README.md

---
language: blk
language_name: Pa'o Karen
language_family: tibetoburman_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-tibetoburman_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: 4.848
  - name: best_isotropy
    type: isotropy
    value: 0.8632
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

# Pa'o Karen - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Pa'o Karen** 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** | 4.022x | 4.02 | 0.0580% | 1,056,850 |
| **16k** | 4.430x | 4.43 | 0.0639% | 959,541 |
| **32k** | 4.613x | 4.61 | 0.0665% | 921,415 |
| **64k** | 4.848x 🏆 | 4.85 | 0.0699% | 876,870 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `မျန်မာခမ်းထီကိုယို တွိုင်ꩻဒေႏသတန် အဝ်ႏ ( ၇ )တွိုင်ꩻ နဝ်ꩻသွူ ။`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁မျန်မာခမ်းထီ ကိုယို ▁တွိုင်ꩻဒေႏသတန် ▁အဝ်ႏ ▁( ▁၇ ▁) တွိုင်ꩻ ▁နဝ်ꩻ သွူ ... (+1 more)` | 11 |
| 16k | `▁မျန်မာခမ်းထီ ကိုယို ▁တွိုင်ꩻဒေႏသတန် ▁အဝ်ႏ ▁( ▁၇ ▁) တွိုင်ꩻ ▁နဝ်ꩻသွူ ▁။` | 10 |
| 32k | `▁မျန်မာခမ်းထီ ကိုယို ▁တွိုင်ꩻဒေႏသတန် ▁အဝ်ႏ ▁( ▁၇ ▁) တွိုင်ꩻ ▁နဝ်ꩻသွူ ▁။` | 10 |
| 64k | `▁မျန်မာခမ်းထီ ကိုယို ▁တွိုင်ꩻဒေႏသတန် ▁အဝ်ႏ ▁( ▁၇ ▁) တွိုင်ꩻ ▁နဝ်ꩻသွူ ▁။` | 10 |

**Sample 2:** `ဝေင်ꩻနောင်ꩻတရားယိုနဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ ၊ ဖြဝ်ꩻခမ်းနယ်ႏအခဝ်နဝ်၊ တောင်ႏကီꩻခရ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ဝေင်ꩻန ောင်ꩻ တရား ယိုနဝ်ꩻ ▁အဝ်ႏဒျာႏ ▁မျန်မာခမ်းထီ ▁၊ ▁ဖြဝ်ꩻခမ်းနယ်ႏ အခဝ်နဝ်၊ ▁တောင်ႏကီꩻခရဲင်ႏ ... (+8 more)` | 18 |
| 16k | `▁ဝေင်ꩻန ောင်ꩻ တရား ယိုနဝ်ꩻ ▁အဝ်ႏဒျာႏ ▁မျန်မာခမ်းထီ ▁၊ ▁ဖြဝ်ꩻခမ်းနယ်ႏ အခဝ်နဝ်၊ ▁တောင်ႏကီꩻခရဲင်ႏ ... (+8 more)` | 18 |
| 32k | `▁ဝေင်ꩻန ောင်ꩻ တရား ယိုနဝ်ꩻ ▁အဝ်ႏဒျာႏ ▁မျန်မာခမ်းထီ ▁၊ ▁ဖြဝ်ꩻခမ်းနယ်ႏ အခဝ်နဝ်၊ ▁တောင်ႏကီꩻခရဲင်ႏ ... (+8 more)` | 18 |
| 64k | `▁ဝေင်ꩻနောင်ꩻ တရားယိုနဝ်ꩻ ▁အဝ်ႏဒျာႏ ▁မျန်မာခမ်းထီ ▁၊ ▁ဖြဝ်ꩻခမ်းနယ်ႏ အခဝ်နဝ်၊ ▁တောင်ႏကီꩻခရဲင်ႏ ▁၊ ▁ဝေင်ꩻနယ်ႏပ ... (+6 more)` | 16 |

