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README.md

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+---
+language: ky
+language_name: Kyrgyz
+language_family: turkic_kipchak
+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_kipchak
+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: 4.474
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7339
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kyrgyz - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kyrgyz** 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** | 3.463x | 3.46 | 0.2417% | 1,335,548 |
+| **16k** | 3.859x | 3.86 | 0.2693% | 1,198,672 |
+| **32k** | 4.202x | 4.20 | 0.2932% | 1,100,887 |
+| **64k** | 4.474x 🏆 | 4.48 | 0.3122% | 1,033,903 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Валенсия - Испания лигасында ойноочу футболдук клуб. Валенсия (Испания). футбол ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁вал енс ия ▁- ▁испания ▁л иг асында ▁ойн оочу ... (+16 more)` | 26 |
+| 16k | `▁вал енс ия ▁- ▁испания ▁лиг асында ▁ойн оочу ▁футбол ... (+13 more)` | 23 |
+| 32k | `▁вал енс ия ▁- ▁испания ▁лигасында ▁ойноочу ▁футболдук ▁клуб . ... (+8 more)` | 18 |
+| 64k | `▁валенсия ▁- ▁испания ▁лигасында ▁ойноочу ▁футболдук ▁клуб . ▁валенсия ▁( ... (+4 more)` | 14 |
+
+**Sample 2:** `Акцентология ( — басым, — сөз, окутууКасевич В. Б. ) — басымды иликтөөчү тил или...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ак цент ология ▁( ▁— ▁басым , ▁— ▁сөз , ... (+25 more)` | 35 |
+| 16k | `▁ак цент ология ▁( ▁— ▁басым , ▁— ▁сөз , ... (+25 more)` | 35 |
+| 32k | `▁ак цент ология ▁( ▁— ▁басым , ▁— ▁сөз , ... (+24 more)` | 34 |
+| 64k | `▁акцент ология ▁( ▁— ▁басым , ▁— ▁сөз , ▁окутуу ... (+21 more)` | 31 |
+
+**Sample 3:** `Реал Овьедо - Испания лигасында ойноочу футболдук клуб.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ре ал ▁о в ь ед о ▁- ▁испания ▁л ... (+8 more)` | 18 |
+| 16k | `▁реал ▁о в ь ед о ▁- ▁испания ▁лиг асында ... (+6 more)` | 16 |
+| 32k | `▁реал ▁ов ь ед о ▁- ▁испания ▁лигасында ▁ойноочу ▁футболдук ... (+2 more)` | 12 |
+| 64k | `▁реал ▁ов ь едо ▁- ▁испания ▁лигасында ▁ойноочу ▁футболдук ▁клуб ... (+1 more)` | 11 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.474x compression
+- **Lowest UNK Rate:** 8k with 0.2417% 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 | 24,309 | 14.57 | 200,338 | 16.1% | 40.8% |
+| **2-gram** | Subword | 401 🏆 | 8.65 | 8,096 | 57.2% | 97.9% |
+| **3-gram** | Word | 13,976 | 13.77 | 213,447 | 20.6% | 52.0% |
+| **3-gram** | Subword | 3,260 | 11.67 | 71,568 | 20.8% | 64.9% |
+| **4-gram** | Word | 20,293 | 14.31 | 405,510 | 19.9% | 50.7% |
+| **4-gram** | Subword | 15,504 | 13.92 | 405,921 | 10.3% | 37.2% |
+| **5-gram** | Word | 14,656 | 13.84 | 318,532 | 21.1% | 54.0% |
+| **5-gram** | Subword | 48,833 | 15.58 | 1,138,729 | 7.2% | 25.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `колдонулган адабияттар` | 36,384 |
+| 2 | `тышкы шилтемелер` | 25,799 |
+| 3 | `тил жана` | 21,797 |
+| 4 | `мамлекеттик тил` | 21,512 |
