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README.md

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+---
+language: pnb
+language_name: Western Panjabi
+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: 3.987
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8211
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Western Panjabi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Western Panjabi** 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** | 3.374x | 3.35 | 0.0495% | 1,253,323 |
+| **16k** | 3.663x | 3.64 | 0.0537% | 1,154,342 |
+| **32k** | 3.861x | 3.84 | 0.0566% | 1,095,265 |
+| **64k** | 3.987x 🏆 | 3.96 | 0.0585% | 1,060,503 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `<font size="+1" بلی size="1" : : : ناں : Pseudotriakis microdon size="1" تے پھرن...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁< font ▁size ="+ 1 " ▁بلی ▁size =" 1 ... (+26 more)` | 36 |
+| 16k | `▁< font ▁size ="+ 1 " ▁بلی ▁size =" 1 ... (+25 more)` | 35 |
+| 32k | `▁< font ▁size ="+ 1 " ▁بلی ▁size =" 1 ... (+22 more)` | 32 |
+| 64k | `▁< font ▁size ="+ 1 " ▁بلی ▁size =" 1 ... (+22 more)` | 32 |
+
+**Sample 2:** `واقعے جم موت ہور دیکھو ہجری شمسی عیسوی کیلنڈر ہجری کیلنڈر حوالے باہرلےجوڑ ہجری ت...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁واقعے ▁جم ▁موت ▁ہور ▁دیکھو ▁ہجری ▁شمسی ▁عیسوی ▁کیلنڈر ▁ہجری ... (+20 more)` | 30 |
+| 16k | `▁واقعے ▁جم ▁موت ▁ہور ▁دیکھو ▁ہجری ▁شمسی ▁عیسوی ▁کیلنڈر ▁ہجری ... (+20 more)` | 30 |
+| 32k | `▁واقعے ▁جم ▁موت ▁ہور ▁دیکھو ▁ہجری ▁شمسی ▁عیسوی ▁کیلنڈر ▁ہجری ... (+20 more)` | 30 |
+| 64k | `▁واقعے ▁جم ▁موت ▁ہور ▁دیکھو ▁ہجری ▁شمسی ▁عیسوی ▁کیلنڈر ▁ہجری ... (+20 more)` | 30 |
+
+**Sample 3:** `thumbnail یورپا مشتری پاندھی دا 6واں چند اے۔ ایہنوں 8 جنوری، وچ گلیلیو نے لبیا س...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁thumbnail ▁یورپ ا ▁مشت ری ▁پاندھی ▁دا ▁ 6 واں ... (+26 more)` | 36 |
+| 16k | `▁thumbnail ▁یورپ ا ▁مشتری ▁پاندھی ▁دا ▁ 6 واں ▁چند ... (+23 more)` | 33 |
+| 32k | `▁thumbnail ▁یورپ ا ▁مشتری ▁پاندھی ▁دا ▁ 6 واں ▁چند ... (+22 more)` | 32 |
+| 64k | `▁thumbnail ▁یورپ ا ▁مشتری ▁پاندھی ▁دا ▁ 6 واں ▁چند ... (+21 more)` | 31 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.987x compression
+- **Lowest UNK Rate:** 8k with 0.0495% 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 | 75,798 | 16.21 | 740,485 | 13.0% | 25.5% |
+| **2-gram** | Subword | 455 🏆 | 8.83 | 31,574 | 58.1% | 95.3% |
+| **3-gram** | Word | 362,363 | 18.47 | 1,592,960 | 4.5% | 12.9% |
+| **3-gram** | Subword | 4,157 | 12.02 | 200,891 | 24.0% | 60.3% |
+| **4-gram** | Word | 1,268,078 | 20.27 | 3,340,374 | 2.5% | 7.4% |
+| **4-gram** | Subword | 25,110 | 14.62 | 1,043,941 | 12.3% | 32.8% |
+| **5-gram** | Word | 1,264,061 | 20.27 | 2,644,505 | 2.5% | 7.3% |
+| **5-gram** | Subword | 106,026 | 16.69 | 3,000,396 | 7.2% | 21.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `د ی` | 636,166 |
