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README.md

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+---
+language: mk
+language_name: Macedonian
+language_family: slavic_south
+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-slavic_south
+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: 4.780
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7374
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Macedonian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Macedonian** 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** | 3.702x | 3.70 | 0.0702% | 2,405,262 |
+| **16k** | 4.123x | 4.12 | 0.0782% | 2,159,772 |
+| **32k** | 4.494x | 4.49 | 0.0852% | 1,981,404 |
+| **64k** | 4.780x 🏆 | 4.78 | 0.0906% | 1,862,766 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `година во архитектурата содржи некои значајни настани. Настани во архитектурата ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+3 more)` | 13 |
+| 16k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+3 more)` | 13 |
+| 32k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+3 more)` | 13 |
+| 64k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+3 more)` | 13 |
+
+**Sample 2:** `година во архитектурата содржи некои значајни настани. Настани во архитектурата`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+1 more)` | 11 |
+| 16k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+1 more)` | 11 |
+| 32k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+1 more)` | 11 |
+| 64k | `▁година ▁во ▁архитектурата ▁содржи ▁некои ▁значајни ▁настани . ▁настани ▁во ... (+1 more)` | 11 |
+
+**Sample 3:** `31 мај — 151-иот ден во годината според грегоријанскиот календар (152-и во прест...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 3 1 ▁мај ▁— ▁ 1 5 1 - ... (+33 more)` | 43 |
+| 16k | `▁ 3 1 ▁мај ▁— ▁ 1 5 1 - ... (+32 more)` | 42 |
+| 32k | `▁ 3 1 ▁мај ▁— ▁ 1 5 1 - ... (+32 more)` | 42 |
+| 64k | `▁ 3 1 ▁мај ▁— ▁ 1 5 1 - ... (+32 more)` | 42 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.780x compression
+- **Lowest UNK Rate:** 8k with 0.0702% 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 | 148,118 | 17.18 | 1,246,589 | 7.0% | 19.9% |
+| **2-gram** | Subword | 310 🏆 | 8.28 | 17,556 | 66.9% | 98.2% |
+| **3-gram** | Word | 382,752 | 18.55 | 2,398,097 | 4.7% | 17.5% |
+| **3-gram** | Subword | 2,638 | 11.37 | 153,828 | 27.1% | 68.9% |
+| **4-gram** | Word | 605,602 | 19.21 | 3,842,232 | 4.8% | 19.7% |
+| **4-gram** | Subword | 15,114 | 13.88 | 929,390 | 13.0% | 37.7% |
+| **5-gram** | Word | 281,875 | 18.10 | 2,561,910 | 6.9% | 27.5% |
+| **5-gram** | Subword | 61,546 | 15.91 | 3,120,609 | 6.8% | 22.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `во година` | 270,904 |
+| 2 | `да се` | 185,526 |
+| 3 | `може да` | 82,758 |
+| 4 | `исто така` | 74,629 |
+| 5 | `година во` | 71,130 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `од страна на` | 47,837 |
+| 2 | `п н е` | 45,911 |
+| 3 | `за време на` | 45,528 |
+| 4 | `во текот на` | 44,568 |
+| 5 | `може да се` | 38,713 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `г п н е` | 26,767 |
+| 2 | `во текот на и` | 13,167 |
+| 3 | `година од страна на` | 13,039 |
+| 4 | `база на податоци на` | 10,253 |
+| 5 | `е вклучен и во` | 10,177 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `новиот општ каталог на длабоконебесни` | 10,166 |
+| 2 | `општ каталог на длабоконебесни тела` | 10,166 |
+| 3 | `тоа е вклучен и во` | 10,165 |
+| 4 | `е вклучен и во други` | 10,165 |
+| 5 | `вршено од повеќе истражувачи па` | 10,165 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `а _` | 16,220,828 |
+| 2 | `н а` | 9,755,201 |
+| 3 | `о _` | 8,545,001 |
+| 4 | `и _` | 8,299,189 |
+| 5 | `_ н` | 7,088,266 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `н а _` | 5,782,550 |
+| 2 | `_ н а` | 5,471,722 |
+| 3 | `_ в о` | 2,895,397 |
+| 4 | `в о _` | 2,774,290 |
+| 5 | `а т а` | 2,545,500 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ н а _` | 3,968,376 |
