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+visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text
+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
+visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language: my
+language_name: Burmese
+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: 5.618
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6934
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Burmese - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Burmese** 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** | 4.094x | 4.09 | 0.0581% | 1,838,036 |
+| **16k** | 4.637x | 4.64 | 0.0658% | 1,622,639 |
+| **32k** | 5.147x | 5.15 | 0.0731% | 1,461,988 |
+| **64k** | 5.618x 🏆 | 5.62 | 0.0797% | 1,339,281 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `လက်ပန်ကွင်းရွာ၊ လက်ပန်ကွင်း ကိုးကား ရွာများ ရွာများ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁လက်ပန် ကွင်းရွာ၊ ▁လက်ပန် ကွင်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 7 |
+| 16k | `▁လက်ပန် ကွင်းရွာ၊ ▁လက်ပန် ကွင်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 7 |
+| 32k | `▁လက်ပန် ကွင်းရွာ၊ ▁လက်ပန် ကွင်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 7 |
+| 64k | `▁လက်ပန် ကွင်းရွာ၊ ▁လက်ပန် ကွင်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 7 |
+
+**Sample 2:** `ကွင်းယားကုန်းရွာ၊ ဇငြွ်ပန်းကုန်း ကိုးကား ရွာများ ရွာများ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ကွင်း ယား ကုန်းရွာ၊ ▁ဇ ငြ ွ ် ပန်း ကုန်း ▁ကိုးကား ... (+2 more)` | 12 |
+| 16k | `▁ကွင်းယား ကုန်းရွာ၊ ▁ဇ ငြ ွ ် ပန်းကုန်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 10 |
+| 32k | `▁ကွင်းယား ကုန်းရွာ၊ ▁ဇငြွ်ပန်းကုန်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 6 |
+| 64k | `▁ကွင်းယား ကုန်းရွာ၊ ▁ဇငြွ်ပန်းကုန်း ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 6 |
+
+**Sample 3:** `ထီတိုလိုအဖျားရွာ၊ ထီတိုလို ကိုးကား ရွာများ ရွာများ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ထ ီတ ိုလို အ ဖျား ရွာ၊ ▁ထ ီတ ိုလို ▁ကိုးကား ... (+2 more)` | 12 |
+| 16k | `▁ထ ီတ ိုလို အဖျား ရွာ၊ ▁ထ ီတ ိုလို ▁ကိုးကား ▁ရွာများ ... (+1 more)` | 11 |
+| 32k | `▁ထီတ ိုလို အဖျား ရွာ၊ ▁ထီတ ိုလို ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 9 |
+| 64k | `▁ထီတ ိုလို အဖျား ရွာ၊ ▁ထီတ ိုလို ▁ကိုးကား ▁ရွာများ ▁ရွာများ` | 9 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.618x compression
+- **Lowest UNK Rate:** 8k with 0.0581% 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 | 8,831 | 13.11 | 97,119 | 30.8% | 47.6% |
+| **2-gram** | Subword | 1,887 🏆 | 10.88 | 72,847 | 36.4% | 73.5% |
+| **3-gram** | Word | 9,813 | 13.26 | 126,512 | 31.4% | 48.2% |
