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README.md

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+---
+language: kaa
+language_name: Kara-Kalpak
+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: 5.231
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8596
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kara-Kalpak - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kara-Kalpak** 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** | 4.095x | 4.10 | 0.0535% | 1,035,724 |
+| **16k** | 4.571x | 4.57 | 0.0597% | 927,895 |
+| **32k** | 4.952x | 4.95 | 0.0647% | 856,500 |
+| **64k** | 5.231x 🏆 | 5.23 | 0.0683% | 810,783 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Bobrovıtsâ () — Ukrainanıń Chernigov wálayatında jaylasqan qala. Bobrovıtsa rayo...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bob r ov ıt s â ▁() ▁— ▁ukrain anıń ... (+29 more)` | 39 |
+| 16k | `▁bob rov ıt s â ▁() ▁— ▁ukrainanıń ▁chern ig ... (+26 more)` | 36 |
+| 32k | `▁bob rov ıt s â ▁() ▁— ▁ukrainanıń ▁chern ig ... (+26 more)` | 36 |
+| 64k | `▁bobrovıt s â ▁() ▁— ▁ukrainanıń ▁chern ig ov ▁wálayatında ... (+22 more)` | 32 |
+
+**Sample 2:** `— Qırǵızstannıń Osh wálayatı Úlken-Alay rayonındaǵı awıl. Úlken-Alay APJ quramın...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁— ▁qırǵızstannıń ▁osh ▁wálayatı ▁úlken - alay ▁rayonındaǵı ▁awıl . ... (+19 more)` | 29 |
+| 16k | `▁— ▁qırǵızstannıń ▁osh ▁wálayatı ▁úlken - alay ▁rayonındaǵı ▁awıl . ... (+19 more)` | 29 |
+| 32k | `▁— ▁qırǵızstannıń ▁osh ▁wálayatı ▁úlken - alay ▁rayonındaǵı ▁awıl . ... (+19 more)` | 29 |
+| 64k | `▁— ▁qırǵızstannıń ▁osh ▁wálayatı ▁úlken - alay ▁rayonındaǵı ▁awıl . ... (+19 more)` | 29 |
+
+**Sample 3:** `— Qırǵızstannıń Batken wálayatı Qadamjay rayonındaǵı awıl. Awıl Maydan awıl okru...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁— ▁qırǵızstannıń ▁batken ▁wálayatı ▁qadamjay ▁rayonındaǵı ▁awıl . ▁awıl ▁maydan ... (+19 more)` | 29 |
+| 16k | `▁— ▁qırǵızstannıń ▁batken ▁wálayatı ▁qadamjay ▁rayonındaǵı ▁awıl . ▁awıl ▁maydan ... (+19 more)` | 29 |
+| 32k | `▁— ▁qırǵızstannıń ▁batken ▁wálayatı ▁qadamjay ▁rayonındaǵı ▁awıl . ▁awıl ▁maydan ... (+19 more)` | 29 |
+| 64k | `▁— ▁qırǵızstannıń ▁batken ▁wálayatı ▁qadamjay ▁rayonındaǵı ▁awıl . ▁awıl ▁maydan ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.231x compression
+- **Lowest UNK Rate:** 8k with 0.0535% 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 | 23,270 | 14.51 | 54,707 | 10.1% | 27.4% |
+| **2-gram** | Subword | 339 🏆 | 8.41 | 4,784 | 62.1% | 98.8% |
+| **3-gram** | Word | 20,253 | 14.31 | 46,477 | 13.1% | 28.7% |
+| **3-gram** | Subword | 2,759 | 11.43 | 39,335 | 21.9% | 68.2% |
+| **4-gram** | Word | 25,858 | 14.66 | 61,893 | 14.2% | 28.0% |
+| **4-gram** | Subword | 13,674 | 13.74 | 197,359 | 11.1% | 37.3% |
+| **5-gram** | Word | 14,234 | 13.80 | 37,066 | 17.5% | 35.1% |
+| **5-gram** | Subword | 43,260 | 15.40 | 503,495 | 6.5% | 24.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sonday aq` | 3,034 |
+| 2 | `menen birge` | 2,841 |
+| 3 | `bolıp tabıladı` | 2,616 |
+| 4 | `sırtqı siltemeler` | 2,295 |
