၸၢၼ်း ၼႆႉ မီးဝႆ...`
3. `သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ ၵေႃႉယဝ်ႉ ၽိုၼ်ဢိင် ၸႄႈဝဵင်းငၸုၼ်ႇ`

**Context Size 4:**

1. `ၸႄႈတိူင်းတႃႈလိူဝ်ႇ ယဝ်ႉ ၶူတ်ႉဢွင်ႈတီႈၼႆႉ ပဵၼ် ယဝ်ႉ ၸွမ်းလူၺ်ႈ သဵၼ်ႈမၢႆႁူဝ်ႁိူၼ်း သေ တီႈ ဢိူင်ႇၵျူဝ်ႉ...`
2. `ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ ၵေႃႉယဝ်ႉ ၽိုၼ်ဢိင် ၸႄႈဝဵင်းမတယႃႇ`
3. `သေ ႁူဝ်ၼပ်ႉၵူၼ်း ယူႇသဝ်း ႁူမ်ႈ မီးယူႇ ၵေႃႉယဝ်ႉ ၽိုၼ်ဢိင် ၸႄႈဝဵင်းပျေႃႇပွႆႇ`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_ပၢၵ်ႈပၼ်း_သီႁဝ်_။_တွ`
2. `းၼၼ်းၸူဝ်း_5)_ဝ၊_ဢေႃ`
3. `ၼ်ပၼ်_၊_ၵေႃႇ_ယိင်ၸိူဝ်းလ`

**Context Size 2:**

1. `ၼ်း။_ၸွမ်_ၵူၼ်းၸုၵျီႇ_(5)`
2. `း_(29)_ဝၼ်း_(1)_ဝၼ်`
3. `)_ဝၢၼ်_ၸၢႆး_(14)_ဝၼ်`

**Context Size 3:**

1. `ဝၼ်း_(2)_ဝၼ်း_ၽၢႆႇတူၵ်း`
2. `_ဝၼ်းဢွၵ်ႇၼႆ_လဝ်ႈထိုင်တႃႇ_`
3. `)_ဝၼ်း_(12)_ဝၼ်း_(28`

**Context Size 4:**

1. `_ဝၼ်း_(21)_ဝၼ်း_(16)_`
2. `)_ဝၼ်း_(20)_ဝၼ်း။_လိူၼ်သႅ`
3. `ဝၼ်း_(24)_ဝၼ်း_(20)_ဝ`


### Key Findings

- **Best Predictability:** Context-4 (word) with 99.4% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (614,095 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 | 47,353 |
| Total Tokens | 767,152 |
| Mean Frequency | 16.20 |
| Median Frequency | 3 |
| Frequency Std Dev | 582.68 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ဝၼ်း | 116,548 |
| 2 | 1 | 32,050 |
| 3 | ယဝ်ႉ | 11,719 |
| 4 | ၽိုၼ်ဢိင် | 11,655 |
| 5 | သေ | 10,963 |
| 6 | ၵေႃႉ | 9,578 |
| 7 | ၼႆႉ | 8,785 |
| 8 | ပဵၼ် | 7,402 |
| 9 | တီႈ | 5,950 |
| 10 | မီးဝႆႉ | 5,835 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ၽိုၼ်မိူၼ် | 2 |
| 2 | copies | 2 |
| 3 | ပဵၼ်တႆးၽိဝ်း | 2 |
| 4 | ၸၢႆးဢၢၼ်းတႆး | 2 |
| 5 | ၼၢင်းယွတ်ႈၼု | 2 |
| 6 | ၸၢႆးၵျီး | 2 |
| 7 | မိူင်းယႆယဝ်ႉ | 2 |
| 8 | ပူဝ်ႇမျႃႉ | 2 |
| 9 | ၶုၼ်ၵျေႃႉၶႅင်ႇ | 2 |
| 10 | လွင်ႈငမ်းယဵၼ်မိူင်းတႆး | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.9775 |
| R² (Goodness of Fit) | 0.985701 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 58.5% |
| Top 1,000 | 73.3% |
| Top 5,000 | 82.5% |
| Top 10,000 | 87.2% |

