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README.md

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+---
+language: lbe
+language_name: Lak
+language_family: caucasian_northeast
+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-caucasian_northeast
+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: 3.877
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2418
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Lak - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lak** 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** | 3.286x | 3.29 | 0.1064% | 106,237 |
+| **16k** | 3.645x | 3.65 | 0.1180% | 95,777 |
+| **32k** | 3.877x 🏆 | 3.89 | 0.1255% | 90,045 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Маз – мазрай гъалгъатӀун ягу чичлан бикӀайссар. Маз дуссар гьарца миллатрал гьан...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁маз ▁– ▁мазрай ▁гъал гъ атӏ ун ▁ягу ▁чич лан ... (+9 more)` | 19 |
+| 16k | `▁маз ▁– ▁мазрай ▁гъалгъ атӏун ▁ягу ▁чич лан ▁бикӏайссар . ... (+7 more)` | 17 |
+| 32k | `▁маз ▁– ▁мазрай ▁гъалгъатӏун ▁ягу ▁чичлан ▁бикӏайссар . ▁маз ▁дуссар ... (+5 more)` | 15 |
+
+**Sample 2:** `ХӀуриет ( «азадшиву») – Туркнал жяматийсса ва сиясийсса кказит Чил сайт кказитру`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁хӏ ури ет ▁( ▁« аз ад шиву ») ▁– ... (+8 more)` | 18 |
+| 16k | `▁хӏ ури ет ▁( ▁« азадшиву ») ▁– ▁туркнал ▁жяматийсса ... (+6 more)` | 16 |
+| 32k | `▁хӏуриет ▁( ▁« азадшиву ») ▁– ▁туркнал ▁жяматийсса ▁ва ▁сиясийсса ... (+4 more)` | 14 |
+
+**Sample 3:** `(, ) — Дагъусттаннал Лакрал райондалун яруссаннал дазуйсса лакрал шяравалу. Бувч...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁(, ▁) ▁— ▁дагъусттаннал ▁лакрал ▁райондалун ▁яруссаннал ▁дазуй сса ▁лакрал ... (+5 more)` | 15 |
+| 16k | `▁(, ▁) ▁— ▁дагъусттаннал ▁лакрал ▁райондалун ▁яруссаннал ▁дазуйсса ▁лакрал ▁шяравалу ... (+4 more)` | 14 |
+| 32k | `▁(, ▁) ▁— ▁дагъусттаннал ▁лакрал ▁райондалун ▁яруссаннал ▁дазуйсса ▁лакрал ▁шяравалу ... (+4 more)` | 14 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 3.877x compression
+- **Lowest UNK Rate:** 8k with 0.1064% 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 | 289 🏆 | 8.17 | 563 | 58.2% | 100.0% |
+| **2-gram** | Subword | 491 | 8.94 | 1,956 | 53.7% | 96.1% |
+| **3-gram** | Word | 292 | 8.19 | 637 | 57.5% | 100.0% |
+| **3-gram** | Subword | 3,297 | 11.69 | 11,342 | 20.8% | 61.9% |
+| **4-gram** | Word | 1,071 | 10.06 | 1,996 | 33.3% | 70.9% |
+| **4-gram** | Subword | 10,634 | 13.38 | 32,543 | 12.1% | 40.2% |
+| **5-gram** | Word | 991 | 9.95 | 1,708 | 32.8% | 74.0% |
+| **5-gram** | Subword | 15,648 | 13.93 | 40,639 | 9.4% | 34.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `агьалинал аьдад` | 264 |
+| 2 | `чил сайт` | 172 |
+| 3 | `бувчӏин баву` | 165 |
+| 4 | `инсан адимина` | 165 |
+| 5 | `туркиянал статистикалул` | 152 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tüi̇k туркиянал статистикалул` | 152 |
+| 2 | `туркиянал статистикалул департамент` | 152 |
+| 3 | `туркиясса шагьру ва` | 140 |
+| 4 | `примечания чил сайт` | 139 |
+| 5 | `район агьалинал аьдад` | 138 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tüi̇k туркиянал статистикалул департамент` | 152 |
+| 2 | `чил сайт къаймакъам муниципалитет` | 124 |
+| 3 | `сайт къаймакъам муниципалитет шагьрурду` | 118 |
+| 4 | `примечания чил сайт къаймакъам` | 116 |
+| 5 | `статистикалул департамент агьалинал аьдад` | 90 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `чил сайт къаймакъам муниципалитет шагьрурду` | 118 |
+| 2 | `примечания чил сайт къаймакъам муниципалитет` | 116 |
+| 3 | `туркиянал статистикалул департамент агьалинал аьдад` | 90 |
+| 4 | `tüi̇k туркиянал статистикалул департамент агьалинал` | 90 |
+| 5 | `статистикалул департамент агьалинал аьдад шин` | 90 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `а л` | 6,845 |
+| 2 | `л _` | 5,250 |
+| 3 | `а _` | 4,594 |
+| 4 | `а н` | 4,356 |
