---
language: azb
language_name: South Azerbaijani
language_family: turkic_oghuz
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-turkic_oghuz
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: 4.154
  - name: best_isotropy
    type: isotropy
    value: 0.8266
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

# South Azerbaijani - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **South Azerbaijani** 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** | 3.140x | 3.14 | 0.4916% | 361,073 |
| **16k** | 3.514x | 3.52 | 0.5501% | 322,683 |
| **32k** | 3.859x | 3.86 | 0.6041% | 293,823 |
| **64k** | 4.154x 🏆 | 4.16 | 0.6502% | 272,975 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `پوچتوووی ( ) روسیه اؤلکه‌سینده یئر آلان بیر کند دیر و آرخانقلسک اوبلاستیندا یئرل...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁پو چ ت وو وی ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ... (+12 more)` | 22 |
| 16k | `▁پو چ ت وو وی ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ... (+12 more)` | 22 |
| 32k | `▁پو چت وووی ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ▁یئر ▁آلان ... (+10 more)` | 20 |
| 64k | `▁پو چت وووی ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ▁یئر ▁آلان ... (+10 more)` | 20 |

**Sample 2:** `هیندوستان اؤلکه‌سینین کارناتاکا ایالتینده بیر کند دیر. بۇ کنده کانادا دیلی دانیش...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁هیندوستان ▁اؤلکه ▁سینین ▁کارناتاکا ▁ایالتینده ▁بیر ▁کند ▁دیر . ▁بۇ ... (+7 more)` | 17 |
| 16k | `▁هیندوستان ▁اؤلکه ▁سینین ▁کارناتاکا ▁ایالتینده ▁بیر ▁کند ▁دیر . ▁بۇ ... (+7 more)` | 17 |
| 32k | `▁هیندوستان ▁اؤلکه ▁سینین ▁کارناتاکا ▁ایالتینده ▁بیر ▁کند ▁دیر . ▁بۇ ... (+7 more)` | 17 |
| 64k | `▁هیندوستان ▁اؤلکه ▁سینین ▁کارناتاکا ▁ایالتینده ▁بیر ▁کند ▁دیر . ▁بۇ ... (+7 more)` | 17 |

**Sample 3:** `پیایو، روسیه ( ) روسیه اؤلکه‌سینده یئر آلان بیر کند دیر و مورمانسک اوبلاستیندا ی...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁پی ای و ، ▁روسیه ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ... (+14 more)` | 24 |
| 16k | `▁پی ای و ، ▁روسیه ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ... (+12 more)` | 22 |
| 32k | `▁پی ایو ، ▁روسیه ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ▁یئر ... (+11 more)` | 21 |
| 64k | `▁پی ایو ، ▁روسیه ▁( ▁) ▁روسیه ▁اؤلکه ▁سینده ▁یئر ... (+11 more)` | 21 |


