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README.md

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+---
+language: gu
+language_name: Gujarati
+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: 4.347
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7575
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Gujarati - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Gujarati** 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** | 3.375x | 3.38 | 0.3164% | 854,027 |
+| **16k** | 3.770x | 3.77 | 0.3535% | 764,449 |
+| **32k** | 4.098x | 4.10 | 0.3842% | 703,344 |
+| **64k** | 4.347x 🏆 | 4.35 | 0.4076% | 662,946 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `સિકર ભારત દેશના પશ્ચિમ ભાગમાં આવેલા રાજસ્થાન રાજ્યનું એક નગર છે. સિકરમાં સિકર જિ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁સિક ર ▁ભારત ▁દેશના ▁પશ્ચિમ ▁ભાગમાં ▁આવેલા ▁રાજસ્થાન ▁રાજ્યનું ▁એક ... (+11 more)` | 21 |
+| 16k | `▁સિક ર ▁ભારત ▁દેશના ▁પશ્ચિમ ▁ભાગમાં ▁આવેલા ▁રાજસ્થાન ▁રાજ્યનું ▁એક ... (+11 more)` | 21 |
+| 32k | `▁સિકર ▁ભારત ▁દેશના ▁પશ્ચિમ ▁ભાગમાં ▁આવેલા ▁રાજસ્થાન ▁રાજ્યનું ▁એક ▁નગર ... (+9 more)` | 19 |
+| 64k | `▁સિકર ▁ભારત ▁દેશના ▁પશ્ચિમ ▁ભાગમાં ▁આવેલા ▁રાજસ્થાન ▁રાજ્યનું ▁એક ▁નગર ... (+9 more)` | 19 |
+
+**Sample 2:** `હસમુખ પટેલ ગુજરાતના અમર‌ઇવાડી લોકસભા મત વિસ્તારમાંથી લોકસભા ચૂંટણીમાં ભારતીય જનત...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁હ સ મુખ ▁પટેલ ▁ગુજરાતના ▁અમર ▁ઇ વાડી ▁લોકસભા ▁મત ... (+14 more)` | 24 |
+| 16k | `▁હસમુખ ▁પટેલ ▁ગુજરાતના ▁અમર ▁ઇ વાડી ▁લોકસભા ▁મત ▁વિસ્તારમાંથી ▁લોકસભા ... (+11 more)` | 21 |
+| 32k | `▁હસમુખ ▁પટેલ ▁ગુજરાતના ▁અમર ▁ઇ વાડી ▁લોકસભા ▁મત ▁વિસ્તારમાંથી ▁લોકસભા ... (+11 more)` | 21 |
+| 64k | `▁હસમુખ ▁પટેલ ▁ગુજરાતના ▁અમર ▁ઇ વાડી ▁લોકસભા ▁મત ▁વિસ્તારમાંથી ▁લોકસભા ... (+11 more)` | 21 |
+
+**Sample 3:** `જય હિન્દ ગુજરાતી ભાષાનું એક રોજીંદુ સમાચારપત્ર છે. બાહ્ય કડીઓ જય હિન્દ વેબસાઇટ સ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁જય ▁હિન્ દ ▁ગુજરાતી ▁ભાષાનું ▁એક ▁રો જી ંદુ ▁સમાચાર ... (+11 more)` | 21 |
+| 16k | `▁જય ▁હિન્ દ ▁ગુજરાતી ▁ભાષાનું ▁એક ▁રોજી ંદુ ▁સમાચારપત્ર ▁છે ... (+8 more)` | 18 |
+| 32k | `▁જય ▁હિન્દ ▁ગુજરાતી ▁ભાષાનું ▁એક ▁રોજી ંદુ ▁સમાચારપત્ર ▁છે . ... (+6 more)` | 16 |
+| 64k | `▁જય ▁હિન્દ ▁ગુજરાતી ▁ભાષાનું ▁એક ▁રોજી ંદુ ▁સમાચારપત્ર ▁છે . ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.347x compression
+- **Lowest UNK Rate:** 8k with 0.3164% 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 | 10,698 | 13.39 | 125,204 | 30.7% | 48.0% |
