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+---
+language: jv
+language_name: Javanese
+language_family: austronesian_javanese
+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_javanese
+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: 4.770
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8468
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Javanese - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Javanese** 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** | 3.761x | 3.76 | 0.0624% | 367,079 |
+| **16k** | 4.158x | 4.16 | 0.0690% | 332,063 |
+| **32k** | 4.504x | 4.51 | 0.0747% | 306,543 |
+| **64k** | 4.770x 🏆 | 4.77 | 0.0791% | 289,433 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Lawang Tamang iku désa ing Kacamatan Kapuas Hulu, Kabupatèn Kapuas, Provinsi Kal...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lawang ▁tam ang ▁iku ▁désa ▁ing ▁kacamatan ▁kapuas ▁hulu , ... (+13 more)` | 23 |
+| 16k | `▁lawang ▁tam ang ▁iku ▁désa ▁ing ▁kacamatan ▁kapuas ▁hulu , ... (+13 more)` | 23 |
+| 32k | `▁lawang ▁tam ang ▁iku ▁désa ▁ing ▁kacamatan ▁kapuas ▁hulu , ... (+13 more)` | 23 |
+| 64k | `▁lawang ▁tam ang ▁iku ▁désa ▁ing ▁kacamatan ▁kapuas ▁hulu , ... (+13 more)` | 23 |
+
+**Sample 2:** `Olimpiade Innsbruck iku tegesé bisa: Olimpiade Mangsa Adhem Olimpiade Mangsa Adh...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁olimpiade ▁in ns br uck ▁iku ▁tegesé ▁bisa : ▁olimpiade ... (+13 more)` | 23 |
+| 16k | `▁olimpiade ▁in ns br uck ▁iku ▁tegesé ▁bisa : ▁olimpiade ... (+13 more)` | 23 |
+| 32k | `▁olimpiade ▁in ns br uck ▁iku ▁tegesé ▁bisa : ▁olimpiade ... (+13 more)` | 23 |
+| 64k | `▁olimpiade ▁innsbruck ▁iku ▁tegesé ▁bisa : ▁olimpiade ▁mangsa ▁adhem ▁olimpiade ... (+7 more)` | 17 |
+
+**Sample 3:** `Tumbang Randang iku désa ing Kacamatan Timpah, Kabupatèn Kapuas, Provinsi Kalima...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁t umbang ▁r andang ▁iku ▁désa ▁ing ▁kacamatan ▁t imp ... (+15 more)` | 25 |
+| 16k | `▁tumbang ▁r andang ▁iku ▁désa ▁ing ▁kacamatan ▁t imp ah ... (+14 more)` | 24 |
+| 32k | `▁tumbang ▁r andang ▁iku ▁désa ▁ing ▁kacamatan ▁t imp ah ... (+14 more)` | 24 |
+| 64k | `▁tumbang ▁r andang ▁iku ▁désa ▁ing ▁kacamatan ▁timpah , ▁kabupatèn ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.770x compression
+- **Lowest UNK Rate:** 8k with 0.0624% 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 | 53,400 | 15.70 | 220,522 | 10.1% | 24.5% |
+| **2-gram** | Subword | 259 🏆 | 8.01 | 15,060 | 68.6% | 99.0% |
+| **3-gram** | Word | 61,400 | 15.91 | 252,205 | 10.0% | 25.8% |
+| **3-gram** | Subword | 2,364 | 11.21 | 78,931 | 26.6% | 70.5% |
+| **4-gram** | Word | 77,247 | 16.24 | 361,150 | 9.9% | 27.0% |
+| **4-gram** | Subword | 14,956 | 13.87 | 384,924 | 13.0% | 38.3% |
+| **5-gram** | Word | 47,870 | 15.55 | 237,597 | 10.3% | 31.3% |
+| **5-gram** | Subword | 61,146 | 15.90 | 1,130,643 | 8.1% | 24.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pranala njaba` | 22,877 |
+| 2 | `ya iku` | 21,546 |
+| 3 | `désa ing` | 18,151 |
+| 4 | `wonten ing` | 17,934 |
+| 5 | `ing kacamatan` | 17,641 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `désa ing kacamatan` | 14,588 |
+| 2 | `iku désa ing` | 12,656 |
+| 3 | `pranala njaba situs` | 10,259 |
+| 4 | `njaba situs resmi` | 7,571 |
+| 5 | `provinsi jawa tengah` | 6,585 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `iku désa ing kacamatan` | 12,424 |
+| 2 | `pranala njaba situs resmi` | 7,568 |
+| 3 | `provinsi jawa tengah indonésia` | 5,971 |
+| 4 | `njaba situs resmi kabupatèn` | 5,917 |
+| 5 | `tengah indonésia uga delengen` | 4,463 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pranala njaba situs resmi kabupatèn` | 5,917 |
+| 2 | `jawa tengah indonésia uga delengen` | 4,458 |
+| 3 | `provinsi jawa tengah indonésia uga` | 4,344 |
+| 4 | `delengen pratélan désa ing nurwègen` | 3,052 |
+| 5 | `uga delengen pratélan désa ing` | 3,052 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 2,445,496 |
+| 2 | `n g` | 2,062,677 |
+| 3 | `n _` | 1,386,666 |
+| 4 | `a _` | 1,357,298 |