### Key Findings

- **Best Compression:** 64k achieves 4.154x compression
- **Lowest UNK Rate:** 8k with 0.4916% 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 | 8,053 | 12.98 | 158,968 | 25.7% | 56.1% |
| **2-gram** | Subword | 528 🏆 | 9.04 | 12,667 | 51.7% | 95.7% |
| **3-gram** | Word | 10,252 | 13.32 | 236,817 | 22.6% | 53.6% |
| **3-gram** | Subword | 3,765 | 11.88 | 106,797 | 23.2% | 62.4% |
| **4-gram** | Word | 17,203 | 14.07 | 427,241 | 19.0% | 47.9% |
| **4-gram** | Subword | 15,109 | 13.88 | 582,040 | 14.6% | 44.8% |
| **5-gram** | Word | 19,665 | 14.26 | 390,296 | 17.2% | 45.0% |
| **5-gram** | Subword | 37,921 | 15.21 | 1,605,166 | 11.7% | 37.8% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ایشلدنلری طرفیندن` | 75,586 |
| 2 | `مقاله‌سیندن گؤتورولوبدور` | 75,505 |
| 3 | `ویکی‌پدیاسی‌نین ایشلدنلری` | 73,736 |
| 4 | `اینگیلیسجه ویکی‌پدیاسی‌نین` | 71,134 |
| 5 | `قایناق‌لار اینگیلیسجه` | 70,887 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن` | 73,736 |
| 2 | `اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری` | 71,134 |
| 3 | `قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین` | 70,813 |
| 4 | `بیر یاشاییش منطقه‌سی‌دیر` | 40,398 |
| 5 | `بیر کند دیر` | 30,448 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن` | 71,134 |
| 2 | `قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری` | 70,813 |
| 3 | `سوْن نۆفوس ساییمی اساسيندا` | 24,567 |
| 4 | `شهرلرین لیستی قایناق‌لار اینگیلیسجه` | 22,936 |
| 5 | `لیستی قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین` | 22,936 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن` | 70,813 |
| 2 | `شهرلرین لیستی قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین` | 22,936 |
| 3 | `لیستی قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری` | 22,936 |
| 4 | `گؤتورولوبدور ۸ آقوست تاریخینده یوْخلانیلیبدیر` | 17,804 |
| 5 | `مقاله‌سیندن گؤتورولوبدور ۸ آقوست تاریخینده` | 17,804 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ی ن` | 1,870,834 |
| 2 | `_ ا` | 1,661,040 |
| 3 | `ی _` | 1,438,867 |
| 4 | `ا ی` | 1,394,440 |
| 5 | `ن _` | 1,218,001 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ا ی` | 718,299 |
| 2 | `ی ن د` | 659,203 |
| 3 | `د ه _` | 586,025 |
| 4 | `ل ا ر` | 581,126 |
| 5 | `ا ی ن` | 470,804 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ن د ه _` | 347,491 |
| 2 | `ل ا ر _` | 329,654 |
| 3 | `ی ن د ه` | 321,093 |
| 4 | `_ ب ی ر` | 258,934 |
| 5 | `ن ی ن _` | 257,847 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ی ن د ه _` | 319,618 |
| 2 | `ق ا ی ن ا` | 236,232 |
| 3 | `_ ق ا ی ن` | 235,936 |
| 4 | `ی ن د ن _` | 199,409 |
| 5 | `ی ن گ ی ل` | 172,619 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 528
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~38% 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 | 0.6633 | 1.584 | 5.08 | 728,851 | 33.7% |
| **1** | Subword | 1.0599 | 2.085 | 9.06 | 3,419 | 0.0% |
| **2** | Word | 0.1969 | 1.146 | 1.48 | 3,698,118 | 80.3% |
| **2** | Subword | 0.9299 | 1.905 | 6.57 | 30,973 | 7.0% |
| **3** | Word | 0.0689 | 1.049 | 1.14 | 5,453,316 | 93.1% |
| **3** | Subword | 0.8407 | 1.791 | 4.70 | 203,342 | 15.9% |
| **4** | Word | 0.0340 🏆 | 1.024 | 1.07 | 6,184,673 | 96.6% |
| **4** | Subword | 0.6999 | 1.624 | 3.22 | 955,119 | 30.0% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `و اؤلوملر باخیر دؤولت موزیک دوْغوملار ۲ مئی eamon gilmore shooter young artist awardbest breakthroug...`
2. `بیر فوتبالیست هوجومچو موقعیتینده اوْیناییب قایناقلار ایلده آمریکالی سیاستچیلر میلادی ایلده آذربایجان...`
3. `اینگیلیسجه phyllis and the new york آمریکانین نبراسکا ایالتینده بیر شهردیر و باتی آجورلو قصبه‌سینده ...`

