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README.md

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+---
+language: iba
+language_name: Iban
+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
+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: 5.202
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8124
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Iban - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Iban** 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** | 4.581x | 4.58 | 0.1303% | 239,370 |
+| **16k** | 4.888x | 4.89 | 0.1391% | 224,316 |
+| **32k** | 5.091x | 5.09 | 0.1449% | 215,386 |
+| **64k** | 5.202x 🏆 | 5.21 | 0.1480% | 210,759 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Gawai, Sebuah kampung di Chitwan, Nepal . Gawai Dayak, pengerami ninting taun ti...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gawai , ▁sebuah ▁kampung ▁di ▁ch it wan , ▁nepal ... (+16 more)` | 26 |
+| 16k | `▁gawai , ▁sebuah ▁kampung ▁di ▁chit wan , ▁nepal ▁. ... (+15 more)` | 25 |
+| 32k | `▁gawai , ▁sebuah ▁kampung ▁di ▁chitwan , ▁nepal ▁. ▁gawai ... (+14 more)` | 24 |
+| 64k | `▁gawai , ▁sebuah ▁kampung ▁di ▁chitwan , ▁nepal ▁. ▁gawai ... (+14 more)` | 24 |
+
+**Sample 2:** `Bangkok tauka nama iya dalam jaku Thai, Krung Thep Maha Nakhon nya indu nengeri ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bangkok ▁tauka ▁nama ▁iya ▁dalam ▁jaku ▁thai , ▁k rung ... (+17 more)` | 27 |
+| 16k | `▁bangkok ▁tauka ▁nama ▁iya ▁dalam ▁jaku ▁thai , ▁k rung ... (+17 more)` | 27 |
+| 32k | `▁bangkok ▁tauka ▁nama ▁iya ▁dalam ▁jaku ▁thai , ▁krung ▁thep ... (+15 more)` | 25 |
+| 64k | `▁bangkok ▁tauka ▁nama ▁iya ▁dalam ▁jaku ▁thai , ▁krung ▁thep ... (+15 more)` | 25 |
+
+**Sample 3:** `Lemari iya nya kabinet bediri ti tinggi tauka sederhana endur nyimpan gari tauka...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lem ari ▁iya ▁nya ▁kabinet ▁bediri ▁ti ▁tinggi ▁tauka ▁sed ... (+13 more)` | 23 |
+| 16k | `▁lemari ▁iya ▁nya ▁kabinet ▁bediri ▁ti ▁tinggi ▁tauka ▁sederhana ▁endur ... (+8 more)` | 18 |
+| 32k | `▁lemari ▁iya ▁nya ▁kabinet ▁bediri ▁ti ▁tinggi ▁tauka ▁sederhana ▁endur ... (+8 more)` | 18 |
+| 64k | `▁lemari ▁iya ▁nya ▁kabinet ▁bediri ▁ti ▁tinggi ▁tauka ▁sederhana ▁endur ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.202x compression
+- **Lowest UNK Rate:** 8k with 0.1303% 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 | 6,394 | 12.64 | 13,442 | 15.3% | 43.4% |
+| **2-gram** | Subword | 194 🏆 | 7.60 | 1,944 | 77.0% | 99.7% |
+| **3-gram** | Word | 9,236 | 13.17 | 13,930 | 9.9% | 32.2% |
+| **3-gram** | Subword | 1,402 | 10.45 | 13,716 | 34.0% | 81.6% |
+| **4-gram** | Word | 12,791 | 13.64 | 15,883 | 6.7% | 22.4% |
+| **4-gram** | Subword | 6,509 | 12.67 | 60,183 | 17.8% | 51.0% |
+| **5-gram** | Word | 5,997 | 12.55 | 7,027 | 8.9% | 30.2% |
+| **5-gram** | Subword | 18,422 | 14.17 | 130,688 | 12.0% | 34.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `iya nya` | 2,053 |
+| 2 | `dalam taun` | 1,897 |
+| 3 | `pelilih menua` | 882 |
+| 4 | `kereban sanding` | 782 |
+| 5 | `kandang menua` | 689 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dikelala enggau nama` | 415 |
+| 2 | `garis entara menua` | 246 |
+| 3 | `dalam taun iya` | 197 |
+| 4 | `nyadi sebagi ari` | 179 |
+| 5 | `web ke bukai` | 165 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `laman web ke bukai` | 158 |
+| 2 | `kereban sanding laman web` | 78 |
+| 3 | `mega dikelala enggau nama` | 74 |
+| 4 | `sanding laman web ke` | 73 |
+| 5 | `ti dikelala enggau nama` | 64 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kereban sanding laman web ke` | 73 |
+| 2 | `sanding laman web ke bukai` | 72 |
+| 3 | `penyanding laman web ke bukai` | 45 |
+| 4 | `bekunsi garis entara menua enggau` | 45 |
+| 5 | `negeri sarawak kunsil negeri sarawak` | 44 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 110,486 |
+| 2 | `n g` | 83,490 |
+| 3 | `i _` | 77,339 |
+| 4 | `e n` | 67,953 |
