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README.md

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+---
+language: ug
+language_name: Uyghur
+language_family: turkic_other
+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_other
+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: 4.777
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8332
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Uyghur - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Uyghur** 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** | 3.736x | 3.74 | 0.1104% | 494,604 |
+| **16k** | 4.184x | 4.19 | 0.1236% | 441,635 |
+| **32k** | 4.530x | 4.54 | 0.1339% | 407,907 |
+| **64k** | 4.777x 🏆 | 4.78 | 0.1412% | 386,795 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `تۇنىسدىكى ئۇيغۇرلارنىڭ سانى 10 ئەتراپىدا بولۇپ، كۆزگە كۆرۈنگەن شەخىسلەر : مەنبەل...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁تۇ نىس دىكى ▁ئۇيغۇرلارنىڭ ▁سانى ▁ 1 0 ▁ئەتراپىدا ▁بولۇپ ... (+6 more)` | 16 |
+| 16k | `▁تۇنىس دىكى ▁ئۇيغۇرلارنىڭ ▁سانى ▁ 1 0 ▁ئەتراپىدا ▁بولۇپ ، ... (+5 more)` | 15 |
+| 32k | `▁تۇنىس دىكى ▁ئۇيغۇرلارنىڭ ▁سانى ▁ 1 0 ▁ئەتراپىدا ▁بولۇپ ، ... (+5 more)` | 15 |
+| 64k | `▁تۇنىس دىكى ▁ئۇيغۇرلارنىڭ ▁سانى ▁ 1 0 ▁ئەتراپىدا ▁بولۇپ ، ... (+5 more)` | 15 |
+
+**Sample 2:** `مۈشۈك ئائىلىسى بەلگە جايلاشماق ئادەت ئاۋۇماق قونالغۇ ئوزۇقلىنىش خۇسۇسىيىتى كەنجى...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁مۈشۈك ▁ئائىلىسى ▁بەلگە ▁جايلاشماق ▁ئادەت ▁ئاۋۇماق ▁قونالغۇ ▁ئوزۇقلىنىش ▁خۇسۇسىيىتى ▁كەنجى ... (+5 more)` | 15 |
+| 16k | `▁مۈشۈك ▁ئائىلىسى ▁بەلگە ▁جايلاشماق ▁ئادەت ▁ئاۋۇماق ▁قونالغۇ ▁ئوزۇقلىنىش ▁خۇسۇسىيىتى ▁كەنجى ... (+5 more)` | 15 |
+| 32k | `▁مۈشۈك ▁ئائىلىسى ▁بەلگە ▁جايلاشماق ▁ئادەت ▁ئاۋۇماق ▁قونالغۇ ▁ئوزۇقلىنىش ▁خۇسۇسىيىتى ▁كەنجى ... (+5 more)` | 15 |
+| 64k | `▁مۈشۈك ▁ئائىلىسى ▁بەلگە ▁جايلاشماق ▁ئادەت ▁ئاۋۇماق ▁قونالغۇ ▁ئوزۇقلىنىش ▁خۇسۇسىيىتى ▁كەنجى ... (+5 more)` | 15 |
+
+**Sample 3:** `ئاپتونوم رايونلۇق تۇرالغۇ ۋە شەھەر - يېزا قۇرۇلۇشى نازارىتى قۇرۇلدى. مەنبەلەر`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ئاپتونوم ▁رايونلۇق ▁تۇر الغۇ ▁ۋە ▁شەھەر ▁- ▁يېزا ▁قۇرۇلۇشى ▁ناز ... (+4 more)` | 14 |
+| 16k | `▁ئاپتونوم ▁رايونلۇق ▁تۇرالغۇ ▁ۋە ▁شەھەر ▁- ▁يېزا ▁قۇرۇلۇشى ▁نازارىتى ▁قۇرۇلدى ... (+2 more)` | 12 |
+| 32k | `▁ئاپتونوم ▁رايونلۇق ▁تۇرالغۇ ▁ۋە ▁شەھەر ▁- ▁يېزا ▁قۇرۇلۇشى ▁نازارىتى ▁قۇرۇلدى ... (+2 more)` | 12 |
+| 64k | `▁ئاپتونوم ▁رايونلۇق ▁تۇرالغۇ ▁ۋە ▁شەھەر ▁- ▁يېزا ▁قۇرۇلۇشى ▁نازارىتى ▁قۇرۇلدى ... (+2 more)` | 12 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.777x compression
