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README.md

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+---
+language: gag
+language_name: Gagauz
+language_family: turkic_oghuz
+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-turkic_oghuz
+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: 3.538
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8240
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Gagauz - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Gagauz** 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** | 2.876x | 2.88 | 0.0916% | 443,197 |
+| **16k** | 3.120x | 3.12 | 0.0994% | 408,594 |
+| **32k** | 3.336x | 3.34 | 0.1062% | 382,142 |
+| **64k** | 3.538x 🏆 | 3.54 | 0.1127% | 360,274 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Üülen Dakota — Amerika Birleşik Devletläri Viliyatı`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁üülen ▁dak ota ▁— ▁amerika ▁birleşik ▁devletläri ▁viliyatı` | 8 |
+| 16k | `▁üülen ▁dakota ▁— ▁amerika ▁birleşik ▁devletläri ▁viliyatı` | 7 |
+| 32k | `▁üülen ▁dakota ▁— ▁amerika ▁birleşik ▁devletläri ▁viliyatı` | 7 |
+| 64k | `▁üülen ▁dakota ▁— ▁amerika ▁birleşik ▁devletläri ▁viliyatı` | 7 |
+
+**Sample 2:** `Gasımuşağı halıları () — Azerbaycan halısı. Dış baalantılar Araşdırmalar "Qasımu...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁g ası muş a ğı ▁h alılar ı ▁() ▁— ... (+27 more)` | 37 |
+| 16k | `▁g ası muş ağı ▁h alılar ı ▁() ▁— ▁azerbaycan ... (+25 more)` | 35 |
+| 32k | `▁g asımuşağı ▁halıları ▁() ▁— ▁azerbaycan ▁hal ısı . ▁dış ... (+14 more)` | 24 |
+| 64k | `▁gasımuşağı ▁halıları ▁() ▁— ▁azerbaycan ▁halısı . ▁dış ▁baalantılar ▁ar ... (+9 more)` | 19 |
+
+**Sample 3:** `Önemli Olaylar Dünnää Gagauz Doğmâk Ölenler kategori:Günler`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁önemli ▁olaylar ▁dünnää ▁gagauz ▁doğmâk ▁ölenler ▁kategori : günler` | 9 |
+| 16k | `▁önemli ▁olaylar ▁dünnää ▁gagauz ▁doğmâk ▁ölenler ▁kategori : günler` | 9 |
+| 32k | `▁önemli ▁olaylar ▁dünnää ▁gagauz ▁doğmâk ▁ölenler ▁kategori : günler` | 9 |
+| 64k | `▁önemli ▁olaylar ▁dünnää ▁gagauz ▁doğmâk ▁ölenler ▁kategori : günler` | 9 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.538x compression
+- **Lowest UNK Rate:** 8k with 0.0916% 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 | 1,971 | 10.94 | 4,598 | 31.2% | 63.5% |
+| **2-gram** | Subword | 446 🏆 | 8.80 | 3,286 | 54.9% | 97.3% |
+| **3-gram** | Word | 1,822 | 10.83 | 5,238 | 34.0% | 64.5% |
+| **3-gram** | Subword | 4,206 | 12.04 | 22,902 | 18.5% | 57.6% |
+| **4-gram** | Word | 5,954 | 12.54 | 16,618 | 24.1% | 43.7% |
+| **4-gram** | Subword | 22,619 | 14.47 | 104,362 | 9.2% | 29.9% |
+| **5-gram** | Word | 5,006 | 12.29 | 14,499 | 25.9% | 45.6% |
+| **5-gram** | Subword | 56,179 | 15.78 | 204,429 | 6.6% | 21.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hem bak` | 1,043 |
+| 2 | `dış baalantılar` | 677 |
+| 3 | `dili laf` | 581 |
+| 4 | `türk dili` | 554 |
+| 5 | `laf edelir` | 538 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dili laf edelir` | 538 |
+| 2 | `hem bak türkiye` | 514 |
+| 3 | `türkiye kasabalar listesi` | 511 |
+| 4 | `bak türkiye türkiye` | 504 |
+| 5 | `türk dili laf` | 503 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hem bak türkiye türkiye` | 504 |
+| 2 | `türkiye türkiye kasabalar listesi` | 501 |
+| 3 | `bak türkiye türkiye kasabalar` | 500 |
+| 4 | `türk dili laf edelir` | 500 |
+| 5 | `resmi türk dili laf` | 500 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `bak türkiye türkiye kasabalar listesi` | 500 |
+| 2 | `hem bak türkiye türkiye kasabalar` | 500 |
+| 3 | `resmi türk dili laf edelir` | 500 |
+| 4 | `türkiye resmi türk dili laf` | 500 |
+| 5 | `bu kasabade türkiye resmi türk` | 499 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a r` | 35,710 |
