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README.md

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+---
+language: ur
+language_name: Urdu
+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: 4.066
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7965
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Urdu - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Urdu** 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** | 3.434x | 3.44 | 0.1597% | 2,494,826 |
+| **16k** | 3.731x | 3.74 | 0.1735% | 2,296,340 |
+| **32k** | 3.936x | 3.95 | 0.1830% | 2,176,646 |
+| **64k** | 4.066x 🏆 | 4.08 | 0.1891% | 2,107,362 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `لائژوو چین کا ایک کاؤنٹی سطح شہر جو ژانگجیانگ میں واقع ہے۔ مزید دیکھیے چین فہرست...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁لائ ژ وو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ... (+21 more)` | 31 |
+| 16k | `▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژ ... (+19 more)` | 29 |
+| 32k | `▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژانگ ... (+18 more)` | 28 |
+| 64k | `▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژانگ ... (+18 more)` | 28 |
+
+**Sample 2:** `ساروی پاکستان کا ایک آباد مقام جو ضلع لاہور میں واقع ہے۔ مزید دیکھیے پاکستان پاک...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)` | 26 |
+| 16k | `▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)` | 26 |
+| 32k | `▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)` | 26 |
+| 64k | `▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)` | 26 |
+
+**Sample 3:** `انڈونیشیا کی ثقافت سے مراد انڈونیشیا کا ثقافتی ورثہ ہے۔ حوالہ جات ثقافت مشرقی ای...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)` | 16 |
+| 16k | `▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)` | 16 |
+| 32k | `▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)` | 16 |
+| 64k | `▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.066x compression
+- **Lowest UNK Rate:** 8k with 0.1597% 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 | 71,880 | 16.13 | 920,952 | 12.1% | 28.0% |
+| **2-gram** | Subword | 407 🏆 | 8.67 | 31,986 | 59.9% | 96.3% |
+| **3-gram** | Word | 315,178 | 18.27 | 2,297,981 | 8.3% | 17.4% |
+| **3-gram** | Subword | 3,547 | 11.79 | 203,673 | 25.5% | 63.2% |
+| **4-gram** | Word | 765,780 | 19.55 | 4,319,755 | 7.4% | 14.2% |
+| **4-gram** | Subword | 19,593 | 14.26 | 1,069,845 | 12.6% | 37.0% |
+| **5-gram** | Word | 617,009 | 19.23 | 3,316,122 | 7.9% | 15.7% |
+| **5-gram** | Subword | 75,628 | 16.21 | 3,267,447 | 7.4% | 25.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `کے لیے` | 246,197 |
+| 2 | `حوالہ جات` | 212,286 |
+| 3 | `واقع ہے` | 138,739 |
+| 4 | `مزید دیکھیے` | 134,662 |
+| 5 | `ہے اور` | 134,251 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `میں واقع ہے` | 98,697 |
+| 2 | `ہے مزید دیکھیے` | 91,225 |
+| 3 | `ریاستہائے متحدہ امریکا` | 75,905 |
