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README.md

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+---
+language: pa
+language_name: Punjabi
+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: 4.042
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8342
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Punjabi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Punjabi** 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** | 3.344x | 3.35 | 0.0292% | 637,303 |
+| **16k** | 3.646x | 3.65 | 0.0318% | 584,610 |
+| **32k** | 3.881x | 3.88 | 0.0339% | 549,074 |
+| **64k** | 4.042x 🏆 | 4.04 | 0.0353% | 527,239 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ਕਸੂੰਬੜੀ ਭਾਰਤੀ ਪੰਜਾਬ ਦੇ ਫ਼ਤਹਿਗੜ੍ਹ ਸਾਹਿਬ ਜ਼ਿਲ੍ਹੇ ਦੇ ਖੇੜਾ ਬਲਾਕ ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ। ਹਵਾਲ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ਕਸ ੂੰ ਬ ੜੀ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਫ਼ਤਹਿਗੜ੍ਹ ▁ਸਾਹਿਬ ▁ਜ਼ਿਲ੍ਹੇ ... (+13 more)` | 23 |
+| 16k | `▁ਕਸ ੂੰ ਬੜੀ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਫ਼ਤਹਿਗੜ੍ਹ ▁ਸਾਹਿਬ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ... (+12 more)` | 22 |
+| 32k | `▁ਕਸ ੂੰ ਬੜੀ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਫ਼ਤਹਿਗੜ੍ਹ ▁ਸਾਹਿਬ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ... (+12 more)` | 22 |
+| 64k | `▁ਕਸ ੂੰ ਬੜੀ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਫ਼ਤਹਿਗੜ੍ਹ ▁ਸਾਹਿਬ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ... (+12 more)` | 22 |
+
+**Sample 2:** `ਚੂੰਗ ਭਾਰਤੀ ਪੰਜਾਬ ਦੇ ਤਰਨਤਾਰਨ ਜ਼ਿਲ੍ਹੇ ਦੇ ਬਲਾਕ ਭਿੱਖੀਵਿੰਡ ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ। ਹਵਾਲੇ ਤਾਰਨ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ਚ ੂੰ ਗ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਤਰਨਤਾਰਨ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ... (+15 more)` | 25 |
+| 16k | `▁ਚ ੂੰ ਗ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਤਰਨਤਾਰਨ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ... (+13 more)` | 23 |
+| 32k | `▁ਚ ੂੰਗ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਤਰਨਤਾਰਨ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਭਿੱ ... (+12 more)` | 22 |
