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README.md

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+---
+language: sd
+language_name: Sindhi
+language_family: indoaryan_central
+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-indoaryan_central
+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: 3.934
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8385
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sindhi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sindhi** 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** | 3.296x | 3.30 | 0.0928% | 803,595 |
+| **16k** | 3.589x | 3.59 | 0.1011% | 737,928 |
+| **32k** | 3.802x | 3.80 | 0.1071% | 696,754 |
+| **64k** | 3.934x 🏆 | 3.94 | 0.1108% | 673,371 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `مؤرخه جو لفظ ڪنهن بہ تاريخ کي حڪايت ڏيڻ يا حوالو ڏيڻ جي لاء استعمال هوندو آهي۔ ج...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁م ؤر خ ه ▁جو ▁لفظ ▁ڪنهن ▁بہ ▁تاريخ ▁کي ... (+30 more)` | 40 |
+| 16k | `▁مؤرخ ه ▁جو ▁لفظ ▁ڪنهن ▁بہ ▁تاريخ ▁کي ▁ح ڪا ... (+26 more)` | 36 |
+| 32k | `▁مؤرخ ه ▁جو ▁لفظ ▁ڪنهن ▁بہ ▁تاريخ ▁کي ▁حڪا يت ... (+23 more)` | 33 |
+| 64k | `▁مؤرخ ه ▁جو ▁لفظ ▁ڪنهن ▁بہ ▁تاريخ ▁کي ▁حڪايت ▁ڏيڻ ... (+22 more)` | 32 |
+
+**Sample 2:** `جنوري فيبروري مارچ اپريل مئي جون جولاءِ آگسٽ سيپٽمبر آڪٽوبر نومبر ڊسمبر صدي`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁جنوري ▁فيبروري ▁مارچ ▁اپريل ▁مئي ▁جون ▁جولاءِ ▁آگسٽ ▁سيپٽمبر ▁آڪٽوبر ... (+3 more)` | 13 |
+| 16k | `▁جنوري ▁فيبروري ▁مارچ ▁اپريل ▁مئي ▁جون ▁جولاءِ ▁آگسٽ ▁سيپٽمبر ▁آڪٽوبر ... (+3 more)` | 13 |
+| 32k | `▁جنوري ▁فيبروري ▁مارچ ▁اپريل ▁مئي ▁جون ▁جولاءِ ▁آگسٽ ▁سيپٽمبر ▁آڪٽوبر ... (+3 more)` | 13 |
+| 64k | `▁جنوري ▁فيبروري ▁مارچ ▁اپريل ▁مئي ▁جون ▁جولاءِ ▁آگسٽ ▁سيپٽمبر ▁آڪٽوبر ... (+3 more)` | 13 |
+
+**Sample 3:** `مويا (شھر) پاڪستان جي صوبي سنڌ جي ضلعي ٽنڊو محمد خان جي تعلقي ٽنڊو غلام حيدر جو ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁مو يا ▁( ش ھر ) ▁پاڪستان ▁جي ▁صوبي ▁سنڌ ... (+33 more)` | 43 |
+| 16k | `▁مو يا ▁( شھر ) ▁پاڪستان ▁جي ▁صوبي ▁سنڌ ▁جي ... (+32 more)` | 42 |
+| 32k | `▁مو يا ▁( شھر ) ▁پاڪستان ▁جي ▁صوبي ▁سنڌ ▁جي ... (+30 more)` | 40 |
+| 64k | `▁مويا ▁( شھر ) ▁پاڪستان ▁جي ▁صوبي ▁سنڌ ▁جي ▁ضلعي ... (+29 more)` | 39 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.934x compression
+- **Lowest UNK Rate:** 8k with 0.0928% 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 | 38,713 | 15.24 | 131,770 | 8.7% | 25.8% |
+| **2-gram** | Subword | 528 🏆 | 9.05 | 10,636 | 53.0% | 94.3% |
+| **3-gram** | Word | 67,235 | 16.04 | 173,925 | 8.2% | 20.0% |
+| **3-gram** | Subword | 4,815 | 12.23 | 79,077 | 21.4% | 55.9% |
+| **4-gram** | Word | 100,042 | 16.61 | 258,539 | 9.6% | 19.9% |
+| **4-gram** | Subword | 27,421 | 14.74 | 394,134 | 10.1% | 30.4% |
+| **5-gram** | Word | 50,768 | 15.63 | 161,354 | 13.9% | 27.3% |
+| **5-gram** | Subword | 99,142 | 16.60 | 989,725 | 5.7% | 19.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `طور تي` | 6,904 |
