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README.md

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+---
+language: id
+language_name: Indonesian
+language_family: austronesian_malay
+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_malay
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 5.355
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6446
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-13
+---
+
+# Indonesian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Indonesian** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 4.240x | 4.24 | 0.0805% | 2,784,763 |
+| **16k** | 4.730x | 4.73 | 0.0899% | 2,496,316 |
+| **32k** | 5.099x | 5.10 | 0.0969% | 2,315,931 |
+| **64k** | 5.355x 🏆 | 5.36 | 0.1017% | 2,205,288 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Marga Karya adalah salah satu desa di kecamatan Kulisusu Barat, Kabupaten Buton ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+14 more)` | 24 |
+| 16k | `▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+13 more)` | 23 |
+| 32k | `▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+12 more)` | 22 |
+| 64k | `▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+12 more)` | 22 |
+
+**Sample 2:** `Sukamaju adalah desa di kecamatan Majalaya, Bandung, Jawa Barat, Indonesia. Refe...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁suk ama ju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ... (+10 more)` | 20 |
+| 16k | `▁suk ama ju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ... (+10 more)` | 20 |
+| 32k | `▁sukamaju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ▁bandung , ... (+8 more)` | 18 |
+| 64k | `▁sukamaju ▁adalah ▁desa ▁di ▁kecamatan ▁majalaya , ▁bandung , ▁jawa ... (+7 more)` | 17 |
+
+**Sample 3:** `Sukarapih adalah desa di kecamatan Sukarame, Tasikmalaya, Jawa Barat, Indonesia....`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+11 more)` | 21 |
+| 16k | `▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+9 more)` | 19 |
+| 32k | `▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+9 more)` | 19 |
+| 64k | `▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukarame , ▁tasikmalaya ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.355x compression
+- **Lowest UNK Rate:** 8k with 0.0805% 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 | 352,571 | 18.43 | 3,589,156 | 5.5% | 15.7% |
+| **2-gram** | Subword | 237 🏆 | 7.89 | 60,775 | 71.6% | 99.3% |
+| **3-gram** | Word | 1,403,108 | 20.42 | 7,774,768 | 5.0% | 12.0% |
+| **3-gram** | Subword | 2,119 | 11.05 | 298,247 | 28.5% | 73.3% |
+| **4-gram** | Word | 2,339,176 | 21.16 | 12,102,231 | 6.6% | 13.7% |
+| **4-gram** | Subword | 13,149 | 13.68 | 1,501,572 | 14.3% | 40.9% |
+| **5-gram** | Word | 1,091,988 | 20.06 | 7,805,199 | 10.1% | 20.0% |
+| **5-gram** | Subword | 56,499 | 15.79 | 5,070,685 | 8.4% | 25.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pada tahun` | 570,229 |
+| 2 | `pranala luar` | 330,544 |
+| 3 | `bagian dari` | 220,724 |
+| 4 | `salah satu` | 204,094 |
+| 5 | `referensi pranala` | 188,446 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `referensi pranala luar` | 188,236 |
+| 2 | `merupakan bagian dari` | 173,920 |
+| 3 | `ini juga merupakan` | 121,570 |
+| 4 | `juga merupakan bagian` | 118,712 |
+| 5 | `spesies ini juga` | 82,513 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `juga merupakan bagian dari` | 118,623 |
+| 2 | `ini juga merupakan bagian` | 118,088 |
+| 3 | `spesies ini juga merupakan` | 82,160 |
+| 4 | `merupakan bagian dari genus` | 74,651 |
+| 5 | `kelas insecta filum arthropoda` | 71,731 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ini juga merupakan bagian dari` | 118,072 |
+| 2 | `spesies ini juga merupakan bagian` | 82,150 |
+| 3 | `kelas insecta filum arthropoda dan` | 71,731 |
+| 4 | `filum arthropoda dan kingdom animalia` | 71,725 |
+| 5 | `insecta filum arthropoda dan kingdom` | 71,724 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 49,403,346 |
+| 2 | `n _` | 31,170,947 |
+| 3 | `a _` | 27,237,962 |
+| 4 | `_ d` | 23,894,955 |
+| 5 | `n g` | 23,074,425 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 24,307,048 |