+| 64k | `▁ਚੂੰਗ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਤਰਨਤਾਰਨ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਭਿੱਖੀਵਿੰਡ ▁ਦਾ ... (+9 more)` | 19 |
+
+**Sample 3:** `ਭੇਲ ਭਾਰਤੀ ਪੰਜਾਬ ਦੇ ਜਲੰਧਰ ਜ਼ਿ��੍ਹੇ ਦੇ ਬਲਾਕ ਆਦਮਪੁਰ ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ। ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ਭ ੇਲ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਜਲੰਧਰ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਆਦ ... (+10 more)` | 20 |
+| 16k | `▁ਭ ੇਲ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਜਲੰਧਰ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਆਦਮਪੁਰ ... (+9 more)` | 19 |
+| 32k | `▁ਭ ੇਲ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਜਲੰਧਰ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਆਦਮਪੁਰ ... (+9 more)` | 19 |
+| 64k | `▁ਭੇਲ ▁ਭਾਰਤੀ ▁ਪੰਜਾਬ ▁ਦੇ ▁ਜਲੰਧਰ ▁ਜ਼ਿਲ੍ਹੇ ▁ਦੇ ▁ਬਲਾਕ ▁ਆਦਮਪੁਰ ▁ਦਾ ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.042x compression
+- **Lowest UNK Rate:** 8k with 0.0292% 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 | 65,512 | 16.00 | 395,139 | 9.9% | 25.1% |
+| **2-gram** | Subword | 1,824 🏆 | 10.83 | 65,167 | 38.3% | 74.2% |
+| **3-gram** | Word | 226,610 | 17.79 | 723,559 | 4.6% | 13.1% |
+| **3-gram** | Subword | 17,627 | 14.11 | 426,051 | 16.0% | 37.7% |
+| **4-gram** | Word | 595,990 | 19.18 | 1,217,646 | 2.1% | 7.1% |
+| **4-gram** | Subword | 101,454 | 16.63 | 1,977,133 | 8.3% | 22.6% |
+| **5-gram** | Word | 481,395 | 18.88 | 795,359 | 2.0% | 6.8% |
+| **5-gram** | Subword | 336,559 | 18.36 | 3,786,103 | 4.3% | 14.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ਜਾਂਦਾ ਹੈ` | 51,096 |
+| 2 | `ਗਿਆ ਸੀ` | 36,408 |
+| 3 | `ਤੌਰ ਤੇ` | 36,131 |
+| 4 | `ਹੈ ਅਤੇ` | 35,656 |
+| 5 | `ਕੀਤਾ ਗਿਆ` | 30,014 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ਕੀਤਾ ਗਿਆ ਸੀ` | 16,375 |
+| 2 | `ਦੇ ਰੂਪ ਵਿੱਚ` | 11,064 |
+| 3 | `ਕਿਹਾ ਜਾਂਦਾ ਹੈ` | 9,910 |
+| 4 | `ਦੇ ਤੌਰ ਤੇ` | 7,251 |
+| 5 | `ਆਮ ਤੌਰ ਤੇ` | 7,156 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ਸਾਲ ਦੀ ਉਮਰ ਵਿੱਚ` | 4,687 |
+| 2 | `ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ` | 4,498 |
+| 3 | `ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ ਪਿੰਡ` | 3,112 |
+| 4 | `ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ` | 2,917 |