+| 2 | `a n` | 34,563 |
+| 3 | `a _` | 34,248 |
+| 4 | `n _` | 31,040 |
+| 5 | `l a` | 29,285 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r` | 14,140 |
+| 2 | `_ k a` | 11,046 |
+| 3 | `a r _` | 9,987 |
+| 4 | `a n _` | 9,910 |
+| 5 | `_ b a` | 7,607 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r _` | 6,472 |
+| 2 | `_ d i l` | 4,896 |
+| 3 | `t ü r k` | 4,490 |
+| 4 | `_ t ü r` | 4,397 |
+| 5 | `_ k a s` | 4,301 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t ü r k` | 4,273 |
+| 2 | `k a s a b` | 3,998 |
+| 3 | `a s a b a` | 3,997 |
+| 4 | `_ k a s a` | 3,991 |
+| 5 | `_ h e m _` | 3,823 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 446
+- **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 | 0.6215 | 1.538 | 3.19 | 70,858 | 37.9% |
+| **1** | Subword | 1.1311 | 2.190 | 8.91 | 872 | 0.0% |
+| **2** | Word | 0.1089 | 1.078 | 1.18 | 224,953 | 89.1% |
+| **2** | Subword | 1.0438 | 2.062 | 5.90 | 7,767 | 0.0% |
+| **3** | Word | 0.0312 | 1.022 | 1.05 | 265,002 | 96.9% |
+| **3** | Subword | 0.8545 | 1.808 | 3.91 | 45,790 | 14.5% |
+| **4** | Word | 0.0143 🏆 | 1.010 | 1.02 | 275,839 | 98.6% |
+| **4** | Subword | 0.6677 | 1.589 | 2.56 | 178,853 | 33.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `hem gezdii erlerdä da var küüyün 2 baskı evindä bulunan derneklär bütün poêtlar ya halk respublikası`
+2. `dili laf edelir görüntüler hem ki evli dört kuruluş evresinde üye tam olarak seçerkendorfman alberto...`
+3. `bir suçtan mahkûm oldu nereiyi bütün gün moldovanın çiftçi pidoş kendi yaratmalarınnan katıldılar av...`
+
+**Context Size 2:**
+
+1. `hem bak laos laoslular laos dili vientiane times i̇ngiliz dili yazı latin alfaviti 50px latin dili l...`
+2. `dış baalantılar en wikipedia turkey kasabalari`
+3. `dili laf edelir görüntüler hem bak türkiye türkiye kasabalar listesi dış baalantılar en wikipedia tu...`
+
+**Context Size 3:**
+
+1. `dili laf edelir görüntüler hem bak türkiye türkiye kasabalar listesi dış baalantılar en wikipedia tu...`
+2. `hem bak türkiye türkiye kasabalar listesi dış baalantılar en wikipedia turkey kasabalari`
+3. `türkiye kasabalar listesi dış baalantılar en wikipedia turkey kasabalari`
+
+**Context Size 4:**
+
+1. `hem bak türkiye türkiye kasabalar listesi dış baalantılar en wikipedia turkey kasabalari`
+2. `türkiye türkiye kasabalar listesi dış baalantılar en wikipedia turkey kasabalari`
+3. `bak türkiye türkiye kasabalar listesi dış baalantılar en wikipedia turkey kasabalari`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_stısızar_la_köl`
+2. `asekome_()_serne`
+3. `i_iyovi,840_9-_k`
+
+**Context Size 2:**
+
+1. `ar_önek_:_kar_uş_`
+2. `an_türkçek_won_ge`
+3. `a_bar_maal_döndad`
+
+**Context Size 3:**
+
+1. `lar_i̇ngilleriyada_`
+2. `_kan_ay_habesinder`
+3. `ar_da,_rayequezdıl`
+
+**Context Size 4:**
+
+1. `lar_list_verdi._bun`
+2. `_dillerinizm,_bir_l`
+3. `türk_koordinatnarı_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (178,853 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 | 26,154 |
+| Total Tokens | 288,661 |
+| Mean Frequency | 11.04 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 61.28 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | hem | 3,845 |
+| 2 | dili | 2,983 |
+| 3 | bir | 2,801 |
+| 4 | da | 2,704 |
+| 5 | 1 | 1,883 |
+| 6 | türkiye | 1,882 |
+| 7 | ay | 1,737 |
+| 8 | bu | 1,733 |
+| 9 | gagauz | 1,519 |
+| 10 | o | 1,516 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | vanların | 2 |
+| 2 | derecede | 2 |
+| 3 | varlığından | 2 |
+| 4 | biolojik | 2 |
+| 5 | koreyada | 2 |
+| 6 | cejuan | 2 |
+| 7 | günümüzdä | 2 |
+| 8 | toscano | 2 |
+| 9 | şenubi | 2 |
+| 10 | grübüdur | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9373 |
+| R² (Goodness of Fit) | 0.991888 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 25.1% |