+| 4 | `شہر حوالہ جات` | 70,046 |
+| 5 | `کے شہر حوالہ` | 69,949 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `کے شہر حوالہ جات` | 69,947 |
+| 2 | `ڈیٹا سے مختلف مختصر` | 60,477 |
+| 3 | `سے مختلف مختصر وضاحت` | 60,477 |
+| 4 | `میں واقع ہے تفصیلات` | 57,274 |
+| 5 | `واقع ہے مزید دیکھیے` | 56,176 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ڈیٹا سے مختلف مختصر وضاحت` | 60,477 |
+| 2 | `مطابقت رکھنے والی مختصر تفصیل` | 36,597 |
+| 3 | `ڈیٹا سے مطابقت رکھنے والی` | 36,597 |
+| 4 | `سے مطابقت رکھنے والی مختصر` | 36,597 |
+| 5 | `ریاستہائے متحدہ امریکا کا ایک` | 32,162 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ک` | 8,939,155 |
+| 2 | `ے _` | 7,506,929 |
+| 3 | `ی _` | 7,229,403 |
+| 4 | `_ ا` | 6,895,736 |
+| 5 | `_ م` | 5,580,612 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ی ں _` | 2,526,926 |
+| 2 | `ک ے _` | 2,439,009 |
+| 3 | `_ ک ے` | 2,399,929 |
+| 4 | `_ ک ی` | 2,309,611 |
+| 5 | `_ م ی` | 2,222,538 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ک ے _` | 2,395,195 |
+| 2 | `م ی ں _` | 1,913,836 |
+| 3 | `_ م ی ں` | 1,894,953 |
+| 4 | `_ ک ی _` | 1,654,644 |
+| 5 | `_ ا و ر` | 1,206,959 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ م ی ں _` | 1,841,071 |
+| 2 | `_ ا و ر _` | 1,180,320 |
+| 3 | `_ ا ی ک _` | 540,019 |
+| 4 | `_ ہ ے ۔ _` | 533,595 |
+| 5 | `ن _ ک ے _` | 281,812 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 407
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% 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 | 0.7878 | 1.726 | 10.02 | 931,321 | 21.2% |
+| **1** | Subword | 1.0778 | 2.111 | 8.71 | 12,123 | 0.0% |
+| **2** | Word | 0.4142 | 1.333 | 2.63 | 9,325,685 | 58.6% |
+| **2** | Subword | 0.7107 | 1.637 | 4.70 | 105,552 | 28.9% |
+| **3** | Word | 0.2036 | 1.152 | 1.51 | 24,486,673 | 79.6% |
+| **3** | Subword | 0.6567 | 1.576 | 3.92 | 496,416 | 34.3% |
+| **4** | Word | 0.0977 🏆 | 1.070 | 1.19 | 36,918,721 | 90.2% |
+| **4** | Subword | 0.6391 | 1.557 | 3.25 | 1,947,168 | 36.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `کے خلاف شمالی افریقی ارکان پارلیمنٹ جان جیکب آباد مقامات ڈیٹا سے قبل مسیح rishi 24`
+2. `میں اس پر پہچان ایک وسیع تحقیق کی جاتی ہے اور دیگر نے بنا مزید دیکھیے`
+3. `کی سپریم کورٹ سی پی سی اے حسینہ اور 626 0 126 نے مسترد کرتے ہوئے`
+
+**Context Size 2:**
+
+1. `کے لیے دو فرسٹ کلاس کرکٹ میں ان کی نمائندگی کرتے ہیں اور طنز کرتے اور حقانیت`
+2. `حوالہ جات بیرونی روابط طرطلیان کا معما بنی ہوئی ایک ترقی یافتہ ڈویژن فور کے لیے حملہ`
+3. `واقع ہے مزید دیکھیے لتھووینیا فہرست لتھووینیا کے نامکمل مضامین ڈیٹا سے مختلف مختصر وضاحت کی پیدائشیں`
+
+**Context Size 3:**
+
+1. `میں واقع ہے تفصیلات ییپچس ضلع کا رقبہ 53 944 مربع کلومیٹر ہے اس کی مجموعی آبادی 6`
+2. `ہے مزید دیکھیے جرمنی کی ریاستیں 16 بھارت کی ریاستیں بلحاظ آبادی حوالہ جات میں قائم ہونے والے`
+3. `ریاستہائے متحدہ امریکا ریاستہائے متحدہ امریکا کا ایک ٹاؤن شپ جو کلنٹن کاؤنٹی اوہائیو اوہائیو 61 310 ...`