+| 5 | `энциклопедия борбору` | 21,464 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `тил жана энциклопедия` | 21,456 |
+| 2 | `жана энциклопедия борбору` | 21,427 |
+| 3 | `мамлекеттик тил жана` | 21,290 |
+| 4 | `колдонулган адабияттар кыргызстан` | 12,535 |
+| 5 | `адабияттар кыргызстан улуттук` | 12,428 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `тил жана энциклопедия борбору` | 21,427 |
+| 2 | `мамлекеттик тил жана энциклопедия` | 21,245 |
+| 3 | `колдонулган адабияттар кыргызстан улуттук` | 12,425 |
+| 4 | `б мамлекеттик тил жана` | 11,940 |
+| 5 | `редактору асанов ү а` | 8,515 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `мамлекеттик тил жана энциклопедия борбору` | 21,216 |
+| 2 | `б мамлекеттик тил жана энциклопедия` | 11,940 |
+| 3 | `башкы редактору асанов ү а` | 8,515 |
+| 4 | `том башкы редактору асанов ү` | 8,513 |
+| 5 | `асанов ү а к 97` | 8,455 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `н _` | 1,699,467 |
+| 2 | `_ к` | 1,353,946 |
+| 3 | `а р` | 1,289,718 |
+| 4 | `а н` | 1,276,765 |
+| 5 | `_ б` | 1,066,604 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ы н _` | 496,520 |
+| 2 | `_ ж а` | 451,616 |
+| 3 | `а р ы` | 399,154 |
+| 4 | `_ к а` | 338,781 |
+| 5 | `а н _` | 333,734 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `н ы н _` | 272,294 |
+| 2 | `а н а _` | 216,178 |
+| 3 | `_ ж а н` | 213,551 |
+| 4 | `ж а н а` | 203,061 |
+| 5 | `ы н ы н` | 161,279 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ж а н а` | 202,652 |
+| 2 | `ж а н а _` | 201,239 |
+| 3 | `ы н ы н _` | 157,972 |
+| 4 | `к ы р г ы` | 103,941 |
+| 5 | `ы р г ы з` | 103,500 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 401
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~25% 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 | 0.9833 | 1.977 | 8.72 | 500,825 | 1.7% |
+| **1** | Subword | 0.9785 | 1.970 | 7.80 | 2,769 | 2.1% |
+| **2** | Word | 0.2540 | 1.192 | 1.59 | 4,365,265 | 74.6% |
+| **2** | Subword | 0.9572 | 1.942 | 6.53 | 21,569 | 4.3% |
+| **3** | Word | 0.0694 | 1.049 | 1.12 | 6,951,636 | 93.1% |
+| **3** | Subword | 0.8644 | 1.821 | 4.81 | 140,847 | 13.6% |
+| **4** | Word | 0.0237 🏆 | 1.017 | 1.04 | 7,750,147 | 97.6% |
+| **4** | Subword | 0.7006 | 1.625 | 3.21 | 676,843 | 29.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `жана comptuex машыгуусуна аткарган милдеттерине төмөндөгүлөр баштапкы материалы первого выступления ...`
+2. `менен нуска аталышы менен мамиленин биринчи планга койгон макулдашуулар байыркы индо европа өлкөлөрү...`
+3. `б кризистик кубулуштардын жардамы менен шартталган айталык асан уулу аны эми санарипке тоскоолдук жа...`
+
+**Context Size 2:**
+
+1. `колдонулган адабияттар каратаев о к фергана ёрёёнъндёгъ кыргыздардын этностук жакындыктарын чагылдыр...`
+2. `тышкы шилтемелер акшнын бардык шаарларынын статистикалары жөнүндө u s census bureau штатынын шаарлар...`
+3. `тил жана энциклопедия борбору физика энциклопедиялык окуу куралы мамлекеттик тил жана энциклопедия б...`
+
+**Context Size 3:**
+
+1. `тил жана энциклопедия борбору isbn 046 1 түшүнүктөрү`