+| 5 | `i n` | 1,235,038 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 1,062,306 |
+| 2 | `a n _` | 825,330 |
+| 3 | `i n g` | 754,596 |
+| 4 | `a n g` | 728,138 |
+| 5 | `_ k a` | 616,864 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i n g _` | 593,220 |
+| 2 | `_ i n g` | 401,502 |
+| 3 | `a n g _` | 300,987 |
+| 4 | `l a n _` | 237,133 |
+| 5 | `_ l a n` | 214,461 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ i n g _` | 314,002 |
+| 2 | `_ l a n _` | 197,495 |
+| 3 | `k a n g _` | 153,856 |
+| 4 | `_ k a n g` | 151,621 |
+| 5 | `n g _ k a` | 91,155 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 259
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~25% 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 | 0.9308 | 1.906 | 8.58 | 468,924 | 6.9% |
+| **1** | Subword | 1.1692 | 2.249 | 7.47 | 10,119 | 0.0% |
+| **2** | Word | 0.2944 | 1.226 | 1.76 | 4,009,882 | 70.6% |
+| **2** | Subword | 0.5600 | 1.474 | 3.25 | 75,466 | 44.0% |
+| **3** | Word | 0.0884 | 1.063 | 1.15 | 7,031,088 | 91.2% |
+| **3** | Subword | 0.5549 | 1.469 | 3.14 | 244,687 | 44.5% |
+| **4** | Word | 0.0284 🏆 | 1.020 | 1.04 | 8,087,514 | 97.2% |
+| **4** | Subword | 0.6012 | 1.517 | 2.96 | 767,333 | 39.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ing wajan utawa rusa sing nganggo cithakan kanggo mesin iki uga dadi sawijining omongané kepeksa nur...`
+2. `lan sawisé sawatara organisasi kabèh kalungguhan punika kanthi dipundalaken kagem nyithak karakter é...`
+3. `kang béda kanggo best lonely island caribbean at cbci siro malabar rajkot sumber daya ekonomi bank`
+
+**Context Size 2:**
+
+1. `pranala njaba situs resmi kabupatèn kendhal pranala njaba master wewengkon ing situs bps data desemb...`
+2. `ya iku 55 20 00 dalu kanthi ritual kesurupan ing pungkasanipun simran remen kaliyan rara oyi diwasa`
+3. `désa ing kacamatan tapin tengah suku bangsa wong sundha kalah lan nagis bilung uga karan nagara panc...`
+
+**Context Size 3:**
+
+1. `désa ing kacamatan tunjungan kurang luwih 12 157 kepala kulawarga lan 67 157 jiwa nglakokaké transmi...`
+2. `iku désa ing kacamatan balongpanggang kabupatèn gresik provinsi jawa wétan indonésia rujukan uga del...`
+3. `pranala njaba situs resmi kabupatèn batang`
+
+**Context Size 4:**
+
+1. `iku désa ing kacamatan samigaluh kabupatèn kulon praga daerah istimewa yogyakarta réferènsi ing kabu...`
+2. `pranala njaba situs resmi luhur ing gorontalo`
+3. `njaba situs resmi kabupatèn pekalongan`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_tedhrapeyi_koke`
+2. `aspingka,_serero`
+3. `ng_ahecahalosung`
+
+**Context Size 2:**
+
+1. `antiong._katuhati`
+2. `ng_bittlenting_so`
+3. `n_bis_ovièrènsijs`
+
+**Context Size 3:**
+
+1. `ng_séjéngge_misuma`
+2. `an_r._kapusahané_k`
+3. `ing_kudu_dhèwèké_j`
+
+**Context Size 4:**
+
+1. `ing_yahya_dhésèmber`
+2. `_ing_wadhisi_déné_k`
+3. `ang_dibat_mliginipu`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (767,333 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 | 206,658 |
+| Total Tokens | 9,650,282 |
+| Mean Frequency | 46.70 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1053.92 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ing | 316,085 |
+| 2 | lan | 198,460 |
+| 3 | kang | 92,968 |
+| 4 | iku | 84,366 |
+| 5 | sing | 79,278 |
+| 6 | saka | 66,802 |
+| 7 | ingkang | 59,183 |
+| 8 | iki | 55,316 |
+| 9 | taun | 54,241 |
+| 10 | kabupatèn | 53,392 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | kaayom | 2 |
+| 2 | paridhiri | 2 |
+| 3 | lakwantara | 2 |
+| 4 | bebakon | 2 |
+| 5 | kadyan | 2 |
+| 6 | nitikira | 2 |
+| 7 | piwoleh | 2 |
+| 8 | llms | 2 |
+| 9 | marosa | 2 |
+| 10 | letan | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0368 |
+| R² (Goodness of Fit) | 0.991631 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 28.5% |
+| Top 1,000 | 54.2% |
+| Top 5,000 | 74.0% |
+| Top 10,000 | 81.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9916 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 28.5% of corpus
+- **Long Tail:** 196,658 words needed for remaining 18.9% 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 | 0.8468 | 0.3355 | N/A | N/A |