+| 5 | `a n` | 64,094 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n g` | 32,899 |
+| 2 | `_ p e` | 27,770 |
+| 3 | `y a _` | 21,779 |
+| 4 | `_ d i` | 21,511 |
+| 5 | `n y a` | 21,129 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g g a` | 16,842 |
+| 2 | `_ n y a` | 16,502 |
+| 3 | `_ e n g` | 16,010 |
+| 4 | `e n g g` | 15,955 |
+| 5 | `g a u _` | 15,431 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n g g a` | 15,887 |
+| 2 | `n g g a u` | 15,423 |
+| 3 | `_ e n g g` | 15,391 |
+| 4 | `g g a u _` | 15,348 |
+| 5 | `_ i y a _` | 9,735 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 194
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.9852 | 1.980 | 6.85 | 34,574 | 1.5% |
+| **1** | Subword | 0.7946 | 1.735 | 5.41 | 1,153 | 20.5% |
+| **2** | Word | 0.3188 | 1.247 | 1.75 | 236,220 | 68.1% |
+| **2** | Subword | 0.8091 | 1.752 | 4.73 | 6,234 | 19.1% |
+| **3** | Word | 0.0977 | 1.070 | 1.16 | 410,924 | 90.2% |
+| **3** | Subword | 0.7951 | 1.735 | 3.72 | 29,465 | 20.5% |
+| **4** | Word | 0.0275 🏆 | 1.019 | 1.04 | 473,414 | 97.3% |
+| **4** | Subword | 0.5984 | 1.514 | 2.54 | 109,660 | 40.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `enggau danau victoria lalu mangku pengawa iya ulih dikena ngumbai diri nyadi tuai republik india sel...`
+2. `iya ari taun 212 iku lebuh 3 711 pampang eksekutif opis pelajar ba waterford sebagi ari`
+3. `ba sarawak chunto pengawa sida penroses beratika sekat bansa bidayuh enggau tuai ba pendam ruti nya`
+
+**Context Size 2:**
+
+1. `iya nya sebengkah menuamultiple sources ba asia tenggara kereban sanding laman web ke bukai baka lil...`
+2. `dalam taun lalu diaku enggau rasmi nya strok lalu ditangkan enggau pemeri sida lalu dimartir kena vi...`
+3. `pelilih menua segamat muar enggau tangkak ba johor karipap dikelala enggau nama il santo sante bemac...`
+
+**Context Size 3:**
+
+1. `dikelala enggau nama highland fold scottish fold longhair longhair fold and coupari pansik udah mada...`
+2. `garis entara menua thailand puangthong rungswasdisab thailands response to the threat of climate cha...`
+3. `dalam taun iya peturun rose fortune siku peranak virginia ke nyadi polis indu keterubah di malaysia ...`
+
+**Context Size 4:**
+
+1. `laman web ke bukai aum besai gerempung bansa bansa beserakup dalam taun iya nerima anugerah indu pem...`
+2. `kereban sanding laman web ke bukai lirik lagu tu ba lirik lagu iban chord gitar lagu tu enggau lagu`
+3. `mega dikelala enggau nama tumpuk pendiau sitak pengawa bepilih enggau bagi mit mukim iya nyadi tuai ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_r._sem_pag_sa'l`
+2. `a_tany)1_e,_nga_`
+3. `nya_bembermplung`
+
+**Context Size 2:**
+
+1. `a_sidur_bang,_ti_`
+2. `ngul_ngka_megoret`
+3. `i_iyadagayuh_peng`
+
+**Context Size 3:**
+
+1. `enggerika_nama_dik`
+2. `_penya_sebeda_karn`
+3. `ya_bic_dite_sebaju`
+
+**Context Size 4:**
+
+1. `nggau_dalam_taun_tu`
+2. `_nyadika_limau)_dik`
+3. `_english_ruhnu._haa`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (109,660 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 | 16,192 |
+| Total Tokens | 490,947 |
+| Mean Frequency | 30.32 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 265.56 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | enggau | 15,341 |
+| 2 | iya | 10,907 |
+| 3 | ba | 10,320 |
+| 4 | ti | 9,965 |
+| 5 | nya | 9,469 |
+| 6 | ke | 8,465 |
+| 7 | ari | 7,379 |
+| 8 | dalam | 5,806 |
+| 9 | nyadi | 5,795 |
+| 10 | taun | 5,418 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | verbum | 2 |
+| 2 | tychy | 2 |
+| 3 | miniaturowej | 2 |
+| 4 | sztuki | 2 |
+| 5 | profesjonalnej | 2 |
+| 6 | wideo | 2 |
+| 7 | nietypowe | 2 |
+| 8 | sztalugi | 2 |
+| 9 | zapałek | 2 |
+| 10 | tuareg | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2366 |
+| R² (Goodness of Fit) | 0.987474 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 43.2% |
+| Top 1,000 | 75.2% |
+| Top 5,000 | 92.5% |