**Sample 3:** `အမုဲင် ခမ်းထီ ကသှိုပ်စဒါႏ ငဝ်းလဝ်းနီꩻ ၃၅လာအို ၉၄ ထူႏတောမ်`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁အမုဲင် ▁ခမ်းထီ ▁က သ ှို ပ် စဒါႏ ▁ငဝ်း လ ဝ်း ... (+6 more)` | 16 |
| 16k | `▁အမုဲင် ▁ခမ်းထီ ▁ကသ ှိုပ် စဒါႏ ▁ငဝ်း လဝ်း နီꩻ ▁၃၅ လာအို ... (+3 more)` | 13 |
| 32k | `▁အမုဲင် ▁ခမ်းထီ ▁ကသှိုပ်စဒါႏ ▁ငဝ်းလဝ်းနီꩻ ▁၃၅လာအို ▁၉ ၄ ▁ထူႏတောမ်` | 8 |
| 64k | `▁အမုဲင် ▁ခမ်းထီ ▁ကသှိုပ်စဒါႏ ▁ငဝ်းလဝ်းနီꩻ ▁၃၅လာအို ▁၉၄ ▁ထူႏတောမ်` | 7 |


### Key Findings

- **Best Compression:** 64k achieves 4.848x compression
- **Lowest UNK Rate:** 8k with 0.0580% 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 | 2,539 | 11.31 | 4,306 | 21.2% | 57.9% |
| **2-gram** | Subword | 1,398 🏆 | 10.45 | 24,285 | 42.8% | 77.0% |
| **3-gram** | Word | 3,862 | 11.92 | 6,537 | 18.8% | 47.3% |
| **3-gram** | Subword | 11,299 | 13.46 | 129,572 | 19.0% | 45.1% |
| **4-gram** | Word | 16,871 | 14.04 | 23,296 | 9.0% | 22.0% |
| **4-gram** | Subword | 54,089 | 15.72 | 405,489 | 10.1% | 25.8% |
| **5-gram** | Word | 15,317 | 13.90 | 19,946 | 8.7% | 21.0% |
| **5-gram** | Subword | 138,288 | 17.08 | 617,898 | 5.8% | 16.6% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `နဝ်ꩻ အဝ်ႏဒျာႏ` | 719 |
| 2 | `အဝ်ႏဒျာႏ မျန်မာခမ်းထီ` | 691 |
| 3 | `ခရိစ်နေင်ႏ ဗာႏ` | 403 |
| 4 | `ဗာႏ စာႏရင်ꩻအလꩻ` | 320 |
| 5 | `မျန်မာခမ်းထီ အခဝ်ထာႏဝ` | 295 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `နဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ` | 624 |
| 2 | `အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ထာႏဝ` | 295 |
| 3 | `ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ` | 261 |
| 4 | `ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ` | 161 |
| 5 | `ထာꩻထွာဖုံႏ လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ` | 153 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `နဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ထာႏဝ` | 282 |
| 2 | `ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ` | 161 |
| 3 | `လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ အထွတ်အမျတ်မွူးနီꩻဖုံႏ` | 153 |
| 4 | `သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ အထွတ်အမျတ်မွူးနီꩻဖုံႏ အာႏကွိုꩻ` | 153 |
| 5 | `ထာꩻထွာဖုံႏ လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ` | 153 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ အထွတ်အမျတ်မွူးနီꩻဖုံႏ အာႏကွိုꩻ` | 153 |
| 2 | `ထာꩻထွာဖုံႏ လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ အထွတ်အမျတ်မွူးနီꩻဖုံႏ` | 153 |
| 3 | `သွူ ထာꩻထွာဖုံႏ လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ` | 151 |
| 4 | `ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ လိုꩻဖြာꩻခြွဉ်းအဝ်ႏ` | 131 |
| 5 | `အဝ်ႏသော့ꩻနဝ်ꩻသွူ ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ` | 111 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ာ ႏ` | 142,384 |
| 2 | `၊ _` | 135,380 |
| 3 | `ꩻ _` | 126,353 |
| 4 | `ဝ် ꩻ` | 102,695 |
| 5 | `င် ꩻ` | 96,805 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `န ဝ် ꩻ` | 77,014 |
| 2 | `ဝ် ꩻ _` | 57,567 |
| 3 | `ꩻ ၊ _` | 31,811 |
| 4 | `သွူ ။ _` | 31,570 |
| 5 | `ႏ ၊ _` | 30,928 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `န ဝ် ꩻ _` | 45,450 |
| 2 | `နေ ာ ဝ် ꩻ` | 23,553 |
| 3 | `ꩻ သွူ ။ _` | 18,993 |
| 4 | `ꩻ န ဝ် ꩻ` | 18,023 |
| 5 | `ႏ န ဝ် ꩻ` | 17,057 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ဝ် ꩻ သွူ ။ _` | 15,761 |
| 2 | `ꩻ န ဝ် ꩻ _` | 12,522 |
| 3 | `နေ ာ ဝ် ꩻ _` | 11,865 |
| 4 | `ႏ န ဝ် ꩻ _` | 10,503 |
| 5 | `န ဝ် ꩻ သွူ ။` | 10,311 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 1,398
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~17% 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 | 0.2308 | 1.173 | 1.60 | 381,069 | 76.9% |
| **1** | Subword | 1.2202 | 2.330 | 20.98 | 2,909 | 0.0% |
| **2** | Word | 0.0412 | 1.029 | 1.06 | 609,269 | 95.9% |
| **2** | Subword | 0.7534 | 1.686 | 5.49 | 61,020 | 24.7% |
| **3** | Word | 0.0155 | 1.011 | 1.02 | 645,305 | 98.5% |
| **3** | Subword | 0.4733 | 1.388 | 2.77 | 335,231 | 52.7% |
| **4** | Word | 0.0088 🏆 | 1.006 | 1.01 | 656,933 | 99.1% |
| **4** | Subword | 0.3156 | 1.245 | 1.90 | 930,014 | 68.4% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `၂ ဖြုံႏလဲ့ အဝ်ႏသွူ ခမ်းတွူးကောင်ꩻယို အမိဉ်ꩻနဝ်ꩻ ဖန်ဖေႏ စဲဉ်ႏဖေႏဒျာႏလွဉ်းလွဉ်းသွူ ယိုလွုမ်ꩻမကာႏ ဗွေႏဗ...`
2. `၃ ပွုမ်ႏယိုသွူ က အဟံ ခွေနဝ်ꩻ ကောလက္ခံႏသား ၂ ၃ ပေါႏပါႏဠိဒျာႏနဝ်ꩻ သော့ꩻတောဝ်းအမုဲင် ဟော်ꩻဖတ်ဗော့ꩻ ပါႏဠ...`
3. `၁ ခြပ် စီ သွံဆီသူ တနတ်တလီꩻ air combat information management unit mimu ဝေင်ꩻနယ်ႏရွုမ်ꩻဖုံႏနဝ်ꩻ အဝ်ႏဒ...`