+2. `жана энциклопедия борбору б isbn районунун суулары суулар`
+3. `мамлекеттик тил жана энциклопедия борбору 784 бет илл isbn 978 4 облусу районунда төрөлгөндөр мугали...`
+
+**Context Size 4:**
+
+1. `тил жана энциклопедия борбору 832 бет илл isbn 978 9 элдери элдери`
+2. `мамлекеттик тил жана энциклопедия борбору 400 бет isbn кыргызстан улуттук энциклопедия 7 том башкы р...`
+3. `колдонулган адабияттар кыргызстан улуттук энциклопедия 7 том башкы ред ү а асанов к 97 б кыргыз энци...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_st»_омисетарты_`
+2. `арызгакм_гулу._с`
+3. `нан_ка_bn_"мөнор`
+
+**Context Size 2:**
+
+1. `н_андүгү,_7_банан`
+2. `_көпчүлгөө_нуу_бү`
+3. `аратын_үзүндары_р`
+
+**Context Size 3:**
+
+1. `ын_кээ_(ги)_(режес`
+2. `_жана_ишет._к_978_`
+3. `арын_ийин_көкөтөрү`
+
+**Context Size 4:**
+
+1. `нын_оң_жээктегереги`
+2. `ана_айдын_макалат._`
+3. `_жаныбар_азайгашкар`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (676,843 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 | 227,514 |
+| Total Tokens | 10,409,036 |
+| Mean Frequency | 45.75 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 744.95 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | жана | 201,356 |
+| 2 | менен | 102,056 |
+| 3 | б | 73,049 |
+| 4 | боюнча | 55,620 |
+| 5 | кыргыз | 49,452 |
+| 6 | суу | 49,420 |
+| 7 | мамлекеттик | 44,758 |
+| 8 | бир | 44,485 |
+| 9 | а | 43,591 |
+| 10 | колдонулган | 39,748 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | никосиянын | 2 |
+| 2 | кипра | 2 |
+| 3 | акротиринин | 2 |
+| 4 | темуриддер | 2 |
+| 5 | phere | 2 |
+| 6 | нарсингхани | 2 |
+| 7 | binibining | 2 |
+| 8 | айтжан | 2 |
+| 9 | колода | 2 |
+| 10 | раскол | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0382 |
+| R² (Goodness of Fit) | 0.992694 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 23.4% |
+| Top 1,000 | 52.7% |
+| Top 5,000 | 72.0% |
+| Top 10,000 | 79.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9927 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 23.4% of corpus
+- **Long Tail:** 217,514 words needed for remaining 20.6% 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 | 0.7339 🏆 | 0.3620 | N/A | N/A |
+| **mono_64d** | 64 | 0.7191 | 0.2908 | N/A | N/A |
+| **mono_128d** | 128 | 0.7165 | 0.2106 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7339 | 0.3558 | 0.0320 | 0.1660 |
+| **aligned_64d** | 64 | 0.7191 | 0.2842 | 0.0600 | 0.2540 |
+| **aligned_128d** | 128 | 0.7165 | 0.2104 | 0.0720 | 0.2880 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7339 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2856. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 7.2% 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 | **1.151** | 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` | көргөндө, калача, карбиддер |
+| `-а` | акылынын, азербайджанда, адамынын |
+| `-ка` | калача, карбиддер, карпас |
+| `-с` | сезгенүүлөрүндө, сүйлөдү, суахили |
+| `-т` | төмөндөшү, тогол, тооруганда |
+| `-б` | брайли, бронхиалдык, болгодуктан |
+| `-ма` | мартьяновго, магнат, мамлекетпн |