+| **2-gram** | Subword | 2,297 🏆 | 11.17 | 62,671 | 37.2% | 69.7% |
+| **3-gram** | Word | 6,094 | 12.57 | 126,108 | 38.3% | 57.0% |
+| **3-gram** | Subword | 19,412 | 14.24 | 340,527 | 15.1% | 39.0% |
+| **4-gram** | Word | 4,835 | 12.24 | 174,488 | 42.1% | 62.1% |
+| **4-gram** | Subword | 84,467 | 16.37 | 1,351,931 | 11.0% | 28.7% |
+| **5-gram** | Word | 2,358 | 11.20 | 99,029 | 46.1% | 70.1% |
+| **5-gram** | Subword | 171,415 | 17.39 | 2,092,817 | 9.1% | 24.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `છે આ` | 59,392 |
+| 2 | `તેમ જ` | 50,730 |
+| 3 | `આવે છે` | 34,138 |
+| 4 | `આ ગામમાં` | 33,314 |
+| 5 | `ભાગમાં આવેલા` | 31,103 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `છે આ ગામમાં` | 33,162 |
+| 2 | `કરવામાં આવે છે` | 19,146 |
+| 3 | `પશ્ચિમ ભાગમાં આવેલા` | 18,413 |
+| 4 | `ભારત દેશના પશ્ચિમ` | 18,409 |
+| 5 | `દેશના પશ્ચિમ ભાગમાં` | 18,400 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ભારત દેશના પશ્ચિમ ભાગમાં` | 18,398 |
+| 2 | `દેશના પશ્ચિમ ભાગમાં આવેલા` | 18,367 |
+| 3 | `પશ્ચિમ ભાગમાં આવેલા ગુજરાત` | 17,959 |
+| 4 | `ભાગમાં આવેલા ગુજરાત રાજ્યના` | 17,656 |
+| 5 | `લોકોનો મુખ્ય વ્યવસાય ખેતી` | 16,402 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ભારત દેશના પશ્ચિમ ભાગમાં આવેલા` | 18,365 |
+| 2 | `દેશના પશ્ચિમ ભાગમાં આવેલા ગુજરાત` | 17,934 |
+| 3 | `પશ્ચિમ ભાગમાં આવેલા ગુજરાત રાજ્યના` | 17,654 |
+| 4 | `ગામના લોકોનો મુખ્ય વ્યવસાય ખેતી` | 16,093 |
+| 5 | `લોકોનો મુખ્ય વ્યવસાય ખેતી ખેતમજૂરી` | 16,009 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. _` | 427,913 |
+| 2 | `, _` | 392,494 |
+| 3 | `_ આ` | 387,226 |
+| 4 | `માં _` | 379,145 |
+| 5 | `_ અ` | 336,386 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ છે .` | 235,479 |
+| 2 | `છે . _` | 225,048 |
+| 3 | `_ અ ને` | 167,804 |
+| 4 | `અ ને _` | 165,365 |
+| 5 | `માં _ આ` | 149,526 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ છે . _` | 224,981 |
+| 2 | `_ અ ને _` | 164,399 |
+| 3 | `માં _ આ વે` | 110,493 |
+| 4 | `. _ આ _` | 68,884 |
+| 5 | `વા માં _ આ` | 67,045 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ છે . _ આ` | 62,651 |
+| 2 | `છે . _ આ _` | 57,724 |
+| 3 | `_ આ વે લા _` | 56,800 |
+| 4 | `માં _ આ વે લા` | 53,829 |
+| 5 | `_ તે મ _ જ` | 50,773 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 2,297
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~25% 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 | 0.8634 | 1.819 | 7.06 | 458,692 | 13.7% |