**Context Size 2:**

1. `နဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ ဖြဝ်ꩻခမ်းနယ်ႏအခဝ်ကွဉ်ႏ မွိုင်ꩻတုံခရဲင်ႏ ဝေင်ꩻနယ်ႏမွိုင်ꩻတုံကို ကပါဒါႏ ဝေင...`
2. `အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ထာႏဝ ပဂိုꩻတွိုင်ꩻဒေႏသတန် အခဝ်ကွဉ်ႏထင်ꩻ တောင်ႏအူခရဲင်ႏ ဝေင်ꩻနယ်ႏဖျူးကို ကပါ...`
3. `ခရိစ်နေင်ႏ ဗာႏ စဲ့ꩻအစိုႏရစိုးကို ကဗွောင်လွေꩻဒါႏ ခမ်းလင်လစ်ꩻခမ်းတောမ်ႏ ဖြေꩻစာကွန်ႏ လွယ်စယ်ခမ်းကူဂဲတ်လ...`

**Context Size 3:**

1. `နဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ကွဉ်ႏထင်ꩻ ဖြဝ်ꩻခမ်းနယ်ႏ အခဝ်ထင်ꩻ ဟိုပန်ခရဲင်ႏ ဝနမ်းပဲင်ႏအိုပ်ချုတ်ခွင...`
2. `အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ထာႏဝ နေပီဒေါ်ခမ်းခြွဉ်းဗူႏဟံႏနယ်ႏ လယ်ဝွေးခရဲင်ႏကို ကအဝ်ႏပါသော့ꩻဒါႏ ဝေင်ꩻနယ...`
3. `ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ လိုꩻဖြာꩻအဝ်ႏ ၁၁ ၃၀၅ ဖြာꩻသွူ အဝ်ႏဒျာႏ ထာဝယ် မေက် ကာꩻတဖူꩻတန်လော...`