+| 2 | `a n g` | 11,583,028 |
+| 3 | `_ m e` | 10,082,401 |
+| 4 | `_ d a` | 9,885,365 |
+| 5 | `n g _` | 9,758,169 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n g _` | 7,357,567 |
+| 2 | `k a n _` | 6,126,631 |
+| 3 | `_ m e n` | 5,037,208 |
+| 4 | `_ d a n` | 4,774,956 |
+| 5 | `d a n _` | 4,773,132 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d a n _` | 4,636,764 |
+| 2 | `y a n g _` | 4,351,516 |
+| 3 | `_ y a n g` | 4,285,242 |
+| 4 | `n g a n _` | 2,901,020 |
+| 5 | `p a d a _` | 2,514,636 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 237
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% 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 | 0.8593 | 1.814 | 13.13 | 2,962,654 | 14.1% |
+| **1** | Subword | 0.4616 | 1.377 | 5.47 | 82,539 | 53.8% |
+| **2** | Word | 0.4377 | 1.354 | 2.70 | 38,824,783 | 56.2% |
+| **2** | Subword | 0.4117 | 1.330 | 2.64 | 451,148 | 58.8% |
+| **3** | Word | 0.1831 | 1.135 | 1.41 | 104,699,371 | 81.7% |
+| **3** | Subword | 0.4412 | 1.358 | 2.78 | 1,192,939 | 55.9% |
+| **4** | Word | 0.0695 🏆 | 1.049 | 1.12 | 147,775,338 | 93.1% |
+| **4** | Subword | 0.5403 | 1.454 | 3.03 | 3,321,158 | 46.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `dan inlandsch schrijver semua lautan mazhab chicago medical top akan tetapi istrinya musik dan yogya...`
+2. `yang dirilis pada tahun pembangunan talud pembangunan ii orthomolybdate feo h2 lebih mudah teroksida...`
+3. `di india jakarta ichtiar baru seri televisi abc nbc selama lamanya yang mendorong serta melumasi lap...`
+
+**Context Size 2:**
+
+1. `pada tahun dengan bubarnya laskar jihad by noorhaidi hasan s ip center sikka 4 maria sharapova dan`
+2. `pranala luar film rusia tahun berikutnya penyelidik ufo dapat berupa kuantitatif misalnya dalam bent...`
+3. `bagian dari ordo diptera kelas insecta filum arthropoda dan kingdom animalia larva kumbang ini biasa...`
+
+**Context Size 3:**
+
+1. `merupakan bagian dari genus bulbophyllum nama ilmiah dari spesies ini didasarkan pada laporan dua or...`
+2. `referensi pranala luar air alps armada air alps telah mencakup pesawat berikut ini per agustus armad...`
+3. `ini juga merupakan bagian dari genus menemerus dan ordo araneae nama ilmiah dari spesies ini pertama...`
+
+**Context Size 4:**
+
+1. `juga merupakan bagian dari genus neoitamus ordo diptera kelas insecta filum arthropoda dan kingdom a...`
+2. `ini juga merupakan bagian dari ordo poales spesies cyperus paniceus sendiri merupakan bagian dari ge...`
+3. `spesies ini juga merupakan bagian dari genus megopis ordo coleoptera kelas insecta filum arthropoda ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ani_arasebeladan`
+2. `_kani,_"tatryang`
+3. `ng_mbantundidmbk`
+
+**Context Size 2:**
+
+1. `angnyangala_pest.`
+2. `n_gi,_ision_untif`
+3. `a_bah_res_porah_a`
+
+**Context Size 3:**
+
+1. `an_:_dan_ada_yang_`
+2. `angan_mencanyimnya`
+3. `_merurandah_dengka`
+
+**Context Size 4:**
+
+1. `ang_dirand_prättige`
+2. `kan_dises_p._london`
+3. `_menyata_panason_me`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (3,321,158 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 | 1,224,888 |
+| Total Tokens | 195,423,598 |
+| Mean Frequency | 159.54 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 8831.73 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | dan | 4,660,597 |
+| 2 | yang | 4,306,521 |
+| 3 | di | 3,661,285 |
+| 4 | pada | 2,304,326 |
+| 5 | dari | 2,124,901 |
+| 6 | dengan | 1,729,322 |
+| 7 | ini | 1,681,032 |
+| 8 | untuk | 1,457,185 |
+| 9 | dalam | 1,438,758 |
+| 10 | adalah | 1,350,786 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | melanthiales | 2 |
+| 2 | trilliales | 2 |
+| 3 | medeolaceae | 2 |
+| 4 | alstroemeriales | 2 |
+| 5 | burmanniales | 2 |
+| 6 | amaryllidales | 2 |
+| 7 | dioscoreanae | 2 |
+| 8 | arecanae | 2 |
+| 9 | mewstadz | 2 |
+| 10 | bithorax | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0756 |
+| R² (Goodness of Fit) | 0.989157 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 29.0% |
+| Top 1,000 | 56.7% |
+| Top 5,000 | 76.2% |
+| Top 10,000 | 83.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9892 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 29.0% of corpus