+| **3-gram** | Subword | 17,172 | 14.07 | 481,303 | 16.6% | 40.4% |
+| **4-gram** | Word | 30,676 | 14.90 | 264,000 | 23.6% | 36.4% |
+| **4-gram** | Subword | 90,180 | 16.46 | 1,884,383 | 10.1% | 25.4% |
+| **5-gram** | Word | 39,876 | 15.28 | 238,225 | 20.9% | 31.1% |
+| **5-gram** | Subword | 269,959 | 18.04 | 3,576,330 | 8.2% | 18.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ကိုးကား ရွာများ` | 58,566 |
+| 2 | `ရွာနေရာကုတ်မှာ ဖြစ်သည်` | 51,588 |
+| 3 | `တည်ရှိသည် ရွာနေရာကုတ်မှာ` | 51,568 |
+| 4 | `ဖြစ်သည် သန်းခေါင်စာရင်းအရ` | 37,043 |
+| 5 | `ဦး မ` | 36,542 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `တည်ရှိသည် ရွာနေရာကုတ်မှာ ဖြစ်သည်` | 51,563 |
+| 2 | `ရွာနေရာကုတ်မှာ ဖြစ်သည် သန်းခေါင်စာရင်းအရ` | 36,945 |
+| 3 | `ဦး လူဦးရေ စုစုပေါင်း` | 34,572 |
+| 4 | `လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည်` | 28,628 |
+| 5 | `ဦးနေထိုင်သည် ကိုးကား ရွာများ` | 27,771 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `တည်ရှိသည် ရွာနေရာကုတ်မှာ ဖြစ်သည် သန်းခေါင်စာရင်းအရ` | 36,927 |
+| 2 | `ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည်` | 28,628 |
+| 3 | `လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား` | 25,411 |
+| 4 | `စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ` | 25,261 |
+| 5 | `မ ဦး လူဦးရေ စုစုပေါင်း` | 22,994 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား` | 25,411 |
+| 2 | `လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ` | 25,261 |
+| 3 | `ဦး မ ဦး လူဦးရေ စုစုပေါင်း` | 22,994 |
+| 4 | `မ ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည်` | 21,852 |
+| 5 | `ကျား ဦး မ ဦး လူဦးရေ` | 21,303 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ာ း` | 1,540,592 |
+| 2 | `င် း` | 1,127,081 |
+| 3 | `သ ည်` | 1,053,771 |
+| 4 | `း _` | 1,020,236 |
+| 5 | `။ _` | 832,045 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `သ ည် ။` | 647,008 |
+| 2 | `ည် ။ _` | 635,498 |
+| 3 | `မျ ာ း` | 557,792 |
+| 4 | `ာ း _` | 379,277 |
+| 5 | `သ ည် _` | 308,511 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `သ ည် ။ _` | 626,895 |
+| 2 | `ဖြ စ် သ ည်` | 152,777 |
+| 3 | `စ် သ ည် ။` | 146,842 |
+| 4 | `း မျ ာ း` | 134,710 |
+| 5 | `မျ ာ း _` | 123,549 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ဖြ စ် သ ည် ။` | 146,362 |
+| 2 | `စ် သ ည် ။ _` | 143,004 |
+| 3 | `_ ဖြ စ် သ ည်` | 102,596 |
+| 4 | `ခဲ့ သ ည် ။ _` | 101,218 |
+| 5 | `း ရွ ာ အု ပ်` | 99,853 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 1,887
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~19% 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 | 0.3898 | 1.310 | 2.70 | 2,269,123 | 61.0% |
