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README.md

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+---
+language: tk
+language_name: Turkmen
+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: 4.949
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8902
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Turkmen - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Turkmen** 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** | 3.867x | 3.87 | 0.1563% | 394,866 |
+| **16k** | 4.295x | 4.30 | 0.1736% | 355,501 |
+| **32k** | 4.665x | 4.67 | 0.1885% | 327,292 |
+| **64k** | 4.949x 🏆 | 4.95 | 0.2000% | 308,505 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Wakalar Sebitler boýunça Tema boýunça <noinclude> Dünýä inenler Aradan çykanlar`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 16k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 32k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 64k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+
+**Sample 2:** `Wakalar Sebitler boýunça Tema boýunça <noinclude> Dünýä inenler Aradan çykanlar`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 16k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 32k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+| 64k | `▁wakalar ▁sebitler ▁boýunça ▁tema ▁boýunça ▁< noinclude > ▁dünýä ▁inenler ... (+2 more)` | 12 |
+
+**Sample 3:** `Seýdi etraby — Lebap welayatynyň bir etrabydyr. etraplary welaýaty welaýatyndaky...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁seý di ▁etraby ▁— ▁lebap ▁welayat ynyň ▁bir ▁etraby dyr ... (+5 more)` | 15 |
+| 16k | `▁seýdi ▁etraby ▁— ▁lebap ▁welayat ynyň ▁bir ▁etraby dyr . ... (+4 more)` | 14 |
+| 32k | `▁seýdi ▁etraby ▁— ▁lebap ▁welayatynyň ▁bir ▁etrabydyr . ▁etraplary ▁welaýaty ... (+2 more)` | 12 |
+| 64k | `▁seýdi ▁etraby ▁— ▁lebap ▁welayatynyň ▁bir ▁etrabydyr . ▁etraplary ▁welaýaty ... (+2 more)` | 12 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.949x compression
+- **Lowest UNK Rate:** 8k with 0.1563% 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 | 11,088 | 13.44 | 23,947 | 14.6% | 32.8% |
+| **2-gram** | Subword | 355 🏆 | 8.47 | 4,493 | 61.5% | 98.3% |
+| **3-gram** | Word | 7,047 | 12.78 | 19,707 | 21.5% | 35.2% |
+| **3-gram** | Subword | 2,934 | 11.52 | 34,530 | 22.8% | 66.5% |
+| **4-gram** | Word | 20,732 | 14.34 | 46,279 | 14.6% | 21.3% |
+| **4-gram** | Subword | 14,717 | 13.85 | 159,071 | 11.4% | 36.9% |
+| **5-gram** | Word | 15,656 | 13.93 | 36,681 | 16.0% | 22.7% |
+| **5-gram** | Subword | 46,546 | 15.51 | 363,230 | 6.8% | 23.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ýa da` | 2,786 |
+| 2 | `aradan çykanlar` | 2,220 |
+| 3 | `tema boýunça` | 2,220 |
+| 4 | `dünýä inenler` | 2,217 |
+| 5 | `sebitler boýunça` | 2,216 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wakalar sebitler boýunça` | 2,208 |
+| 2 | `boýunça tema boýunça` | 2,201 |
+| 3 | `sebitler boýunça tema` | 2,201 |
+| 4 | `dünýä inenler aradan` | 2,174 |
+| 5 | `inenler aradan çykanlar` | 2,174 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sebitler boýunça tema boýunça` | 2,201 |
+| 2 | `wakalar sebitler boýunça tema` | 2,196 |
+| 3 | `dünýä inenler aradan çykanlar` | 2,174 |
+| 4 | `tema boýunça noinclude dünýä` | 2,119 |
+| 5 | `boýunça noinclude dünýä inenler` | 2,119 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wakalar sebitler boýunça tema boýunça` | 2,196 |
+| 2 | `tema boýunça noinclude dünýä inenler` | 2,119 |
+| 3 | `sebitler boýunça tema boýunça noinclude` | 2,112 |
+| 4 | `boýunça tema boýunça noinclude dünýä` | 2,112 |
+| 5 | `noinclude dünýä inenler aradan çykanlar` | 2,085 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a r` | 188,493 |
+| 2 | `l a` | 152,165 |
+| 3 | `a n` | 151,310 |
+| 4 | `_ b` | 146,537 |
+| 5 | `a _` | 138,776 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r` | 82,667 |
+| 2 | `a r y` | 58,594 |
+| 3 | `y ň _` | 57,971 |