**Context Size 4:**

1. `နဝ်ꩻ အဝ်ႏဒျာႏ မျန်မာခမ်းထီ အခဝ်ထာႏဝ ဧရာႏဝတီႏတွိုင်ꩻဒေႏသတန် မအူပိဉ်ခရဲင်ႏ ကို ကပါဒါႏ ဝေင်ꩻနယ်ႏတဖြုံႏဒ...`
2. `ခရိစ်နေင်ႏ ဗာႏ စာႏရင်ꩻအလꩻ ဝေင်ꩻကိုနဝ်ꩻ လိုꩻဖြာꩻအဝ်ႏ ၁၀ ၄၄၃ ဖြာꩻသွူ အဉ်းမယို ကရီးခါနဝ်ꩻ ထွာဒျာႏ ဒုံအဉ...`
3. `ထာꩻထွာဖုံႏ လွူးဖွာꩻသားဖုံႏ သီမားသားဖုံႏ မွူးနီꩻအုံပဆားနီꩻဖုံႏတောမ်ႏ အထွတ်အမျတ်မွူးနီꩻဖုံႏ အာႏကွိုꩻ`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_တသီႏပေႏစမုံးဝါꩻစွဉ်းထဲ`
2. `ꩻနဝ်ႏပုဂ္ဂိုလ်ႏ_ဇာဝ်ꩻသား`
3. `ႏအခြာဏ်ႏတောယိုတဲ့_ဟောႏရာ`

**Context Size 2:**

1. `ာႏနဝ်ꩻ_အောဝ်ꩻသွူကျောင်ႏဒျာ`
2. `၊_တွိုက်_ကြွဲႏ_ဖန်_သွူ။_ဓမ္မပ`
3. `ꩻ_ခင်ႏငံႏ_မန်း"ကို_ကတဲမ်`

**Context Size 3:**

1. `နဝ်ꩻသွူ။_နီကွဉ်ကꩻမွိုန်း။_၂။`
2. `ဝ်ꩻ_အံႏဖြာꩻနောဝ်ꩻ_ပအိုဝ်ႏယို`
3. `ꩻ၊_မဲ့သျင်ႏကျင်ꩻ။_နီလိတ်_အဝ်`

**Context Size 4:**

1. `နဝ်ꩻ_ဟဲ့ꩻဗာႏသꩻ_ကွား_ကွန်ပေ`
2. `နောဝ်ꩻ_ဘဝပေါင်ꩻ_ရွဉ်ခန်ဗီႏ_`
3. `ꩻသွူ။_ပအိုဝ်ႏစွိုးခွိုꩻသီး_သွိုန်ႏသ`


### Key Findings

- **Best Predictability:** Context-4 (word) with 99.1% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (930,014 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 | 67,819 |
| Total Tokens | 396,228 |
| Mean Frequency | 5.84 |
| Median Frequency | 2 |
| Frequency Std Dev | 39.85 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ၂ | 3,796 |
| 2 | ၃ | 3,380 |
| 3 | ၁ | 3,330 |
| 4 | အာႏကွိုꩻ | 3,141 |
| 5 | နဝ်ꩻ | 2,717 |
| 6 | ၄ | 2,608 |
| 7 | ၅ | 2,058 |
| 8 | ထွာဒျာႏ | 1,623 |
| 9 | ၆ | 1,585 |
| 10 | အဝ်ႏဒျာႏ | 1,494 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | တထာနမ်းနောဝ်ꩻ | 2 |
| 2 | တထာဖြွီꩻဖုံႏ | 2 |
| 3 | antihistamine | 2 |
| 4 | ပထမခွိုꩻ | 2 |
| 5 | ဒုတိယခွိုꩻ | 2 |
| 6 | histamine | 2 |
| 7 | တနယ်ႏလိုမ်းဆဲင်ႏရာꩻ | 2 |
| 8 | အခြေပြုမူလတန်ꩻ | 2 |
| 9 | ပထမကြီးတန်ꩻတွမ်ႏ | 2 |
| 10 | ရန်ႏကုန်ႏတုံး | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.7916 |
| R² (Goodness of Fit) | 0.998007 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 17.9% |
| Top 1,000 | 34.4% |
| Top 5,000 | 51.9% |
| Top 10,000 | 61.5% |