+| 2 | `تو ں` | 423,885 |
+| 3 | `نو ں` | 352,128 |
+| 4 | `ا ے` | 155,281 |
+| 5 | `دے لئی` | 102,000 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `اس د ی` | 29,046 |
+| 2 | `انہاں د ی` | 27,692 |
+| 3 | `انہاں نو ں` | 24,454 |
+| 4 | `font size 1` | 24,402 |
+| 5 | `د ی طرف` | 22,242 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ی وجہ تو ں` | 15,972 |
+| 2 | `د ی وجہ تو` | 15,769 |
+| 3 | `font size 1 size` | 9,010 |
+| 4 | `size 1 color black` | 8,781 |
+| 5 | `دے ناں تو ں` | 8,743 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `د ی وجہ تو ں` | 15,758 |
+| 2 | `font size 1 size 1` | 8,428 |
+| 3 | `د ی طرف تو ں` | 7,772 |
+| 4 | `size 1 size 1 color` | 6,657 |
+| 5 | `1 size 1 color black` | 5,232 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ے _` | 5,428,043 |
+| 2 | `ی _` | 4,517,358 |
+| 3 | `_ ا` | 4,456,935 |
+| 4 | `_ د` | 3,754,809 |
+| 5 | `ں _` | 3,049,226 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `د ے _` | 1,633,658 |
+| 2 | `ا ں _` | 1,428,031 |
+| 3 | `_ د ے` | 1,418,307 |
+| 4 | `ت ے _` | 1,198,221 |
+| 5 | `_ و چ` | 983,245 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ د ے _` | 1,415,850 |
+| 2 | `_ و چ _` | 931,900 |
+| 3 | `_ ت ے _` | 767,638 |
+| 4 | `‏ ‏ ی _` | 616,950 |
+| 5 | `د ‏ ‏ ی` | 612,667 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ د ‏ ‏ ی` | 612,245 |
+| 2 | `د ‏ ‏ ی _` | 604,110 |
+| 3 | `_ ت و ‏ ں` | 423,919 |
+| 4 | `ت و ‏ ں _` | 421,873 |
+| 5 | `و ‏ ‏ ں _` | 329,449 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 455
+- **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 | 0.7943 | 1.734 | 9.73 | 826,993 | 20.6% |
+| **1** | Subword | 0.7157 | 1.642 | 6.80 | 15,827 | 28.4% |
+| **2** | Word | 0.3933 | 1.313 | 2.41 | 8,042,106 | 60.7% |
+| **2** | Subword | 0.6609 | 1.581 | 4.50 | 107,498 | 33.9% |
+| **3** | Word | 0.1776 | 1.131 | 1.44 | 19,356,492 | 82.2% |
+| **3** | Subword | 0.6554 | 1.575 | 3.88 | 483,762 | 34.5% |
+| **4** | Word | 0.0843 🏆 | 1.060 | 1.16 | 27,821,467 | 91.6% |
+| **4** | Subword | 0.6318 | 1.550 | 3.14 | 1,876,646 | 36.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `دے مطابق لکڑی چھوٹی انتظامی ونڈ سمیں روچک کھیڈ کلب وغیرہ مسجد تعمیر کیتے اکبر خان`
+2. `ں ختم ہوئے گئے جتھے تک نوبت وی کِسے نظام الدین جنرل دے لئی جو تپسیا`
+3. `وچ ریک تو ں اپنے تعلق اُتے محمول منقول مشاہدہ کرنا چاہندا سی جو عورتاں د`
+
+**Context Size 2:**
+
+1. `د ی قدیم تریخ دی تریخ ہندستان دی ونڈ پیکنگ اوپیرا چین دا سارا دار و مدار`
+2. `��و ں انہاں دا ناں خرزادہ سی اصل وچ کمانڈر کمیسار تے ممبر ملکاں د ی فیکٹری`
+3. `نو ں جدید بناؤن لئی ورتے جاسکدے نیں کیلیفورنیا وچ اک آفریدی پشتون معاشرے دے رہنماواں تو`
+
+**Context Size 3:**
+
+1. `اس د ی معرفت کہیا گیا سی کہ علم مثلثات کوریاضی دے اک علیحدہ موضوع دے طورپرمتعارف کروائے`
+2. `انہاں د ی نظر تو ں مضمون دے مسودہ نگاراں وچو ں اک برج د ی شناخت دا`
+3. `انہاں نو ں عذاب خدا دے ذریعہ سزا یافتہ افراد وچو ں چار ملکہ بطور ملکہ نيں پہلی`
