README.md

---
language: rki
language_name: Rakhine
language_family: tibetoburman_burmese
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_burmese
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: 4.868
  - name: best_isotropy
    type: isotropy
    value: 0.8300
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Rakhine** 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** | 3.940x | 3.94 | 0.1358% | 871,776 |
| **16k** | 4.360x | 4.36 | 0.1503% | 787,889 |
| **32k** | 4.558x | 4.56 | 0.1571% | 753,628 |
| **64k** | 4.868x 🏆 | 4.87 | 0.1678% | 705,565 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `နို့လက်ဖက်ရည် (အင်္ဂလိပ်: milk tea) ရေ လက်ဖက်ရည်နန့် နွားနို့နန့် ပြုလုပ်ထားရေ ဖ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁နို့ လက် ဖက်ရည် ▁( အင်္ဂလိပ် : ▁m il k ▁t ... (+16 more)` | 26 |
| 16k | `▁နို့ လက်ဖက်ရည် ▁( အင်္ဂလိပ် : ▁m il k ▁t e ... (+12 more)` | 22 |
| 32k | `▁နို့လက်ဖက်ရည် ▁( အင်္ဂလိပ် : ▁m il k ▁t e a ... (+10 more)` | 20 |
| 64k | `▁နို့လက်ဖက်ရည် ▁( အင်္ဂလိပ် : ▁milk ▁tea ) ▁ရေ ▁လက်ဖက်ရည်နန့် ▁နွားနို့နန့် ... (+3 more)` | 13 |

**Sample 2:** `ပုလဲနို့လက်ဖက်ရည် (တရုတ်: 珍珠奶茶) ဆိုရေမှာ ထိုင်ဝမ်တွင် လူကြိုက်များရေ လက်ဖက်ရည်အအ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ပုလဲ နို့ လက် ဖက်ရည် ▁( တရုတ် : ▁ 珍珠奶茶 ) ... (+20 more)` | 30 |
| 16k | `▁ပုလဲ နို့ လက်ဖက်ရည် ▁( တရုတ် : ▁ 珍珠奶茶 ) ▁ဆိုရေမှာ ... (+14 more)` | 24 |
| 32k | `▁ပုလဲ နို့လက်ဖက်ရည် ▁( တရုတ် : ▁ 珍珠奶茶 ) ▁ဆိုရေမှာ ▁ထိုင်ဝ ... (+11 more)` | 21 |
| 64k | `▁ပုလဲ နို့လက်ဖက်ရည် ▁( တရုတ် : ▁ 珍珠奶茶 ) ▁ဆိုရေမှာ ▁ထိုင်ဝမ်တွင် ... (+7 more)` | 17 |

**Sample 3:** `ကိုယ်ရေးအကျဉ်း အလုပ်အကိုင် ဂီတလမ်းကြောင်း အယ်လ်ဘမ်တိ Single သီချင်းတိ ပါဝင်သီဆို...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ကိုယ်ရေးအကျဉ်း ▁အလုပ်အကိုင် ▁ဂီတ လမ်းကြောင်း ▁အယ်လ် ဘမ်တိ ▁s ing le ▁သီချင်းတိ ... (+9 more)` | 19 |
| 16k | `▁ကိုယ်ရေးအကျဉ်း ▁အလုပ်အကိုင် ▁ဂီတလမ်းကြောင်း ▁အယ်လ်ဘမ်တိ ▁single ▁သီချင်းတိ ▁ပါဝင်သီဆိုဖူးရေ ▁သီချင်းတိ ▁ကိုးကား ▁ပြင်ပလင့် ... (+2 more)` | 12 |
| 32k | `▁ကိုယ်ရေးအကျဉ်း ▁အလုပ်အကိုင် ▁ဂီတလမ်းကြောင်း ▁အယ်လ်ဘမ်တိ ▁single ▁သီချင်းတိ ▁ပါဝင်သီဆိုဖူးရေ ▁သီချင်းတိ ▁ကိုးကား ▁ပြင်ပလင့် ... (+1 more)` | 11 |
| 64k | `▁ကိုယ်ရေးအကျဉ်း ▁အလုပ်အကိုင် ▁ဂီတလမ်းကြောင်း ▁အယ်လ်ဘမ်တိ ▁single ▁သီချင်းတိ ▁ပါဝင်သီဆိုဖူးရေ ▁သီချင်းတိ ▁ကိုးကား ▁ပြင်ပလင့် ... (+1 more)` | 11 |