+| 5 | `bir neshe` | 2,269 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `derekler sırtqı siltemeler` | 1,685 |
+| 2 | `légales geografiyası jer` | 1,398 |
+| 3 | `adampopulations légales geografiyası` | 1,398 |
+| 4 | `geografiyası jer maydanı` | 1,374 |
+| 5 | `sonıń menen birge` | 1,344 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `adampopulations légales geografiyası jer` | 1,398 |
+| 2 | `légales geografiyası jer maydanı` | 1,374 |
+| 3 | `jaylasqan kommuna xalqı xalqı` | 1,319 |
+| 4 | `sırtqı siltemeler departamenti kommunaları` | 1,319 |
+| 5 | `derekler sırtqı siltemeler departamenti` | 1,318 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `adampopulations légales geografiyası jer maydanı` | 1,374 |
+| 2 | `departamentinde jaylasqan kommuna xalqı xalqı` | 1,318 |
+| 3 | `derekler sırtqı siltemeler departamenti kommunaları` | 1,318 |
+| 4 | `km2 derekler sırtqı siltemeler departamenti` | 1,317 |
+| 5 | `franciyanıń seine maritime departamentinde jaylasqan` | 707 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a r` | 340,214 |
+| 2 | `l a` | 332,558 |
+| 3 | `a n` | 303,317 |
+| 4 | `n _` | 291,907 |
+| 5 | `a _` | 281,704 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r` | 145,814 |
+| 2 | `a n _` | 91,773 |
+| 3 | `l e r` | 91,522 |
+| 4 | `i y a` | 90,612 |
+| 5 | `_ h á` | 90,529 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ h á m` | 74,987 |
+| 2 | `h á m _` | 73,954 |
+| 3 | `l a r ı` | 52,831 |
+| 4 | `ı n d a` | 52,080 |
+| 5 | `l ı q _` | 47,017 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ h á m _` | 73,759 |
+| 2 | `ı n d a _` | 37,981 |
+| 3 | `a l ı q _` | 26,249 |
+| 4 | `a d ı . _` | 25,896 |
+| 5 | `e n e n _` | 25,107 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 339
+- **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 | 0.9475 | 1.928 | 7.14 | 215,380 | 5.3% |
+| **1** | Subword | 0.9483 | 1.930 | 8.43 | 1,371 | 5.2% |
+| **2** | Word | 0.2249 | 1.169 | 1.49 | 1,535,314 | 77.5% |
+| **2** | Subword | 1.0052 | 2.007 | 6.59 | 11,538 | 0.0% |
+| **3** | Word | 0.0563 | 1.040 | 1.09 | 2,281,857 | 94.4% |
+| **3** | Subword | 0.8603 | 1.815 | 4.43 | 75,969 | 14.0% |
+| **4** | Word | 0.0154 🏆 | 1.011 | 1.02 | 2,476,994 | 98.5% |
+| **4** | Subword | 0.6640 | 1.584 | 2.96 | 336,428 | 33.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `hám oqıw orınları la capital hám jazıwdı buyırıw sistemasınan wear os 1 1 sıyaqlı uluwmalıq yamasa`
+2. `menen baylanıs kanalların usınǵan sorawları jiberiletuǵın reklamalardı alıp keledi generikler c php ...`
+3. `ushın paydalanıladı óytkeni biraq bul kompilyatorǵa tán juwap beriw jolı qol menen qatnasqan hám mád...`
+
+**Context Size 2:**
+
+1. `sonday aq aldıńǵı qosıqlarınıń tariyxın izertley aladı internet protokolı 4 versiyası ipv4 ip adresi...`
+2. `menen birge orınlanatuǵın programma kerek óytkeni ájiniyazǵa shekemgi qaraqalpaq shayırlarında bul f...`
+3. `bolıp tabıladı bes juldız berip dosınıń mına sózlerin keltiredi windows api sonshelli keń tarqaldı b...`
+
+**Context Size 3:**
+
+1. `derekler sırtqı siltemeler departamenti kommunaları`