+
+**Context Size 4:**
+
+1. `ی وجہ تو ں غیر واضح نيں حالاں کہ تبت دے ناں نو ں شری حمیرہ لکھیا گیا سی`
+2. `د ی وجہ تو ں قیدیاں نو ں قتل کر دتا فرانسیسی گورنر ڈوپلے نے مظفر جنگ کيت ی`
+3. `font size 1 size 1 color black lonoke county arkansas font 250px دیس صوبہ ساؤتھ ڈیکوٹا راجکعر کلیر ل...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_راٹی،_اٹہہد_حمی`
+2. `اوچ_وہنے_منی_و‏ں`
+3. `یالأنیدے_آشدھ_مب`
+
+**Context Size 2:**
+
+1. `ے_علیسانہار_کيتاں`
+2. `ی_اک_صد_نے_آخری_ش`
+3. `_اپنے_بھ_کيتائنگ_`
+
+**Context Size 3:**
+
+1. `دے_لفظاں_وچ_انہ_مظ`
+2. `اں_دار_تے_اس_گئی۔_`
+3. `_دے_نيں_کہ_ہوری_بی`
+
+**Context Size 4:**
+
+1. `_دے_خیبر_پانی_ھود_ک`
+2. `_وچ_سرکارڈ_،_क्रिस_कुलथा`
+3. `_تے_انہاں_دے_ہور_اے`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 91.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,876,646 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 | 354,441 |
+| Total Tokens | 38,365,731 |
+| Mean Frequency | 108.24 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4606.26 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | دے | 1,417,871 |
+| 2 | ں | 946,354 |
+| 3 | وچ | 938,439 |
+| 4 | تے | 775,429 |
+| 5 | ی | 685,094 |
+| 6 | د | 647,998 |
+| 7 | دا | 502,834 |
+| 8 | نے | 448,856 |
+| 9 | اے | 445,649 |
+| 10 | تو | 435,054 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | گوکلے | 2 |
+| 2 | gokula | 2 |
+| 3 | سہورا | 2 |
+| 4 | سنسنوار | 2 |
+| 5 | کٹھمبر | 2 |
+| 6 | آغر | 2 |
+| 7 | انیردھ | 2 |
+| 8 | imadus | 2 |
+| 9 | چورامان | 2 |
+| 10 | بُندیل | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1062 |
+| R² (Goodness of Fit) | 0.989961 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 39.9% |
+| Top 1,000 | 64.4% |
+| Top 5,000 | 82.1% |
+| Top 10,000 | 87.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9900 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 39.9% of corpus
+- **Long Tail:** 344,441 words needed for remaining 12.1% 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 | 0.8211 🏆 | 0.4072 | N/A | N/A |
+| **mono_64d** | 64 | 0.8095 | 0.3302 | N/A | N/A |
+| **mono_128d** | 128 | 0.7605 | 0.2826 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8211 | 0.3992 | 0.0680 | 0.2880 |
+| **aligned_64d** | 64 | 0.8095 | 0.3176 | 0.1360 | 0.4980 |
+| **aligned_128d** | 128 | 0.7605 | 0.2618 | 0.2180 | 0.6080 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8211 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3331. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 21.8% 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 | **-0.655** | 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` | الحرکۃ, الرُّکنِ, الجارود |
+| `-ا` | اثرہويا, اورسرکشی, انورؔ |
+| `-م` | مستنگ, مولاناعبدالرؤف, مرحمت |
+| `-ب` | بیشکتاش, بیوکس, بانسری |
+| `-ک` | کومچ, کاراگنڈا, کیبی |
+| `-س` | سفین, سپردگی, سامع |
+| `-و` | والصلۃ, ویلفئیر, وطواط |