+| 2 | `_ в о _` | 2,496,500 |
+| 3 | `а т а _` | 2,159,054 |
+| 4 | `и т е _` | 1,510,803 |
+| 5 | `_ о д _` | 1,503,838 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `а _ н а _` | 1,123,156 |
+| 2 | `_ г о д и` | 801,639 |
+| 3 | `г о д и н` | 793,128 |
+| 4 | `о д и н а` | 717,809 |
+| 5 | `а _ в о _` | 641,767 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 310
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~22% 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 | 0.9313 | 1.907 | 11.18 | 1,397,869 | 6.9% |
+| **1** | Subword | 0.9537 | 1.937 | 6.98 | 8,643 | 4.6% |
+| **2** | Word | 0.3725 | 1.295 | 2.41 | 15,610,954 | 62.7% |
+| **2** | Subword | 0.7745 | 1.711 | 5.49 | 60,305 | 22.6% |
+| **3** | Word | 0.1516 | 1.111 | 1.34 | 37,555,740 | 84.8% |
+| **3** | Subword | 0.8197 | 1.765 | 4.77 | 330,722 | 18.0% |
+| **4** | Word | 0.0598 🏆 | 1.042 | 1.11 | 50,433,239 | 94.0% |
+| **4** | Subword | 0.7470 | 1.678 | 3.67 | 1,576,045 | 25.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `на минотаурот најстарото болничко лекување на електронот може да го ставаат во ноември се од овие`
+2. `во г п н е независна држава за аварите да се случиле неколку минути по неколку`
+3. `и надгледувајќи радикални реакции како и главен увозник skycom сад и во романија историјата како ugc`
+
+**Context Size 2:**
+
+1. `во година во полска и украина реката е 117 км2 дитмаршен 132 965 1 861 година пред`
+2. `да се натпреварува водачи на земјата развојот на препарати за атрофичната кожа многу поширок опфат т...`
+3. `може да има изразени оддавања на стронциум и алуминиум изопрооксиди соодветно првиот е анонимното ск...`
+
+**Context Size 3:**
+
+1. `од страна на данците кои се подолги од аксијалната пиридилна ga n врска со должини на страните а`
+2. `за време на вечерата иван илич е веќе многу пијан кога линдорф влегува со пејачката стела и го`
+3. `во текот на 367 и 368 исламска година настани 1 јануари ссср започнува со својата хуманитарна активн...`
+
+**Context Size 4:**
+
+1. `г п н е според продолжениот јулијански календар истата трае во текот на и година според асирскиот ка...`
+2. `во текот на и година според асирскиот календар во којшто мерењето на времето започнува со 622 година...`
+3. `година од страна на бугарските истражувачи генеричките лекови го формираат столбот на локалната екон...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_о_зна_скан_идо_`
+2. `а._во_и_нина_н_с`
+3. `ова_дичеме_на_ка`
+
+**Context Size 2:**
+
+1. `а_изенизвине_на_м`
+2. `на_доне_перетски_`
+3. `о_улигна_арларист`
+
+**Context Size 3:**
+
+1. `на_јанско-лисковек`
+2. `_на_ост_попрата_же`
+3. `_во_френ_каквиот_п`
+
+**Context Size 4:**
+
+1. `_на_чашките_заливор`
+2. `_во_сличн_кривале_д`
+3. `ата_долна_свињарски`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,576,045 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 | 629,840 |
+| Total Tokens | 66,539,192 |
+| Mean Frequency | 105.64 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 7439.52 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | на | 3,984,194 |
+| 2 | во | 2,517,366 |
+| 3 | и | 2,001,305 |
+| 4 | од | 1,514,717 |
+| 5 | се | 1,235,287 |
+| 6 | за | 987,031 |
+| 7 | со | 823,175 |
+| 8 | е | 782,070 |
+| 9 | година | 672,383 |
+| 10 | да | 610,844 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | калеуче | 2 |
+| 2 | chiloé | 2 |
+| 3 | преживениот | 2 |
+| 4 | делевиш | 2 |
+| 5 | platessoides | 2 |
+| 6 | pleco | 2 |
+| 7 | метарма | 2 |
+| 8 | алалаона | 2 |
+| 9 | octodecimguttata | 2 |
+| 10 | домбасл | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9604 |
+| R² (Goodness of Fit) | 0.996757 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 37.1% |
+| Top 1,000 | 56.3% |
+| Top 5,000 | 72.8% |
+| Top 10,000 | 79.8% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9968 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 37.1% of corpus
+- **Long Tail:** 619,840 words needed for remaining 20.2% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7374 | 0.3633 | N/A | N/A |