+| **1** | Subword | 1.1880 | 2.278 | 16.40 | 12,091 | 0.0% |
+| **2** | Word | 0.0846 | 1.060 | 1.16 | 6,111,017 | 91.5% |
+| **2** | Subword | 0.7455 | 1.677 | 6.00 | 198,292 | 25.5% |
+| **3** | Word | 0.0245 | 1.017 | 1.04 | 7,076,304 | 97.5% |
+| **3** | Subword | 0.5456 | 1.460 | 3.39 | 1,190,344 | 45.4% |
+| **4** | Word | 0.0104 🏆 | 1.007 | 1.02 | 7,324,998 | 99.0% |
+| **4** | Subword | 0.4066 | 1.326 | 2.29 | 4,039,178 | 59.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ဖြစ်သည် ဒါဘန်မြို့ကြီးဒေသသည် တောင်အာဖရိကတွင် နယ်လ်ဆင်မင်ဒဲလားနှင့် တွေ့ဆုံခဲ့ပြီး ကောင်းကင်တမန်များန...`
+2. `သည် မကွေးတိုင်းဒေသကြီး မင်းဘူးခရိုင် ကုန်ကူးသန်းရောင်းဝယ်ရေးအတွက် အရေးပါသောဆိပ်ကမ်းမြို့ဖြစ်လေသည် သင...`
+3. `ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ ရွာများ ရွာများ ရွာများ ရွာများ ရွာများ ရွာများ ရွ...`
+
+**Context Size 2:**
+
+1. `ရွာနေရာကုတ်မှာ ဖြစ်သည် ၁၄ ဂိုး mohammad al sahlawi မန်နေဂျာ juan antonio gk 1 igor akinfeev c rb 2`
+2. `တည်ရှိသည် ရွာနေရာကုတ်မှာ ဖြစ်သည် ကိုးကား ဘူတာရုံများ ဘူတာရုံများ`
+3. `ဖြစ်သည် သန်းခေါင်စာရင်းအရ လိန်တောကျေးရွာအုပ်စုတွင် ကျား ဦး မ ၅၈၈ ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် က...`
+
+**Context Size 3:**
+
+1. `တည်ရှိသည် ရွာနေရာကုတ်မှာ ဖြစ်သည် ကိုးကား ရွာများ ရွာများ ပြိုင်းချောင်းရွာ ကိုးကား ရွာများ ရွာများ စ...`
+2. `ရွာနေရာကုတ်မှာ ဖြစ်သည် သန်းခေါင်စာရင်းအရ ကော့လှိုင်ကျေးရွာအုပ်စုတွင် ကျား ဦး မ ဦး လူဦးရေ စုစုပေါင်း ...`
+3. `ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ ကျားသားလေးများ နေထိုင်ရာ အအိုဆေး အပျိုဆေး ရှိ`
+
+**Context Size 4:**
+
+1. `တည်ရှိသည် ရွာနေရာကုတ်မှာ ဖြစ်သည် သန်းခေါင်စာရင်းအရ ကဝါးပန်းကျေးရွာအုပ်စုတွင် ကျား ၆၁ ဦး မ ၅၈ ဦး လူဦး...`
+2. `ဦး လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ ကျားသားလေးများ နေထိုင်ရာ အအိုဆေး အပျိုဆေး ရှိ`
+3. `လူဦးရေ စုစုပေါင်း ဦးနေထိုင်သည် ကိုးကား ရွာများ ကျားသားလေးများ နေထိုင်ရာ အအိုဆေး အပျိုဆေး ရှိ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ကာခဲ့သဖြစ်ဇဘာအုပ်ငန်းချွ`
+2. `း၊_ဆန်း_ပညာနာ_နှင့်_`
+3. `ာင်းလ)သည်ရှိခဲ့ပြီ_နာတ်_မ`
+
+**Context Size 2:**
+
+1. `ား_လူဦးနေ_အမေရိကန်တောက်ထု`
+2. `င်းပါမောင်ရွာအုပ်စု၌_အပိုင်`
+3. `သည်_အလယ်လုပ်ကိုင်_ဘာသည်။_`
+
+**Context Size 3:**
+
+1. `သည်။_ကိုးကား_ကိုယ်ပိုင်သီချင်း`
+2. `ည်။_ရွာများ_ဓားရှာဖွေတွေ့ရှိနို`
+3. `များ)_အလယ်တန်းကျေးဇူးကြောင်`
+
+**Context Size 4:**
+
+1. `သည်။_6|39|_စင်စစ်_ဖြစ်ပြီး`
+2. `ဖြစ်သည်။_ဒွတ္တပေါင်မင်းကြီးကျေးရွ`
+3. `စ်သည်။_ယနေ့ခေတ်၏_အိပ်ရာခင်းပြီ`
+
+
+### 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 (4,039,178 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 | 535,794 |
+| Total Tokens | 7,184,049 |