+| 5 | `ਹੈ ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ` | 2,408 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ਹੈ ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ ਪਿੰਡ` | 2,358 |
+| 2 | `ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ ਹਵਾਲੇ` | 2,190 |
+| 3 | `ਪਿੰਡ ਹੈ ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ` | 1,587 |
+| 4 | `ਇੱਕ ਪਿੰਡ ਹੈ ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ` | 1,551 |
+| 5 | `ਜੂਨ ਜੁਲਾਈ ਸਤੰਬਰ ਅਕਤੂਬਰ ਦਸੰਬਰ` | 1,224 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ਰ _` | 970,300 |
+| 2 | `_ ਅ` | 824,969 |
+| 3 | `, _` | 781,870 |
+| 4 | `ਨ _` | 746,764 |
+| 5 | `। _` | 733,291 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ਵਿੱ ਚ` | 572,750 |
+| 2 | `ਵਿੱ ਚ _` | 533,677 |
+| 3 | `_ ਦੇ _` | 530,516 |
+| 4 | `ਅ ਤੇ _` | 432,213 |
+| 5 | `_ ਅ ਤੇ` | 431,849 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ਵਿੱ ਚ _` | 533,074 |
+| 2 | `_ ਅ ਤੇ _` | 431,071 |
+| 3 | `_ ਹੈ । _` | 249,093 |
+| 4 | `_ ਇੱ ਕ _` | 216,221 |
+| 5 | `_ ਲ ਈ _` | 135,834 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ਹ ਨ । _` | 79,400 |
+| 2 | `ਦਾ _ ਹੈ । _` | 69,651 |
+| 3 | `_ ਕ ਰ ਨ _` | 56,253 |
+| 4 | `_ ਉ ਸ ਨੇ _` | 53,553 |
+| 5 | `_ ਹੈ । _ ਇ` | 51,650 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 1,824
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~14% 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 | 0.7819 | 1.719 | 8.40 | 605,913 | 21.8% |
+| **1** | Subword | 0.7222 | 1.650 | 10.75 | 17,141 | 27.8% |
+| **2** | Word | 0.3690 | 1.291 | 2.26 | 5,085,870 | 63.1% |
+| **2** | Subword | 0.7395 | 1.670 | 6.01 | 184,279 | 26.0% |
+| **3** | Word | 0.1540 | 1.113 | 1.34 | 11,467,166 | 84.6% |
+| **3** | Subword | 0.5675 | 1.482 | 3.86 | 1,107,278 | 43.2% |
+| **4** | Word | 0.0639 🏆 | 1.045 | 1.11 | 15,379,661 | 93.6% |
+| **4** | Subword | 0.4313 | 1.348 | 2.39 | 4,276,620 | 56.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ਵਿੱਚ ਫੁੱਟਾਂ ਦੀ ਵਕਾਲਤ ਵੀ ਹਨ ਨਿੱਜੀ ਅਤੇ ਫਿਰ ਹੈਵੀ ਕੇਕ ਵਿੱਚ ਮਾਰੀਆਂ ਜਾਂਦੀਆਂ ਹਨ ਜਨਮ`
+2. `ਦੇ ਨਾਲ ਸਨਮਾਨਿਤ ਕੀਤਾ ਤਾਂ ਦੇਵੀ ਮਹਾਤਮਯਮ ਅਨੁਸਾਰ ਉਸਨੇ ਊਰਜਾ ਕੁਸ਼ਲਤਾ ਨਾਲ ਹਰਾਇਆ 24 ਵਿੱਚ ਪੈਸਾ`
+3. `ਹੈ 3 october egyptclay sai jayalakshmy jayaram montinee tangphong thassha december retrieved 25 ਸ੍ਰੀ...`
