README.md

---
language: xmf
language_name: Mingrelian
language_family: kartvelian
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-kartvelian
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: 4.270
  - name: best_isotropy
    type: isotropy
    value: 0.8723
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-11
---

# Mingrelian - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Mingrelian** 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** | 3.307x | 3.31 | 0.0486% | 395,121 |
| **16k** | 3.672x | 3.68 | 0.0540% | 355,865 |
| **32k** | 3.993x | 4.00 | 0.0587% | 327,283 |
| **64k** | 4.270x 🏆 | 4.27 | 0.0627% | 306,011 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `— ახალი წანეფიშ ეჭარუაშახ 821 წანა. მოლინეფი დუნაბადი ნაღურა კატეგორია:`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 8 2 1 ▁წანა . ... (+5 more)` | 15 |
| 16k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 8 2 1 ▁წანა . ... (+5 more)` | 15 |
| 32k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 8 2 1 ▁წანა . ... (+5 more)` | 15 |
| 64k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 8 2 1 ▁წანა . ... (+5 more)` | 15 |

**Sample 2:** `წანა — ჯვ. წ. XIII ოშწანურაშ ჯვ. წ. რანწკო 4-ა წანა. ახალი წანეფიშ ეჭარუაშახ წან...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁წანა ▁— ▁ჯვ . ▁წ . ▁xiii ▁ოშწანურაშ ▁ჯვ . ... (+19 more)` | 29 |
| 16k | `▁წანა ▁— ▁ჯვ . ▁წ . ▁xiii ▁ოშწანურაშ ▁ჯვ . ... (+19 more)` | 29 |
| 32k | `▁წანა ▁— ▁ჯვ . ▁წ . ▁xiii ▁ოშწანურაშ ▁ჯვ . ... (+19 more)` | 29 |
| 64k | `▁წანა ▁— ▁ჯვ . ▁წ . ▁xiii ▁ოშწანურაშ ▁ჯვ . ... (+19 more)` | 29 |

**Sample 3:** `— ახალი წანეფიშ ეჭარუაშახ 319 წანა. მოლინეფი დუნაბადი ნაღურა კატეგორია:`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 3 1 9 ▁წანა . ... (+5 more)` | 15 |
| 16k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 3 1 9 ▁წანა . ... (+5 more)` | 15 |
| 32k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 3 1 9 ▁წანა . ... (+5 more)` | 15 |
| 64k | `▁— ▁ახალი ▁წანეფიშ ▁ეჭარუაშახ ▁ 3 1 9 ▁წანა . ... (+5 more)` | 15 |