+
+**Context Size 4:**
+
+1. `کے شہر حوالہ جات میں آباد ہونے والے مقامات ڈیٹا سے مختلف مختصر وضاحت کے آباد مقامات میں مرگ`
+2. `ڈیٹا سے مختلف مختصر وضاحت مزاحیہ ڈراما فلمیں فلمیں متحدہ میں زنا کے بارے میں فلمیں فلمیں سے تخلیق`
+3. `میں واقع ہے تفصیلات لا شاپیل این والگوڈیمار کا رقبہ 108 02 مربع کلومیٹر ہے اور اس کی مجموعی`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_16_دیکاس_مد_کے_`
+2. `ا_اتھروساہے_لے۔_`
+3. `یں_tad_ا_نیا_205`
+
+**Context Size 2:**
+
+1. `_کی_پان_ال_ہور_بر`
+2. `ے_ان_میں_معا_مغرب`
+3. `ی_ول_گار_پیدارکھی`
+
+**Context Size 3:**
+
+1. `یں_کا_کورپینیجرینڈ`
+2. `کے_بلندیر_بِکری_علی`
+3. `_کے_تھے۔_ابھ_انھوں`
+
+**Context Size 4:**
+
+1. `_کے_نتیجے_میں_واقع_`
+2. `میں_جہاں_i_tehsil_w`
+3. `_میں_وشون-شوگر_پار،`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 90.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,947,168 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 | 395,742 |
+| Total Tokens | 58,544,950 |
+| Mean Frequency | 147.94 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 7177.12 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | کے | 2,399,956 |
+| 2 | میں | 1,897,386 |
+| 3 | کی | 1,727,992 |
+| 4 | اور | 1,185,297 |
+| 5 | ہے | 1,085,895 |
+| 6 | سے | 991,149 |
+| 7 | کا | 802,866 |
+| 8 | نے | 660,570 |
+| 9 | اس | 581,153 |
+| 10 | پر | 570,105 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | yarns | 2 |
+| 2 | anika | 2 |
+| 3 | dailystar | 2 |
+| 4 | دامنیوں | 2 |
+| 5 | دریاچۂ | 2 |
+| 6 | murgap | 2 |
+| 7 | دیمقراطیت | 2 |
+| 8 | الممتنعة | 2 |
+| 9 | کرداراے | 2 |
+| 10 | قیطابای | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1596 |
+| R² (Goodness of Fit) | 0.989996 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 40.8% |
+| Top 1,000 | 67.7% |
+| Top 5,000 | 84.7% |
+| Top 10,000 | 89.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9900 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 40.8% of corpus
+- **Long Tail:** 385,742 words needed for remaining 10.1% 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 | 0.7965 🏆 | 0.3746 | N/A | N/A |
+| **mono_64d** | 64 | 0.7804 | 0.3072 | N/A | N/A |
+| **mono_128d** | 128 | 0.7411 | 0.2584 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7965 | 0.3667 | 0.0900 | 0.3980 |
+| **aligned_64d** | 64 | 0.7804 | 0.3243 | 0.1900 | 0.5220 |
+| **aligned_128d** | 128 | 0.7411 | 0.2599 | 0.2640 | 0.6360 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7965 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3152. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 26.4% 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 | **-0.387** | 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 |
+|--------|----------|
+| `-ال` | الازہار, الحسينی, الكرخی |
+| `-ا` | الازہار, اعادی, اسکن |
+| `-م` | موزولینی, مشہورہ, میلوم |
+| `-ب` | بیٹاہے, بمبی, بدیشگرام |
+| `-س` | سکھانے, سگاچے, سووموٹو |
+| `-ک` | کورینتینے, کتابتِ, کٹمور |
