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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
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README.md

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+---
+language: km
+language_name: Khmer
+language_family: austroasiatic_khmer
+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-austroasiatic_khmer
+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: 4.889
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8701
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Khmer - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Khmer** 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** | 3.556x | 3.54 | 0.1756% | 741,877 |
+| **16k** | 4.063x | 4.05 | 0.2006% | 649,413 |
+| **32k** | 4.511x | 4.49 | 0.2228% | 584,909 |
+| **64k** | 4.889x 🏆 | 4.87 | 0.2415% | 539,636 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `សាវតារ ភូមិតាបឹបនេះយើងពុំបានជ្រាបច្បាស់ទេ ។ តែយើងបានដឹងថាក្នុងភូមិនេះមានទួលកប់ខ្...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁សាវ តារ ▁ភូមិ ត ាប ឹប នេះ យើង ពុំ បាន ... (+24 more)` | 34 |
+| 16k | `▁សាវ តារ ▁ភូមិ តាប ឹប នេះ យើង ពុំ បាន ជ្រាប ... (+21 more)` | 31 |
+| 32k | `▁សាវ តារ ▁ភូមិ តាប ឹប នេះ យើង ពុំបាន ជ្រាប ច្បាស់ ... (+17 more)` | 27 |
+| 64k | `▁សាវតារ ▁ភូមិ តាប ឹប នេះយើង ពុំបាន ជ្រាប ច្បាស់ទេ ▁។ ▁តែ ... (+13 more)` | 23 |
+
+**Sample 2:** `៖ ឃុំស៊ុង ឃុំមានជ័យ ឃុំសំឡូត ឃុំកំពង់ល្ពៅ ឃុំអូរសំរិល ឃុំតាតោក ឃុំតាសាញ សូមមើលផង...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁៖ ▁ឃុំ ស៊ ុង ▁ឃុំ មានជ័យ ▁ឃុំ សំ ឡ ូត ... (+18 more)` | 28 |
+| 16k | `▁៖ ▁ឃុំ ស៊ ុង ▁ឃុំមានជ័យ ▁ឃុំ សំឡូត ▁ឃុំកំពង់ ល ្ពៅ ... (+13 more)` | 23 |
+| 32k | `▁៖ ▁ឃុំ ស៊ុង ▁ឃុំមានជ័យ ▁ឃុំ សំឡូត ▁ឃុំកំពង់ ល្ពៅ ▁ឃុំអូរ សំរ ... (+10 more)` | 20 |
+| 64k | `▁៖ ▁ឃុំ ស៊ុង ▁ឃុំមានជ័យ ▁ឃុំ សំឡូត ▁ឃុំកំពង់ល្ពៅ ▁ឃុំអូរ សំរិល ▁ឃុំ ... (+7 more)` | 17 |
+
+**Sample 3:** `ម៉ៃឃើលអាចសំដៅលើ៖ ម៉ៃឃើល ហ្វារ៉ាដេយ ម៉ៃឃើល ចាកសាន់ ម៉ៃឃើល វីកឃឺវី`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ម៉ ៃ ឃ ើល អាច សំដៅលើ ៖ ▁ម៉ ៃ ឃ ... (+22 more)` | 32 |
+| 16k | `▁ម៉ៃឃើល អាច សំដៅលើ៖ ▁ម៉ៃឃើល ▁ហ ្វារ ៉ា ដ េយ ▁ម៉ៃឃើល ... (+8 more)` | 18 |
+| 32k | `▁ម៉ៃឃើល អាចសំដៅលើ៖ ▁ម៉ៃឃើល ▁ហ្វារ ៉ា ដេយ ▁ម៉ៃឃើល ▁ចាក សាន់ ▁ម៉ៃឃើល ... (+4 more)` | 14 |
+| 64k | `▁ម៉ៃឃើល អាចសំដៅលើ៖ ▁ម៉ៃឃើល ▁ហ្វារ៉ាដេយ ▁ម៉ៃឃើល ▁ចាក សាន់ ▁ម៉ៃឃើល ▁វីក ឃឺ ... (+1 more)` | 11 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.889x compression
+- **Lowest UNK Rate:** 8k with 0.1756% 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 | 29,102 | 14.83 | 72,055 | 8.9% | 24.7% |
+| **2-gram** | Subword | 5,212 🏆 | 12.35 | 88,256 | 22.4% | 57.4% |
+| **3-gram** | Word | 53,084 | 15.70 | 103,452 | 6.4% | 17.4% |
