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README.md

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+---
+language: mad
+language_name: Madurese
+language_family: austronesian_other
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-austronesian_other
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.690
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8668
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Madurese - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Madurese** 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** | 3.672x | 3.68 | 0.0762% | 283,323 |
+| **16k** | 4.063x | 4.07 | 0.0844% | 255,999 |
+| **32k** | 4.409x | 4.41 | 0.0915% | 235,937 |
+| **64k** | 4.690x 🏆 | 4.69 | 0.0974% | 221,777 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Kolami iyâ arèya dhisa è Kacamadhân Walea Kapoloan, Tojo Una-Una, Sulawesi Tengn...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ko lami ▁iyâ ▁arèya ▁dhisa ▁è ▁kacamadhân ▁wa lea ▁kapoloan ... (+12 more)` | 22 |
+| 16k | `▁ko lami ▁iyâ ▁arèya ▁dhisa ▁è ▁kacamadhân ▁walea ▁kapoloan , ... (+10 more)` | 20 |
+| 32k | `▁ko lami ▁iyâ ▁arèya ▁dhisa ▁è ▁kacamadhân ▁walea ▁kapoloan , ... (+10 more)` | 20 |
+| 64k | `▁kolami ▁iyâ ▁arèya ▁dhisa ▁è ▁kacamadhân ▁walea ▁kapoloan , ▁tojo ... (+9 more)` | 19 |
+
+**Sample 2:** `jmpl Nyarang ojhen biasanah è kalakoh parappâèn bâdâ acara mantân`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁jmpl ▁ny arang ▁o jh en ▁biasanah ▁è ▁kala koh ... (+9 more)` | 19 |
+| 16k | `▁jmpl ▁ny arang ▁o jh en ▁biasanah ▁è ▁kala koh ... (+8 more)` | 18 |
+| 32k | `▁jmpl ▁ny arang ▁o jhen ▁biasanah ▁è ▁kala koh ▁para ... (+6 more)` | 16 |
+| 64k | `▁jmpl ▁nyarang ▁ojhen ▁biasanah ▁è ▁kalakoh ▁parappâ èn ▁bâdâ ▁acara ... (+1 more)` | 11 |
+
+**Sample 3:** `jmpl cer bawang, iâ area kakanan dâri Mekasân, Madhurâ. èghâbây dâri teppong bân...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁jmpl ▁cer ▁ba wang , ▁i â ▁area ▁kakanan ▁dâri ... (+13 more)` | 23 |
+| 16k | `▁jmpl ▁cer ▁bawang , ▁i â ▁area ▁kakanan ▁dâri ▁me ... (+11 more)` | 21 |
+| 32k | `▁jmpl ▁cer ▁bawang , ▁iâ ▁area ▁kakanan ▁dâri ▁me kasân ... (+10 more)` | 20 |
+| 64k | `▁jmpl ▁cer ▁bawang , ▁iâ ▁area ▁kakanan ▁dâri ▁mekasân , ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.690x compression
+- **Lowest UNK Rate:** 8k with 0.0762% 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 | 7,660 | 12.90 | 15,927 | 15.5% | 38.8% |
+| **2-gram** | Subword | 284 🏆 | 8.15 | 2,917 | 65.5% | 99.2% |
+| **3-gram** | Word | 8,331 | 13.02 | 12,743 | 10.9% | 33.7% |
+| **3-gram** | Subword | 2,475 | 11.27 | 21,754 | 25.0% | 69.0% |
+| **4-gram** | Word | 11,782 | 13.52 | 16,213 | 9.8% | 26.2% |
+| **4-gram** | Subword | 14,165 | 13.79 | 105,104 | 11.2% | 37.1% |
+| **5-gram** | Word | 6,142 | 12.58 | 8,427 | 13.4% | 34.7% |
+| **5-gram** | Subword | 47,465 | 15.53 | 258,432 | 7.5% | 23.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `iyâ arèya` | 3,079 |
+| 2 | `è taon` | 2,005 |
+| 3 | `sala sèttong` | 1,545 |