+| 2 | `ڪيو ويو` | 6,538 |
+| 3 | `ان جي` | 6,124 |
+| 4 | `سنڌ جي` | 5,981 |
+| 5 | `کان پوءِ` | 5,924 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `سنڌي ادبي بورڊ` | 2,484 |
+| 2 | `پاڪستان جون جنرل` | 2,294 |
+| 3 | `آرٽيڪل پاڪستان جون` | 2,294 |
+| 4 | `اصل آرٽيڪل پاڪستان` | 2,294 |
+| 5 | `جون جنرل اليڪشن` | 2,294 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `اصل آرٽيڪل پاڪستان جون` | 2,294 |
+| 2 | `پاڪستان جون جنرل اليڪشن` | 2,294 |
+| 3 | `آرٽيڪل پاڪستان جون جنرل` | 2,294 |
+| 4 | `جنرل اليڪشن اصل آرٽيڪل` | 2,292 |
+| 5 | `اليڪشن اصل آرٽيڪل پاڪستان` | 2,292 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `آرٽيڪل پاڪستان جون جنرل اليڪشن` | 2,294 |
+| 2 | `اصل آرٽيڪل پاڪستان جون جنرل` | 2,294 |
+| 3 | `اليڪشن اصل آرٽيڪل پاڪستان جون` | 2,292 |
+| 4 | `جنرل اليڪشن اصل آرٽيڪل پاڪستان` | 2,292 |
+| 5 | `جنرل اليڪشن جنرل اليڪشن اصل` | 1,838 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ي _` | 1,114,749 |
+| 2 | `ن _` | 753,311 |
+| 3 | `_ ج` | 557,070 |
+| 4 | `و _` | 411,945 |
+| 5 | `ا ن` | 385,837 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ج ي` | 277,174 |
+| 2 | `ج ي _` | 273,527 |
+| 3 | `ا ن _` | 231,693 |
+| 4 | `_ ۾ _` | 172,549 |
+| 5 | `_ ۽ _` | 138,576 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ج ي _` | 239,630 |
+| 2 | `_ ج و _` | 103,948 |
+| 3 | `_ آ ه ي` | 88,375 |
+| 4 | `ن _ ج ي` | 75,849 |
+| 5 | `_ ک ي _` | 60,920 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ن _ ج ي _` | 72,823 |
+| 2 | `_ آ ه ي .` | 45,747 |
+| 3 | `_ ک ا ن _` | 45,181 |
+| 4 | `آ ه ي . _` | 42,956 |
+| 5 | `_ س ا ن _` | 37,158 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 528
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~19% 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 | 0.9555 | 1.939 | 9.12 | 226,862 | 4.4% |
+| **1** | Subword | 0.9881 | 1.984 | 9.49 | 3,118 | 1.2% |
+| **2** | Word | 0.3286 | 1.256 | 1.91 | 2,067,523 | 67.1% |
+| **2** | Subword | 0.8362 | 1.785 | 5.73 | 29,575 | 16.4% |
+| **3** | Word | 0.1126 | 1.081 | 1.21 | 3,948,825 | 88.7% |
+| **3** | Subword | 0.7606 | 1.694 | 4.20 | 169,537 | 23.9% |
+| **4** | Word | 0.0359 🏆 | 1.025 | 1.05 | 4,752,539 | 96.4% |
+| **4** | Subword | 0.6343 | 1.552 | 2.94 | 712,269 | 36.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `جي ماءُ فلاد قيا قبيلي جا ضلعا شامل ٿي ويا آرامي قبيلو ٻين اڳواڻن به سنڌو`
+2. `جو پڌرنامو asean بيمسٽيڪ جو اندازو ٿي ھي ھڪ نگران حڪومت سياست آيو هو هن تڪ`
+3. `آهي ان فارسي شعر چيل ھجي جتي ايراني ٻولين جا وڏا ڪن ٿيون جون شاخون مشق`
+
+**Context Size 2:**
+
+1. `طور تي هڪ رسالو تحقيق الخلافة لکيو جو حيدرآباد بيورو جو چيئرمئن به ٿيو محمد شاھ جو`
+2. `ڪيو ويو هو ان جو استحصال ڪندي احتياط سان ھلائڻو ھوندو آھي ٻيو تھھ پھرئين تھھ جي`
+3. `ان جي ئي صحبت آسو صوفي بڻيو آسو رام جي قتل واري الزام تي گرفتار ڪيو ويو`
+
+**Context Size 3:**
+
+1. `سنڌي ادبي بورڊ حوالا جي تاريخ جي تاريخ جون ڳالهيون 180 سنڌ جي مختصر تاريخ ص84 85 سال`