+| **3-gram** | Subword | 51,695 | 15.66 | 499,965 | 8.2% | 24.3% |
+| **4-gram** | Word | 118,314 | 16.85 | 213,260 | 4.3% | 12.7% |
+| **4-gram** | Subword | 260,843 | 17.99 | 1,609,249 | 4.4% | 12.4% |
+| **5-gram** | Word | 100,822 | 16.62 | 180,877 | 4.2% | 13.0% |
+| **5-gram** | Subword | 609,986 | 19.22 | 2,327,771 | 3.0% | 8.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `example example` | 21,905 |
+| 2 | `of the` | 4,908 |
+| 3 | `ត្រូវ បាន` | 3,687 |
+| 4 | `នៅ ក្នុង` | 3,249 |
+| 5 | `ព្រះ អង្គ` | 2,574 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `example example example` | 10,790 |
+| 2 | `villageភូមិ villageភូមិ villageភូមិ` | 1,612 |
+| 3 | `ត្រូវ បាន គេ` | 1,169 |
+| 4 | `៤៩៣ ប្រ ក` | 995 |
+| 5 | `សាសនា ព្រះពុទ្ធសាសនា វត្ត` | 640 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `example example example example` | 1,615 |
+| 2 | `villageភូមិ villageភូមិ villageភូមិ villageភូមិ` | 1,380 |
+| 3 | `អនុវិទ្យាល័យ សាសនា ព្រះពុទ្ធសាសនា វត្ត` | 558 |
+| 4 | `បឋមសិក្សា អនុវិទ្យាល័យ សាសនា ព្រះពុទ្ធសាសនា` | 536 |
+| 5 | `អប់រំ បឋមសិក្សា អនុវិទ្យាល័យ សាសនា` | 535 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `villageភូមិ villageភូមិ villageភូមិ villageភូមិ villageភូមិ` | 1,151 |
+| 2 | `អប់រំ បឋមសិក្សា អនុវិទ្យាល័យ សាសនា ព្រះពុទ្ធសាសនា` | 535 |
+| 3 | `បឋមសិក្សា អនុវិទ្យាល័យ សាសនា ព្រះពុទ្ធសាសនា វត្ត` | 528 |
+| 4 | `e លិច w ត្បូង s` | 455 |
+| 5 | `n កើត e លិច w` | 454 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `។ _` | 199,513 |
+| 2 | `បា ន` | 145,143 |
+| 3 | `ង _` | 128,650 |
+| 4 | `កា រ` | 123,593 |
+| 5 | `e _` | 121,925 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ និ ង` | 83,168 |
+| 2 | `_ ។ _` | 67,258 |
+| 3 | `រ ប ស់` | 64,716 |
+| 4 | `_ ដែ ល` | 42,564 |
+| 5 | `_ t h` | 39,828 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `m p l e` | 34,032 |
+| 2 | `p l e _` | 33,694 |
+| 3 | `_ e x a` | 33,362 |
+| 4 | `a m p l` | 33,310 |
+| 5 | `e x a m` | 33,310 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ e x a m` | 33,301 |
+| 2 | `a m p l e` | 33,292 |
+| 3 | `e x a m p` | 33,273 |
+| 4 | `x a m p l` | 33,273 |
+| 5 | `m p l e _` | 33,105 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 5,212
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~8% 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 | 0.2782 | 1.213 | 2.41 | 859,644 | 72.2% |
+| **1** | Subword | 1.0301 | 2.042 | 17.81 | 14,759 | 0.0% |
+| **2** | Word | 0.1500 | 1.110 | 1.34 | 2,064,587 | 85.0% |
+| **2** | Subword | 0.6645 | 1.585 | 5.47 | 262,778 | 33.5% |
+| **3** | Word | 0.0584 | 1.041 | 1.09 | 2,764,478 | 94.2% |