+| 4 | `è bâkto` | 1,201 |
+| 5 | `ka angghuy` | 1,038 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `panèka sala sèttong` | 583 |
+| 2 | `al qur an` | 334 |
+| 3 | `sè bâḍâ è` | 250 |
+| 4 | `arèya sala sèttong` | 249 |
+| 5 | `iyâ arèya sala` | 218 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `iyâ arèya sala sèttong` | 205 |
+| 2 | `sala sèttong naghârâ è` | 116 |
+| 3 | `sè tamaso ka ḍâlem` | 114 |
+| 4 | `tamaso ka ḍâlem famili` | 112 |
+| 5 | `panèka sala sèttong sastrawan` | 106 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sè tamaso ka ḍâlem famili` | 111 |
+| 2 | `panèka sala sèttong naghârâ è` | 97 |
+| 3 | `arèya tombuwân sè tamaso ka` | 83 |
+| 4 | `iyâ arèya tombuwân sè tamaso` | 81 |
+| 5 | `panèka sala sèttong sastrawan bân` | 76 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 135,108 |
+| 2 | `a _` | 111,120 |
+| 3 | `n _` | 106,453 |
+| 4 | `n g` | 96,416 |
+| 5 | `_ s` | 84,557 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a` | 39,553 |
+| 2 | `a n _` | 38,801 |
+| 3 | `â n _` | 37,878 |
+| 4 | `n g _` | 34,520 |
+| 5 | `a n g` | 34,407 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `b â n _` | 25,412 |
+| 2 | `_ s è _` | 23,221 |
+| 3 | `_ b â n` | 22,209 |
+| 4 | `_ p a n` | 12,960 |
+| 5 | `g h i _` | 12,282 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b â n _` | 19,896 |
+| 2 | `a g h i _` | 10,512 |
+| 3 | `a n g g h` | 7,941 |
+| 4 | `a n è k a` | 6,131 |
+| 5 | `r è y a _` | 6,117 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 284
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% 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 | 0.8768 | 1.836 | 5.67 | 85,149 | 12.3% |
+| **1** | Subword | 0.9174 | 1.889 | 5.75 | 1,785 | 8.3% |
+| **2** | Word | 0.2172 | 1.162 | 1.45 | 481,154 | 78.3% |
+| **2** | Subword | 0.7767 | 1.713 | 4.61 | 10,251 | 22.3% |
+| **3** | Word | 0.0556 | 1.039 | 1.08 | 694,348 | 94.4% |
+| **3** | Subword | 0.8058 | 1.748 | 3.95 | 47,246 | 19.4% |
+| **4** | Word | 0.0147 🏆 | 1.010 | 1.02 | 750,067 | 98.5% |
+| **4** | Subword | 0.6526 | 1.572 | 2.80 | 186,332 | 34.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `è sosol empa kecamaḍhân bone èkennal mènangka am jungen rhein è ḍâlem ghâbâyânna james tautan sè`
+2. `sè ajhârâ neng pernata dhârurat politik filsafat tiongkok akennalaghi kendaraan rèya kalabân lo polo...`
+3. `bân sayatan è tèmor gedenken an panèka èlakonè marèna dâpa sè terlibat ḍâlem abentu pandhengngan man...`
+
+**Context Size 2:**
+
+1. `iyâ arèya katettapân ḍâri allah kaangghuy ngalakonè imsak molaè bâkto teknologi transistor mulaè a n...`
+2. `è taon schrödinger dhâddhi asisten exner sombher`
+3. `sala sèttong naghârâ è èropa lao provinsi kapolowan kanary ceuta melilla è afrika kantor perserikata...`
+
+**Context Size 3:**
+
+1. `panèka sala sèttong sastrawan bân panolès inḍonèsia karjâ buku bidadari untuk dewa assalamualaikum b...`
+2. `al qur an bapa èn serring nghâjhâk potra potrana akompol samarèna maghrib kaângguy abahas tafsir al ...`