+| **1** | Subword | 0.9952 | 1.993 | 15.09 | 11,981 | 0.5% |
+| **2** | Word | 0.2300 | 1.173 | 1.59 | 3,234,951 | 77.0% |
+| **2** | Subword | 0.6872 | 1.610 | 5.08 | 180,749 | 31.3% |
+| **3** | Word | 0.0669 | 1.047 | 1.13 | 5,125,144 | 93.3% |
+| **3** | Subword | 0.5539 | 1.468 | 3.49 | 917,629 | 44.6% |
+| **4** | Word | 0.0220 🏆 | 1.015 | 1.04 | 5,806,762 | 97.8% |
+| **4** | Subword | 0.4165 | 1.335 | 2.13 | 3,199,363 | 58.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `છે તે સામેલ કરવાનો છે એઆરડીએ arda ના વેટરર્નસ ઘડાયેલા સ્તંભો વિમલ સંધિવિગ્રહિક દામોદર ઠાકરસી મહિલા`
+2. `અને એ ઇ સ્મિથ એંથની જીડેંસ મિશેલ ચાર્લ્સ સ્ટેઇનમેટ્ઝ અને ગાયક તરીકે પસંદ નથી તેમણે સ્ટેલીના`
+3. `આ ગામમાં પ્રાથમિક શાળા તેમ જ દૂધની ડેરી જેવી સવલતો પ્રાપ્ય થયેલી છે સંદર્ભ કર્યા છે`
+
+**Context Size 2:**
+
+1. `છે આ ગામ સ્થાપિત સહયોગ કુષ્ટ યજ્ઞ ટ્રસ્ટ નામની સંસ્થા પણ જીવનસંચારવાદ પણ શીખવે છે તે દરમિયાન`
+2. `તેમ જ અન્ય શાકભાજીના પાકની ખેતી કરવામાં આવે છે આ શહેરની અર્થતંત્રએ ભારતમા દસમો ક્રમ ધરાવે છે`
+3. `આવે છે જો કે બંગાળીઓ વિશે કેટલીક કાલ્પનિક વાર્તાઓ ડબ્લ્યુડબ્લ્યુઇમાં બિહાઇન્ડ ધ પેઇન્ટેડ સ્માઇલ માં ...`
+
+**Context Size 3:**
+
+1. `છે આ ગામમાં પ્રાથમિક શાળા આંગણવાડી પંચાયતઘર દૂધની ડેરી વગેરે સવલતો પ્રાપ્ય છે ગામના લોકો વ્યવસાયમાં ...`
+2. `કરવામાં આવે છે આ તત્વનું નામ મેન્ડેલિવીયમ ને શુદ્ધ અને ઉપયોગિ રસાયણ શાસ્તની આંતરરાષ્ટ્રીય સંસ્થા દ્વ...`
+3. `પશ્ચિમ ભાગમાં આવેલા ગુજરાત રાજ્યના મધ્ય ભાગમાં આવેલા પંચમહાલ જિલ્લામાં આવેલા કુલ ૬ છ તાલુકાઓ પૈકીના ...`
+
+**Context Size 4:**
+
+1. `ભારત દેશના પશ્ચિમ ભાગમાં આવેલા ગુજરાત રાજ્યમાં આવેલા સૌરાષ્ટ્ર વિસ્તારમાં આવેલા પોરબંદર જિલ્લામાં આવ...`
+2. `દેશના પશ્ચિમ ભાગમાં આવેલા ગુજરાત રાજ્યના દક્ષિણ ભાગમાં આવેલા તાપી જિલ્લાના કુલ ૭ સાત તાલુકાઓ પૈકીના ...`
+3. `પશ્ચિમ ભાગમાં આવેલા ગુજરાત રાજ્યના ઉત્તર પૂર્વ ભાગમાં આવેલા અરવલ્લી જિલ્લામાં આવેલા કુલ ૬ છ તાલુકાઓ ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_મેજયેલી_રોજના_અને_અને_`
+2. `રજકોનો_તા_દરાજ્યની_મ��ટેની_`
+3. `કર_hie_સ્વતંત્ર્ય_તાં_પદ્ધની`
+
+**Context Size 2:**
+
+1. `._૧૯_રોહિતો_અને_ભારત_મા`
+2. `,_entના_પાકની_ઉત્તરાધિક_`
+3. `_આર_સૂર્યના_મૃત્યુ_ગાંધીનગર,`
+
+**Context Size 3:**
+
+1. `_છે._આ_ગામમાં_મુખ્યત્વે_આદિવાસી`
+2. `છે._તાલુકો_ખેતી_કરવાની_પણ_`
+3. `_અનેક_આવેલા_અભ્યાસ_દ્વારા_પહોં`
+
+**Context Size 4:**
+
+1. `_છે._ગોડ્ડામાં_ગોડ્ડા_જિલ્લામાં_આવેલા_`
+2. `_અને_ક્લાસિકલ_માર્ગ_પર_એમિગાઃ_`