+| Top 10,000 | 97.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9875 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 43.2% of corpus
+- **Long Tail:** 6,192 words needed for remaining 2.8% 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 | 0.8124 | 0.3506 | N/A | N/A |
+| **mono_64d** | 64 | 0.4625 | 0.3269 | N/A | N/A |
+| **mono_128d** | 128 | 0.0966 | 0.3153 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8124 🏆 | 0.3472 | 0.0680 | 0.3200 |
+| **aligned_64d** | 64 | 0.4625 | 0.3265 | 0.0760 | 0.3900 |
+| **aligned_128d** | 128 | 0.0966 | 0.3184 | 0.0900 | 0.3580 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8124 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3308. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 9.0% 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 | **-0.134** | 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` | sisal, siaran, sebilion |
+| `-di` | diarkib, digambarka, dipendam |
+| `-be` | bebilion, beting, besaing |
+| `-a` | acutis, annie, alice |
+| `-b` | bebilion, beting, barito |
+| `-p` | perfectus, pansut, pengirau |
+| `-m` | music, mutuska, materials |
+| `-pe` | perfectus, pengirau, pengari |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | telekomunikasyen, lateran, siaran |
+| `-a` | mutuska, digambarka, ikea |
+| `-s` | perfectus, acutis, materials |
+| `-i` | nyapai, pengari, diganti |
+| `-ng` | beting, besaing, petang |
+| `-g` | beting, besaing, petang |
+| `-an` | lateran, siaran, labuan |
+| `-e` | annie, code, divide |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ngka` | 1.53x | 69 contexts | engka, angka, bangka |
+| `enga` | 1.41x | 60 contexts | lenga, lengan, dengan |
+| `ngga` | 1.49x | 39 contexts | rongga, anggap, enggay |
+| `dang` | 1.58x | 30 contexts | udang, kadang, undang |
+| `enya` | 1.50x | 35 contexts | menya, kenya, lenyau |
+| `syen` | 1.79x | 19 contexts | fesyen, mosyen, aksyen |
+| `engk` | 1.50x | 27 contexts | engka, engku, tengku |
+| `nger` | 1.64x | 19 contexts | ngeri, ranger, ngerak |
+| `ndan` | 1.60x | 20 contexts | undan, undang, pandan |
+| `enge` | 1.71x | 16 contexts | mengeri, nengeri, avenged |
+| `peny` | 1.70x | 16 contexts | penyu, penyah, penyai |
+| `pema` | 1.44x | 27 contexts | pemar, pemai, pemali |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-di` | `-a` | 111 words | dikuingka, diformalka |
+| `-p` | `-n` | 105 words | penulin, patron |
+| `-di` | `-ka` | 93 words | dikuingka, diformalka |
+| `-k` | `-n` | 84 words | kondisyen, kolonisasyen |
+| `-p` | `-a` | 82 words | panglima, praha |
+| `-p` | `-an` | 69 words | pengkalan, persamaan |
+| `-s` | `-n` | 65 words | sensasyen, sain |
+| `-p` | `-i` | 64 words | perai, pagi |
+| `-p` | `-ng` | 57 words | pesaing, putting |
+| `-p` | `-g` | 57 words | pesaing, putting |
+
+### 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 |
+|------|-----------------|------------|------|
+| malacañang | **`malacañ-a-ng`** | 7.5 | `a` |
+| pengurang | **`pengu-ra-ng`** | 7.5 | `ra` |
+| inchinnan | **`inchin-n-an`** | 7.5 | `n` |
+| kandungan | **`kandu-ng-an`** | 7.5 | `ng` |
+| pengeringat | **`pengeri-ng-at`** | 7.5 | `ng` |
+| centuries | **`centur-i-es`** | 7.5 | `i` |
+| pengerekai | **`pengere-ka-i`** | 7.5 | `ka` |
+| pengerugi | **`penger-u-gi`** | 7.5 | `u` |
+| prasekula | **`p-ra-sekula`** | 7.5 | `sekula` |
+| nicholson | **`nichol-s-on`** | 7.5 | `s` |
+| ngasingka | **`ngasi-ng-ka`** | 7.5 | `ng` |
+| admission | **`a-d-mission`** | 7.5 | `mission` |
+| inggerisjaku | **`inggerisja-k-u`** | 7.5 | `k` |
+| interamna | **`interam-n-a`** | 7.5 | `n` |
+| haubjerre | **`haubjer-r-e`** | 7.5 | `r` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Iban 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 (5.20x) |
+| N-gram | **2-gram** | Lowest perplexity (194) |
+| Markov | **Context-4** | Highest predictability (97.3%) |
+| 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 03:50:03*

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