+| 5 | `с а` | 4,141 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `а л _` | 3,177 |
+| 2 | `с с а` | 2,851 |
+| 3 | `н а л` | 2,180 |
+| 4 | `с а _` | 1,620 |
+| 5 | `_ б у` | 1,513 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `н а л _` | 1,990 |
+| 2 | `с с а _` | 1,561 |
+| 3 | `с с а р` | 832 |
+| 4 | `_ в а _` | 767 |
+| 5 | `н н а л` | 632 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `н н а л _` | 577 |
+| 2 | `и н а л _` | 528 |
+| 3 | `а г ь р у` | 514 |
+| 4 | `ш а г ь р` | 509 |
+| 5 | `_ ш а г ь` | 506 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (word) with 289
+- **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 | 0.5464 | 1.460 | 2.38 | 14,824 | 45.4% |
+| **1** | Subword | 1.4264 | 2.688 | 9.63 | 447 | 0.0% |
+| **2** | Word | 0.0829 | 1.059 | 1.13 | 35,065 | 91.7% |
+| **2** | Subword | 1.0905 | 2.130 | 5.40 | 4,302 | 0.0% |
+| **3** | Word | 0.0248 | 1.017 | 1.04 | 39,281 | 97.5% |
+| **3** | Subword | 0.7425 | 1.673 | 2.86 | 23,223 | 25.7% |
+| **4** | Word | 0.0131 🏆 | 1.009 | 1.02 | 40,171 | 98.7% |
+| **4** | Subword | 0.4036 | 1.323 | 1.73 | 66,320 | 59.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ва къазах кирил алфавит جثتپباذدڅخحچشسژڗزرعظطضڝصکقڢفڠغنملگݤګيوه усларал алфавит алеут лугъат хъанай ...`
+2. `аьдад 27 освенцим кӏану бугьлай бушиву му бакъа бувну бачи учирчагу жу дурсса чӏумал бикӏу гьануну`
+3. `бур иш тагьар щищал ссащал ттущал вищал танащал жущал зущал тайннащал кӏанттул улклухсса ччаврин бут...`
+
+**Context Size 2:**
+
+1. `агьалинал аьдад шин шагьру шяравалу total 9 008 18 646 27 654 6 102 24 227 30`
+2. `чил сайт къаймакъам муниципалитет шагьрурду`
+3. `инсан адимина 23 058 хъамитайпа 23 710 tüi̇k туркиянал статистикалул департамент агьалинал аьдад 434...`
+
+**Context Size 3:**
+
+1. `tüi̇k туркиянал статистикалул департамент примечания чил сайт къаймакъам муниципалитет шагьрурду`
+2. `туркиянал статистикалул департамент примечания чил сайт къаймакъам муниципалитет шагьрурду`
+3. `туркиясса шагьру ва артвин ильданул центр район агьалинал аьдад 18 072 инсан адимина 9 211 хъамитайп...`
+
+**Context Size 4:**
+
+1. `tüi̇k туркиянал статистикалул департамент районну адыяман adıyaman merkez бесни besni челикхан çelik...`
+2. `чил сайт къаймакъам муниципалитет шагьрурду`
+3. `примечания чил сайт къаймакъам муниципалитет шагьрурду`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_xvilstücaziulyu`
+2. `азивуххагьанун_w`
+3. `укумаласаймесаль`
+
+**Context Size 2:**
+
+1. `алеххаврал_ин_т_к`
+2. `л_шинатни._чиви._`
+3. `а_лакӏалуну_дусса`
+
+**Context Size 3:**
+
+1. `ал_жуж_xvii—xvi_el`
+2. `сса_гьаейссавних_ш`
+3. `нал_шярава_26_эски`
+
+**Context Size 4:**
+
+1. `нал_маз_(аьрабнал_а`
+2. `сса_щарая_ингилис_b`
+3. `ссар._агьалинал_ста`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (66,320 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 | 5,374 |
+| Total Tokens | 38,623 |
+| Mean Frequency | 7.19 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 21.50 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ва | 771 |
+| 2 | аьдад | 394 |
+| 3 | бур | 362 |
+| 4 | инсан | 309 |
+| 5 | шагьру | 295 |
+| 6 | шинал | 274 |
+| 7 | агьалинал | 267 |
+| 8 | маз | 217 |
+| 9 | чил | 217 |
+| 10 | ягу | 194 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | спорт | 2 |
+| 2 | нурмагомедов | 2 |
+| 3 | гренландия | 2 |
+| 4 | советских | 2 |
+| 5 | социалистических | 2 |
+| 6 | республик | 2 |
+| 7 | адолф | 2 |
+| 8 | гьитлер | 2 |
+| 9 | саллатіир | 2 |
+| 10 | сталинград | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8339 |
+| R² (Goodness of Fit) | 0.982815 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 32.5% |
+| Top 1,000 | 67.1% |
+| Top 5,000 | 98.1% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9828 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 32.5% of corpus
+- **Long Tail:** -4,626 words needed for remaining 100.0% 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 | 0.2418 | 0.5099 | N/A | N/A |