+3. `માં_આવે_ત્યારે_બાસાલ્ટિક_કે_દારૂનુ_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (3,199,363 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 | 193,639 |
+| Total Tokens | 7,238,200 |
+| Mean Frequency | 37.38 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1013.36 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | છે | 321,831 |
+| 2 | અને | 165,458 |
+| 3 | આ | 104,369 |
+| 4 | જ | 69,831 |
+| 5 | એક | 66,411 |
+| 6 | આવેલા | 56,885 |
+| 7 | તેમ | 53,009 |
+| 8 | કે | 43,256 |
+| 9 | ગામમાં | 39,326 |
+| 10 | માટે | 39,310 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | લંપુરને | 2 |
+| 2 | શ્રુહાદ | 2 |
+| 3 | ફાર્મહાઉસમાં | 2 |
+| 4 | જંબુદ્દીવપણ્ણત્તિ | 2 |
+| 5 | જંબુસામિચરિઉ | 2 |
+| 6 | સુધર્મસ્વામી | 2 |
+| 7 | જંબુને | 2 |
+| 8 | વૈરાગ્યવિરોધી | 2 |
+| 9 | જંબુની | 2 |
+| 10 | સુધર્માસ્વામીએ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0403 |
+| R² (Goodness of Fit) | 0.997228 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 34.5% |
+| Top 1,000 | 58.3% |
+| Top 5,000 | 75.1% |
+| Top 10,000 | 81.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9972 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 34.5% of corpus
+- **Long Tail:** 183,639 words needed for remaining 18.4% 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 | 0.7412 | 0.3587 | N/A | N/A |
+| **mono_64d** | 64 | 0.7575 | 0.2728 | N/A | N/A |
+| **mono_128d** | 128 | 0.7575 🏆 | 0.2077 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7412 | 0.3577 | 0.0380 | 0.1860 |
+| **aligned_64d** | 64 | 0.7575 | 0.2690 | 0.0580 | 0.2540 |
+| **aligned_128d** | 128 | 0.7575 | 0.2042 | 0.0660 | 0.2820 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_128d with 0.7575 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2783. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.6% 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 | **3.181** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **1.816** | 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 |
+|--------|----------|
+| `-સ` | સંસ્કૃતભાષામાં, સામાન્યત, સ્થપાતી |
+| `-ક` | કોબોલ્ડ, કાલક્રમાનુસાર, કાળજીપુર્વક |
+| `-મ` | મિંઢા, મહાવીરને, માયથિકલ |
+| `-પ` | પોપચા, પાડવા, પંચગવ્ય |
+| `-બ` | બૉલની, બલાઢા, બિન્કસ |
+| `-વ` | વાસ્તે, વાલીના, વિદેશોની |
+| `-ર` | રેટની, રાસમસ, રેતીયાની |
+| `-અ` | અવસ્થાના, અપ્રમાણિકતાનો, અતુલનીય |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ર` | કાલક્રમાનુસાર, ઈન્કાર, છાપર |
+| `-ન` | નાસારિસ્તાન, ફિઝિશ્યન, અંકન |
