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README.md

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+---
+language: kbd
+language_name: Kabardian
+language_family: caucasian_northwest
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-caucasian_northwest
+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: 4.542
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6517
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kabardian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kabardian** 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** | 3.541x | 3.54 | 0.1767% | 352,078 |
+| **16k** | 3.908x | 3.91 | 0.1950% | 319,043 |
+| **32k** | 4.190x | 4.19 | 0.2091% | 297,527 |
+| **64k** | 4.542x 🏆 | 4.55 | 0.2266% | 274,517 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Публий Овидий Назон (, 43 гъатхэпэм и 20, Сулмо — 17-18, Томис) — Урым империэм ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁п убл ий ▁о в ид ий ▁н аз он ... (+31 more)` | 41 |
+| 16k | `▁публ ий ▁о в ид ий ▁наз он ▁(, ▁ ... (+29 more)` | 39 |
+| 32k | `▁публий ▁о в идий ▁назон ▁(, ▁ 4 3 ▁гъатхэпэм ... (+26 more)` | 36 |
+| 64k | `▁публий ▁овидий ▁назон ▁(, ▁ 4 3 ▁гъатхэпэм ▁и ▁ ... (+21 more)` | 31 |
+
+**Sample 2:** `Адэипс () — Урысейм хэт Къэбэрдей-Балъкъэрым и щӀыпӀэм хэж псыщ Шэджэмым хэлъадэ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁адэ и пс ▁() ▁— ▁урысейм ▁хэт ▁къэбэрдей - балъкъэрым ... (+22 more)` | 32 |
+| 16k | `▁адэ и пс ▁() ▁— ▁урысейм ▁хэт ▁къэбэрдей - балъкъэрым ... (+22 more)` | 32 |
+| 32k | `▁адэ и пс ▁() ▁— ▁урысейм ▁хэт ▁къэбэрдей - балъкъэрым ... (+21 more)` | 31 |
+| 64k | `▁адэипс ▁() ▁— ▁урысейм ▁хэт ▁къэбэрдей - балъкъэрым ▁и ▁щӏыпӏэм ... (+19 more)` | 29 |
+
+**Sample 3:** `Шонэпс () — Урысейм хэт Къэрэшей-Шэрджэсым и щӀыпӀэм хэж псыщ Псыжъым хэлъадэу, ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ш онэ пс ▁() ▁— ▁урысейм ▁хэт ▁къэрэшей - шэрджэсым ... (+23 more)` | 33 |
+| 16k | `▁ш онэ пс ▁() ▁— ▁урысейм ▁хэт ▁къэрэшей - шэрджэсым ... (+23 more)` | 33 |
+| 32k | `▁ш онэ пс ▁() ▁— ▁урысейм ▁хэт ▁къэрэшей - шэрджэсым ... (+23 more)` | 33 |
+| 64k | `▁шонэпс ▁() ▁— ▁урысейм ▁хэт ▁къэрэшей - шэрджэсым ▁и ▁щӏы��ӏэм ... (+21 more)` | 31 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.542x compression
+- **Lowest UNK Rate:** 8k with 0.1767% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 1,558 | 10.61 | 2,836 | 28.9% | 70.6% |
+| **2-gram** | Subword | 394 🏆 | 8.62 | 2,782 | 58.6% | 97.2% |
+| **3-gram** | Word | 1,116 | 10.12 | 2,525 | 37.0% | 74.5% |
+| **3-gram** | Subword | 3,004 | 11.55 | 20,702 | 26.1% | 64.9% |
+| **4-gram** | Word | 1,940 | 10.92 | 4,554 | 31.6% | 59.6% |
+| **4-gram** | Subword | 13,210 | 13.69 | 77,181 | 13.2% | 39.7% |
+| **5-gram** | Word | 1,471 | 10.52 | 3,389 | 34.1% | 65.4% |
+| **5-gram** | Subword | 31,176 | 14.93 | 127,435 | 7.7% | 27.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `адыгэхэм я` | 416 |