### Key Findings

- **Best Compression:** 64k achieves 4.868x compression
- **Lowest UNK Rate:** 8k with 0.1358% 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 | 2,475 | 11.27 | 3,711 | 18.7% | 59.1% |
| **2-gram** | Subword | 1,997 🏆 | 10.96 | 21,097 | 35.0% | 70.8% |
| **3-gram** | Word | 3,510 | 11.78 | 5,274 | 15.1% | 51.1% |
| **3-gram** | Subword | 16,910 | 14.05 | 105,452 | 13.3% | 36.3% |
| **4-gram** | Word | 13,278 | 13.70 | 18,246 | 8.0% | 25.9% |
| **4-gram** | Subword | 74,444 | 16.18 | 313,099 | 6.3% | 19.3% |
| **5-gram** | Word | 12,446 | 13.60 | 16,164 | 7.7% | 25.0% |
| **5-gram** | Subword | 151,855 | 17.21 | 428,759 | 3.6% | 12.6% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `တွီးတောမှု သံသယကုက္ကုစ္စ` | 226 |
| 2 | `ဒုက္ကဋ်အာပတ် သင့်` | 225 |
| 3 | `ပါရာဇိကအာပတ် သင့်ဧ့` | 217 |
| 4 | `ပ ရဟန်း` | 216 |
| 5 | `အယင်ခေါက်ကခါ ရဟန်းတစ်ပါးစွာ` | 203 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ထိုရဟန်းအား တွီးတောမှု သံသယကုက္ကုစ္စ` | 178 |
| 2 | `သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ` | 172 |
| 3 | `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့` | 144 |
| 4 | `ဖြစ်ဧ့ ပ ရဟန်း` | 142 |
| 5 | `မဟုတ်မမှန် ပြောဆိုရေ ရဟန်းအား` | 80 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ` | 135 |
| 2 | `သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း` | 128 |
| 3 | `ထိုရဟန်းအား တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့` | 113 |
| 4 | `သံသယကုက္ကုစ္စ ဖြိုက်ဧ့ ပ ရဟန်း` | 67 |
| 5 | `ရှေးဟောင်းအဆောက်အဦးများ ရှေးဟောင်းသုတေသနနှင့် အမျိုးသားပြတိုက်ဦးစီးဌာန ယဉ်ကျေးမူဝန်ကြီးဌာန` | 66 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ထိုရဟန်းအား တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ` | 110 |
| 2 | `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း` | 93 |
| 3 | `ကိုးကား မြောက်ဦးဒေသ ရှေးဟောင်းအဆောက်အဦးများ ရှေးဟောင်းသုတေသနနှင့် အမျိုးသားပြတိုက်ဦးစီးဌာန` | 66 |
| 4 | `ရှေးဟောင်းအဆောက်အဦးများ ရှေးဟောင်းသုတေသနနှင့် အမျိုးသားပြတိုက်ဦးစီးဌာန ယဉ်ကျေးမူဝန်ကြီးဌာန ပုထိုးတော်တိ` | 66 |
| 5 | `မြောက်ဦးဒေသ ရှေးဟောင်းအဆောက်အဦးများ ရှေးဟောင်းသုတေသနနှင့် အမျိုးသားပြတိုက်ဦးစီးဌာန ယဉ်ကျေးမူဝန်ကြီးဌာန` | 66 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `င် း` | 70,638 |
| 2 | `ာ း` | 65,449 |
| 3 | `_ အ` | 56,551 |
| 4 | `။ _` | 52,197 |
| 5 | `း _` | 50,866 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ရေ ။ _` | 31,071 |
| 2 | `ာ င် း` | 18,078 |
| 3 | `တွ င် _` | 14,734 |
| 4 | `န န့် _` | 14,037 |
| 5 | `ာ း _` | 12,271 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `လ ည် း _` | 6,741 |
| 2 | `ရ ဟ န် း` | 5,765 |
| 3 | `ကေ ာ င် း` | 5,150 |
| 4 | `ဖြ စ် ရေ ။` | 4,615 |
| 5 | `စ် ရေ ။ _` | 4,465 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ဖြ စ် ရေ ။ _` | 4,438 |
| 2 | `_ ရ ဟ န် း` | 3,712 |
| 3 | `ပ ါ ရေ ။ _` | 3,105 |
| 4 | `က တ် ရေ ။ _` | 2,654 |
| 5 | `_ ဖြ စ် ရေ ။` | 2,073 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 1,997
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~13% 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 | 0.2818 | 1.216 | 1.76 | 243,192 | 71.8% |
| **1** | Subword | 1.4978 | 2.824 | 24.80 | 2,290 | 0.0% |
| **2** | Word | 0.0459 | 1.032 | 1.07 | 427,212 | 95.4% |
| **2** | Subword | 0.7632 | 1.697 | 5.15 | 56,790 | 23.7% |
| **3** | Word | 0.0165 | 1.012 | 1.02 | 454,829 | 98.3% |
| **3** | Subword | 0.4942 | 1.409 | 2.68 | 292,573 | 50.6% |
| **4** | Word | 0.0100 🏆 | 1.007 | 1.01 | 463,846 | 99.0% |
| **4** | Subword | 0.3219 | 1.250 | 1.80 | 783,810 | 67.8% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `နန့် ဝဇီရာကိလာယ vajrakilaya ရို့ပါဝင်လီရေ ဒါကီနီတိ dakini ကောင်းကင်သို့ကြွလှမ်းသူ ရေ ဒေဝိဉာဉ်သဘာဝကနေ...`
2. `ဖြစ်ရေ အာရုံခံကိရိယာတိကို နေ့စဉ်သုံး အရာဝတ္ထုတိတွင် အကန့်အသတ်ဖြင့်သာ အသုံးချနိုင်ရေ ကိုးကား နိုင်ငံတ...`
3. `ရေ ဖိုင်တိကို ဖလှယ်နိုင်ပြီးကေ ဝီမျှရန် ဝှိုက်ဘုတ် ပေါ်တွင်ရီးဆွဲခြင်း ယပိုင်မဟုတ်ကေ ဘောင်နီရာအကျယ်အ...`

