README.md

---
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_માટેની_`
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*