+| 2 | `я къуалэбзу` | 386 |
+| 3 | `брат хьэсин` | 386 |
+| 4 | `къуалэбзу щӏэныгъэр` | 386 |
+| 5 | `тхылъхэр брат` | 299 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `адыгэхэм я къуалэбзу` | 386 |
+| 2 | `я къуалэбзу щӏэныгъэр` | 386 |
+| 3 | `тхылъхэр брат хьэсин` | 299 |
+| 4 | `брат хьэсин адыгэхэм` | 299 |
+| 5 | `хьэсин адыгэхэм я` | 299 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `адыгэхэм я къуалэбзу щӏэныгъэр` | 386 |
+| 2 | `хьэсин адыгэхэм я къуалэбзу` | 299 |
+| 3 | `брат хьэсин адыгэхэм я` | 299 |
+| 4 | `тхылъхэр брат хьэсин адыгэхэм` | 299 |
+| 5 | `я къуалэбзу щӏэныгъэр черкеск` | 211 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `хьэсин адыгэхэм я къуалэбзу щӏэныгъэр` | 299 |
+| 2 | `брат хьэсин адыгэхэм я къуалэбзу` | 299 |
+| 3 | `тхылъхэр брат хьэсин адыгэхэм я` | 299 |
+| 4 | `адыгэхэм я къуалэбзу щӏэныгъэр черкеск` | 211 |
+| 5 | `я къуалэбзу щӏэныгъэр черкеск къ` | 206 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `э _` | 32,580 |
+| 2 | `м _` | 29,279 |
+| 3 | `э м` | 26,549 |
+| 4 | `э р` | 26,396 |
+| 5 | `х э` | 25,875 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `э м _` | 16,638 |
+| 2 | `_ к ъ` | 15,408 |
+| 3 | `э р _` | 12,826 |
+| 4 | `ъ у э` | 10,448 |
+| 5 | `г ъ у` | 10,296 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `х э р _` | 6,565 |
+| 2 | `г ъ у э` | 5,997 |
+| 3 | `х э м _` | 5,976 |
+| 4 | `м _ и _` | 4,974 |
+| 5 | `э х э м` | 4,168 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ н э х ъ` | 3,022 |
+| 2 | `ы г ъ у э` | 2,854 |
+| 3 | `э х э р _` | 2,785 |
+| 4 | `э х э м _` | 2,662 |
+| 5 | `х э м _ я` | 2,645 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 394
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.5371 | 1.451 | 2.80 | 58,099 | 46.3% |
+| **1** | Subword | 1.1013 | 2.145 | 8.35 | 788 | 0.0% |
+| **2** | Word | 0.1119 | 1.081 | 1.19 | 162,358 | 88.8% |
+| **2** | Subword | 1.0773 | 2.110 | 6.05 | 6,578 | 0.0% |
+| **3** | Word | 0.0277 | 1.019 | 1.04 | 192,783 | 97.2% |
+| **3** | Subword | 0.8756 | 1.835 | 3.66 | 39,780 | 12.4% |
+| **4** | Word | 0.0099 🏆 | 1.007 | 1.01 | 199,396 | 99.0% |
+| **4** | Subword | 0.5274 | 1.441 | 2.15 | 145,401 | 47.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `и нэхъыбэм къэрал хуит ящӏат адыгэ къуэджэхэм щыропсалъэ къаджищыр ашхаруа псалъэкӏэ йоджэ щопсэу ӏу...`
+2. `я нэхъыбапӏэм административнэ сыхьэт чым пэщхъан адыгэ мазэцӏэхэр къипсэлъу къыгурыӏуэу ди лъэхъэнэм...`
+3. `нэхъ ину щыт шъхьао хуэкӏэкӏыу къэрууфӏу итхьакӏумэхэр инкъым теплъэр нэхъыщхьэу хьэпщхупщ лъэпкъхэр...`
+
+**Context Size 2:**
+
+1. `адыгэхэм я къуалэбзу щӏэныгъэр черкесск гъ теплъэхэр лъэпкъхэр`
+2. `къуалэбзу щӏэныгъэр черкеск къ гъ лъэпкъыр лъэпкъэгъухэр`
+3. `брат хьэсин адыгэхэм я къуалэбзу щӏэныгъэр брат хьэсин черкесск гъ лъэпкъыр лъэпкъхэр`
+
+**Context Size 3:**
+
+1. `адыгэхэм я къуалэбзу щӏэныгъэр черкесск гъ теплъэхэр лъэпкъхэр`