### Key Findings

- **Zipf Compliance:** R²=0.9980 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 17.9% of corpus
- **Long Tail:** 57,819 words needed for remaining 38.5% 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 | 0.8632 🏆 | 0.3270 | N/A | N/A |
| **mono_64d** | 64 | 0.8595 | 0.2722 | N/A | N/A |
| **mono_128d** | 128 | 0.6854 | 0.2261 | N/A | N/A |
| **aligned_32d** | 32 | 0.8632 | 0.3317 | 0.0135 | 0.1716 |
| **aligned_64d** | 64 | 0.8595 | 0.2717 | 0.0745 | 0.2844 |
| **aligned_128d** | 128 | 0.6854 | 0.2281 | 0.1625 | 0.3386 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8632 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2762. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 16.3% 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 | **0.267** | 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 |
|--------|----------|
| `-လိ` | လိက်ပအိုဝ်ႏ, လိုꩻစားယိုဖုံႏနဝ်ꩻ, လိတ်လုံးကို |
| `-လို` | လိုꩻစားယိုဖုံႏနဝ်ꩻ, လိုꩻခိုဖုံႏလဲ့, လိုꩻမွိုက်နဝ်ꩻ |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ꩻ` | ဝန်ႏကိုတွော့ꩻ, ရခဲင်ႏခွန်ဟော်ခံꩻ, သဗ္ဗညုဘုရာꩻ |
| `-ႏ` | လဲဉ်အံႏ, အလင်္ကာႏ, မာꩻသွော့ကုသိုလ်ႏ |
| `-်ꩻ` | လာအိုခမ်းထီနဝ်ꩻ, တသီႏအံႏနယ်ꩻနဝ်ꩻ, ယိုသွံပါꩻထွာနဝ်ꩻ |
| `-း` | အောဝ်ႏဟမ်ႏခမ်ႏဖာႏလောင်း, လူထုအလောင်း, ဉာဏ်ႏတောႏဆꩻချာလွဉ်းလွဉ်း |
| `-ဝ်ꩻ` | လာအိုခမ်းထီနဝ်ꩻ, တသီႏအံႏနယ်ꩻနဝ်ꩻ, ယိုသွံပါꩻထွာနဝ်ꩻ |
| `-်း` | အောဝ်ႏဟမ်ႏခမ်ႏဖာႏလောင်း, လူထုအလောင်း, ဉာဏ်ႏတောႏဆꩻချာလွဉ်းလွဉ်း |
| `-နဝ်ꩻ` | လာအိုခမ်းထီနဝ်ꩻ, တသီႏအံႏနယ်ꩻနဝ်ꩻ, ယိုသွံပါꩻထွာနဝ်ꩻ |
| `-ာႏ` | အလင်္ကာႏ, ဖန်ဆင်ꩻမာꩻခါꩻဒျာႏ, ကိုꩻကွယ်ႏသားအာဗာႏ |