+| `-м` | миллиметрдик, мартьяновго, магнат |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-н` | акылынын, шейбанилердин, адамынын |
+| `-а` | өзалдынча, жазгандарга, калача |
+| `-ын` | акылынын, адамынын, барын |
+| `-ы` | звёзды, вазийпасы, юаны |
+| `-ин` | шейбанилердин, шалтаевдин, илинин |
+| `-ан` | болгодуктан, жасалмалуулуктан, алған |
+| `-р` | карбиддер, гребнер, багбандар |
+| `-к` | миллиметрдик, бронхиалдык, акшыйрак |
+
+### 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 |
+|------|----------|------------------|----------|
+| `арды` | 1.76x | 180 contexts | зарды, барды, дарды |
+| `ргыз` | 2.39x | 35 contexts | ыргыз, хыргыз, кыргыз |
+| `ктар` | 1.47x | 245 contexts | ыктар, уктар, актар |
+| `асын` | 1.44x | 274 contexts | гасын, тасын, жасын |
+| `лган` | 1.49x | 192 contexts | ылган, алган, қилган |
+| `арын` | 1.38x | 241 contexts | барын, жарын, шарын |
+| `леке` | 2.29x | 26 contexts | келеке, белеке, телеке |
+| `улга` | 1.48x | 136 contexts | кулга, уулга, тулга |
+| `рдын` | 1.86x | 46 contexts | ырдын, крдын, тардын |
+| `екет` | 2.07x | 28 contexts | зекет, секет, рекет |
+| `ыргы` | 1.61x | 64 contexts | ыргып, кыргы, ыргыз |
+| `етти` | 1.46x | 69 contexts | шетти, четти, бетти |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-к` | `-н` | 294 words | кенжекаранын, кылжейрен |
+| `-а` | `-н` | 198 words | агарткан, акимдерин |
+| `-т` | `-н` | 185 words | телефондоштуруунун, таралуунун |
+| `-б` | `-н` | 154 words | буковинанын, боткодон |
+| `-к` | `-а` | 138 words | каарданса, курмана |
+| `-с` | `-н` | 137 words | системасынын, сапарларынын |
+| `-а` | `-а` | 92 words | арина, алматыга |
+| `-м` | `-н` | 86 words | макрофагдын, мамтелерадиосунун |
+| `-к` | `-ы` | 85 words | коллекцияларды, колдонулгандыгы |
+| `-к` | `-ын` | 81 words | кенжекаранын, кыяктын |
+
+### 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 |
+|------|-----------------|------------|------|
+| референдумдан | **`референдум-да-н`** | 7.5 | `да` |
+| калининская | **`калининск-а-я`** | 7.5 | `а` |
+| өнөктөштөрүнө | **`өнөктөштөрү-н-ө`** | 7.5 | `н` |
+| табакчалардан | **`табакчалар-да-н`** | 7.5 | `да` |
+| шарпылдак | **`шарпыл-да-к`** | 7.5 | `да` |
+| коомдоштуруунун | **`коомдоштуруу-н-ун`** | 7.5 | `н` |
+| топтолушунун | **`топтолушу-н-ун`** | 7.5 | `н` |
+| облустардан | **`облустар-да-н`** | 7.5 | `да` |
+| үйрөтүүнү | **`үйрөтүү-н-ү`** | 7.5 | `н` |
+| конуштардагы | **`конуштар-да-гы`** | 7.5 | `да` |
+| келишкени | **`келишке-н-и`** | 7.5 | `н` |
+| сактандыруунун | **`сактандыруу-н-ун`** | 7.5 | `н` |
+| стадионунда | **`стадиону-н-да`** | 7.5 | `н` |
+| секрециянын | **`секреция-н-ын`** | 7.5 | `н` |
+| пёшинская | **`пёшинс-ка-я`** | 7.5 | `ка` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kyrgyz 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 (4.47x) |
+| N-gram | **2-gram** | Lowest perplexity (401) |
+| Markov | **Context-4** | Highest predictability (97.6%) |
+| 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: 2026-01-10 10:13:59*

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