+
+**Context Size 2:**
+
+1. `ਜਾਂਦਾ ਹੈ ਮੁਗ਼ਲ ਸਮਰਾਟ ਅਕਬਰ ਦੀ ਮੁੱਖ ਭੂਮਿਕਾ ਵਿੱਚ ਲਿਖਦੇ ਹਨ ਕਿ ਆਜ਼ਾਦੀ ਤੋਂ ਬਾਅਦ ਉਸਨੂੰ ਗਿਆਨ`
+2. `ਗਿਆ ਸੀ ਵਿੱਚ ਇਸ ਸਥਿਤੀ ਨੂੰ ਖਤਮ ਹੋ ਗਿਆ ਇਸ ਗੱਲ ਦੀ ਪੁਸ਼ਟੀ ਕੀਤੀ ਕਿ ਸਾਰਾ ਨੇ`
+3. `ਤੌਰ ਤੇ ਰਾਜ ਬਿਹਾਰ ਵਿੱਚ ਚੋਖੇ ਸੁਧਾਰ ਦੇ ਸਮੇਂ ਤੋਂ ਇੱਥੇ ਆ ਕੇ ਜਾਂ ਮਿਰਚਾਂ ਸ਼ਾਮਲ ਕਰਦਾ`
+
+**Context Size 3:**
+
+1. `ਕੀਤਾ ਗਿਆ ਸੀ ਮਹਾਰਾਸ਼ਟਰ ਸਰਕਾਰ ਨੇ ਸਮਾਜਿਕ ਵਿਗਿਆਨ ਵਿੱਚ ਦੇਸ਼ ਦਾ ਸਭ ਤੋਂ ਮਕਬੂਲ ਕਹਾਣੀ big two hearted`
+2. `ਦੇ ਰੂਪ ਵਿੱਚ ਸ਼ਾਮਲ ਕੀਤਾ ਗਿਆ ਹੈ ਜਿਸ ਵਿੱਚ ਫਾਰਸੀ ਅਤੇ ਯੂਨਾਨੀਆਂ ਨੇ ਮਸ਼ਹੂਰ ਕਾਨਾ ਜੋ ਮਧੂਮੱਖੀਆਂ ਤੋਂ`
+3. `ਕਿਹਾ ਜਾਂਦਾ ਹੈ ਪਰੰਪਰਾ ਦੇ ਸੰਬੰਧ ਵਿੱਚ ਕੈਂਬਰਿਜ ਯੂਨੀਵਰਸਿਟੀ ਦੇ ਹਿਸਾਬਦਾਨ ਐੱਚ ਐੱਫ਼ ਬੇਕਰ ਅਤੇ ਈ ਡਬਲਿਊ ਹੌਬਸਨ`
+
+**Context Size 4:**
+
+1. `ਸਾਲ ਦੀ ਉਮਰ ਵਿੱਚ ਉਸਦੀ ਮੌਤ ਹੋ ਗਈ ਉਸਨੇ ਆਪਣਾ ਜੀਵਨ ਆਪਣੇ ਪਤੀ ਅਤੇ ਇੱਕ ਬੇਟੇ ਨਾਲ ਕੈਨੇਡਾ ਦੇ`
+2. `ਦਾ ਇੱਕ ਪਿੰਡ ਹੈ ਹਵਾਲੇ ਜ਼ਿਲ੍ਹੇ ਦੇ ਪਿੰਡ ਬਲਾਕ ਦੇ ਪਿੰਡ`
+3. `ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ ਦੁਆਰਾ ਵਰਤੀ ਜਾਂਦੀ ਇੱਕ ਵੱਖਰੀ ਕਿਸਮ ਦੀ ਕਿਤਾਬ ਹੈ ਜਿਸ ਗੀਹੋਨ ਦਰਿਆ ਮੰਨਿਆ ਗਿਆ ਹੈ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_rstederishjh_ਗ੍ਰਾ`
+2. `ਰ_ਮੁੱਖ_ਵਿਚ_they:_ਮਾਂ_`
+3. `ਸਨੇ_ਪੈਦਾ_ਅਭਿਨੇ_ਸੀ।_ਹੈ।_`
+
+**Context Size 2:**
+
+1. `ਰ_ਬਣੀ_ਸੰਗ੍ਰਹਿਣ_ਵਾਈਆਂ_ਬਾਹ`
+2. `_ਅਤੇ_ਜ਼ੈਨ_ਯੂਨੀਵਰਮ_ਜਿਸਨੂੰ_`
+3. `,_ਖੇਡਾਂ_ਵਿੱਚ_ਉਹ_ਕੁਮਾ_ਪੜ੍ਹਾ`
+
+**Context Size 3:**
+
+1. `_ਵਿੱਚ_ਪੋਲੀਆਂ_ਹਨ।_ਉਹ_ਆਪਣੇ`
+2. `ਵਿੱਚ_ਹੋਇਆ_ਅਤੇ_ਰਸਮੀ)_ਜਾਂ_ਬ`
+3. `_ਦੇ_ਨਾਲ_ਸੰਬੰਧ_ਰੱਖਦੇ_ਹਨ।_`
+
+**Context Size 4:**
+
+1. `_ਵਿੱਚ_ਇੱਕ_ਸਰੋਤ_ਜਿਸ_ਵਿੱਚ_ਲਿਆ`
+2. `_ਅਤੇ_ਕਰਨੈਲ_ਨਪੋਲੀਅਨ(20_ਫੁੱ`
+3. `_ਹੈ।_ਮੁਫਤ_ਸਿਰਾਜ-ਉਦ-ਦੌਲਾ_ਦੀ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (4,276,620 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 | 242,047 |
+| Total Tokens | 18,725,732 |
+| Mean Frequency | 77.36 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 2689.48 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ਵਿੱਚ | 572,433 |
+| 2 | ਦੇ | 531,722 |
+| 3 | ਹੈ | 471,753 |
+| 4 | ਅਤੇ | 432,771 |
+| 5 | ਦੀ | 370,327 |
+| 6 | ਨੂੰ | 275,364 |
+| 7 | ਦਾ | 267,922 |
+| 8 | ਸੀ | 222,609 |
+| 9 | ਇੱਕ | 219,966 |
+| 10 | ਤੋਂ | 188,860 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ਸੁੰਦਰਨਰ | 2 |
+| 2 | ਚੱਕਰਾਈ | 2 |
+| 3 | divyakirti | 2 |
+| 4 | csie | 2 |
+| 5 | ਵਿਟਾਲੀ | 2 |
+| 6 | ਸ਼ਮਤੀਕੋਵ | 2 |
+| 7 | bvsc | 2 |
+| 8 | mvph | 2 |
+| 9 | ਉੱਲੀਮਾਰਾਂ | 2 |