+| `-و` | وملل, وففینگے, وولڈز |
+| `-ت` | تبصروں, تحس, ترسیلات |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ی` | نمامی, اعادی, بمبی |
+| `-ا` | آئیڈیلا, چھیڈا, سنگڑا |
+| `-ن` | اسکن, ڑککن, لعبدالرحمن |
+| `-s` | anthonys, carpets, condoles |
+| `-n` | usenon, areairon, bannerman |
+| `-e` | linkage, lafitte, ampère |
+| `-ں` | پنجابیوں, بیروتژاں, تبصروں |
+| `-ر` | الازہار, کٹمور, خائر |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ھارت` | 2.29x | 63 contexts | ہھارت, پھارت, دھارت |
+| `مریک` | 2.26x | 41 contexts | امریک, مریکی, مریکہ |
+| `اؤنٹ` | 2.16x | 43 contexts | ماؤنٹ, گاؤنٹ, کاؤنٹ |
+| `کاؤن` | 2.21x | 39 contexts | کاؤنا, کاؤنی, کاؤنٹ |
+| `اریخ` | 1.86x | 54 contexts | فاریخ, تاریخ, تاریخٰ |
+| `لاقو` | 2.48x | 18 contexts | علاقو, الاقو, لاقوۃ |
+| `ھلاڑ` | 2.91x | 11 contexts | ڈھلاڑ, کھلاڑ, لھلاڑی |
+| `اقوا` | 2.16x | 27 contexts | اقوام, جاقوا, اقوال |
+| `ختلف` | 2.31x | 20 contexts | اختلف, يختلف, مختلف |
+| `ختصر` | 2.07x | 23 contexts | اختصر, مختصر, مختصرا |
+| `الاق` | 1.77x | 39 contexts | الاقو, الاقصي, الاقصى |
+| `تحدہ` | 2.47x | 11 contexts | متحدہ, 1متحدہ, المتحدہ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ال` | `-ی` | 59 words | النفیسی, الأولی |
+| `-ا` | `-ا` | 45 words | اورلینزلوویزیانا, اماٹیلا |
+| `-ا` | `-ی` | 43 words | انڈیانامیامی, النفیسی |
+| `-س` | `-ی` | 35 words | سریمورالی, سیارچوی |
+| `-ک` | `-ی` | 32 words | کندی, کولاتیری |
+| `-ال` | `-ن` | 31 words | الوالدين, الیکزاندرپشکن |
+| `-ال` | `-ہ` | 28 words | العربیہ, السیارہ |
+| `-م` | `-ی` | 26 words | مہرؤلی, مورنسی |
+| `-ب` | `-ی` | 24 words | بحیری, بریطانی |
+| `-ا` | `-ن` | 23 words | ابوالرین, انبان |
+
+### 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 |
+|------|-----------------|------------|------|
+| ایکسیلنسی | **`ایکسیلن-س-ی`** | 7.5 | `س` |
+| ریٹروگریڈ | **`ریٹروگر-ی-ڈ`** | 7.5 | `ی` |
+| گراؤنڈبیونس | **`گراؤنڈبیو-ن-س`** | 7.5 | `ن` |
+| کوستنجویکانہ | **`کوستنجویک-ان-ہ`** | 7.5 | `ان` |
+| اورتعلیمی | **`اور-تعلیم-ی`** | 6.0 | `تعلیم` |
+| مالمزبیری | **`م-الم-زبیری`** | 6.0 | `زبیری` |
+| composers | **`composer-s`** | 4.5 | `composer` |
+| نصیرآبادی | **`نصیرآباد-ی`** | 4.5 | `نصیرآباد` |
+| تھیوبالڈس | **`تھیوبالڈ-س`** | 4.5 | `تھیوبالڈ` |
+| ہائیڈریٹس | **`ہائیڈریٹ-س`** | 4.5 | `ہائیڈریٹ` |
+| پیرالمپکس | **`پیرالمپک-س`** | 4.5 | `پیرالمپک` |
+| dwellings | **`dwelling-s`** | 4.5 | `dwelling` |
+| violations | **`violation-s`** | 4.5 | `violation` |
+| positional | **`position-al`** | 4.5 | `position` |
+| oscillators | **`oscillator-s`** | 4.5 | `oscillator` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Urdu 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 (4.07x) |
+| N-gram | **2-gram** | Lowest perplexity (407) |
+| Markov | **Context-4** | Highest predictability (90.2%) |
+| 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 06:46:29*

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