### Key Findings

- **Best Compression:** 64k achieves 4.270x compression
- **Lowest UNK Rate:** 8k with 0.0486% 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 | 14,545 | 13.83 | 37,338 | 12.9% | 33.4% |
| **2-gram** | Subword | 483 🏆 | 8.92 | 6,848 | 54.1% | 96.3% |
| **3-gram** | Word | 14,526 | 13.83 | 36,176 | 13.3% | 35.0% |
| **3-gram** | Subword | 4,386 | 12.10 | 52,208 | 19.0% | 58.2% |
| **4-gram** | Word | 20,697 | 14.34 | 53,331 | 13.3% | 31.9% |
| **4-gram** | Subword | 24,158 | 14.56 | 264,428 | 8.9% | 31.2% |
| **5-gram** | Word | 12,424 | 13.60 | 34,098 | 17.4% | 37.8% |
| **5-gram** | Subword | 76,448 | 16.22 | 649,486 | 5.5% | 20.9% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `რესურსეფი ინტერნეტის` | 10,643 |
| 2 | `ჯვ წ` | 2,869 |
| 3 | `ართ ართი` | 2,539 |
| 4 | `of the` | 2,084 |
| 5 | `ქოძირით თაშნეშე` | 1,913 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `მოლინეფი დუნაბადი ნაღურა` | 1,341 |
| 2 | `დუნაბადი ნაღურა კატეგორია` | 1,341 |
| 3 | `ახალი წანეფიშ ეჭარუაშახ` | 1,200 |
| 4 | `წანა მოლინეფი დუნაბადი` | 1,191 |
| 5 | `ოფიციალური ვებ ხასჷლა` | 717 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `მოლინეფი დუნაბადი ნაღურა კატეგორია` | 1,336 |
| 2 | `წანა მოლინეფი დუნაბადი ნაღურა` | 1,191 |
| 3 | `ღურთუთა ფურთუთა მელახი პირელი` | 660 |
| 4 | `ფურთუთა მელახი პირელი მესი` | 658 |
| 5 | `ეკენია გჷმათუთა გერგობათუთა ქირსეთუთა` | 656 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `წანა მოლინეფი დუნაბადი ნაღურა კატეგორია` | 1,191 |
| 2 | `ღურთუთა ფურთუთა მელახი პირელი მესი` | 654 |
| 3 | `ფურთუთა მელახი პირელი მესი მანგი` | 647 |
| 4 | `მელახი პირელი მესი მანგი კვირკვე` | 646 |
| 5 | `მანგი კვირკვე მარაშინათუთა ეკენია გჷმათუთა` | 642 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ი _` | 316,429 |
| 2 | `შ _` | 280,108 |
| 3 | `ა ნ` | 206,994 |
| 4 | `ა რ` | 189,457 |
| 5 | `რ ი` | 178,820 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ი შ _` | 142,356 |
| 2 | `ე ფ ი` | 121,504 |
| 3 | `ა შ _` | 105,502 |
| 4 | `ლ ი _` | 74,000 |
| 5 | `_ დ ო` | 69,476 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ დ ო _` | 54,635 |
| 2 | `ე ფ ი _` | 51,940 |
| 3 | `ე ფ ი შ` | 38,103 |
| 4 | `_ წ ა ნ` | 37,247 |
| 5 | `ფ ი შ _` | 35,972 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ე ფ ი შ _` | 35,235 |
| 2 | `_ წ ა ნ ა` | 29,928 |
| 3 | `, _ ნ ა მ` | 16,612 |
| 4 | `_ ნ ა მ უ` | 15,215 |
| 5 | `წ ა ნ ა შ` | 14,803 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 483
- **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 | 0.7664 | 1.701 | 4.87 | 268,026 | 23.4% |
| **1** | Subword | 0.8477 | 1.800 | 6.70 | 2,905 | 15.2% |
| **2** | Word | 0.1728 | 1.127 | 1.35 | 1,300,406 | 82.7% |
| **2** | Subword | 0.9102 | 1.879 | 5.61 | 19,472 | 9.0% |
| **3** | Word | 0.0491 | 1.035 | 1.08 | 1,752,396 | 95.1% |
| **3** | Subword | 0.8316 | 1.780 | 4.23 | 109,244 | 16.8% |
| **4** | Word | 0.0176 🏆 | 1.012 | 1.03 | 1,882,972 | 98.2% |
| **4** | Subword | 0.6760 | 1.598 | 2.91 | 461,858 | 32.4% |

### Generated Text Samples (Word-based)

Below are text samples generated from each word-based Markov chain model:

**Context Size 1:**

1. `დო მალურიე აკოანჯარაფა ავტომობილეფი ლეგენდარულო გინართინუ ირდიხას ექიაქო შეთმოფხვადუთ ინფორმაციას მუ...`
2. `რე გენუა ღურჷ პუბლიკაციაშე ოთხი თარანგელოზიშე უნჩაშო წანეფს ჯავახიშვილი შ აკაკი გელოვანი მითოლოგიური...`
3. `წანას ქიანაქ ტურისტეფიშ დო მუსხირენ წანას რანკექ მუშობა რსულაშე ახალ ზელანდიას პროვინცია ადმინისტრაც...`

**Context Size 2:**

1. `რესურსეფი ინტერნეტის უილიამ ბლეიკიშ ციტატას მიარცხუ if the doors delacorte press isbn eden paul gene...`
2. `ჯვ წ 293 261 თიშენი ნამდა თაქ მუდგაზმარენ აბანობურ ფლროაშ დო ფაუნაშ გოვითარაფაშ დო გჷმორინაფაშ ნება ...`
3. `ართ ართი მუკნაჭარას ნამუსჷთ წანას მიკრობიოლოგი ანტონ ვან ლევენჰუკი ინგლ antonie van leeuwenhoek დ 24...`