+3. `sè bâḍâ è antara kompolan polo polo è tèmorra polo maḍhurâ sapuḍi aropa aghi polo palèng lowas nomer`
+
+**Context Size 4:**
+
+1. `iyâ arèya sala sèttong ghunong wisata sè baḍâ è banyuwangi bân bândâbâsa jhâbâ tèmor inḍonèsia sè an...`
+2. `sala sèttong naghârâ è èropa bârâ antillen belanda provinsi bonaire sint eustatius bân saba è amerik...`
+3. `sè tamaso ka ḍâlem famili cucurbitaceae tombuwân arèya èkoca kèya jambu bol inḍonesia malay apple in...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ewây_bâtè_se_pa`
+2. `a'_jon_è._kana_l`
+3. `n_-la_al_paasèra`
+
+**Context Size 2:**
+
+1. `an_kaapès_jaktunt`
+2. `a_bia_al_nèkentuh`
+3. `n_ton:_enta_pem-m`
+
+**Context Size 3:**
+
+1. `_kaoḍi’_“propa_kuf`
+2. `an_krèpublik_ngalo`
+3. `ân_sè_labân_kapa_l`
+
+**Context Size 4:**
+
+1. `bân_smp_3_ḍésémber_`
+2. `_sè_abârra_sala_oli`
+3. `_bân_bân_demi_abhâr`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (186,332 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 | 37,097 |
+| Total Tokens | 741,682 |
+| Mean Frequency | 19.99 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 232.38 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | è | 23,535 |
+| 2 | sè | 23,401 |
+| 3 | bân | 20,011 |
+| 4 | ka | 7,685 |
+| 5 | panèka | 5,706 |
+| 6 | taon | 5,597 |
+| 7 | ḍâri | 4,979 |
+| 8 | kalabân | 4,663 |
+| 9 | arèya | 4,306 |
+| 10 | orèng | 4,157 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | eghunaaghin | 2 |
+| 2 | pengatorannah | 2 |
+| 3 | ngelaksanaaghin | 2 |
+| 4 | sampèr | 2 |
+| 5 | geluk | 2 |
+| 6 | tekuk | 2 |
+| 7 | rasmè | 2 |
+| 8 | maddhekka | 2 |
+| 9 | uttarkashi | 2 |
+| 10 | spillway | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0120 |
+| R² (Goodness of Fit) | 0.991547 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 31.6% |
+| Top 1,000 | 58.3% |
+| Top 5,000 | 79.8% |
+| Top 10,000 | 87.8% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9915 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 31.6% of corpus
+- **Long Tail:** 27,097 words needed for remaining 12.2% 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 | 0.8668 🏆 | 0.3020 | N/A | N/A |
+| **mono_64d** | 64 | 0.6062 | 0.2632 | N/A | N/A |
+| **mono_128d** | 128 | 0.1633 | 0.2527 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8668 | 0.3113 | 0.0380 | 0.2740 |
+| **aligned_64d** | 64 | 0.6062 | 0.2737 | 0.0720 | 0.3700 |
+| **aligned_128d** | 128 | 0.1633 | 0.2516 | 0.1100 | 0.4080 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8668 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2757. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 11.0% 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 | **0.495** | 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 |
+|--------|----------|
+| `-a` | advokasi, aobâna, alias |
+| `-s` | sekabbhinna, sahabatta, salajâ |
+| `-ka` | kakosongan, kapalana, kaodi |
+| `-ma` | macmillan, marapi, mareh |
+| `-k` | kemaluan, kakosongan, khadijah |
+| `-pa` | paragraf, parsiapân, panyâbâb |
+| `-b` | berry, biography, bhâdâ |