+| Top 1,000 | 53.2% |
+| Top 5,000 | 76.4% |
+| Top 10,000 | 86.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9919 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 25.1% of corpus
+- **Long Tail:** 16,154 words needed for remaining 13.4% 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 | 0.8240 | 0.3585 | N/A | N/A |
+| **mono_64d** | 64 | 0.5076 | 0.3424 | N/A | N/A |
+| **mono_128d** | 128 | 0.1196 | 0.3318 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8240 🏆 | 0.3601 | 0.0340 | 0.1900 |
+| **aligned_64d** | 64 | 0.5076 | 0.3378 | 0.0780 | 0.3180 |
+| **aligned_128d** | 128 | 0.1196 | 0.3296 | 0.1000 | 0.4120 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8240 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3434. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 10.0% 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 | **1.113** | 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 |
+|--------|----------|
+| `-ka` | kafasını, kastela, kaçanik |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | asirin, sarıboyun, bolton |
+| `-an` | ardından, hazırlanan, komrattan |
+| `-ar` | aaraştırerlar, aznar, aktrisalar |
+| `-er` | çalışer, techner, muzaffer |
+| `-da` | olgularında, sţenasında, moskvada |
+
+### 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 |
+|------|----------|------------------|----------|
+| `leri` | 1.84x | 88 contexts | lerik, ileri, galeri |
+| `ları` | 1.73x | 87 contexts | onları, otları, yuları |
+| `ller` | 2.12x | 36 contexts | aller, moller, ullern |
+| `asın` | 1.72x | 59 contexts | basın, klasın, alasın |
+| `anın` | 1.83x | 39 contexts | canın, hanın, sanını |
+| `nnar` | 1.90x | 32 contexts | onnar, onnara, gunnar |
+| `ille` | 1.85x | 29 contexts | lille, pille, ville |
+| `arın` | 1.82x | 30 contexts | uların, karının, boyarın |
+| `ında` | 1.62x | 40 contexts | sında, adında, ilında |
+| `gauz` | 2.18x | 14 contexts | gagauz, gauzlar, gagauzça |
+| `nsan` | 1.75x | 19 contexts | insan, insanı, insana |
+| `evle` | 2.10x | 11 contexts | devlet, evleri, devleti |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ka` | `-n` | 36 words | kantakuzenin, karaçoban |
+| `-ka` | `-ar` | 28 words | katılannar, karaullar |
+| `-ka` | `-an` | 16 words | karaçoban, karannıktan |
+| `-ka` | `-da` | 13 words | kasabalarda, katkıda |
+| `-ka` | `-er` | 6 words | kaybettiler, kazaner |
+
+### 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 |
+|------|-----------------|------------|------|
+| argentinada | **`argentina-da`** | 4.5 | `argentina` |
+| tehnikada | **`tehnika-da`** | 4.5 | `tehnika` |
+| bakannıında | **`bakannıın-da`** | 4.5 | `bakannıın` |
+| konferenţiyada | **`konferenţiya-da`** | 4.5 | `konferenţiya` |
+| devletlerinda | **`devletlerin-da`** | 4.5 | `devletlerin` |
+| delegaţiyada | **`delegaţiya-da`** | 4.5 | `delegaţiya` |
+| vyetnamda | **`vyetnam-da`** | 4.5 | `vyetnam` |
+| kasabalarda | **`ka-sabal-ar-da`** | 4.5 | `sabal` |
+| forrester | **`forrest-er`** | 4.5 | `forrest` |
+| vakıdında | **`vakıdın-da`** | 4.5 | `vakıdın` |
+| karıştırêrlar | **`ka-rıştırêrl-ar`** | 3.0 | `rıştırêrl` |
+| çayırlarda | **`çayırl-ar-da`** | 3.0 | `çayırl` |
+| karikaturacılar | **`ka-rikaturacıl-ar`** | 3.0 | `rikaturacıl` |
+| karşılaşan | **`ka-rşılaş-an`** | 3.0 | `rşılaş` |
+| katılaceklar | **`ka-tılacekl-ar`** | 3.0 | `tılacekl` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Gagauz shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (3.54x) |
+| N-gram | **2-gram** | Lowest perplexity (446) |
+| Markov | **Context-4** | Highest predictability (98.6%) |
+| 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: 2026-01-04 14:49:17*

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