**Context Size 2:**

1. `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း သင်စွာ ပါရာဇိက ကျယာလို့ မိန့်တော်မှုဧ့ ၁၁၆ ၁၅၆ အယင်ခေါက်ကခါ ...`
2. `ဒုက္ကဋ်အာပတ် သင့် ပ အကြောင်းသုံးမျိုးရို့နန့် မသိစွာကို ငါစွာ သိလည်း သိ မြင်လည်း မြင်ရေဟု ပ အကြောင်း...`
3. `ပါရာဇိကအာပတ် သင့်ဧ့ အချိန်းအချက်လုပ်ခြင်း ဆိုစော် စစောပင်ဖြစ်စီ ညနိန်ချမ်းပင်ဖြစ်စီ ညဉ့်ပင်ဖြစ်စီ နိ...`

**Context Size 3:**

1. `ထိုရဟန်းအား တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း ပါရာဇိကအာပတ် မသင့် ဒုက္ကဋ်အာပတ် သင့်ဧ့လို့ မိန့်...`
2. `သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်းရို့ ပါရာဇိကအာပတ် မသင့်လို့ မိန့်တော်မူဧ့ ၆၉ အယင်ခေါက်ကခါ သတ္တရသဝဂ္ဂီရဟန...`
3. `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း သင်စွာ ပါရာဇိက ကျယာလို့ မိန့်တော်မူဧ့ ၆၅ အယင်ခေါက်ကခါ ဝမ်းကြ...`

**Context Size 4:**

1. `တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း သင်စွာ ပါရာဇိက ကျယာလို့ မိန့်တော်မူဧ့ ၄၆ အယင်ခေါက်ကခါ ရဟန်းတ...`
2. `သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း သင်စွာပါရာဇိက ကျယာလို့ မိန့်တော်မူဧ့ ၁၁၅ အယင်ခေါက်ကခါ ရဟန်းတစ်ပါးစွာ မြ...`
3. `ထိုရဟန်းအား တွီးတောမှု သံသယကုက္ကုစ္စ ဖြစ်ဧ့ ပ ရဟန်း သင်စွာ ပါရာဇိက ကျရာလို့ မိန့်တော်မူဧ့ ၈၆ ဝိနီတဝထ...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_ဆည်းအတွင်ထက်နန့်_အသျှင်ဒို`
2. `းလျှပ်ရို့ဖြစ်ခေါ်ရာင်းကိုအတွဲပ`
3. `ားရေ။_ဇာ_သင့်ရန်ကြီးတိန`