+| **mono_64d** | 64 | 0.7024 | 0.2990 | N/A | N/A |
+| **mono_128d** | 128 | 0.6203 | 0.2691 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7374 🏆 | 0.3635 | 0.1520 | 0.5340 |
+| **aligned_64d** | 64 | 0.7024 | 0.2953 | 0.2380 | 0.6560 |
+| **aligned_128d** | 128 | 0.6203 | 0.2655 | 0.3760 | 0.7180 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7374 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3093. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 37.6% 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** | 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 |
+|--------|----------|
+| `-с` | стенливил, североисточен, салминен |
+| `-ка` | кантрел, кач, католски |
+| `-а` | аргас, азилантите, акнисте |
+| `-ма` | мамуци, мажените, маринадата |
+| `-по` | помориска, подјазичната, положат |
+| `-к` | кантрел, кладошница, коинон |
+| `-ко` | коинон, кошланд, копродукт |
+| `-s` | sbordone, superluminal, stralsunder |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-а` | душевина, електроника, пијаната |
+| `-и` | издатоци, рудници, гликобелковини |
+| `-е` | најневообичаените, субкултурните, царице |
+| `-те` | најневообичаените, субкултурните, регистраторите |
+| `-та` | пијаната, проверката, подјазичната |
+| `-т` | еукариот, реверсот, меѓупарламентарниот |
+| `-от` | еукариот, реверсот, меѓупарламентарниот |
+| `-о` | витешкото, интимно, пароло |
+
+### 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 |
+|------|----------|------------------|----------|
+| `уваа` | 2.43x | 85 contexts | уваат, чуваа, жуваат |
+| `увањ` | 2.04x | 160 contexts | лување, рување, чување |
+| `увал` | 2.00x | 172 contexts | увала, јувал, дувал |
+| `ијат` | 1.76x | 300 contexts | лијат, хијат, ријат |
+| `ички` | 1.82x | 235 contexts | кички, нички, лички |
+| `кедо` | 2.77x | 33 contexts | македо, алкедо, македон |
+| `ањет` | 2.27x | 71 contexts | рањето, вањето, кањете |
+| `нски` | 1.58x | 402 contexts | ронски, менски, ренски |
+| `анск` | 1.34x | 935 contexts | канск, анска, данск |
+| `иски` | 1.56x | 353 contexts | киски, тиски, писки |
+| `инск` | 1.39x | 722 contexts | пинск, инско, минск |
+| `онск` | 1.41x | 510 contexts | ронски, јонско, шонски |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-п` | `-а` | 118 words | пресбикуза, пентесилеја |
+| `-с` | `-а` | 108 words | самбра, скаса |
+| `-п` | `-и` | 79 words | повелбени, пољани |
+| `-п` | `-е` | 76 words | питите, поиде |
+| `-к` | `-а` | 74 words | клитика, куиксама |
+| `-с` | `-и` | 70 words | сукотаи, сапрофитии |
+| `-с` | `-е` | 67 words | служите, софите |
+| `-по` | `-а` | 66 words | поситна, почесна |
+| `-а` | `-а` | 64 words | адарсана, аеторема |
+| `-б` | `-а` | 62 words | безлисна, бозонската |
+
+### 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 |
+|------|-----------------|------------|------|
+| клинетите | **`клинет-и-те`** | 7.5 | `и` |
+| карантанците | **`карантанц-и-те`** | 7.5 | `и` |
+| ҫемҫелӗхпалли | **`ҫемҫелӗхпал-л-и`** | 7.5 | `л` |
+| кедровата | **`кедров-а-та`** | 7.5 | `а` |
+| тркачката | **`тркач-ка-та`** | 7.5 | `ка` |
+| пантотенат | **`пантотен-а-т`** | 7.5 | `а` |
+| наранџито | **`наранџ-и-то`** | 7.5 | `и` |
+| епросартан | **`епросар-та-н`** | 7.5 | `та` |
+| стивенсовиот | **`стивенсов-и-от`** | 7.5 | `и` |
+| организирано | **`организир-а-но`** | 7.5 | `а` |
+| евроазијците | **`евроазијц-и-те`** | 7.5 | `и` |
+| епистазата | **`епистаз-а-та`** | 7.5 | `а` |
+| страдачите | **`страдач-и-те`** | 7.5 | `и` |
+| поштарината | **`поштарин-а-та`** | 7.5 | `а` |
+| дебатирано | **`дебатир-а-но`** | 7.5 | `а` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Macedonian 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 (4.78x) |
+| N-gram | **2-gram** | Lowest perplexity (310) |
+| Markov | **Context-4** | Highest predictability (94.0%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 18:37:02*

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