### 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.

*No significant bound stems detected.*


### 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 |
|--------|--------|-----------|----------|
| `-လိ` | `-ꩻ` | 83 words | လိုꩻမဉ်ꩻ, လိုꩻနမ်းအကိုအထန်ႏနီဖဲ့ꩻ |
| `-လိ` | `-ႏ` | 64 words | လိုꩻစွဲဉ်ႏ, လိုꩻမုရေꩻအစွိုꩻအဗူႏဖုံႏ |
| `-လိ` | `-်ꩻ` | 61 words | လိုꩻမဉ်ꩻ, လိုꩻယုက်နဝ်ꩻ |
| `-လိ` | `-ဝ်ꩻ` | 45 words | လိုꩻယုက်နဝ်ꩻ, လိတ်မွူးပအိုဝ်ႏယိုခါနဝ်ꩻ |
| `-လိ` | `-နဝ်ꩻ` | 37 words | လိုꩻယုက်နဝ်ꩻ, လိတ်မွူးပအိုဝ်ႏယိုခါနဝ်ꩻ |
| `-လိ` | `-း` | 36 words | လိုꩻခမ်း, လိုႏတဝ်း |
| `-လိ` | `-်ႏ` | 23 words | လိုꩻစွဲဉ်ႏ, လိုꩻသွုန်ႏထီဓာတ်တွမ်ႏ |
| `-လိ` | `-်း` | 19 words | လိုꩻခမ်း, လိုႏတဝ်း |
| `-လိ` | `-ာႏ` | 15 words | လိုꩻမျိုꩻတွမ်ႏခမ်းထီအတာႏ, လိတ်လုဲင်ꩻတွမ်ႏအနုပညာႏ |
| `-လိ` | `-ွူ` | 5 words | လိုꩻမဉ်အံႏနွောင်ꩻနိစ်စက်ဒါႏဝင်ꩻဖုံႏနဝ်ꩻသွူ, လိုႏမာꩻထူႏလွလဲဉ်းဒျာႏနောဝ်ꩻသွူ |

### 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 |
|------|-----------------|------------|------|
| ကွဲညညနဝ်ꩻ | **`ကွဲညည-နဝ်ꩻ`** | 4.5 | `ကွဲညည` |
| သꩻကိုနဝ်ꩻ | **`သꩻကို-နဝ်ꩻ`** | 4.5 | `သꩻကို` |
| လိုꩻယင်ဟန်ႏနဝ်ꩻ | **`လို-ꩻယင်ဟန-်ႏ-နဝ်ꩻ`** | 4.5 | `ꩻယင်ဟန` |
| နင်ꩻသုမနာနဝ်ꩻ | **`နင်ꩻသုမနာ-နဝ်ꩻ`** | 4.5 | `နင်ꩻသုမနာ` |
| ပုဏ္ဏာꩻနဝ်ꩻ | **`ပုဏ္ဏာꩻ-နဝ်ꩻ`** | 4.5 | `ပုဏ္ဏာꩻ` |
| နာꩻတဲ့နဝ်ꩻ | **`နာꩻတဲ့-နဝ်ꩻ`** | 4.5 | `နာꩻတဲ့` |
| ခန္ဓာႏတန်ယိုနဝ်ꩻ | **`ခန္ဓာႏတန်ယို-နဝ်ꩻ`** | 4.5 | `ခန္ဓာႏတန်ယို` |
| ရောင်ထာꩻနဝ်ꩻ | **`ရောင်ထာꩻ-နဝ်ꩻ`** | 4.5 | `ရောင်ထာꩻ` |
| ခယ်ႏမူႏနဝ်ꩻ | **`ခယ်ႏမူႏ-နဝ်ꩻ`** | 4.5 | `ခယ်ႏမူႏ` |
| အနာႏဂတ်နဝ်ꩻ | **`အနာႏဂတ်-နဝ်ꩻ`** | 4.5 | `အနာႏဂတ်` |
| ရဟန်ꩻသာႏမဏေႏနဝ်ꩻ | **`ရဟန်ꩻသာႏမဏေႏ-နဝ်ꩻ`** | 4.5 | `ရဟန်ꩻသာႏမဏေႏ` |
| ထွို့ꩻစွဲႏနဝ်ꩻ | **`ထွို့ꩻစွဲႏ-နဝ်ꩻ`** | 4.5 | `ထွို့ꩻစွဲႏ` |
| စူမွူးနဝ်ꩻ | **`စူမွူး-နဝ်ꩻ`** | 4.5 | `စူမွူး` |
| ပွိုးနဝ်ꩻ | **`ပွိုး-နဝ်ꩻ`** | 4.5 | `ပွိုး` |
| သင်္ဃာႏတောႏနဝ်ꩻ | **`သင်္ဃာႏတေ-ာႏ-နဝ်ꩻ`** | 3.0 | `သင်္ဃာႏတေ` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Pa'o Karen 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 (4.85x) |
| N-gram | **2-gram** | Lowest perplexity (1,398) |
| Markov | **Context-4** | Highest predictability (99.1%) |
| 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: 2026-01-03 19:13:44*