+| 10 | sarkaryawah | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1016 |
+| R² (Goodness of Fit) | 0.993300 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 40.3% |
+| Top 1,000 | 64.7% |
+| Top 5,000 | 81.6% |
+| Top 10,000 | 87.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9933 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 40.3% of corpus
+- **Long Tail:** 232,047 words needed for remaining 12.7% 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 | 0.8342 🏆 | 0.3762 | N/A | N/A |
+| **mono_64d** | 64 | 0.8303 | 0.3067 | N/A | N/A |
+| **mono_128d** | 128 | 0.8116 | 0.2410 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8342 | 0.3832 | 0.0760 | 0.3300 |
+| **aligned_64d** | 64 | 0.8303 | 0.3087 | 0.1300 | 0.4120 |
+| **aligned_128d** | 128 | 0.8116 | 0.2355 | 0.1700 | 0.4920 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8342 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3085. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 17.0% 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 | **-0.529** | 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 |
+|--------|----------|
+| `-ਸ` | ਸੱਬਲਕਸ਼ਮੀ, ਸਪਿਨੋਜ਼ਾ, ਸਵਰੋ |
+| `-ਕ` | ਕਿਸਮੇਟ, ਕੋਲਵਿਨ, ਕੀਮਾਰ |
+| `-ਮ` | ਮੰਡੀ, ਮਾਰਟਨੀ, ਮੋਹਨਕਾਧਲ |
+| `-ਬ` | ਬਿਭੂਤੀਭੂਸ਼ਣ, ਬੈਰੂਨੀ, ਬਚਾਏ |
+| `-ਪ` | ਪਲੱਕਡ਼, ਪੀਡਬਲਯੂਏ, ਪੱਟਮੱਲ |
+| `-ਅ` | ਅਤਨੂ, ਅਸਾਂਜ, ਅਵਾਰਡxbiz |
+| `-ਰ` | ਰਾਏਚੂਰ, ਰੇਸ਼ੇਬਾਜ਼, ਰਵਾਇਤੀ |
+| `-ਵ` | ਵਲੱਲੀ, ਵਿਸਾਯਨ, ਵਿਦਆਉਟ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ਨ` | ਦਵੈਪਾਇਨ, ਜੁਬੀਨ, ਫਾਰੇਨ |
+| `-ਰ` | ਗੁਰਬੀਰ, ਯੋਗਤਾਸੁਪਰ, ਆਲਿਵਰ |
+| `-ਸ` | ਓਵਰਟੋਨਸ, ਟੈਨਿਨਸ, ਜੋਨਜਸ |
+| `-s` | missions, legs, democracies |
+| `-ਲ` | ਮੋਹਨਕਾਧਲ, ਸਕੂਲ, ਪੱਟਮੱਲ |
+| `-ਕ` | ਨਾਸਤਾਲਿਕ, ਓਟਕ, ਅਕ |
+| `-ਮ` | ਦੇਮ, ਨਿਮਾਜਨਮ, ਭਾਗਮ |
+| `-n` | anchan, broughton, ceylon |
+
+### 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 |
+|------|----------|------------------|----------|
+| `indi` | 3.26x | 45 contexts | indic, hindi, indie |
+| `ress` | 3.17x | 50 contexts | cress, press, dress |
+| `atio` | 3.32x | 38 contexts | ratio, lation, nation |
+| `vers` | 3.08x | 47 contexts | versa, verso, verse |
+| `nter` | 3.08x | 45 contexts | enter, inter, unter |