+| **3** | Subword | 0.4625 | 1.378 | 2.82 | 1,436,052 | 53.8% |
+| **4** | Word | 0.0205 🏆 | 1.014 | 1.03 | 3,007,497 | 98.0% |
+| **4** | Subword | 0.3127 | 1.242 | 1.86 | 4,049,871 | 68.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `និង ឡាវ ព្រះឧបជ្ឈាហ៍ ទេពវង្ស សម្តេច ព្រះអភិសិរីសុគន្ធាមហាសង្ឃរាជាធិបតី សម្តេចព្រះមហាសង្ឃរាជ បួរ គ្រី...`
+2. `example example example example ៧ លោកស្រី គាត់ បាន សម្រាប់ និកាយ ហ្សេន តានត្រិក និងដែនដីបរិសុទ្ធ ដែន...`
+3. `the united states union premier league cup នេះក៏ជាការប្រកួតផ្លូវការណ៍ក្រោមការគ្រប់គ្រងរបស់ cambodian...`
+
+**Context Size 2:**
+
+1. `example example example ៣ example example ២៧ example example ១១ example example ៧ example example ex...`
+2. `of the mahayana idea that such an attack scenario dynamically shall make use of both the dmt`
+3. `ត្រូវ បាន អភិវឌ្ឍន សម្រាប់ kde 3 បាន ការ តែង តាំង ជា អភិបាល នៃ តំបន់អុីវាណូ ហ្វ្រែនគីវស៍ ក្នុង នាម`
+
+**Context Size 3:**
+
+1. `example example example ៤១ example example example ៦ example example example ១២ example example exam...`
+2. `villageភូមិ villageភូមិ villageភូមិ villageភូមិ village ព្រំប្រទល់នៃ ទិសខាងកើត e ខាងត្បូង s ខាងលិច w...`
+3. `ត្រូវ បាន គេ ធ្វើ តេស្ដ នៅ ក្នុង ថ្នាក់ b និង c គឺជារង្វាស់នៃជ្រុងនៃ ត្រីកោណ ដែលមាន ក្រលាផ្ទៃ f និង ...`
+
+**Context Size 4:**
+
+1. `example example example example ៣ ស្រី ៨ example example example ៣៣ example example example ៩ exampl...`
+2. `villageភូមិ villageភូមិ villageភូមិ villageភូមិ villageភូមិ villageភូមិ villageភូមិ village ព្រំប្រទ...`
+3. `អនុវិទ្យាល័យ សាសនា ព្រះពុទ្ធសាសនា វត្ត ផ្សារ រមណីដ្ឋាន ឯកសារពិគ្រោះ គណកម្មការជាតិរៀបចំការបោះឆ្នោត ខេ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_plovon_(ហៅថានេះ​សេចក្តី`
+2. `​ច្បាប់​ជាជន៍ជា​គួរលាវបាទទួ`
+3. `ង​ថា_មាគរ_ck_និងសែន​`
+
+**Context Size 2:**
+
+1. `។_rel.2_សង្ខិត្តំ។]_(_s`
+2. `បាន​លទ្ធផលស្គាល់ច្បាស់លាស់_។_ស`
+3. `ង_ត្រឡប់​យកមន្រ្តីខុទ្ទកាល័យ_និ`
+
+**Context Size 3:**
+
+1. `_និង_កម្រិត។_ផ្លូវថូម៉ាស"_(r`
+2. `_។_នាម៉ឺន​ពិធី​មាំ​ថែមទៀតផង`
+3. `របស់វីតាមីន_atter_leve`
+
+**Context Size 4:**
+
+1. `mple_៥០_និងប្រទេសអូស្រ្ដាលី_កេន`
+2. `ple_example_example`
+3. `_example_example_ex`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (4,049,871 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 | 168,571 |
+| Total Tokens | 2,917,143 |
+| Mean Frequency | 17.31 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 265.83 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | និង | 40,023 |
+| 2 | example | 33,205 |
+| 3 | the | 28,680 |
+| 4 | ជា | 28,379 |
+| 5 | បាន | 26,100 |
+| 6 | មាន | 21,881 |
+| 7 | of | 20,677 |
+| 8 | ដែល | 18,961 |
+| 9 | នៅ | 18,044 |