+| **mono_64d** | 64 | 0.7745 | 0.2697 | N/A | N/A |
+| **mono_128d** | 128 | 0.7659 | 0.1964 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8468 🏆 | 0.3396 | 0.1700 | 0.4900 |
+| **aligned_64d** | 64 | 0.7745 | 0.2725 | 0.2720 | 0.6640 |
+| **aligned_128d** | 128 | 0.7659 | 0.1970 | 0.4020 | 0.7520 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8468 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2684. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 40.2% 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 | **0.262** | 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 |
+|--------|----------|
+| `-s` | sejajar, savages, sigifredo |
+| `-a` | arepé, apla, aristizábal |
+| `-ka` | kakangipun, kari, kambu |
+| `-k` | kinali, kakangipun, kari |
+| `-ma` | mansel, mangkunegoro, matar |
+| `-di` | diah, dipompa, disebutnang |
+| `-m` | mesiu, michail, mansel |
+| `-sa` | savages, samsat, sandler |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | sokawatèn, tekukan, kakangipun |
+| `-a` | rayya, apla, archuleta |
+| `-e` | oise, cave, scalable |
+| `-an` | tekukan, panerbitan, pegelaran |
+| `-s` | fasciatus, liturgis, savages |
+| `-i` | kinali, nareswari, kari |
+| `-ng` | dhuwung, nonggunong, widianing |
+| `-g` | dhuwung, nonggunong, widianing |
+
+### 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` | 1.62x | 487 contexts | angka, angké, angki |
+| `puni` | 2.39x | 38 contexts | punia, punik, punis |
+| `nthi` | 2.24x | 47 contexts | knthi, anthi, sonthi |
+| `nten` | 1.80x | 122 contexts | enten, onten, inten |
+| `angg` | 1.40x | 471 contexts | anggy, anggo, anggi |
+| `ngka` | 1.47x | 336 contexts | angka, ongka, ingka |
+| `enga` | 1.54x | 237 contexts | menga, denga, engau |
+| `gkan` | 2.05x | 60 contexts | angkan, igkang, ngkana |
+| `ingk` | 1.63x | 161 contexts | ingka, singka, ingkah |
+| `angi` | 1.49x | 229 contexts | tangi, rangi, angie |
+| `ngin` | 1.63x | 128 contexts | ngina, nging, angin |
+| `akak` | 1.71x | 93 contexts | lakak, sakak, kakak |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-n` | 129 words | sekuningan, suwukan |
+| `-pa` | `-n` | 102 words | patuan, parwanosen |
+| `-k` | `-n` | 91 words | kondhan, kin |
+| `-di` | `-i` | 90 words | disigèni, dipungameli |
+| `-s` | `-a` | 82 words | shimojima, spinella |
+| `-ka` | `-n` | 82 words | karenggan, kamawen |
+| `-di` | `-é` | 75 words | diwajibaké, dijodokaké |
+| `-pa` | `-an` | 72 words | patuan, parengkuan |
+| `-k` | `-an` | 60 words | kondhan, kutukan |
+| `-a` | `-a` | 54 words | angkawijaya, anzola |
+
+### 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 |
+|------|-----------------|------------|------|
+| duryudana | **`duryud-an-a`** | 7.5 | `an` |
+| banjengan | **`banje-ng-an`** | 7.5 | `ng` |
+| ngrencana | **`ngrenc-an-a`** | 7.5 | `an` |
+| indowebster | **`indowebs-t-er`** | 7.5 | `t` |
+| tengkorake | **`tengko-ra-ke`** | 7.5 | `ra` |
+| dentawyanjana | **`dentawyanj-an-a`** | 7.5 | `an` |
+| dhongkrak | **`dhongk-ra-k`** | 7.5 | `ra` |
+| kayubiranga | **`kayubira-ng-a`** | 7.5 | `ng` |
+| kathosana | **`kathos-an-a`** | 7.5 | `an` |
+| tunjungan | **`tunju-ng-an`** | 7.5 | `ng` |
+| dengannya | **`dengan-n-ya`** | 7.5 | `n` |
+| västergötland | **`västergötl-an-d`** | 7.5 | `an` |
+| romandini | **`romandi-n-i`** | 7.5 | `n` |
+| kentingan | **`kenti-ng-an`** | 7.5 | `ng` |
+| çuklapaksa | **`çuklapak-s-a`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Javanese 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 (4.77x) |
+| N-gram | **2-gram** | Lowest perplexity (259) |
+| Markov | **Context-4** | Highest predictability (97.2%) |
+| 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 06:50:22*
diff --git a/jv_morph_tokenizer.json b/jv_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..c00b09502c113f4d484e0f49ac1967af6bf8a09e
--- /dev/null
+++ b/jv_morph_tokenizer.json
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diff --git a/models/tokenizer/jv_tokenizer_64k.vocab b/models/tokenizer/jv_tokenizer_64k.vocab
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