+| `-સ` | બિન્કસ, રાસમસ, ન્યૂટ્સ |
+| `-ક` | આસ્ક, કાળજીપુર્વક, ટેલિફોનિક |
+| `-લ` | માયથિકલ, ખારોલ, લીવરપૂલ |
+| `-ટ` | ઓરિએન્ટ, બેરોનેટ, કન્ફ્લિક્ટ |
+| `-એ` | શાલ્વએ, વેબએ, ટેસ્લાએ |
+| `-s` | guianensis, libraries, rhodes |
+
+### 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` | 3.61x | 29 contexts | motion, nation, notion |
+| `atio` | 3.66x | 25 contexts | ratio, nation, station |
+| `indi` | 3.58x | 24 contexts | india, hindi, indic |
+| `વનગર` | 3.22x | 14 contexts | ઇવનગર, ધુવનગર, ભાવનગર |
+| `નવલક` | 3.19x | 5 contexts | નવલકથા, નવલકથાએ, નવલકથાઓ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ક` | `-ન` | 26 words | કુલન, ક્રુશિયન |
+| `-સ` | `-સ` | 25 words | સ્પીકર્સ, સેટેલાઇટ્સ |
+| `-ક` | `-સ` | 22 words | કનેક્શન્સ, કાલપેર્સ |
+| `-ક` | `-ર` | 22 words | કેલનર, કૃષ્ણકુમાર |
+| `-સ` | `-ક` | 18 words | સબસોનિક, સ્ટ્રેટેજીક |
+| `-સ` | `-ર` | 17 words | સુમેર, સ્ટ્રાઈકર |
+| `-પ` | `-ર` | 17 words | પાઉડર, પીયર |
+| `-સ` | `-ન` | 17 words | સફરજન, સવેન |
+| `-મ` | `-ન` | 16 words | માર્જીન, મિત્રસેન |
+| `-પ` | `-ન` | 16 words | પરાધીન, પ્રણોદન |
+
+### 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 |
+|------|-----------------|------------|------|
+| conventional | **`convention-al`** | 4.5 | `convention` |
+| કસોટીઓમાં | **`ક-સ-ોટીઓમાં`** | 4.5 | `ોટીઓમાં` |
+| એનડબલ્યુએ | **`એનડબલ્યુ-એ`** | 4.5 | `એનડબલ્યુ` |
+| ઇન્સ્ટ્રુમેન્ટલ | **`ઇન્સ્ટ્રુમેન્ટ-લ`** | 4.5 | `ઇન્સ્ટ્રુમેન્ટ` |
+| અવિશ્વાસની | **`અ-વિશ્વાસની`** | 4.5 | `વિશ્વાસની` |
+| હેલેનીકોન | **`હેલેનીકો-ન`** | 4.5 | `હેલેનીકો` |
+| manifestations | **`manifestation-s`** | 4.5 | `manifestation` |
+| ટેકનોલોજીએ | **`ટેકનોલોજી-એ`** | 4.5 | `ટેકનોલોજી` |
+| festivals | **`festival-s`** | 4.5 | `festival` |
+| citations | **`citation-s`** | 4.5 | `citation` |
+| લક્ષણોમાં | **`લ-ક્ષણોમાં`** | 4.5 | `ક્ષણોમાં` |
+| પહોંચાડવામાં | **`પ-હ-ોંચાડવામાં`** | 4.5 | `ોંચાડવામાં` |
+| બિનસહસંયોજક | **`બ-િનસહસંયોજ-ક`** | 3.0 | `િનસહસંયોજ` |
+| રુખમાબાઈને | **`ર-ુખમાબાઈને`** | 1.5 | `ુખમાબાઈને` |
+| ભવિષ્યકથન | **`ભવિષ્યકથ-ન`** | 1.5 | `ભવિષ્યકથ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Gujarati 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 (4.35x) |
+| N-gram | **2-gram** | Lowest perplexity (2,297) |
+| Markov | **Context-4** | Highest predictability (97.8%) |
+| 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 00:30:07*

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