**Context Size 3:**

1. `მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა კატეგ...`
2. `ახალი წანეფიშ ეჭარუაშახ 576 წანა მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა კატეგორ...`
3. `წანა მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა ...`

**Context Size 4:**

1. `წანა მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა კატეგორია მოლინეფი დუნაბადი ნაღურა ...`
2. `ღურთუთა ფურთუთა მელახი პირელი მესი მანგი კვირკვე მარაშინათუთა ეკენია გჷმათუთა გერგობათუთა ქირსეთუთა ...`
3. `ფურთუთა მელახი პირელი მესი მანგი კვირკვე მარაშინათუთა ეკენია გჷმათუთა გერგობათუთა ქირსეთუთა 22 ქირსე...`


### Generated Text Samples (Subword-based)

Below are text samples generated from each subword-based Markov chain model:

**Context Size 1:**

1. `_fatanacis_გლიურ`
2. `აზი_9075_280_ას_`
3. `ი_ნტე“._გმანი_რმ`

**Context Size 2:**

1. `ი_მელანმოჭმენიამბ`
2. `შ_ლჷ_დელი_მოშწანკ`
3. `ანი_მუე-ტემელუას_`

**Context Size 3:**

1. `იშ_ეპისკი_ანურეატი`
2. `ეფი_ართალი_წანერი_`
3. `აშ_გოძველ_ურთუალეს`

**Context Size 4:**

1. `_დო_ჯოხოდჷ_ვითოში._`
2. `ეფი_(ინგლისარიშ_ნუტ`
3. `ეფიშ_მონწყუ_ბიგნეფი`


### Key Findings

- **Best Predictability:** Context-4 (word) with 98.2% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (461,858 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 | 105,542 |
| Total Tokens | 1,961,354 |
| Mean Frequency | 18.58 |
| Median Frequency | 3 |
| Frequency Std Dev | 236.83 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | დო | 54,771 |
| 2 | რე | 28,199 |
| 3 | წანას | 11,878 |
| 4 | წანაშ | 11,129 |
| 5 | რესურსეფი | 10,818 |
| 6 | ინტერნეტის | 10,733 |
| 7 | the | 10,417 |
| 8 | of | 9,251 |
| 9 | რდჷ | 8,188 |
| 10 | 1 | 7,138 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | რაროტონგას | 2 |
| 2 | efo | 2 |
| 3 | ჟირულ | 2 |
| 4 | ლეგიონური | 2 |
| 5 | ანტონესკუშ | 2 |
| 6 | მოსლიქ | 2 |
| 7 | ფბრ | 2 |
| 8 | შპეერიშ | 2 |
| 9 | შერერიქ | 2 |
| 10 | კანემანი | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.9583 |
| R² (Goodness of Fit) | 0.995191 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 21.7% |
| Top 1,000 | 47.9% |
| Top 5,000 | 67.7% |
| Top 10,000 | 76.1% |

### Key Findings

- **Zipf Compliance:** R²=0.9952 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 21.7% of corpus
- **Long Tail:** 95,542 words needed for remaining 23.9% 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 | 0.8716 | 0.3197 | N/A | N/A |
| **mono_64d** | 64 | 0.8723 🏆 | 0.2350 | N/A | N/A |
| **mono_128d** | 128 | 0.7382 | 0.1853 | N/A | N/A |
| **aligned_32d** | 32 | 0.8716 | 0.3267 | 0.0320 | 0.2240 |
| **aligned_64d** | 64 | 0.8723 | 0.2335 | 0.0720 | 0.3200 |
| **aligned_128d** | 128 | 0.7382 | 0.1809 | 0.0820 | 0.3860 |