+| `-پ` | پرفائزتھے, پیچیدگى, پستاں |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ی` | گھمری, کیبی, کوتای |
+| `-ں` | دواخاناں, پستاں, تکبراں |
+| `-ا` | کاراگنڈا, شانامتا, اثرہويا |
+| `-ن` | ڈینوبیئن, سفین, ٹراجن |
+| `-اں` | دواخاناں, پستاں, تکبراں |
+| `-s` | uvs, hylocereus, sectors |
+| `-ر` | جَور, نذير, فچنر |
+| `-ہ` | آئنہ, تےحملہ, ریاضشہزادہ |
+
+### 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` | 3.07x | 58 contexts | tiong, action, kition |
+| `ادشا` | 2.58x | 40 contexts | پادشا, ادشاہ, بادشا |
+| `بادش` | 2.73x | 27 contexts | بادشا, بادشان, بادشاہ |
+| `ھارت` | 2.32x | 48 contexts | طھارت, دھارت, مھارت |
+| `یتاں` | 1.94x | 74 contexts | حیتاں, گیتاں, جیتاں |
+| `مریک` | 2.32x | 35 contexts | امریک, مریکل, مریکہ |
+| `لاقے` | 3.13x | 12 contexts | غلاقے, علاقے, علاقےِ |
+| `ردار` | 1.66x | 119 contexts | كردار, قردار, کردار |
+| `کومت` | 2.34x | 28 contexts | حکومت, کومتے, ہکومت |
+| `حکوم` | 2.07x | 43 contexts | حکومت, حکومٹ, حکومۃ |
+| `سلطن` | 2.35x | 26 contexts | سلطنت, سلطنة, سلطنتِ |
+| `ستعم` | 2.21x | 26 contexts | مستعمل, استعمی, ستعمال |
+
+### 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` | `-ی` | 59 words | ابچلی, البیرنی |
+| `-ال` | `-ی` | 41 words | البیرنی, السلیمی |
+| `-ا` | `-ں` | 40 words | ایواناں, اخواندیاں |
+| `-ا` | `-ا` | 37 words | اڈاندا, اینٹونیا |
+| `-ا` | `-اں` | 35 words | ایواناں, اخواندیاں |
+| `-م` | `-ی` | 33 words | مائکرونیشی, مرزاجانی |
+| `-ک` | `-ی` | 32 words | کابلی, کوریری |
+| `-س` | `-ی` | 32 words | سرکھائی, سنگتراشی |
+| `-ک` | `-ا` | 28 words | کانازاوا, کيتاگیاتھا |
+| `-ا` | `-ن` | 27 words | اوزگین, اکورگان |
+
+### 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 |
+|------|-----------------|------------|------|
+| انقلابیان | **`انقلاب-ی-ان`** | 7.5 | `ی` |
+| حبیریمنیا | **`حبیریم-ن-یا`** | 7.5 | `ن` |
+| منموہنیاں | **`منموہن-ی-اں`** | 7.5 | `ی` |
+| بناچاہندے | **`ب-نا-چاہندے`** | 7.5 | `چاہندے` |
+| والزیارات | **`و-ال-زیارات`** | 6.0 | `زیارات` |
+| یونانیدیس | **`یونانی-دی-س`** | 6.0 | `یونانی` |
+| protestants | **`protestant-s`** | 4.5 | `protestant` |
+| destinations | **`destination-s`** | 4.5 | `destination` |
+| والانحطاط | **`و-الانحطاط`** | 4.5 | `الانحطاط` |
+| reprinted | **`reprint-ed`** | 4.5 | `reprint` |
+| ناخوشگوار | **`نا-خوشگوار`** | 4.5 | `خوشگوار` |
+| بازنطینیاں | **`بازنطینی-اں`** | 4.5 | `بازنطینی` |
+| اسماعیلاں | **`اسماعیل-اں`** | 4.5 | `اسماعیل` |
+| respected | **`respect-ed`** | 4.5 | `respect` |
+| اندازاًجنوب | **`ان-د-ازاًجنوب`** | 4.5 | `ازاًجنوب` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Western Panjabi 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 (3.99x) |
+| N-gram | **2-gram** | Lowest perplexity (455) |
+| Markov | **Context-4** | Highest predictability (91.6%) |
+| 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 21:07:05*

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