+- **Lowest UNK Rate:** 8k with 0.1104% 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 | 31,118 | 14.93 | 65,522 | 7.0% | 22.8% |
+| **2-gram** | Subword | 453 🏆 | 8.82 | 10,807 | 59.0% | 94.8% |
+| **3-gram** | Word | 26,893 | 14.71 | 61,707 | 9.8% | 27.8% |
+| **3-gram** | Subword | 3,539 | 11.79 | 73,196 | 23.7% | 64.2% |
+| **4-gram** | Word | 113,663 | 16.79 | 204,980 | 5.9% | 16.1% |
+| **4-gram** | Subword | 17,568 | 14.10 | 306,560 | 10.8% | 35.0% |
+| **5-gram** | Word | 102,988 | 16.65 | 180,010 | 6.1% | 16.5% |
+| **5-gram** | Subword | 57,887 | 15.82 | 660,169 | 6.0% | 21.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `font family` | 1,559 |
+| 2 | `style direction` | 1,545 |
+| 3 | `div style` | 1,544 |
+| 4 | `قاماق جازاسى` | 1,444 |
+| 5 | `يەنە بىر` | 1,422 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `div style direction` | 1,538 |
+| 2 | `مۇددەتلىك قاماق جازاسى` | 1,253 |
+| 3 | `style direction rtl` | 1,242 |
+| 4 | `direction rtl font` | 1,239 |
+| 5 | `rtl font family` | 1,239 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `style direction rtl font` | 1,239 |
+| 2 | `direction rtl font family` | 1,239 |
+| 3 | `div style direction rtl` | 1,237 |
+| 4 | `تۆۋەن مۇددەتلىك قاماق جازاسى` | 971 |
+| 5 | `يىلدىن تۆۋەن مۇددەتلىك قاماق` | 963 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `style direction rtl font family` | 1,239 |
+| 2 | `div style direction rtl font` | 1,234 |
+| 3 | `يىلدىن تۆۋەن مۇددەتلىك قاماق جازاسى` | 955 |
+| 4 | `tom microsoft uighur uyghur ekran` | 802 |
+| 5 | `ukij tuz tom microsoft uighur` | 802 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ئ` | 601,928 |
+| 2 | `ى _` | 466,826 |
+| 3 | `ل ى` | 449,775 |
+| 4 | `ن ى` | 375,089 |
+| 5 | `ى ل` | 331,034 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ئ ا` | 151,323 |
+| 2 | `ن ى ڭ` | 143,621 |
+| 3 | `ى ڭ _` | 142,034 |
+| 4 | `ى ن ى` | 138,625 |
+| 5 | `ى ل ى` | 138,069 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ن ى ڭ _` | 135,065 |
+| 2 | `_ ب و ل` | 68,419 |
+| 3 | `غ ا ن _` | 58,174 |
+| 4 | `د ى ن _` | 57,430 |
+| 5 | `ل ى ر ى` | 56,859 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ى ن ى ڭ _` | 44,317 |
+| 2 | `_ ق ى ل ى` | 35,323 |
+| 3 | `ن ى ڭ _ ئ` | 33,703 |
+| 4 | `د ى ك ى _` | 29,476 |
+| 5 | `_ ب ى ر _` | 29,261 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 453
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~21% 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 | 0.8072 | 1.750 | 5.72 | 320,664 | 19.3% |