+2. `پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستا...`
+3. `آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل...`
+
+**Context Size 4:**
+
+1. `آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن جنرل اليڪش...`
+2. `اصل آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آر...`
+3. `پاڪستان جون جنرل اليڪشن جنرل اليڪشن اصل آرٽيڪل پاڪستان جون جنرل اليڪشن پاڪستان جي قومي اسيمبلي جون ع...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_آهي_و_انن_ٻڌائي`
+2. `يو_پري_سيوامحالي`
+3. `اڌرنهد_ٽين_و_ويءَ`
+
+**Context Size 2:**
+
+1. `ي_وس،_سنڌ_۾_ڪله_ج`
+2. `ن_ورتحري_قبضو_ٿين`
+3. `_جو_واقرر_ڳاٽوزين`
+
+**Context Size 3:**
+
+1. `_جي_ذيلي_يا_مريڪٽر`
+2. `جي_ويندو_هو._اسي_ڪ`
+3. `ان_علائي،_وچ_۾_ٻٽي`
+
+**Context Size 4:**
+
+1. `_جي_حيثيت_۾،_شمشيرن`
+2. `_جو_هڪ_هندستان_ھٿ_ڏ`
+3. `_آهي.،_عضويات)_۾_ڪي`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (712,269 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 | 101,453 |
+| Total Tokens | 5,390,213 |
+| Mean Frequency | 53.13 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1038.92 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | جي | 240,979 |
+| 2 | جو | 104,513 |
+| 3 | آهي | 87,558 |
+| 4 | کي | 61,555 |
+| 5 | تي | 51,826 |
+| 6 | کان | 45,610 |
+| 7 | سان | 38,559 |
+| 8 | جا | 33,418 |
+| 9 | ان | 33,002 |
+| 10 | the | 32,948 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | جوالامکي | 2 |
+| 2 | باريري | 2 |
+| 3 | آسامائي | 2 |
+| 4 | بالمِڪي | 2 |
+| 5 | شويتامبر | 2 |
+| 6 | چوٽرا | 2 |
+| 7 | هديارا | 2 |
+| 8 | سُکوچڪ | 2 |
+| 9 | ڪاٺواري | 2 |
+| 10 | گرودوارا | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0832 |
+| R² (Goodness of Fit) | 0.989336 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 32.9% |
+| Top 1,000 | 60.7% |
+| Top 5,000 | 80.7% |
+| Top 10,000 | 87.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9893 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 32.9% of corpus
+- **Long Tail:** 91,453 words needed for remaining 12.5% 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 | 0.8385 🏆 | 0.3803 | N/A | N/A |
+| **mono_64d** | 64 | 0.8313 | 0.3087 | N/A | N/A |
+| **mono_128d** | 128 | 0.8167 | 0.2309 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8385 | 0.3802 | 0.0300 | 0.2040 |
+| **aligned_64d** | 64 | 0.8313 | 0.3038 | 0.0820 | 0.3320 |
+| **aligned_128d** | 128 | 0.8167 | 0.2420 | 0.1040 | 0.3860 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8385 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3077. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 10.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.436** | 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 |
+|--------|----------|
+| `-ا` | اسڪيمون, اجهامي, انڊونيشا |
+| `-م` | مبينہ, مسلمہ, مون |
+| `-س` | سيليبس, ساون, ساهيڙين |
+| `-ڪ` | ڪﺌﻲ, ڪلنگ, ڪئٽئلن |
+| `-ال` | الدراسات, اللھ, المنورہ |
+| `-ب` | بادشاهتي, بنتِ, باک |