+2. `я къуалэбзу щӏэныгъэр черкеск къ гъ лъэпкъхэр лъэпкъыр`
+3. `брат хьэсин адыгэхэм я къуалэбзу щӏэныгъэр черкесск гъ теплъэхэр хэкӏыгъуэхэр`
+
+**Context Size 4:**
+
+1. `адыгэхэм я къуалэбзу щӏэныгъэр черкеск къ гъ лъэпкъэгъухэр лъэпкъыр`
+2. `брат хьэсин адыгэхэм я къуалэбзу щӏэныгъэр черкесск гъ хэкӏыгъуэхэр теплъэхэр`
+3. `хьэсин адыгэхэм я къуалэбзу щӏэныгъэр черкеск къ гъ хэкӏыгъуэр лъэпкъхэр`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ргаууе_кӏыр_я_д`
+2. `эр_зуэконгуэр_ɬʼ`
+3. `ыхьэкӏыхъагу_сей`
+
+**Context Size 2:**
+
+1. `э_амазововой_идж_`
+2. `м_нэм_щӏауэдардей`
+3. `эм_щӏэубгъуэсилым`
+
+**Context Size 3:**
+
+1. `эм_и_литекӏэ,_абгъ`
+2. `_къэошӏукӏэ_абы_ещ`
+3. `эр_антар_иужь_кал_`
+
+**Context Size 4:**
+
+1. `хэр_и_нэхуаку,_налщ`
+2. `хэм_я_нэхъ_инщ._апх`
+3. `гъуэжь_лэчтхыгъэр_а`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (145,401 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 | 18,198 |
+| Total Tokens | 179,236 |
+| Mean Frequency | 9.85 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 74.58 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | и | 8,299 |
+| 2 | я | 3,463 |
+| 3 | нэхъ | 1,395 |
+| 4 | гъэм | 1,150 |
+| 5 | хы | 930 |
+| 6 | м | 915 |
+| 7 | а | 847 |
+| 8 | хэт | 669 |
+| 9 | зы | 634 |
+| 10 | км | 602 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | дэплъейр | 2 |
+| 2 | хьэзыр | 2 |
+| 3 | мэгурым | 2 |
+| 4 | мывэр | 2 |
+| 5 | уанэр | 2 |
+| 6 | хабзэрэ | 2 |
+| 7 | шыдым | 2 |
+| 8 | зылъагъур | 2 |
+| 9 | бзыпхъэ | 2 |
+| 10 | куб | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9480 |
+| R² (Goodness of Fit) | 0.991228 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 26.5% |
+| Top 1,000 | 56.6% |
+| Top 5,000 | 80.3% |
+| Top 10,000 | 90.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9912 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 26.5% of corpus
+- **Long Tail:** 8,198 words needed for remaining 9.5% 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 | 0.6517 | 0.3536 | N/A | N/A |
+| **mono_64d** | 64 | 0.2166 | 0.3347 | N/A | N/A |
+| **mono_128d** | 128 | 0.0438 | 0.3380 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6517 🏆 | 0.3583 | 0.0120 | 0.1220 |
+| **aligned_64d** | 64 | 0.2166 | 0.3384 | 0.0260 | 0.1680 |
+| **aligned_128d** | 128 | 0.0438 | 0.3433 | 0.0440 | 0.1920 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6517 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3444. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.4% 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 | **1.374** | 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 |
+|--------|----------|
+| `-къ` | къыкӏожыр, къупщхьэм, къакӏуэ |
+| `-к` | куб, къыкӏожыр, къупщхьэм |
+| `-п` | півострова, пурус, парку |
+| `-а` | абгъуэр, атлантикэ, амазонэхэм |
+| `-и` | икӏагъыр, ихахэм, иӏыгъыр |
+| `-б` | бжыгъэри, бджэхьашъхьам, бзыр |
+| `-зэ` | зэфӏэувэныгъэ, зэрщыту, зэхуакухэм |