+| **1** | Subword | 1.1829 | 2.270 | 8.19 | 4,322 | 0.0% |
+| **2** | Word | 0.2037 | 1.152 | 1.42 | 1,831,690 | 79.6% |
+| **2** | Subword | 0.7055 | 1.631 | 4.46 | 35,371 | 29.5% |
+| **3** | Word | 0.0501 | 1.035 | 1.07 | 2,595,280 | 95.0% |
+| **3** | Subword | 0.7110 | 1.637 | 3.49 | 157,874 | 28.9% |
+| **4** | Word | 0.0173 🏆 | 1.012 | 1.02 | 2,783,101 | 98.3% |
+| **4** | Subword | 0.5528 | 1.467 | 2.45 | 550,624 | 44.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ۋە شەكلىمۇ ئۆزگىچە ھەر دەرىجىلىك قوغدىلىدىغان مەدەنىيەت گۇرۇپپىلىرى ديكى ۋە ئىزچىللىقىدۇر يەنگىن 85 ...`
+2. `بىر خىزمەت ئورنىڭىزدىن تۇرالامسىز ــ ئۇيغۇر قاغانلىقىغا ئەلچى ئەۋەتتى بۇ كىشى ئىسمى بولغان جورنالىست...`
+3. `بىلەن ئۇيغۇر ئاپتونوم جايلارنىڭ ئاپتونومىيە ئورگانلىرى تەسىس قىلىنغان ئۇ يەردىكى ۋەتەنسىز ئادەم توپل...`
+
+**Context Size 2:**
+
+1. `font family ukij tuz tom microsoft uighur uyghur ekran arial unicode ms alpida_unicode system ukij t...`
+2. `style direction rtl font family tahoma ئۇمۇمىي ئىسمى سىمۋولى نومۇرى ئىلمېنىت كاتېگورىيىسى گۇرۇپپىسى ...`
+3. `div style direction rtl font family ukij tuz tom microsoft uighur uyghur ekran arial unicode ms row`
+
+**Context Size 3:**
+
+1. `div style direction rtl font family tahoma wirginiye shitati bolsa amérika qoshma shtatliri dikibixa...`
+2. `مۇددەتلىك قاماق جازاسى ياكى تۇتۇپ تۇرۇپ ئەمگەككە سېلىش جازاسى بېرىلىدۇ ئاقىۋىتى پەۋقۇلئاددە ئېغىر بو...`
+3. `style direction rtl font family tahoma misori shitati bolsa amérika qoshma shtatliri dikibixahar nop...`
+
+**Context Size 4:**
+
+1. `style direction rtl font family alkatip tor alpida_unicode system ukij tuz tom microsoft uighur uygh...`
+2. `direction rtl font family alkatip tor alpida_unicode system ukij tuz tom microsoft uighur uyghur ekr...`
+3. `div style direction rtl font family ukij tuz basma microsoft uighur uyghur ekran arial unicode ms بې...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_سەي_پان-_«ﺗﻪﻧﻰ_`
+2. `ى،_شلىسارىرىقاھر`
+3. `ابىشكىلىرلىقىئۇ_`
+
+**Context Size 2:**
+
+1. `_ئىق_كېلىقىليەت_ي`
+2. `ى_ۋە_ئەككىن_يولغا`
+3. `لىك_ئادىغا_يا_مېر`
+
+**Context Size 3:**
+
+1. `_ئاياغا_ۋە_ھازى_يو`
+2. `نىڭ_يۈرۈپ_كەلتۈركچ`
+3. `ىڭ_تۆۋەتەنتتە_يۈرى`
+
+**Context Size 4:**
+
+1. `نىڭ_نەتىجىلىك_ماۋۇ_`
+2. `_بولسا_نىسبەت_قىلىد`
+3. `غان_ھايۋانات_دولقۇن`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (550,624 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 | 135,750 |
+| Total Tokens | 3,012,380 |
+| Mean Frequency | 22.19 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 244.00 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ۋە | 48,299 |