+| 4 | `a n _` | 55,883 |
+| 5 | `r . _` | 53,874 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r y` | 41,638 |
+| 2 | `n y ň _` | 30,386 |
+| 3 | `_ w e _` | 29,297 |
+| 4 | `y n d a` | 26,755 |
+| 5 | `l e r i` | 26,718 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b i l e` | 16,563 |
+| 2 | `i l e n _` | 16,493 |
+| 3 | `y n d a _` | 16,259 |
+| 4 | `y n y ň _` | 15,844 |
+| 5 | `b i l e n` | 14,698 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 355
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% 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 | 0.8425 | 1.793 | 5.31 | 167,857 | 15.8% |
+| **1** | Subword | 1.0332 | 2.047 | 8.72 | 1,227 | 0.0% |
+| **2** | Word | 0.1779 | 1.131 | 1.35 | 888,328 | 82.2% |
+| **2** | Subword | 1.0291 | 2.041 | 6.32 | 10,675 | 0.0% |
+| **3** | Word | 0.0393 | 1.028 | 1.06 | 1,193,586 | 96.1% |
+| **3** | Subword | 0.8531 | 1.806 | 4.15 | 67,431 | 14.7% |
+| **4** | Word | 0.0110 🏆 | 1.008 | 1.01 | 1,255,469 | 98.9% |
+| **4** | Subword | 0.6220 | 1.539 | 2.69 | 279,783 | 37.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `we hemişe eline düşüpdir aýallaryñ häkimlik edýär bangkokdaky ýurduň 12 15 eretriýadan hem de ýylyň ...`
+2. `bilen icc bütindünýä güni kyýamat gününi alada üns berilýär asteroidler ýaly düzüp ol birwagtlar zaý...`
+3. `hem satuwa çykaryldy awstro wengriýa bilen kagyz ýüzündeligine galdy ž gulart käbir bölekleriniň geç...`
+
+**Context Size 2:**
+
+1. `ýa da mikaýyl bin seljuk bin dükak ýylda mälik şa üçin jelaly kalendaryny hijri kalendaryny mysal hö...`
+2. `tema boýunça noinclude dünýä inenler aradan çykanlar kategoriýa`
+3. `dünýä inenler aradan çykanlar salgylanmalar`
+
+**Context Size 3:**
+
+1. `wakalar sebitler boýunça tema boýunça noinclude dünýä inenler aradan çykanlar 31`
+2. `boýunça tema boýunça noinclude dünýä inenler aradan çykanlar 104`
+3. `sebitler boýunça tema boýunça noinclude dünýä inenler aradan çykanlar 29`
+
+**Context Size 4:**
+
+1. `sebitler boýunça tema boýunça noinclude dünýä inenler aradan çykanlar 26`
+2. `wakalar sebitler boýunça tema boýunça noinclude dünýä inenler aradan çykanlar baýramçylyklar`
+3. `boýunça noinclude dünýä inenler aradan çykanlar towşan esenowa hydyr derýaýew kerim gurbannepesow`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_botaýärkmp),_öz`
+2. `a_gumgitdar_nyle`
+3. `ebury_der._ýasah`
+
+**Context Size 2:**
+
+1. `ar._oduşli_düşdir`
+2. `lar.ilbaşdyry,_ob`
+3. `an_emlündama_(ýar`
+
+**Context Size 3:**
+
+1. `laryň_daşly_şübhes`
+2. `ary_12-150-nji_mil`
+3. `yň_aýatynyň_keşler`
+
+**Context Size 4:**
+
+1. `lary_deňde_gölli,_o`
+2. `nyň_bolandygynda_ru`
+3. `_we_goşuny,_hassa_t`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (279,783 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 | 70,850 |
+| Total Tokens | 1,266,247 |
+| Mean Frequency | 17.87 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 172.27 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | we | 29,419 |
+| 2 | bilen | 14,593 |
+| 3 | hem | 9,723 |
+| 4 | bu | 9,296 |
+| 5 | bir | 7,148 |
+| 6 | üçin | 7,116 |
+| 7 | da | 6,676 |
+| 8 | boýunça | 6,346 |
+| 9 | ol | 6,099 |
+| 10 | ýylda | 5,569 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | halaçda | 2 |
+| 2 | byradarlygynyň | 2 |
+| 3 | halaja | 2 |
+| 4 | bakynyň | 2 |
+| 5 | esaslandyrylanlar | 2 |
+| 6 | ailəsi | 2 |
+| 7 | yörükler | 2 |
+| 8 | ýarymgoragçysy | 2 |
+| 9 | jizak | 2 |
+| 10 | kolhozçi | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9487 |
+| R² (Goodness of Fit) | 0.992202 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 22.4% |
+| Top 1,000 | 47.7% |
+| Top 5,000 | 70.0% |
+| Top 10,000 | 79.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9922 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 22.4% of corpus
+- **Long Tail:** 60,850 words needed for remaining 20.9% 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 | 0.8902 | 0.2916 | N/A | N/A |