+| **mono_64d** | 64 | 0.0556 | 0.4959 | N/A | N/A |
+| **mono_128d** | 128 | 0.0084 | 0.4715 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2418 🏆 | 0.4977 | 0.0178 | 0.1869 |
+| **aligned_64d** | 64 | 0.0556 | 0.4695 | 0.0445 | 0.1869 |
+| **aligned_128d** | 128 | 0.0084 | 0.4738 | 0.0386 | 0.2285 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.2418 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4864. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.5% 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 | **1.143** | 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 |
+|--------|----------|
+| `-к` | комаровлул, конья, ккули |
+| `-а` | аллагьнал, аьлил, апрель |
+| `-б` | бакъахьурча, баглар, буру |
+| `-м` | мазсулейман, мукьилчинмур, мармара |
+| `-д` | дянив, даву, дишайссар |
+| `-с` | сарычам, сахих, стакан |
+| `-бу` | буру, буллалаву, буххлаххиссар |
+| `-ба` | бакъахьурча, баглар, бакӏрал |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-л` | комаровлул, аллагьнал, аьлил |
+| `-а` | цукунчӏавсса, бакъахьурча, хауса |
+| `-ал` | аллагьнал, бакӏрал, бакӏчитал |
+| `-у` | хӏакьину, буру, чичрурду |
+| `-са` | цукунчӏавсса, хауса, гъансса |
+| `-н` | ттуйн, стакан, ттун |
+| `-р` | баглар, невшехир, мукьилчинмур |
+| `-ул` | комаровлул, барзул, мургул |
+
+### 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 |
+|------|----------|------------------|----------|
+| `айсс` | 1.77x | 27 contexts | байсса, дайсса, шайсса |
+| `ссар` | 1.82x | 19 contexts | дуссар, ухссар, буссар |
+| `йсса` | 1.69x | 14 contexts | байсса, дайсса, шайсса |
+| `хъан` | 1.89x | 9 contexts | хъанан, хъанай, ляхъан |
+| `улла` | 1.59x | 12 contexts | дуллан, буллай, арулла |
+| `мазр` | 1.82x | 8 contexts | мазри, мазру, мазрай |
+| `унна` | 1.51x | 12 contexts | кунна, сунна, куннал |
+| `аьра` | 1.81x | 7 contexts | аьрал, аьраб, аьрали |
+| `лчин` | 1.69x | 8 contexts | цалчин, цалчинми, цалчинмур |
+| `нсса` | 1.90x | 6 contexts | бансса, чансса, гъансса |
+| `ннал` | 1.68x | 8 contexts | куннал, миннал, ханнал |
+| `асса` | 1.66x | 8 contexts | чассаг, кьасса, журасса |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-а` | `-л` | 45 words | аллагьнал, аьлил |
+| `-к` | `-л` | 42 words | комаровлул, къарачайнал |
+| `-б` | `-а` | 35 words | бакъахьурча, ба |
+| `-б` | `-у` | 35 words | буру, буллалаву |
+| `-а` | `-ал` | 33 words | аллагьнал, арантурал |
+| `-м` | `-а` | 32 words | мармара, муратпаш�� |
+| `-б` | `-л` | 30 words | бакӏрал, барзул |
+| `-к` | `-ал` | 30 words | къарачайнал, куннал |
+| `-к` | `-н` | 27 words | камерун, къаплан |
+| `-б` | `-р` | 27 words | баглар, буххлаххиссар |
+
+### 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 |
+|------|-----------------|------------|------|
+| къабивкӏун | **`къ-а-бивкӏун`** | 7.5 | `бивкӏун` |
+| советская | **`советск-а-я`** | 7.5 | `а` |
+| балайрдаву | **`балайрд-а-ву`** | 7.5 | `а` |
+| макьаларду | **`макьал-ар-ду`** | 7.5 | `ар` |
+| бартольдлул | **`бартольд-л-ул`** | 6.0 | `бартольд` |
+| агьрамнал | **`агьрам-н-ал`** | 6.0 | `агьрам` |
+| миллатиял | **`миллат-ия-л`** | 6.0 | `миллат` |
+| къаяевлул | **`къаяев-л-ул`** | 6.0 | `къаяев` |
+| республикалул | **`республик-ал-ул`** | 6.0 | `республик` |
+| бакъанугу | **`бакъа-ну-гу`** | 6.0 | `бакъа` |
+| ущущулгъилун | **`ущущулгъи-л-ун`** | 6.0 | `ущущулгъи` |
+| дунияллул | **`дуниял-л-ул`** | 6.0 | `дуниял` |
+| абумуслим | **`а-бу-муслим`** | 6.0 | `муслим` |
+| шамхалнал | **`шамхал-н-ал`** | 6.0 | `шамхал` |
+| закуевлул | **`закуев-л-ул`** | 6.0 | `закуев` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lak 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 | **32k BPE** | Best compression (3.88x) |
+| N-gram | **2-gram** | Lowest perplexity (289) |
+| Markov | **Context-4** | Highest predictability (98.7%) |
+| 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 10:21:18*

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