**Context Size 2:**

1. `ایشلدنلری طرفیندن ballard مقاله‌سیندن گؤتورولوبدور ۳۰ نوْوامبر تاریخینده رالی قوزئی کارولینا ایالتین...`
2. `مقاله‌سیندن گؤتورولوبدور ۲۲ آقوست تاریخینده یوْخلانیلیبدیر ایالتین شهرلری آمریکا بیرلشمیش ایالتلری ک...`
3. `ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن piguet مقاله‌سیندن گؤتورولوبدور ۱۹ جولای یوْخلانیلیبدیر شهرلری en ...`

**Context Size 3:**

1. `ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن phosphate مقاله‌سیندن گؤتورولوبدور ۳۰ نوْوامبر تاریخینده یوْخلانیل...`
2. `اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن mała مقاله‌سیندن گؤتورولوبدور ۱۲ آقوست تاریخینده یوْخلا...`
3. `قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن nigra مقاله‌سیندن گؤتورولوبدور ۲۷ جولای تاری...`

**Context Size 4:**

1. `اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن tachov district مقاله‌سیندن گؤتورولوبدور ۱۹ جولای یوْخل...`
2. `قایناق‌لار اینگیلیسجه ویکی‌پدیاسی‌نین ایشلدنلری طرفیندن reed مقاله‌سیندن گؤتورولوبدور ۲۲ ژانویه تاری...`
3. `سوْن نۆفوس ساییمی اساسيندا نفر ایمیش قایناقلار جومهوریتینین شهرلری en bədəlan`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_طین_اینالرشه_می`
2. `ینینویره_ب.st_آذ`
3. `اؤلیلده_s_مول_کل`

**Context Size 2:**

1. `ینی_اوبونیرلرین_ش`
2. `_این_چاری_اوربّع_د`
3. `ی_حؤکواءنینه‌سیناق`

**Context Size 3:**

1. `_ایشتیرامبر_charah`
2. `ینده_یئرلشیرکت_()_`
3. `ده_یوْخلو_"_the_ism`

**Context Size 4:**

1. `نده_یئرلشیر._بۇ_شهر`
2. `لار_اینسانی._۲۴_آقو`
3. `ینده_چیخماق_شکیلات)`


### Key Findings

- **Best Predictability:** Context-4 (word) with 96.6% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (955,119 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 | 271,726 |
| Total Tokens | 12,485,100 |
| Mean Frequency | 45.95 |
| Median Frequency | 3 |
| Frequency Std Dev | 1144.86 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | و | 284,866 |
| 2 | بیر | 169,436 |
| 3 | اینگیلیسجه | 149,744 |
| 4 | قایناقلار | 142,037 |
| 5 | the | 114,223 |
| 6 | تاریخینده | 92,091 |
| 7 | قایناق‌لار | 90,964 |
| 8 | ایلده | 83,776 |
| 9 | شهرلری | 81,908 |
| 10 | طرفیندن | 80,193 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ائششکین | 2 |
| 2 | لابی‌سی | 2 |
| 3 | آذری‌ها | 2 |
| 4 | داشناکلارلا | 2 |
| 5 | قۇلان | 2 |
| 6 | آسینۇس | 2 |
| 7 | ائششه‌یینین | 2 |
| 8 | تاپؽلمیشدیر | 2 |
| 9 | kulan | 2 |
| 10 | کسا | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.1608 |
| R² (Goodness of Fit) | 0.995522 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 34.4% |
| Top 1,000 | 64.8% |
| Top 5,000 | 79.6% |
| Top 10,000 | 84.6% |

### Key Findings

- **Zipf Compliance:** R²=0.9955 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 34.4% of corpus
- **Long Tail:** 261,726 words needed for remaining 15.4% 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 | 0.8266 🏆 | 0.3562 | N/A | N/A |
| **mono_64d** | 64 | 0.7978 | 0.2932 | N/A | N/A |
| **mono_128d** | 128 | 0.7560 | 0.2495 | N/A | N/A |
| **aligned_32d** | 32 | 0.8266 | 0.3594 | 0.0580 | 0.2760 |
| **aligned_64d** | 64 | 0.7978 | 0.3041 | 0.1220 | 0.4360 |
| **aligned_128d** | 128 | 0.7560 | 0.2442 | 0.2380 | 0.6200 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8266 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3011. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 23.8% R@1 in cross-lingual retrieval.
- **Recommendation:** 128d aligned for best cross-lingual performance

---
## 6.  Morphological Analysis (Experimental)

This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.