+| 2 | بىر | 30,257 |
+| 3 | بىلەن | 28,269 |
+| 4 | بۇ | 23,896 |
+| 5 | بولۇپ | 15,446 |
+| 6 | بولغان | 12,330 |
+| 7 | ياكى | 11,679 |
+| 8 | ئۇيغۇر | 11,561 |
+| 9 | ئۇ | 10,650 |
+| 10 | دەپ | 9,455 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | hâmid | 2 |
+| 2 | arî | 2 |
+| 3 | alâ | 2 |
+| 4 | tevhîd | 2 |
+| 5 | kitâbu | 2 |
+| 6 | âsım | 2 |
+| 7 | بىرقانداق | 2 |
+| 8 | گەنبىيەن | 2 |
+| 9 | توغرامچىسى | 2 |
+| 10 | باۋزا | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9498 |
+| R² (Goodness of Fit) | 0.987707 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 18.6% |
+| Top 1,000 | 43.9% |
+| Top 5,000 | 66.4% |
+| Top 10,000 | 75.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9877 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 18.6% of corpus
+- **Long Tail:** 125,750 words needed for remaining 24.6% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8332 🏆 | 0.3495 | N/A | N/A |
+| **mono_64d** | 64 | 0.8220 | 0.2582 | N/A | N/A |
+| **mono_128d** | 128 | 0.8294 | 0.1749 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8332 | 0.3542 | 0.0200 | 0.1300 |
+| **aligned_64d** | 64 | 0.8220 | 0.2639 | 0.0460 | 0.2060 |
+| **aligned_128d** | 128 | 0.8294 | 0.1693 | 0.0880 | 0.2980 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8332 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2617. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.8% 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 | **-0.217** | 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 |
+|--------|----------|
+| `-ئ` | ئۇقۇتۇش, ئۇبباد, ئاپتوبۇسنىڭ |
+| `-ئا` | ئاپتوبۇسنىڭ, ئاياغنى, ئاقساشقا |
+| `-ت` | تەلەيسىزلىكىدىن, تاتلىقنىڭ, توشمىغانلار |
+| `-م` | مۇشتەرى, مەنبئەدىن, مۇزلار |
+| `-ئە` | ئەتتۈرىدۇكى, ئەﻫﻤﯩﻴﻪﺗﻠﯩﻚ, ئەﮬلى |
+| `-ك` | كېچىلىرى, كۋانتى, كارتىنىڭ |
+| `-ب` | بەرداشلىق, بەئەينى, بېكىتمىگەن |
+| `-س` | سەرەتان, سەيياھنىڭ, سوغۇقچان |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ى` | كېچىلىرى, كۋانتى, ھېسىياتلىرى |
+| `-ىڭ` | شەكلىنىڭ, سەيياھنىڭ, ئاپتوبۇسنىڭ |
+| `-ن` | چاپلانغان, سەرەتان, غەزەپتىن |
+| `-نى` | شىركەتلەرنى, نوچىنى, بەئەينى |
+| `-ڭ` | شەكلىنىڭ, سەيياھنىڭ, ئاپتوبۇسنىڭ |
+| `-ىن` | غەزەپتىن, دەرەختىن, تەلەيسىزلىكىدىن |
+| `-ا` | لايكا, ئىستىقبالغا, ھېساپلىغاندا |
+| `-ە` | ئۆزلەشتۈرۈشىگە, سەۋىيىگىچە, كەچلەردە |
+
+### 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 |
+|------|----------|------------------|----------|