+| Mean Frequency | 13.41 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 366.75 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ဖြစ်သည် | 101,666 |
+| 2 | သည် | 96,325 |
+| 3 | ကိုးကား | 92,437 |
+| 4 | ဦး | 83,872 |
+| 5 | ရွာများ | 67,205 |
+| 6 | ရက် | 60,957 |
+| 7 | တည်ရှိသည် | 57,556 |
+| 8 | ရွာနေရာကုတ်မှာ | 51,593 |
+| 9 | နှင့် | 40,151 |
+| 10 | မ | 38,196 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ကျောက်ကြီးမြို့နယ်အတွင်းရှိ | 2 |
+| 2 | ယမတိုခဆွတ်ရဂိတောင် | 2 |
+| 3 | ခအိနတောင် | 2 |
+| 4 | ခမိုတောင် | 2 |
+| 5 | တန်းရှင်းတောင် | 2 |
+| 6 | ရှိနိုဂတောင်ထိပ် | 2 |
+| 7 | ယောဆွတ်ဘတို | 2 |
+| 8 | ယောဆွတ်ဘ | 2 |
+| 9 | 1xbet | 2 |
+| 10 | seppiko | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8889 |
+| R² (Goodness of Fit) | 0.998993 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 22.8% |
+| Top 1,000 | 38.0% |
+| Top 5,000 | 52.0% |
+| Top 10,000 | 58.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9990 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 22.8% of corpus
+- **Long Tail:** 525,794 words needed for remaining 41.3% 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 | 0.6749 | 0.3233 | N/A | N/A |
+| **mono_64d** | 64 | 0.6458 | 0.2438 | N/A | N/A |
+| **mono_128d** | 128 | 0.6934 | 0.1709 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6749 | 0.3433 | 0.0640 | 0.3360 |
+| **aligned_64d** | 64 | 0.6458 | 0.2465 | 0.1420 | 0.4260 |
+| **aligned_128d** | 128 | 0.6934 🏆 | 0.1662 | 0.2060 | 0.5080 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_128d with 0.6934 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2490. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.6% 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 | **0.664** | 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` | အလားတူလင့်ခ်များ, အလက်စကားပြည်နယ်, အလ္လာဟ် |
+| `-မ` | မေးမြန်းလျက်, မဟာသုတသောမဇာတ်အဆုံး, မသိစေရန် |
+| `-က` | ကက်စတီးပြည်နယ်ကို, ကံသစ်ကျေးရွာအုပ်စု, ကိလေသာဝဋ် |
+| `-သ` | သုံးရက်မြောက်နေ့တွင်, သံဃာ့ဆေးရုံ, သက်ဝင်ယုံကြည်မှု |
+| `-ပ` | ပုဂံနေပြည်တော်သို့, ပြစ်မှုအမျိုးအစားပေါ်မူတည်, ပစ္စည်းများစွာ |
+| `-တ` | တင်းနစ်နှင့်, တစ်ယောက်နဲ့, တက်လာသည် |
+| `-ရ` | ရူသာဖို့ဒ်က, ရွေးကောက်ပွဲကျင်းပခဲ့သည်, ရိုးရာသည် |
+| `-လ` | လေ့ရှိပေသည်, လှုပ်ခါမှုကို, လိုချင်သည့် |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-က` | ရူသာဖို့ဒ်က, အလက, သိဝက |
+| `-s` | investments, watsons, hispidissimus |
+| `-e` | capacitance, stéphane, awardsfavorite |
+| `-n` | balujun, maccabean, မိုဘိုင်းvpn |
+| `-ရ` | နိဗ္ဗာန်ရ, ဘဒ္ဒန္တဉာဏိဿရ, ာက်ရ |
+| `-a` | ghulja, kinema, ida |