+2. `légales geografiyası jer maydanı 20 49 km2 derekler sırtqı siltemeler departamenti kommunaları`
+3. `adampopulations légales geografiyası jer maydanı 19 09 km2 derekler sırtqı siltemeler departamenti k...`
+
+**Context Size 4:**
+
+1. `adampopulations légales geografiyası jer maydanı 14 37 km2 derekler sırtqı siltemeler departamenti k...`
+2. `légales geografiyası jer maydanı 5 55 km2 derekler sırtqı siltemeler departamenti kommunaları`
+3. `jaylasqan kommuna xalqı xalqı 2 635 adampopulations légales geografiyası jer maydanı 17 47 km2 derek...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_qın_ticenendaya`
+2. `a_1460_deberoliy`
+3. `idayamgi_—_p_tia`
+
+**Context Size 2:**
+
+1. `arın_dáwilladı_do`
+2. `lar_twajları_dá_s`
+3. `anlatınǵan_ionıń_`
+
+**Context Size 3:**
+
+1. `lar_bazlıq_derek,_`
+2. `an_ashqada_basında`
+3. `iyatlar_bolıwı_anı`
+
+**Context Size 4:**
+
+1. `_hám_ol_hası_qatnas`
+2. `hám_g_sui_skepti_de`
+3. `ında_kóterilgerisiw`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (336,428 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 | 94,344 |
+| Total Tokens | 2,550,053 |
+| Mean Frequency | 27.03 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 320.88 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | hám | 74,114 |
+| 2 | menen | 22,644 |
+| 3 | ushın | 19,490 |
+| 4 | bul | 18,802 |
+| 5 | bir | 13,691 |
+| 6 | ol | 12,270 |
+| 7 | bolıp | 9,798 |
+| 8 | yamasa | 8,778 |
+| 9 | bolǵan | 8,505 |
+| 10 | dep | 8,012 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | allaxabad | 2 |
+| 2 | shaqapshasına | 2 |
+| 3 | pondar | 2 |
+| 4 | shechen | 2 |
+| 5 | álimsultanov | 2 |
+| 6 | alimsultanovtıń | 2 |
+| 7 | xasavyurt | 2 |
+| 8 | şebinkarahisar | 2 |
+| 9 | 042 | 2 |
+| 10 | i̇zel | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9824 |
+| R² (Goodness of Fit) | 0.989215 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 21.4% |
+| Top 1,000 | 49.2% |
+| Top 5,000 | 71.8% |
+| Top 10,000 | 80.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9892 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 21.4% of corpus
+- **Long Tail:** 84,344 words needed for remaining 19.5% 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 | 0.8596 🏆 | 0.3821 | N/A | N/A |
+| **mono_64d** | 64 | 0.8357 | 0.2373 | N/A | N/A |
+| **mono_128d** | 128 | 0.8393 | 0.1678 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8596 | 0.3758 | 0.0640 | 0.2900 |
+| **aligned_64d** | 64 | 0.8357 | 0.2292 | 0.1320 | 0.4080 |
+| **aligned_128d** | 128 | 0.8393 | 0.1697 | 0.1560 | 0.4740 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8596 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2603. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 15.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.422** | 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 |
+|--------|----------|
+| `-s` | sovxozı, sibirdiń, shakuriy |
+| `-a` | arturo, adewir, aǵası |
+| `-t` | toplaydı, talantın, túsiminiń |
+| `-b` | besten, barri, bahalı |
+| `-k` | komandiriniń, kaliforniyada, komponentleri |
+| `-m` | mamanlıǵı, mellanox, materigin |