+| 10 | ក្នុង | 16,838 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | កេលីម៉ាន់តាន់ | 2 |
+| 2 | สทิงพระ | 2 |
+| 3 | ទេសបាលតំបន់ | 2 |
+| 4 | វត្តច័ន្ទ | 2 |
+| 5 | និងការអភិវឌ្ឍខ្លួនឯង | 2 |
+| 6 | milliontimes | 2 |
+| 7 | អក្សរចិនបុរាណ | 2 |
+| 8 | នៅលើផ្ទៃខាងក្រោយងងឹត | 2 |
+| 9 | វគ្គជម្រុះជុំទី៣ | 2 |
+| 10 | wagnalls | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0175 |
+| R² (Goodness of Fit) | 0.996035 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 27.0% |
+| Top 1,000 | 51.0% |
+| Top 5,000 | 68.7% |
+| Top 10,000 | 75.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9960 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 27.0% of corpus
+- **Long Tail:** 158,571 words needed for remaining 24.4% 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 | 0.8684 | 0.3333 | N/A | N/A |
+| **mono_64d** | 64 | 0.8701 🏆 | 0.2501 | N/A | N/A |
+| **mono_128d** | 128 | 0.7385 | 0.2098 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8684 | 0.3316 | 0.0940 | 0.3400 |
+| **aligned_64d** | 64 | 0.8701 | 0.2521 | 0.1220 | 0.4760 |
+| **aligned_128d** | 128 | 0.7385 | 0.2166 | 0.2480 | 0.6260 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_64d with 0.8701 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2656. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 24.8% 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 | **0.614** | 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 |
+|--------|----------|
+| `-ស` | សឧត្តរំ, ស្វាហ៊ីលី, សម្បកក្រៅរុំ |
+| `-ប` | បានដល់ការទាយគតិរបស់ព្រះសិទ្ធត្ថរាជកុមារ, បឋមជ្ឈានតោ, ប្រាសាទបាក់បែកនៅខាងក្រោយនៃវត្តស្រីមឿងនៅវាំងចន្ទន៍ភាគកណ្ដាល |
+| `-ក` | ក្រាំងចិន, ក្រមាខ្មែរ, ក្នុងកាលខាងក្រោយ |
+| `-អ` | អង្គុយក្នុងទីសមគួរហើយ, អេអូនីសេ, អូរាំងអាស្លី |
+| `-ន` | និងបន្លែ, នៃម៉ាស់សរុបនៃប្រព័ន្ធព្រះអាទិត្យ, និងបរិវារមួយក្រុមបានភៀសទៅជ្រកកោនក្នុងប្រទេសសៀមជាមួយព្រះ |
+| `-ម` | ម��នប្រាសាទ, ម្យ៉ាងទៀតសោត, មានឱកាស |
+| `-s` | supra, sharia, signals |
+| `-រ` | រមែងសញ្ជប់សញ្ជឹង, រណ្តៅតូច, របស់ព្រះពុទ្ធមួយភាគដែរ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ង` | រមែងសញ្ជប់សញ្ជឹង, ត្បូងពណ៌បៃតង, ដើម្បីនឹង |
+| `-យ` | អង្គុយក្នុងទីសមគួរហើយ, ធ្វើឱ្យជាស្ថានទីរីករាយ, គ្មានមន្ទីរពេទ្យ |
+| `-ន` | ក្រាំងចិន, យោន, គឺមិនមាន |
+| `-រ` | បានដល់ការទាយគតិរបស់ព្រះសិទ្ធត្ថរាជកុមារ, ក្រមាខ្មែរ, របស់ព្រះពុទ្ធមួយភាគដែរ |
+| `-ត` | គឺមិនមាននិមិត្ត, ម្យ៉ាងទៀតសោត, និងរារាំងការពង្រីកខ្លួនរបស់ចិនបន្តទៅទៀត |
+| `-ក` | នៃតំបន់ប្រាសាទសំបូរព្រៃគុក, ក្នុងសំដីរបស់អ្នក, និងចក |
+| `-ម` | ទៅកាន់មនុស្សទាំងអស់ក្នុងសង្គម, ដូចជាកោះត្រល់ជាដើម, ទឹកនោមផ្អែម |
+| `-s` | nicolas, thoughts, characters |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ight` | 2.39x | 50 contexts | fight, night, sight |