+| `-х` | хэхъурэ, хъыбарылъэ, хэтхэмрэ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-э` | хэхъурэ, хъыбарылъэ, убыхыбзэ |
+| `-м` | щытехьэм, ихахэм, къупщхьэм |
+| `-р` | икӏагъыр, абгъуэр, къыкӏожыр |
+| `-эм` | щытехьэм, ихахэм, къупщхьэм |
+| `-эр` | абгъуэр, гуэрэнхэр, ӏутхэр |
+| `-у` | къимыгъану, хэтху, шху |
+| `-ым` | ишхкъым, виетнамым, сыбырым |
+| `-ӏэ` | ищӏагъымкӏэ, гъэмахуэкӏэ, бгъухэмкӏэ |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ыгъэ` | 1.62x | 51 contexts | шыгъэ, тыгъэ, дыгъэ |
+| `агъэ` | 1.71x | 40 contexts | уагъэ, дагъэ, дагъэр |
+| `эпкъ` | 1.83x | 31 contexts | нэпкъ, жэпкъ, лэпкъ |
+| `эхэм` | 1.49x | 68 contexts | жэхэм, пэхэм, дэхэм |
+| `эхэр` | 1.57x | 54 contexts | фэхэр, нэхэр, сэхэр |
+| `шъхь` | 1.63x | 35 contexts | шъхьэ, ишъхьэ, шъхьэм |
+| `эгъу` | 1.46x | 52 contexts | жэгъу, нэгъу, мэгъу |
+| `ыгъу` | 1.47x | 47 contexts | шыгъу, яӏыгъу, мыгъуэ |
+| `ъэра` | 2.08x | 14 contexts | къэрал, гъэращ, гъэрауэ |
+| `эхъу` | 1.41x | 43 contexts | мэхъу, нэхъу, мэхъур |
+| `эхъы` | 1.71x | 21 contexts | нэхъыжъ, нэхъыжь, нэхъыбэ |
+| `къым` | 1.44x | 34 contexts | пкъым, инкъым, ипкъым |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-къ` | `-э` | 154 words | къашыргъэгъуабжэ, къамэ |
+| `-къ` | `-р` | 105 words | къодор, къызэрагъэсэбэпыр |
+| `-п` | `-э` | 95 words | плӏыуэ, псыӏуфэ |
+| `-х` | `-э` | 94 words | хымрэ, хухуабжэ |
+| `-п` | `-м` | 86 words | пэкъыухэм, прусиэм |
+| `-къ` | `-м` | 84 words | къущхьэхэм, къуэхьэпӏэм |
+| `-и` | `-э` | 80 words | испаныбзэкӏэ, ицӏэ |
+| `-зэ` | `-э` | 80 words | зэмылӏаужыгъуэ, зэригъэунэхумкӏэ |
+| `-къ` | `-у` | 80 words | къэлэлэху, къыгъану |
+| `-т` | `-э` | 79 words | техуауэ, таукъуэ |
+
+### 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 |
+|------|-----------------|------------|------|
+| здэпсомрэ | **`здэпсо-м-рэ`** | 7.5 | `м` |
+| ухуэмеймэ | **`ухуэмей-м-э`** | 7.5 | `м` |
+| ирихьэлӏэу | **`ирихьэл-ӏэ-у`** | 7.5 | `ӏэ` |
+| мэзджэдщхьэтумэ | **`мэзджэдщхьэту-м-э`** | 7.5 | `м` |
+| ягъэлъапӏэт | **`ягъэлъап-ӏэ-т`** | 7.5 | `ӏэ` |
+| уругуаимрэ | **`уругуаи-м-рэ`** | 7.5 | `м` |
+| щӏыӏэтыӏэщ | **`щӏыӏэты-ӏэ-щ`** | 7.5 | `ӏэ` |
+| виетнамым | **`виетна-м-ым`** | 7.5 | `м` |
+| шъхьафхэу | **`шъхьаф-хэ-у`** | 7.5 | `хэ` |
+| абдежьхэм | **`абдежь-хэ-м`** | 7.5 | `хэ` |
+| раджбаромэ | **`раджбаро-м-э`** | 7.5 | `м` |
+| праягамрэ | **`праяга-м-рэ`** | 7.5 | `м` |
+| къаукъазми | **`къаукъаз-м-и`** | 7.5 | `м` |
+| цитоплазмэ | **`цитоплаз-м-э`** | 7.5 | `м` |
+| цӏэрыӏуэт | **`цӏэрыӏу-э-т`** | 6.0 | `цӏэрыӏу` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kabardian 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 (4.54x) |
+| N-gram | **2-gram** | Lowest perplexity (394) |
+| Markov | **Context-4** | Highest predictability (99.0%) |
+| 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 07:17:37*

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