+| `tion` | 3.01x | 48 contexts | lation, option, nation |
+| `ment` | 3.15x | 37 contexts | mente, mentem, cement |
+| `stor` | 3.11x | 35 contexts | astor, jstor, stork |
+| `ture` | 3.05x | 34 contexts | mature, nature, future |
+| `iver` | 3.13x | 29 contexts | diver, river, giver |
+| `ctio` | 3.05x | 25 contexts | action, auction, section |
+| `mber` | 3.12x | 22 contexts | ember, amber, number |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ਕ` | `-ਨ` | 41 words | ਕੈਲੇਡੋਨੀਅਨ, ਕਰਤੱਵਪੂਰਨ |
+| `-ਸ` | `-ਨ` | 37 words | ਸੰਰਚਨ, ਸਿਵਨ |
+| `-ਸ` | `-ਰ` | 31 words | ਸੇਲਾਂਗੋਰ, ਸਾਰਤ੍ਰ |
+| `-ਸ` | `-ਕ` | 26 words | ਸਮਾਨਆਰਥਕ, ਸਕੈਪਟਿਕ |
+| `-ਕ` | `-ਰ` | 26 words | ਕੈਨਰ, ਕੈਬਰ |
+| `-ਮ` | `-ਨ` | 19 words | ਮੇਰੀਨ, ਮੁਕੁੰਦਨ |
+| `-ਮ` | `-ਰ` | 17 words | ਮੰਜਰੇਕਰ, ਮਿਊਰ |
+| `-ਬ` | `-ਨ` | 17 words | ਬੈਗੁਈਸੇਨ, ਬ੍ਰੇਮੇਨ |
+| `-ਪ` | `-ਨ` | 16 words | ਪੋਥਨ, ਪ੍ਰਸਾਸਨ |
+| `-ਰ` | `-ਨ` | 16 words | ਰੂਪਵਾਨ, ਰਘੂਨਾਥਨ |
+
+### 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 |
+|------|-----------------|------------|------|
+| intentions | **`intention-s`** | 4.5 | `intention` |
+| ਅਤਾਉੱਲ੍ਹਾ | **`ਅ-ਤ-ਾਉੱਲ੍ਹਾ`** | 4.5 | `ਾਉੱਲ੍ਹਾ` |
+| orientale | **`oriental-e`** | 4.5 | `oriental` |
+| presented | **`present-ed`** | 4.5 | `present` |
+| ecosystems | **`ecosystem-s`** | 4.5 | `ecosystem` |
+| ਵਿਸ਼ਵੰਭਰਨ | **`ਵਿਸ਼ਵੰਭਰ-ਨ`** | 4.5 | `ਵਿਸ਼ਵੰਭਰ` |
+| commissioner | **`commission-er`** | 4.5 | `commission` |
+| potentials | **`potential-s`** | 4.5 | `potential` |
+| ਅਜ਼ਹੇਂਦਰਾ | **`ਅ-ਜ-਼ਹੇਂਦਰਾ`** | 4.5 | `਼ਹੇਂਦਰਾ` |
+| manhattans | **`manhattan-s`** | 4.5 | `manhattan` |
+| neighbors | **`neighbor-s`** | 4.5 | `neighbor` |
+| ਹਾਰਪਰਕੋਲਿਨਸ | **`ਹਾਰਪਰਕੋਲਿਨ-ਸ`** | 4.5 | `ਹਾਰਪਰਕੋਲਿਨ` |
+| audiobooks | **`audiobook-s`** | 4.5 | `audiobook` |
+| capitalists | **`capitalist-s`** | 4.5 | `capitalist` |
+| ਇਲੈਕਟ੍ਰਾਨ | **`ਇਲੈਕਟ੍ਰਾ-ਨ`** | 4.5 | `ਇਲੈਕਟ੍ਰਾ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Punjabi 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 (4.04x) |
+| N-gram | **2-gram** | Lowest perplexity (1,824) |
+| Markov | **Context-4** | Highest predictability (93.6%) |
+| 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 19:32:35*

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