+| `-پ` | پرزور, پڙھي, پتن |
+| `-و` | وهندي, واڊ, وسيامل |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ن` | اسڪيمون, ھڙتالن, دماغن |
+| `-ي` | ڳائجي, وهندي, کي |
+| `-s` | minorities, indies, endophytes |
+| `-ا` | انڊونيشا, ڌاڍا, سنزا |
+| `-e` | hoernle, dengue, deville |
+| `-n` | marathon, ruskin, cern |
+| `-و` | کیو, سھتو, ماپبو |
+| `-ون` | اسڪيمون, مون, ساون |
+
+### 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 |
+|------|----------|------------------|----------|
+| `tion` | 3.01x | 49 contexts | notion, nation, cation |
+| `ريون` | 2.35x | 135 contexts | ڪريون, فريون, دريون |
+| `يندا` | 2.25x | 112 contexts | نيندا, ويندا, ڏيندا |
+| `atio` | 3.03x | 30 contexts | natio, ratio, nation |
+| `يندي` | 1.83x | 114 contexts | ڪيندي, ٿيندي, ميندي |
+| `يائي` | 1.69x | 117 contexts | بيائي, پيائي, ديائي |
+| `يندڙ` | 1.79x | 89 contexts | ڏيندڙ, ايندڙ, ويندڙ |
+| `ائون` | 1.53x | 148 contexts | مائون, ٹائون, لائون |
+| `نهنج` | 2.12x | 34 contexts | تنهنجي, تنهنجو, پنهنجي |
+| `اريخ` | 2.19x | 18 contexts | تاريخ, ٿاريخ, پاريخ |
+| `علائ` | 2.47x | 10 contexts | علائق, علائي, علائقي |
+| `ڪستا` | 2.24x | 11 contexts | سڪستان, پاڪستان, ترڪستان |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ا` | `-ن` | 55 words | اُنھَن, افشاريان |
+| `-م` | `-ي` | 35 words | مائوزي, مھاڏي |
+| `-ا` | `-ي` | 30 words | السنوسي, ائڪمي |
+| `-پ` | `-ن` | 29 words | پبليڪشن, پپن |
+| `-ڪ` | `-ن` | 29 words | ڪارواين, ڪنٽينرن |
+| `-م` | `-ن` | 26 words | مارلن, ملهايون |
+| `-ا` | `-ا` | 25 words | اورا, الما |
+| `-ب` | `-ن` | 23 words | بوسٽن, بُڪين |
+| `-س` | `-ن` | 23 words | سرنامن, سون |
+| `-ا` | `-و` | 22 words | ايلنبرو, اجھو |
+
+### 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 |
+|------|-----------------|------------|------|
+| انڊيپينڊنس | **`انڊيپينڊ-ن-س`** | 7.5 | `ن` |
+| وڪيذريعات | **`وڪيذريع-ا-ت`** | 7.5 | `ا` |
+| برهمپوترا | **`برهمپوت-ر-ا`** | 7.5 | `ر` |
+| اصطلاحيات | **`اصطلاح-يا-ت`** | 6.0 | `اصطلاح` |
+| interests | **`inter-es-ts`** | 6.0 | `inter` |
+| المهاجرين | **`ال-مهاجرين`** | 4.5 | `مهاجرين` |
+| periodical | **`periodic-al`** | 4.5 | `periodic` |
+| ڊيموگرافيا | **`ڊيموگرافي-ا`** | 4.5 | `ڊيموگرافي` |
+| interactions | **`interaction-s`** | 4.5 | `interaction` |
+| anglicans | **`anglican-s`** | 4.5 | `anglican` |
+| lansdowne | **`lansdown-e`** | 4.5 | `lansdown` |
+| شاهواڻيءَ | **`ش-ا-هواڻيءَ`** | 4.5 | `هواڻيءَ` |
+| presidente | **`president-e`** | 4.5 | `president` |
+| orientales | **`oriental-es`** | 4.5 | `oriental` |
+| شاگردياڻيون | **`شاگردياڻي-ون`** | 4.5 | `شاگردياڻي` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sindhi 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 (3.93x) |
+| N-gram | **2-gram** | Lowest perplexity (528) |
+| Markov | **Context-4** | Highest predictability (96.4%) |
+| 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 20:08:57*

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