+| `-ng` | retracing, chantanayingyong, luang |
+| `-on` | relation, washinton, baryon |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ment` | 3.58x | 41 contexts | ament, ement, mental |
+| `tion` | 3.31x | 50 contexts | tiong, notion, option |
+| `nter` | 3.41x | 44 contexts | inter, enter, center |
+| `atio` | 3.41x | 39 contexts | ratio, nation, cations |
+| `inte` | 3.45x | 34 contexts | inter, intel, intent |
+| `vers` | 3.09x | 50 contexts | versa, verse, versed |
+| `iona` | 3.50x | 15 contexts | fiona, dionaea, nasional |
+| `onal` | 3.46x | 9 contexts | tonal, donald, ronald |
+
+### 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` | `-က` | 26 words | အချို့ကတည်းက, အင်အားကြီးသူများက |
+| `-ပ` | `-က` | 18 words | ပေါထုဇ္ဇနိက, ပဒေသရာဇ်ခေတ်က |
+| `-မ` | `-က` | 17 words | မန္ဒာယုက, မြန်မာကလေးများက |
+| `-ရ` | `-က` | 17 words | ရှူမိပါက, ရေနံကုမ္ပဏီများက |
+| `-အ` | `-ရ` | 9 words | အဋ္ဌင်္ဂိကဝါရ, အဘိဓမ္မာဝတာရ |
+| `-က` | `-က` | 8 words | ကလိဗ်လန်းက, ကိုဘဟိန်းက |
+| `-စ` | `-က` | 8 words | စကြာမင်းဖြစ်စဉ်က, စာရေးသူများက |
+| `-တ` | `-က` | 8 words | တတိယပါရာဇိက, တက္ကသိုလ်ဆရာတစ်ဦးက |
+| `-သ` | `-ရ` | 7 words | သတ္တင်္ဂုတ္တရ, သတ္တန္တရ |
+| `-လ` | `-က` | 7 words | လက်ခံနိုင်ပါက, လက်ဝဲနော်ရထာအမတ်က |
+
+### 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 |
+|------|-----------------|------------|------|
+| မနှစ်သက်ကြသဖြင့် | **`မ-နှစ်သက်ကြသဖြင့်`** | 4.5 | `နှစ်သက်ကြသဖြင့်` |
+| အဆောင်အဖြစ် | **`အ-ဆောင်အဖြစ်`** | 4.5 | `ဆောင်အဖြစ်` |
+| ဝတ္ထုတိုဆုနှင့် | **`ဝ-တ-္ထုတိုဆုနှင့်`** | 4.5 | `္ထုတိုဆုနှင့်` |
+| လတွင်လည်းကောင်း | **`လ-တွင်လည်းကောင်း`** | 4.5 | `တွင်လည်းကောင်း` |
+| တိုင်းတာမှုအရ | **`တိုင်းတာမှု-အရ`** | 4.5 | `တိုင်းတာမှု` |
+| နစ်နာရသည် | **`န-စ-်နာရသည်`** | 4.5 | `်နာရသည်` |
+| မအောင်မြင်ခဲ့ | **`မ-အောင်မြင်ခဲ့`** | 4.5 | `အောင်မြင်ခဲ့` |
+| cardinals | **`cardinal-s`** | 4.5 | `cardinal` |
+| နဂါးမောက် | **`န-ဂ-ါးမောက်`** | 4.5 | `ါးမောက်` |
+| လက်ဝှေ့ပညာ | **`လ-က-်ဝှေ့ပညာ`** | 4.5 | `်ဝှေ့ပညာ` |
+| ကျိုက်လတ်မြို့က | **`ကျိုက်လတ်မြို့-က`** | 4.5 | `ကျိုက်လတ်မြို့` |
+| အစာတို့တွင် | **`အ-စာတို့တွင်`** | 4.5 | `စာတို့တွင်` |
+| စကားတော်များကို | **`စ-က-ားတော်များကို`** | 4.5 | `ားတော်များကို` |
+| တယ်လီဖပ်စ်က | **`တယ်လီဖပ်စ်-က`** | 4.5 | `တယ်လီဖပ်စ်` |
+| ဆယ်ယူပြီး | **`ဆ-ယ-်ယူပြီး`** | 4.5 | `်ယူပြီး` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Burmese 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 (5.62x) |
+| N-gram | **2-gram** | Lowest perplexity (1,887) |
+| 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 15:48:31*

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