+| `-ma` | mamanlıǵı, materigin, makbet |
+| `-sh` | shakuriy, shıǵır, shtatı |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | dawamın, daǵdarısın, besten |
+| `-a` | kaliforniyada, ıqlımına, evropaǵa |
+| `-ı` | mamanlıǵı, toplaydı, sovxozı |
+| `-ń` | komandiriniń, sibirdiń, oppengeymernıń |
+| `-ıń` | oppengeymernıń, dárwazamanlardıń, klarustıń |
+| `-i` | rsetti, komponentleri, xarakterlewshi |
+| `-an` | aspan, gúmannan, saban |
+| `-r` | populyar, ústinler, adewir |
+
+### 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 |
+|------|----------|------------------|----------|
+| `lard` | 1.63x | 167 contexts | larda, lardı, alardı |
+| `atla` | 1.64x | 122 contexts | atlas, atlan, atlar |
+| `tler` | 1.65x | 98 contexts | etler, bitler, pátler |
+| `asın` | 1.45x | 170 contexts | basın, pasın, tasın |
+| `ardı` | 1.86x | 47 contexts | yardı, bardı, lardı |
+| `ayla` | 1.45x | 107 contexts | layla, aylar, zayla |
+| `shıl` | 1.74x | 47 contexts | aqshıl, shılım, oyshıl |
+| `alıq` | 1.41x | 104 contexts | xalıq, salıq, balıq |
+| `tuǵı` | 2.22x | 18 contexts | tuǵın, atatuǵın, ótetuǵın |
+| `wshı` | 1.85x | 30 contexts | suwshı, oyıwshı, oqıwshı |
+| `ciya` | 1.76x | 34 contexts | raciya, akciya, faciya |
+| `ladı` | 1.61x | 47 contexts | aladı, oyladı, aqladı |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-a` | 140 words | sozılıwǵa, samaveda |
+| `-s` | `-n` | 123 words | sportın, sedan |
+| `-a` | `-ı` | 109 words | alındı, aleksandriyalı |
+| `-k` | `-i` | 104 words | kúndizgi, keńeytpeni |
+| `-a` | `-n` | 97 words | ańlatpaytuǵının, australian |
+| `-b` | `-n` | 95 words | báhárinen, baylanısıwınan |
+| `-s` | `-ı` | 94 words | sırtqı, sawatlı |
+| `-t` | `-ı` | 94 words | tartısıwlardı, tulı |
+| `-t` | `-n` | 92 words | talqılaǵan, turatuǵının |
+| `-a` | `-a` | 88 words | albina, auditoriyasına |
+
+### 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 |
+|------|-----------------|------------|------|
+| vetnamnıń | **`vetnam-n-ıń`** | 7.5 | `n` |
+| raketalardı | **`raketal-ar-dı`** | 7.5 | `ar` |
+| bruklindaǵı | **`bruklin-da-ǵı`** | 7.5 | `da` |
+| waqıyadan | **`waqıya-da-n`** | 7.5 | `da` |
+| freymvorkları | **`freymvorkl-ar-ı`** | 7.5 | `ar` |
+| galitsina | **`galitsi-n-a`** | 7.5 | `n` |
+| futbolshılardı | **`futbolshıl-ar-dı`** | 7.5 | `ar` |
+| kolonnası | **`kolon-na-sı`** | 7.5 | `na` |
+| redaktorlarda | **`redaktorl-ar-da`** | 7.5 | `ar` |
+| sanktgallendaǵı | **`sanktgallen-da-ǵı`** | 7.5 | `da` |
+| abdujalil | **`abdujal-i-l`** | 7.5 | `i` |
+| singlların | **`singll-ar-ın`** | 7.5 | `ar` |
+| zanjibarda | **`zanjib-ar-da`** | 7.5 | `ar` |
+| kóringenindey | **`kóringenin-de-y`** | 7.5 | `de` |
+| nuqsanların | **`nuqsanl-ar-ın`** | 7.5 | `ar` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kara-Kalpak 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 (5.23x) |
+| N-gram | **2-gram** | Lowest perplexity (339) |
+| Markov | **Context-4** | Highest predictability (98.5%) |
+| 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 07:05:40*

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