**Context Size 2:**

1. `င်းဆောင်း၌_သူဆင်း_ဖြစ်ပြော`
2. `ားရေးနှင့်လှောင်ထူထပ်ကမ်းမှုအ`
3. `_အလာက_ဟဲ့_မိမိကိုယ်_စေတီ_ဒွ`

**Context Size 3:**

1. `ရေ။_ကူးစက်တိနန့်_ရွိ့လျားလည်း`
2. `ာင်းကို_တိစွာလဲ။_အမှတ်အသစ်တိ`
3. `တွင်_ရှီးကရက်ဒေါက်တာဆွန်ယက်တိ`

**Context Size 4:**

1. `လည်း_နန့်_မျက်နှာလည်း_ကျရုံသာ`
2. `ရဟန်းဘောင်ပေါ်သတင်း_(wave`
3. `ကောင်းလေးမျက်နှာပြင်တစ်ခုဖြစ်၍_`


### Key Findings

- **Best Predictability:** Context-4 (word) with 99.0% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (783,810 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 | 47,832 |
| Total Tokens | 309,275 |
| Mean Frequency | 6.47 |
| Median Frequency | 3 |
| Frequency Std Dev | 31.95 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | နန့် | 2,540 |
| 2 | ဖြစ်ရေ | 2,301 |
| 3 | ရေ | 2,228 |
| 4 | ဟု | 1,547 |
| 5 | ပ | 1,498 |
| 6 | ကို | 1,222 |
| 7 | ခုနှစ် | 1,163 |
| 8 | ကိုးကား | 1,147 |
| 9 | ၁ | 1,146 |
| 10 | ဟိရေ | 1,132 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ရီကူးစွာပါရာဖြစ်ဖြစ် | 2 |
| 2 | asr | 2 |
| 3 | mothers | 2 |
| 4 | pdp | 2 |
| 5 | evans | 2 |
| 6 | မြောက်ဦးမြို့မာပင် | 2 |
| 7 | ၁၉ရ၃ | 2 |
| 8 | ၁၉ရရ | 2 |
| 9 | ၁၉၉ရ | 2 |
| 10 | စာနယ်ဇင်းအဖွဲ့တွင် | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.8062 |
| R² (Goodness of Fit) | 0.995656 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 15.9% |
| Top 1,000 | 35.9% |
| Top 5,000 | 57.4% |
| Top 10,000 | 68.4% |

### Key Findings

- **Zipf Compliance:** R²=0.9957 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 15.9% of corpus
- **Long Tail:** 37,832 words needed for remaining 31.6% 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 | 0.8300 🏆 | 0.3077 | N/A | N/A |
| **mono_64d** | 64 | 0.7613 | 0.2695 | N/A | N/A |
| **mono_128d** | 128 | 0.3041 | 0.2391 | N/A | N/A |
| **aligned_32d** | 32 | 0.8300 | 0.3073 | 0.0260 | 0.1900 |
| **aligned_64d** | 64 | 0.7613 | 0.2529 | 0.0400 | 0.2280 |
| **aligned_128d** | 128 | 0.3041 | 0.2467 | 0.0940 | 0.3080 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8300 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2705. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 9.4% 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 | **0.985** | 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 |
|--------|----------|
| `-အ` | အရှေ့ဘက်တွင်, အမြင့်ဆုံးနီရာမှာ, အဆင်း |
| `-မ` | မတွိ့သည်ကို, မိမမာ, မေထုန်အမှုတဲးလို့ |
| `-သ` | သီရိဝိဇယဧ, သဗြုတ်သီးကို, သောတာပတ္တိဖိုလ်သို့ |
| `-ပ` | ပရိုတင်းဓာတ်, ပြင်ရေ, ပြန်လာရေအခါ |
| `-ရ` | ရီဆိုးရီညစ်တိကို, ရာစုသစ်အလင်္ကာ, ရေပီးဝေရီး |
| `-က` | ကွန်ယက်ရေ, ကပ်ပနာ, ကွန်ယက်ချိတ်ဆက်မှု |
| `-တ` | တောင်း, တနင်္ဂနွေသားဖြစ်လျှင်, တိုင်းရင်းသားလူမျိုး |
| `-လ` | လက္ခဏ, လို့ပြောရာ, လူမှုဆက်ဆံရီး |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ဧ` | သီရိဝိဇယဧ, နိုင်ငံစာချုပ်ဧ, ရခိုင်အမျိုးသားကောင်စီဧ |
| `-က` | ထိုစဉ်အခါက, ရဟန်းရို့က, ယင်းရို့က |
| `-s` | indus, forces, patients |
| `-e` | amplitude, made, initiative |
| `-n` | radiation, chain, transcription |
| `-on` | radiation, transcription, sanitation |
| `-ရ` | ဥပဒေအရ, လေ့လာမှုတိအရ, ပြောပြချက်အရ |
| `-y` | pty, viceroy, complexity |