+- **Long Tail:** 1,214,888 words needed for remaining 17.0% 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 | 0.6145 | 0.3978 | N/A | N/A |
+| **mono_64d** | 64 | 0.6446 | 0.3232 | N/A | N/A |
+| **mono_128d** | 128 | 0.6017 | 0.2493 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6145 | 0.3840 | 0.5320 | 0.8980 |
+| **aligned_64d** | 64 | 0.6446 🏆 | 0.3083 | 0.7760 | 0.9520 |
+| **aligned_128d** | 128 | 0.6017 | 0.2548 | 0.8760 | 0.9860 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_64d with 0.6446 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3196. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 87.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.225** | Low formulaic 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` | sarmizegetusa, sounkyoensis, savoia |
+| `-a` | anakboru, analdie, aerotaxi |
+| `-ma` | mantellodon, mariarosa, manaruh |
+| `-m` | mengahruskan, muhadatsatul, menyalahkan |
+| `-k` | khathib, kunžak, kar98k |
+| `-p` | parungi, perusaahaan, pinsot |
+| `-b` | burdi, bumbong, bagiab |
+| `-t` | theridioides, teymourtash, talana |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | westa, grazilla, lefkosia |
+| `-n` | discrimination, jörn, mengahruskan |
+| `-s` | gomphrenoides, zimdars, sounkyoensis |
+| `-i` | parungi, burdi, aerotaxi |
+| `-e` | analdie, herne, iratsume |
+| `-an` | mengahruskan, fatchurohman, perusaahaan |
+| `-ya` | bungkusnya, oksidatifnya, berkembangbiaknya |
+| `-r` | vbr, legitimator, sattar |
+
+### 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 |
+|------|----------|------------------|----------|
+| `engu` | 1.64x | 240 contexts | cengu, dengu, wengu |
+| `ebag` | 2.05x | 77 contexts | sebag, tebag, lebaga |
+| `gkan` | 1.83x | 118 contexts | ingkan, ongkan, tigkan |
+| `ebua` | 2.11x | 62 contexts | sebua, ebuah, zebua |
+| `rkan` | 1.74x | 146 contexts | arkan, mrkan, erkan |
+| `egar` | 1.61x | 200 contexts | jegar, degar, cegar |
+| `rseb` | 2.00x | 68 contexts | terseb, ersebut, trsebut |
+| `njad` | 2.11x | 51 contexts | njadi, anjad, anjadi |
+| `ingk` | 1.37x | 376 contexts | singk, hingk, ingky |
+| `menj` | 1.88x | 63 contexts | menju, menja, menje |
+| `terb` | 1.49x | 188 contexts | terbai, terbis, terbat |
+| `nnya` | 1.65x | 106 contexts | annya, ionnya, lannya |
+
+### 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` | 117 words | pankorben, pembumian |
+| `-p` | `-a` | 104 words | puncumania, paradera |
+| `-s` | `-a` | 93 words | sylviatata, saaka |
+| `-a` | `-a` | 85 words | ajidarma, anisotricha |
+| `-k` | `-n` | 82 words | kipin, kylián |
+| `-p` | `-an` | 81 words | pembumian, pacinan |
+| `-k` | `-a` | 78 words | kreuta, kepemimpinanya |
+| `-m` | `-n` | 77 words | mistakon, mengkonsentrasikan |
+| `-s` | `-n` | 74 words | sajikdan, saefudin |
+| `-t` | `-a` | 72 words | tubicinella, typhlonyphia |
+
+### 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 |
+|------|-----------------|------------|------|
+| coelosphaeridae | **`coelosphaerid-a-e`** | 7.5 | `a` |
+| iguanidae | **`iguanid-a-e`** | 7.5 | `a` |
+| wijayaanwar | **`wijayaanw-a-r`** | 7.5 | `a` |
+| kerarajaan | **`keraraj-a-an`** | 7.5 | `a` |
+| pandjhoerit | **`pandjhoer-i-t`** | 7.5 | `i` |
+| fauthouxsandrine | **`fauthouxsandri-n-e`** | 7.5 | `n` |
+| retnowati | **`retnow-a-ti`** | 7.5 | `a` |
+| encontrar | **`encontr-a-r`** | 7.5 | `a` |
+| prasekolah | **`p-ra-sekolah`** | 7.5 | `sekolah` |
+| penamamaan | **`penama-ma-an`** | 7.5 | `ma` |
+| samatorsemarang | **`samatorsemar-a-ng`** | 7.5 | `a` |
+| keshavrao | **`keshavr-a-o`** | 7.5 | `a` |
+| mencatatnya | **`mencatat-n-ya`** | 7.5 | `n` |
+| interkelasi | **`interke-la-si`** | 7.5 | `la` |
+| siberpunk | **`siberpu-n-k`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Indonesian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (5.35x) |
+| N-gram | **2-gram** | Lowest perplexity (237) |
+| Markov | **Context-4** | Highest predictability (93.1%) |
+| 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: 2026-01-13 19:55:01*

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