+| `رىنى` | 2.13x | 126 contexts | برىنى, يىرىنى, نارىنى |
+| `ىدىك` | 2.04x | 131 contexts | خىدىكى, سىدىكى, ئىدىكى |
+| `لارن` | 1.92x | 162 contexts | لارنى, لارنىڭ, سۇلارنى |
+| `ىرىن` | 2.05x | 80 contexts | سىرىن, شىرىن, بىرىن |
+| `ىلىش` | 1.62x | 247 contexts | بىلىش, ئىلىش, تىلىش |
+| `ارنى` | 1.96x | 72 contexts | لارنى, قارنى, بارنى |
+| `يغۇر` | 2.39x | 29 contexts | ئۇيغۇر, ئويغۇر, ئ‍ۇيغۇر |
+| `انلى` | 1.60x | 116 contexts | شانلى, جانلىق, دانلىد |
+| `دىغا` | 2.14x | 34 contexts | دىغار, مودىغا, سودىغا |
+| `ىلىق` | 1.54x | 132 contexts | سىلىق, بىلىق, يىلىق |
+| `رنىڭ` | 2.14x | 29 contexts | ئەرنىڭ, كورنىڭ, يەرنىڭ |
+| `ىغان` | 1.59x | 94 contexts | پىغان, جىغان, قلىغان |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ئ` | `-ى` | 327 words | ئۇزىرىشى, ئۇلاغلىرىنى |
+| `-ت` | `-ى` | 181 words | تاۋارلارنى, تاۋابىئاتلىرى |
+| `-ئ` | `-ن` | 176 words | ئەرەبچىدىن, ئىپادىلەيدىغان |
+| `-ئ` | `-ا` | 164 words | ئاقسارايغا, ئۇلاپلا |
+| `-ئ` | `-ڭ` | 154 words | ئۇنىۋېرسىتېتنىڭ, ئىلتىماسنىڭ |
+| `-ئ` | `-ىڭ` | 151 words | ئۇنىۋېرسىتېتنىڭ, ئىلتىماسنىڭ |
+| `-ئ` | `-نى` | 136 words | ئۇلاغلىرىنى, ئەترەتنى |
+| `-ئ` | `-ە` | 117 words | ئىبرانىيچە, ئۆستۈرۈشتە |
+| `-م` | `-ى` | 113 words | مايورى, مەسۇلاتلىرىنى |
+| `-ك` | `-ى` | 100 words | كىنوچىلىقى, كۈنپېتىشتىكى |
+
+### 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 |
+|------|-----------------|------------|------|
+| پەرقلەنسىمۇ | **`پەرقلەن-سى-مۇ`** | 7.5 | `سى` |
+| لەپتىننىڭ | **`لەپتىن-ن-ىڭ`** | 7.5 | `ن` |
+| تەشكىلاتنىڭ | **`تەشكىلات-ن-ىڭ`** | 7.5 | `ن` |
+| تاشيولنىڭ | **`تاشيول-ن-ىڭ`** | 7.5 | `ن` |
+| ئىستاكاننىڭ | **`ئىستاكان-ن-ىڭ`** | 7.5 | `ن` |
+| ئۆستۈرۈشنىڭ | **`ئۆستۈرۈش-ن-ىڭ`** | 7.5 | `ن` |
+| ھۈجەيرىسىنى | **`ھۈجەيرى-سى-نى`** | 7.5 | `سى` |
+| دوستويېۋىسكىي | **`دوستويېۋىس-كى-ي`** | 7.5 | `كى` |
+| قۇتۇلۇشنىڭ | **`قۇتۇلۇش-ن-ىڭ`** | 7.5 | `ن` |
+| ئىقتىدارىمنىڭ | **`ئىقتىدارىم-ن-ىڭ`** | 7.5 | `ن` |
+| پۇقرالىقىغا | **`پۇقرالىق-ى-غا`** | 6.0 | `پۇقرالىق` |
+| ئوخشىماسلىقىغا | **`ئوخشىماسلىق-ى-غا`** | 6.0 | `ئوخشىماسلىق` |
+| ئاسساسلىق | **`ئا-س-ساسلىق`** | 6.0 | `ساسلىق` |
+| ئادەمىنىڭ | **`ئادەم-ىن-ىڭ`** | 6.0 | `ئادەم` |
+| ئۈچۈنچىسى | **`ئۈچۈن-چى-سى`** | 6.0 | `ئۈچۈن` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Uyghur 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 (4.78x) |
+| N-gram | **2-gram** | Lowest perplexity (453) |
+| Markov | **Context-4** | Highest predictability (98.3%) |
+| 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 02:36:16*

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