### 6.1 Productivity & Complexity

| Metric | Value | Interpretation | Recommendation |
|--------|-------|----------------|----------------|
| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
| Idiomaticity Gap | **0.190** | 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 |
|--------|----------|

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ین` | کومیته‌سی‌‌نین, قورانین, پاقلئنین |
| `-ان` | قافقازدان, آتاسین‌دان, تیتانلاردان |

### 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 |
|------|----------|------------------|----------|
| `رلری` | 2.05x | 205 contexts | یرلری, ارلری, خطرلری |
| `اقلا` | 1.90x | 131 contexts | ناقلا, اقلایس, داقلاس |
| `یبدی` | 2.33x | 31 contexts | ییبدیر, گلیبدی, آلیبدی |
| `قلار` | 2.01x | 54 contexts | حقلاری, لیقلار, حاقلار |
| `اریخ` | 2.11x | 41 contexts | تاریخ, ‌تاریخ, تواریخ |
| `ولوب` | 1.85x | 60 contexts | اولوب, قولوب, بولوب |
| `تیند` | 1.73x | 73 contexts | تینده, تیندل, تیندال |
| `یناق` | 2.07x | 27 contexts | ایناق, قیناق, سیناق |
| `ئرلش` | 2.13x | 24 contexts | یئرلشن, يئرلشن, یئرلشه |
| `ریخی` | 2.00x | 22 contexts | مریخی, ریخین, مریخین |
| `قاین` | 2.31x | 14 contexts | قاینا, قاینی, قاینز |
| `هرلر` | 2.14x | 17 contexts | شهرلر, شهرلري, شهرلری |

### 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.

*No significant affix co-occurrences detected.*


### 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 |
|------|-----------------|------------|------|
| دۆکانلارینین | **`دۆکانلار-ین-ین`** | 6.0 | `دۆکانلار` |
| سیلاحینین | **`سیلاح-ین-ین`** | 6.0 | `سیلاح` |
| باشچی‌لارینین | **`باشچی‌لار-ین-ین`** | 6.0 | `باشچی‌لار` |
| دوخالارین | **`دوخالار-ین`** | 4.5 | `دوخالار` |
| آمارلارین | **`آمارلار-ین`** | 4.5 | `آمارلار` |
| تاریخچیلرین | **`تاریخچیلر-ین`** | 4.5 | `تاریخچیلر` |
| اؤدوللرین | **`اؤدوللر-ین`** | 4.5 | `اؤدوللر` |
| شکیلچیلرین | **`شکیلچیلر-ین`** | 4.5 | `شکیلچیلر` |
| کوْمونیستلرین | **`کوْمونیستلر-ین`** | 4.5 | `کوْمونیستلر` |
| بیوفیزیکین | **`بیوفیزیک-ین`** | 4.5 | `بیوفیزیک` |
| تاپینتیلارین | **`تاپینتیلار-ین`** | 4.5 | `تاپینتیلار` |
| میکروبلارین | **`میکروبلار-ین`** | 4.5 | `میکروبلار` |
| تیکیلینین | **`تیکیل-ین-ین`** | 3.0 | `تیکیل` |
| نفتالینین | **`نفتال-ین-ین`** | 3.0 | `نفتال` |
| والنتاینین | **`والنتا-ین-ین`** | 3.0 | `والنتا` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language South Azerbaijani 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 (4.15x) |
| N-gram | **2-gram** | Lowest perplexity (528) |
| Markov | **Context-4** | Highest predictability (96.6%) |
| 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: 2026-01-03 19:16:27*