+| `tion` | 2.28x | 46 contexts | option, nation, lotion |
+| `ment` | 2.30x | 39 contexts | cement, moment, mental |
+| `atio` | 2.39x | 33 contexts | ratio, nation, horatio |
+| `nter` | 2.15x | 37 contexts | enter, inter, winter |
+| `inte` | 2.29x | 29 contexts | intel, inter, winter |
+| `stor` | 2.31x | 27 contexts | story, jstor, storm |
+| `ctio` | 2.40x | 23 contexts | action, section, actions |
+| `illa` | 2.19x | 27 contexts | illam, villa, silla |
+| `ubli` | 2.35x | 19 contexts | dublin, public, publié |
+| `pres` | 2.24x | 22 contexts | press, ypres, presse |
+| `iver` | 2.18x | 22 contexts | liver, river, waiver |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ប` | `-ន` | 50 words | បណ្តាញសាកលវិទ្យាល័យអាស៊ាន, បង្ហាញខ្លួន |
+| `-ក` | `-ង` | 49 words | ការប្រើដំណរក្នុង, ក្រាំងខ្លុង |
+| `-ប` | `-យ` | 46 words | បានត្រាស់សេចក្តីនេះរួចហើយ, បន្សាយ |
+| `-ន` | `-យ` | 44 words | និងសម្តែងដោយ, និងបានយសសក្ដិគ្រប់សព្វណាស់ទៅហើយ |
+| `-ក` | `-យ` | 40 words | កម្លាំងថយ, ក៏ពោលពាក្យ |
+| `-ក` | `-ន` | 39 words | កៈទឿន, ការឈ្លានពានរបស់ជប៉ុន |
+| `-ន` | `-ង` | 38 words | និងចៅប្រមាញ់វិងស៊ុង, និងនៅសងខាង |
+| `-ន` | `-រ` | 37 words | និងវិចិត្រសិល្បៈខេត្តព្រះវិហារ, នាយសមុទ្រ |
+| `-ស` | `-ន` | 36 words | សីលជាស្ពាន, សារពត័មាន |
+| `-ស` | `-រ` | 35 words | សុពាហុត្ថេរ, សភិយត្ថេរ |
+
+### 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 |
+|------|-----------------|------------|------|
+| abdagases | **`abdaga-s-es`** | 7.5 | `s` |
+| នៅពីក្រោយខ្នង | **`នៅពីក្រោយខ្-ន-ង`** | 7.5 | `ន` |
+| tlaxcaltecas | **`tlaxcalteca-s`** | 4.5 | `tlaxcalteca` |
+| instrumental | **`instrument-al`** | 4.5 | `instrument` |
+| អន្តរជាតិ | **`អ-ន-្តរជាតិ`** | 4.5 | `្តរជាតិ` |
+| អបដិក្កូលេ | **`អ-បដិក្កូលេ`** | 4.5 | `បដិក្កូលេ` |
+| scholarships | **`scholarship-s`** | 4.5 | `scholarship` |
+| ស្រមោចហែរ | **`ស្រ��ោចហែ-រ`** | 4.5 | `ស្រមោចហែ` |
+| replacements | **`replacement-s`** | 4.5 | `replacement` |
+| ពួកសត្វតែងមាន | **`ព-ួកសត្វតែងមា-ន`** | 3.0 | `ួកសត្វតែងមា` |
+| grancrest | **`grancr-es-t`** | 3.0 | `grancr` |
+| ប្រទាញសងខាង | **`ប្រទាញសងខា-ង`** | 1.5 | `ប្រទាញសងខា` |
+| ក្នុងថ្ងៃនេះបាន | **`ក្នុងថ្ងៃនេះបា-ន`** | 1.5 | `ក្នុងថ្ងៃនេះបា` |
+| vidyādhara | **`vidyādhar-a`** | 1.5 | `vidyādhar` |
+| ក្រុមហាម៉ាស់ | **`ក-្រុមហាម៉ាស់`** | 1.5 | `្រុមហាម៉ាស់` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Khmer 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 (4.89x) |
+| N-gram | **2-gram** | Lowest perplexity (5,212) |
+| Markov | **Context-4** | Highest predictability (98.0%) |
+| 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 08:23:26*

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