+| **mono_64d** | 64 | 0.8799 | 0.2188 | N/A | N/A |
+| **mono_128d** | 128 | 0.6945 | 0.1696 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8902 🏆 | 0.2952 | 0.0120 | 0.1680 |
+| **aligned_64d** | 64 | 0.8799 | 0.2224 | 0.0560 | 0.2240 |
+| **aligned_128d** | 128 | 0.6945 | 0.1700 | 0.0840 | 0.3140 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8902 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2279. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.4% 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 | **-0.035** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | awyny, andrýu, alta |
+| `-s` | saklanýandyr, saýylmadyk, stories |
+| `-g` | gyşy, guzlar, gallipoli |
+| `-b` | beloklaryny, basílio, basylan |
+| `-m` | meýi, maersk, mortier |
+| `-k` | kekene, klisfeniň, kesil |
+| `-d` | diskriminasiýa, deňlemek, dakylýar |
+| `-t` | theodore, territoriýasyndaky, taýynlapdyr |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ň` | operasiýalaryň, klisfeniň, aşgabadyň |
+| `-r` | saklanýandyr, guzlar, mortier |
+| `-y` | beloklaryny, gyşy, awyny |
+| `-a` | diskriminasiýa, alta, gatyşmagynda |
+| `-yň` | operasiýalaryň, aşgabadyň, wahýyň |
+| `-n` | humaýun, basylan, araçäkleşýän |
+| `-i` | meýi, redmi, erişleri |
+| `-an` | basylan, gan, barylýan |
+
+### 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 |
+|------|----------|------------------|----------|
+| `kmen` | 3.11x | 26 contexts | rkmen, sökmen, çekmen |
+| `anla` | 1.82x | 155 contexts | sanlar, panlar, hanlar |
+| `asyn` | 1.76x | 181 contexts | ýasyn, masyn, gasyn |
+| `erin` | 1.91x | 103 contexts | lerin, erine, yerin |
+| `rkme` | 3.11x | 14 contexts | rkmen, türkmer, turkmen |
+| `tlar` | 1.70x | 133 contexts | atlar, otlar, otlara |
+| `rler` | 1.83x | 86 contexts | ärler, ÿrler, ýerler |
+| `nlar` | 1.84x | 79 contexts | onlar, gunlar, hunlar |
+| `erle` | 1.63x | 96 contexts | ýerler, ỳerler, gerlen |
+| `ylar` | 1.63x | 72 contexts | lylar, kylar, sylar |
+| `rlar` | 1.60x | 76 contexts | arlar, durlar, ýarlar |
+| `klar` | 1.67x | 63 contexts | uklar, klark, oklar |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-g` | `-y` | 123 words | gaşy, gatnawy |
+| `-g` | `-r` | 121 words | gaçypdyrlar, girilýär |
+| `-g` | `-a` | 96 words | gidrogeologiýa, graflyklara |
+| `-b` | `-r` | 92 words | bir, bazaar |
+| `-g` | `-n` | 89 words | gaýtarylan, gelmeýän |
+| `-g` | `-i` | 88 words | geçmegi, güýçli |
+| `-s` | `-ň` | 87 words | sahypalaryň, süýümleriniň |
+| `-s` | `-y` | 80 words | sostawyny, satmagy |
+| `-g` | `-ň` | 76 words | goýumdarlarynyň, guramaklygyň |
+| `-b` | `-y` | 75 words | bozulmagy, bidgatçy |
+
+### 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 |
+|------|-----------------|------------|------|
+| slawýanlarda | **`slawýanl-ar-da`** | 7.5 | `ar` |
+| görkezipdir | **`görkezip-di-r`** | 7.5 | `di` |
+| oktýabrdan | **`oktýabr-da-n`** | 7.5 | `da` |
+| balyklaryñ | **`balykl-ar-yñ`** | 7.5 | `ar` |
+| sazandalary | **`sazandal-ar-y`** | 7.5 | `ar` |
+| bolanlary | **`bolanl-ar-y`** | 7.5 | `ar` |
+| garşydaşlary | **`garşydaşl-ar-y`** | 7.5 | `ar` |
+| halykynyň | **`halyky-n-yň`** | 7.5 | `n` |
+| manjurlaryň | **`manjurl-ar-yň`** | 7.5 | `ar` |
+| mukdarlary | **`mukdarl-ar-y`** | 7.5 | `ar` |
+| ybadatlarda | **`ybadatl-ar-da`** | 7.5 | `ar` |
+| guşaklyklary | **`guşaklykl-ar-y`** | 7.5 | `ar` |
+| amallaryň | **`amall-ar-yň`** | 7.5 | `ar` |
+| ugurlarda | **`ugurl-ar-da`** | 7.5 | `ar` |
+| ýakynlarda | **`ýakynl-ar-da`** | 7.5 | `ar` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Turkmen 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 (4.95x) |
+| N-gram | **2-gram** | Lowest perplexity (355) |
+| Markov | **Context-4** | Highest predictability (98.9%) |
+| 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: 2026-01-11 01:05:04*

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