### Key Findings

- **Zipf Compliance:** R²=0.9857 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 58.5% of corpus
- **Long Tail:** 37,353 words needed for remaining 12.8% 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 | 0.7537 🏆 | 0.3337 | N/A | N/A |
| **mono_64d** | 64 | 0.3939 | 0.2857 | N/A | N/A |
| **mono_128d** | 128 | 0.0610 | 0.2919 | N/A | N/A |
| **aligned_32d** | 32 | 0.7537 | 0.3194 | 0.0180 | 0.1380 |
| **aligned_64d** | 64 | 0.3939 | 0.2880 | 0.0300 | 0.1900 |
| **aligned_128d** | 128 | 0.0610 | 0.2969 | 0.0420 | 0.2220 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.7537 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3026. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 4.2% 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 | **1.149** | High formulaic/idiomatic 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 |
|--------|----------|
| `-ပ` | ပၢၼ်လဵဝ်ၵၼ်တင်း, ပူႇတၢၼ်ႇ, ပုဂံ |
| `-တ` | တီႈၼႂ်းၸႄႈဝဵင်းယိၼ်းမႃႇပိၼ်ႇၼႆႉ, တွင်ႇလိူဝ်း, တီႈၼၼ်ႈၵေႃႈ |
| `-မ` | မိူင်းလႂ်မိူင်းၼၼ်ႉ, မိူင်းမျၢၼ်ႇမႃႇယဝ်ႉ, မရဵင်း |
| `-သ` | သမ်ႉၸၢင်ႈ, သင်ႇဝႆႉဝႃႈ, သေဢမ်ႇဝႃႈ |
| `-ဢ` | ဢၼ်တႄႉ, ဢိူင်ႇသီးၵူင်းၼႆႉ, ဢဝ်ပရိၼိပ်ႉပၢၼ်ႇယဝ်ႉ |
| `-ၵ` | ၵေႃလိၵ်ႈလၢႆးလႄႈ, ၵႂႃႇဢႅဝ်ႇ, ၵၢၼ်ၸိူင်ႉႁိုဝ်လႄႈသင် |
| `-လ` | လုမ်းတႅၼ်းၽွင်းသၢင်ႈၾိင်ႈမိူင်း, လိူင်ႇၼမ်ယဝ်ႉ, လိၵ်ႈတႆးလူင်မိူဝ်ႈပၢၼ်ၵဵင်းဝီ |
| `-ဢၼ` | ဢၼ်တႄႉ, ဢၼ်ႁဵတ်းၵၢၼ်တႆး, ဢၼ်ဢွၼ်ႇၸၢႆး |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-s` | classes, dress, layouts |
| `-n` | foreign, christian, berlin |
| `-e` | give, aubange, lifestyle |
| `-d` | passed, afraid, ဝၢၼ်ႈလူင်တွင်းgad |
| `-on` | migration, opinion, xenophon |
| `-ng` | achang, trading, zhejiang |
| `-y` | day, modernity, turkey |
| `-t` | east, recordsost, crescent |

### 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 |
|------|----------|------------------|----------|
| `tion` | 2.53x | 13 contexts | action, nation, options |
| `atio` | 2.48x | 11 contexts | nation, nations, station |

### 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 |
|--------|--------|-----------|----------|
| `-s` | `-s` | 8 words | scales, shows |
| `-s` | `-t` | 6 words | scoot, significant |
| `-s` | `-d` | 5 words | statehood, switzerland |
| `-s` | `-y` | 5 words | study, slowly |
| `-s` | `-n` | 4 words | sangken, sovereign |
| `-s` | `-e` | 3 words | spike, shwe |
| `-s` | `-ed` | 3 words | supported, specialized |
| `-s` | `-ng` | 2 words | shandong, sung |
| `-s` | `-g` | 2 words | shandong, sung |
| `-s` | `-on` | 2 words | simpson, scorpion |

### 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 |
|------|-----------------|------------|------|
| operations | **`operation-s`** | 4.5 | `operation` |
| ႁၵ်ႉမိူင်း | **`ႁ-ၵ-်ႉမိူင်း`** | 4.5 | `်ႉမိူင်း` |
| လၵ်းမိူင်းၼႆႉ | **`လ-ၵ-်းမိူင်းၼႆႉ`** | 4.5 | `်းမိူင်းၼႆႉ` |
| လၵ်းမိူင်း | **`လ-ၵ-်းမိူင်း`** | 4.5 | `်းမိူင်း` |
| လဝ်ႈထိုင် | **`လ-ဝ-်ႈထိုင်`** | 3.0 | `်ႈထိုင်` |
| တီႈလူႇတၢၼ်း | **`တ-ီႈလူႇတၢၼ်း`** | 1.5 | `ီႈလူႇတၢၼ်း` |
| expressway | **`expresswa-y`** | 1.5 | `expresswa` |
| လိူၼ်ႁူၵ်း | **`လ-ိူၼ်ႁူၵ်း`** | 1.5 | `ိူၼ်ႁူၵ်း` |
| ဢဝ်ငဝ်းလႅင်း | **`ဢဝ-်ငဝ်းလႅင်း`** | 1.5 | `်ငဝ်းလႅင်း` |
| ၶဝ်တွၼ်းလိူဝ်သေ | **`ၶဝ-်တွၼ်းလိူဝ်သေ`** | 1.5 | `်တွၼ်းလိူဝ်သေ` |
| ဢေႃးၽႃႇမင်ႇၵလႃႇ | **`ဢ-ေႃးၽႃႇမင်ႇၵလႃႇ`** | 1.5 | `ေႃးၽႃႇမင်ႇၵလႃႇ` |
| ဢမ်ႇလီလိုမ်း | **`ဢ-မ်ႇလီလိုမ်း`** | 1.5 | `မ်ႇလီလိုမ်း` |
| မိူင်းဢႃႇဝႃႉၵေႃႈ | **`မ-ိူင်းဢႃႇဝႃႉၵေႃႈ`** | 1.5 | `ိူင်းဢႃႇဝႃႉၵေႃႈ` |
| ဢၼ်မီးၵုင်ႇမုၼ် | **`ဢၼ-်မီးၵုင်ႇမုၼ်`** | 1.5 | `်မီးၵုင်ႇမုၼ်` |
| ဢိင်ၼိူဝ်လူၺ်ႈ | **`ဢ-ိင်ၼိူဝ်လူၺ်ႈ`** | 1.5 | `ိင်ၼိူဝ်လူၺ်ႈ` |

### 6.6 Linguistic Interpretation

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

> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.

---
## 7. Summary & Recommendations

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

### Production Recommendations

| Component | Recommended | Rationale |
|-----------|-------------|-----------|
| Tokenizer | **64k BPE** | Best compression (4.91x) |
| N-gram | **2-gram** | Lowest perplexity (304) |
| Markov | **Context-4** | Highest predictability (99.4%) |
| 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 20:12:17*