+| `-p` | penolès, paragraf, parsiapân |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | kemaluan, kakosongan, parsiapân |
+| `-a` | sekabbhinna, sahabatta, aobâna |
+| `-an` | kemaluan, kakosongan, macmillan |
+| `-i` | èghâdhui, advokasi, ègabungaghi |
+| `-hi` | ègabungaghi, aningghâlaghi, èdebataghi |
+| `-na` | sekabbhinna, aobâna, rilisna |
+| `-s` | waprès, penolès, cutlass |
+| `-ng` | gâmpang, tambâng, torkaḍâng |
+
+### 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 |
+|------|----------|------------------|----------|
+| `angk` | 1.72x | 122 contexts | angka, angko, èangka |
+| `nggh` | 1.57x | 158 contexts | ongghe, èngghi, èngghâ |
+| `gghu` | 1.88x | 60 contexts | agghu, negghu, ongghu |
+| `ngka` | 1.55x | 131 contexts | angka, èangka, mengka |
+| `angg` | 1.47x | 151 contexts | anggâ, anggun, rangga |
+| `ddhi` | 1.98x | 37 contexts | eddhi, seddhi, deddhi |
+| `gghâ` | 1.73x | 63 contexts | cegghâ, èngghâ, logghâ |
+| `tton` | 2.08x | 25 contexts | ottone, èttong, button |
+| `âddh` | 2.13x | 16 contexts | bâddhâ, ḍâddhi, sâddhi |
+| `hâdd` | 2.12x | 15 contexts | dhâddi, dhâddih, dhâddhi |
+| `aren` | 1.66x | 33 contexts | karen, arena, areng |
+| `labâ` | 1.84x | 22 contexts | labân, alabân, labâng |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-n` | 162 words | pangobhâdhân, panganjhuân |
+| `-pa` | `-n` | 161 words | pangobhâdhân, panganjhuân |
+| `-ka` | `-n` | 154 words | kabendherran, kaodhiân |
+| `-s` | `-a` | 130 words | sèvilla, sadaja |
+| `-k` | `-n` | 124 words | kabendherran, kaodhiân |
+| `-p` | `-an` | 122 words | pakarangan, pangamatan |
+| `-pa` | `-an` | 106 words | pakarangan, pangamatan |
+| `-k` | `-an` | 99 words | kabendherran, karegghingan |
+| `-a` | `-i` | 91 words | adhâddiyaghi, azeri |
+| `-ka` | `-an` | 90 words | kabendherran, karegghingan |
+
+### 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 |
+|------|-----------------|------------|------|
+| bertasbih | **`bertasb-i-h`** | 7.5 | `i` |
+| pertamina | **`pertam-i-na`** | 7.5 | `i` |
+| fakultassa | **`fakultas-s-a`** | 7.5 | `s` |
+| pendukungnga | **`pendukung-ng-a`** | 7.5 | `ng` |
+| parèntana | **`parènt-an-a`** | 7.5 | `an` |
+| terlarang | **`terla-ra-ng`** | 7.5 | `ra` |
+| kebijaksanaan | **`kebijaksa-na-an`** | 7.5 | `na` |
+| ibukottana | **`ibukott-an-a`** | 7.5 | `an` |
+| kapotosanna | **`kapotos-an-na`** | 7.5 | `an` |
+| rangsangan | **`rangsa-ng-an`** | 7.5 | `ng` |
+| pangangghuy | **`pa-ng-angghuy`** | 7.5 | `angghuy` |
+| tangghungan | **`tangghu-ng-an`** | 7.5 | `ng` |
+| polinesia | **`poline-si-a`** | 7.5 | `si` |
+| pematangan | **`pe-ma-tangan`** | 7.5 | `tangan` |
+| ètampilkan | **`ètampil-k-an`** | 7.5 | `k` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Madurese 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 (4.69x) |
+| N-gram | **2-gram** | Lowest perplexity (284) |
+| Markov | **Context-4** | Highest predictability (98.5%) |
+| 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 11:30:57*

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