### 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 |
|------|----------|------------------|----------|
| `atio` | 2.95x | 11 contexts | ratio, nation, nations |
| `tion` | 2.92x | 9 contexts | action, nation, motion |

### 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 |
|--------|--------|-----------|----------|
| `-အ` | `-ဧ` | 22 words | အမှုန်တိဧ, အုပ်ချုပ်သူရို့ဧ |
| `-က` | `-ဧ` | 16 words | ကျယ်ဧ, ကော်မတီဧ |
| `-မ` | `-က` | 11 words | မြာဒင်ထက်က, မိဖုရားက |
| `-အ` | `-က` | 11 words | အုပ်ချုပ်သူတိက, အာကာသက |
| `-မ` | `-ဧ` | 10 words | မည်သည့်ကိန်းနှစ်ခုဧ, မျိုးရိုးဗီဇအင်ဂျင်နီယာဧ |
| `-ရ` | `-က` | 8 words | ရသေ့ကြီးက, ရာစုခန့်က |
| `-အ` | `-ရ` | 8 words | အနှစ်သာရ, အခြီအနီအရ |
| `-သ` | `-ဧ` | 7 words | သဘာဝကိန်းစုဧ, သဘာဝတရားဧ |
| `-တ` | `-က` | 7 words | တတိယပဏ္ဏာသက, တောင်အာဖရိက |
| `-သ` | `-က` | 6 words | သံသယဟိပါက, သက္က |

### 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 |
|------|-----------------|------------|------|
| ဟွန်ရှူးကျွန်းဧ | **`ဟွန်ရှူးကျွန်း-ဧ`** | 4.5 | `ဟွန်ရှူးကျွန်း` |
| ခက်ခဲပြီး | **`ခ-က-်ခဲပြီး`** | 4.5 | `်ခဲပြီး` |
| စေတီတော်ဧ | **`စေတီတော်-ဧ`** | 4.5 | `စေတီတော်` |
| တရားကိုကေလေ့ | **`တ-ရ-ားကိုကေလေ့`** | 4.5 | `ားကိုကေလေ့` |
| ခရီးထွက်ပြန်ရာ | **`ခ-ရ-ီးထွက်ပြန်ရာ`** | 4.5 | `ီးထွက်ပြန်ရာ` |
| ပြည့်နှစ်က | **`ပြည့်နှစ်-က`** | 4.5 | `ပြည့်နှစ်` |
| ဘဒ္ဒကမ္ဘာမှ | **`ဘ-ဒ-္ဒကမ္ဘာမှ`** | 3.0 | `္ဒကမ္ဘာမှ` |
| နတ်မြစ်ဧ့ | **`န-တ-်မြစ်ဧ့`** | 3.0 | `်မြစ်ဧ့` |
| ရင်ကွဲနာနန့် | **`ရ-င-်ကွဲနာနန့်`** | 3.0 | `်ကွဲနာနန့်` |
| မလက်ကာအား | **`မ-လက-်ကာအား`** | 3.0 | `်ကာအား` |
| နတ်ရုပ်တိ | **`န-တ-်ရုပ်တိ`** | 3.0 | `်ရုပ်တိ` |
| အပေါ်သို့ | **`အ-ပ-ေါ်သို့`** | 3.0 | `ေါ်သို့` |
| ဆန္ဒပြမှု | **`ဆ-န-္ဒပြမှု`** | 3.0 | `္ဒပြမှု` |
| ရဟိသဖြင့် | **`ရ-ဟ-ိသဖြင့်`** | 3.0 | `ိသဖြင့်` |
| ထိုနည်းကို | **`ထ-ိုနည်းကို`** | 1.5 | `ိုနည်းကို` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Rakhine 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 (4.87x) |
| N-gram | **2-gram** | Lowest perplexity (1,997) |
| Markov | **Context-4** | Highest predictability (99.0%) |
| 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 18:35:21*