### Key Findings

- **Best Isotropy:** mono_64d with 0.8723 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2469. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 8.2% 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 | **0.809** | 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 |
|--------|----------|
| `-ა` | ანკლავი, ახვალამას, ალაბამაშ |
| `-მ` | მოლუსკეფიშ, მიწონუ, მერცხილი |
| `-მა` | მანჩურალიშ, მაბირე, მაიერჰოფი |
| `-ს` | სოკომპა, საკონდიტრო, სათავადო |
| `-გ` | გუდაუთას, გეოლოგიქ, გეორგიოს |
| `-სა` | საკონდიტრო, სათავადო, სარაი |
| `-კ` | კინოშე, კონტრ, კარდამონიშ |
| `-ბ` | ბანკირი, ბრელშა, ბამიანი |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ი` | ანკლავი, პიჩი, ბანკირი |
| `-შ` | ფორტეპიანოშ, ლოშიშ, მოლუსკეფიშ |
| `-იშ` | ლოშიშ, მოლუსკეფიშ, კარდამონიშ |
| `-ს` | გუდაუთას, ახვალამას, ფურთუთას |
| `-ა` | ბრელშა, ეკონომიკა, სოკომპა |
| `-ლი` | მერცხილი, რეკორდული, გჷშმაკოროცხალი |
| `-აშ` | ზუღაშ, ალაბამაშ, ჯანთხილუაშ |
| `-რი` | ბანკირი, ჯალპაიგური, ბედინერი |

### 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 |
|------|----------|------------------|----------|
| `ალურ` | 1.96x | 86 contexts | ცალურ, გალურ, ალურე |
| `ანეფ` | 1.65x | 147 contexts | წანეფ, წანეფც, ხანეფც |
| `რეფი` | 1.65x | 143 contexts | ერეფი, არეფი, ცირეფი |
| `ნეფი` | 1.55x | 148 contexts | თნეფი, ენეფი, ინეფი |
| `ლეფი` | 1.55x | 139 contexts | შლეფი, დღლეფი, თულეფი |
| `ობაშ` | 1.86x | 48 contexts | ტობაშ, ნობაშ, უობაში |
| `ტეფი` | 1.60x | 78 contexts | ჩიტეფი, კეტეფი, ერტეფი |
| `ნტერ` | 1.83x | 44 contexts | ნტერი, ინტერ, ნტერო |
| `რმალ` | 1.98x | 29 contexts | ქარმალი, ქარმალქ, წყარმალ |
| `ურსე` | 2.19x | 19 contexts | კურსეფი, კურსეფს, რსურსეფი |
| `ტერნ` | 1.91x | 25 contexts | ტერნი, შტერნი, სტერნი |
| `უეფი` | 1.44x | 66 contexts | ჭუეფი, კუეფი, სუეფი |

### 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 |
|--------|--------|-----------|----------|
| `-მ` | `-ი` | 174 words | მუნაღელი, მიზანტროპი |
| `-ა` | `-ი` | 150 words | ანდალუსიარი, ალიფი |
| `-მ` | `-შ` | 136 words | მარსულებერეფიშ, მიოცენიშ |
| `-კ` | `-ი` | 110 words | კარერი, კუჩხეფი |
| `-გ` | `-ი` | 103 words | გიბრალტარი, გჷმორკვიაფილი |
| `-მ` | `-ს` | 91 words | მონძეეფს, მანუსკრიპტეფს |
| `-კ` | `-შ` | 87 words | კირქუაშ, კონგილიოშ |
| `-ბ` | `-ი` | 87 words | ბრაზავილი, ბურჟი |
| `-მ` | `-იშ` | 87 words | მარსულებერეფიშ, მიოცენიშ |
| `-ს` | `-ი` | 86 words | სტარი, სუმერი |

### 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 |
|------|-----------------|------------|------|
| განვითარების | **`განვითარებ-ი-ს`** | 7.5 | `ი` |
| ტრაგედიას | **`ტრაგედ-ი-ას`** | 7.5 | `ი` |
| მანერჰეიმი | **`მანერჰე-ი-მი`** | 7.5 | `ი` |
| აკოდგინელი | **`აკოდგინ-ე-ლი`** | 7.5 | `ე` |
| ქვერსემიაშ | **`ქვერსემ-ი-აშ`** | 7.5 | `ი` |
| დეფინიციათ | **`დეფინიც-ი-ათ`** | 7.5 | `ი` |
| ვიკივოიაჟის | **`ვიკივოიაჟ-ი-ს`** | 7.5 | `ი` |
| აპლიკაცია | **`აპლიკაც-ი-ა`** | 7.5 | `ი` |
| მუჭომეფითიე | **`მუჭომეფით-ი-ე`** | 7.5 | `ი` |
| ბანჯარმასინი | **`ბანჯარმა-სი-ნი`** | 7.5 | `სი` |
| ჟირსქესამიე | **`ჟირსქესამ-ი-ე`** | 7.5 | `ი` |
| ქუდასქიდჷ | **`ქუდასქ-ი-დჷ`** | 7.5 | `ი` |
| რეზერვაცია | **`რეზერვაც-ი-ა`** | 7.5 | `ი` |
| აეროპორტიე | **`აეროპორტ-ი-ე`** | 7.5 | `ი` |
| დისტილაცია | **`დისტილაც-ი-ა`** | 7.5 | `ი` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Mingrelian 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 (4.27x) |
| N-gram | **2-gram** | Lowest perplexity (483) |
| Markov | **Context-4** | Highest predictability (98.2%) |
| Embeddings | **100d** | Balanced semantic capture and isotropy |


---
## Appendix: Metrics Glossary & Interpretation Guide

This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.

### Tokenizer Metrics

**Compression Ratio**
> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
>
> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
>
> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.

**Average Token Length (Fertility)**
> *Definition:* Mean number of characters per token produced by the tokenizer.
>
> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
>
> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.

**Unknown Token Rate (OOV Rate)**
> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
>
> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
>
> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.

### N-gram Model Metrics

**Perplexity**
> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
>
> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
>
> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.

**Entropy**
> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
>
> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
>
> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.

**Coverage (Top-K)**
> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
>
> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
>
> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.

### Markov Chain Metrics

**Average Entropy**
> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
>
> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
>
> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.

**Branching Factor**
> *Definition:* Average number of unique next tokens observed for each context.
>
> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
>
> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.

**Predictability**
> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
>
> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
>
> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.

### Vocabulary & Zipf's Law Metrics

**Zipf's Coefficient**
> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
>
> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
>
> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.

**R² (Coefficient of Determination)**
> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
>
> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
>
> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.

**Vocabulary Coverage**
> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
>
> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
>
> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.

### Word Embedding Metrics

**Isotropy**
> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
>
> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
>
> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.

**Average Norm**
> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
>
> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
>
> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).

**Cosine Similarity**
> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
>
> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
>
> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.

**t-SNE Visualization**
> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
>
> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
>
> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.

### General Interpretation Guidelines

1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.


### Visualizations Index

| Visualization | Description |
|---------------|-------------|
| Tokenizer Compression | Compression ratios by vocabulary size |
| Tokenizer Fertility | Average token length by vocabulary |
| Tokenizer OOV | Unknown token rates |
| Tokenizer Total Tokens | Total tokens by vocabulary |
| N-gram Perplexity | Perplexity by n-gram size |
| N-gram Entropy | Entropy by n-gram size |
| N-gram Coverage | Top pattern coverage |
| N-gram Unique | Unique n-gram counts |
| Markov Entropy | Entropy by context size |
| Markov Branching | Branching factor by context |
| Markov Contexts | Unique context counts |
| Zipf's Law | Frequency-rank distribution with fit |
| Vocab Frequency | Word frequency distribution |
| Top 20 Words | Most frequent words |
| Vocab Coverage | Cumulative coverage curve |
| Embedding Isotropy | Vector space uniformity |
| Embedding Norms | Vector magnitude distribution |
| Embedding Similarity | Word similarity heatmap |
| Nearest Neighbors | Similar words for key terms |
| t-SNE Words | 2D word embedding visualization |
| t-SNE Sentences | 2D sentence embedding visualization |
| Position Encoding | Encoding method comparison |
| Model Sizes | Storage requirements |
| Performance Dashboard | Comprehensive performance overview |

---
## About This Project

### Data Source

Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.

### Project

A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.

### Maintainer

[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)

### Citation

If you use these models in your research, please cite:

```bibtex
@misc{wikilangs2025,
  author = {Kamali, Omar},
  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
  year = {2025},
  doi = {10.5281/zenodo.18073153},
  publisher = {Zenodo},
  url = {https://huggingface.co/wikilangs}
  institution = {Omneity Labs}
}
```

### License

MIT License - Free for academic and commercial use.

### Links

- 🌐 Website: [wikilangs.org](https://wikilangs.org)
- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
---
*Generated by Wikilangs Models Pipeline*

*Report Date: 2026-01-11 05:18:26*