---
language: arz
language_name: Egyptian Arabic
language_family: arabic
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-arabic
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: 3.899
  - name: best_isotropy
    type: isotropy
    value: 0.7938
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

# Egyptian Arabic - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Egyptian Arabic** 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** | 2.872x | 2.87 | 0.8437% | 1,716,209 |
| **16k** | 3.211x | 3.21 | 0.9431% | 1,535,351 |
| **32k** | 3.553x | 3.55 | 1.0437% | 1,387,311 |
| **64k** | 3.899x 🏆 | 3.90 | 1.1453% | 1,264,296 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `سينافريدى ( الاسم العلمى: Synaphridae ) هوا فصيله من العنكبيات بيتبع عنكبوت. لين...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁سين اف ريد ى ▁( ▁الاسم ▁العلم ى : ▁s ... (+29 more)` | 39 |
| 16k | `▁سين اف ريدى ▁( ▁الاسم ▁العلمى : ▁s yn ap ... (+24 more)` | 34 |
| 32k | `▁سين اف ريدى ▁( ▁الاسم ▁العلمى : ▁syn ap h ... (+22 more)` | 32 |
| 64k | `▁سين اف ريدى ▁( ▁الاسم ▁العلمى : ▁syn aph rida ... (+20 more)` | 30 |

**Sample 2:** `اينديرا باچت لاعبه شطرنج من سلوفينيا و كازاخستان. حياتها اينديرا باچت من مواليد ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ايند يرا ▁با چ ت ▁لاعبه ▁شطرنج ▁من ▁سلوفينيا ▁و ... (+24 more)` | 34 |
| 16k | `▁ايند يرا ▁با چ ت ▁لاعبه ▁شطرنج ▁من ▁سلوفينيا ▁و ... (+24 more)` | 34 |
| 32k | `▁ايند يرا ▁باچ ت ▁لاعبه ▁شطرنج ▁من ▁سلوفينيا ▁و ▁كازاخستان ... (+22 more)` | 32 |
| 64k | `▁ايند يرا ▁باچ ت ▁لاعبه ▁شطرنج ▁من ▁سلوفينيا ▁و ▁كازاخستان ... (+22 more)` | 32 |

**Sample 3:** `مفطورة الخنازير ( الاسم العلمى: Mycoplasma suis ) هوا نوع من بدائيات النوى بيتبع...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁مف ط ورة ▁الخ نا زير ▁( ▁الاسم ▁العلم ى ... (+32 more)` | 42 |
| 16k | `▁مف ط ورة ▁الخ نا زير ▁( ▁الاسم ▁العلمى : ... (+30 more)` | 40 |
| 32k | `▁مف ط ورة ▁الخ نا زير ▁( ▁الاسم ▁العلمى : ... (+30 more)` | 40 |
| 64k | `▁مف ط ورة ▁الخ نا زير ▁( ▁الاسم ▁العلمى : ... (+29 more)` | 39 |


### Key Findings

- **Best Compression:** 64k achieves 3.899x compression
- **Lowest UNK Rate:** 8k with 0.8437% 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 | 5,833 | 12.51 | 1,079,967 | 30.2% | 66.4% |
| **2-gram** | Subword | 317 🏆 | 8.31 | 15,559 | 62.6% | 98.6% |
| **3-gram** | Word | 8,334 | 13.02 | 1,690,048 | 28.5% | 62.7% |
| **3-gram** | Subword | 2,031 | 10.99 | 130,688 | 30.0% | 73.9% |
| **4-gram** | Word | 12,878 | 13.65 | 3,065,781 | 27.3% | 59.4% |
| **4-gram** | Subword | 7,269 | 12.83 | 793,433 | 19.5% | 56.8% |
| **5-gram** | Word | 13,448 | 13.72 | 3,166,704 | 28.9% | 59.2% |
| **5-gram** | Subword | 18,103 | 14.14 | 2,865,423 | 14.0% | 48.6% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `لينكات برانيه` | 1,294,219 |
| 2 | `برانيه مصادر` | 1,167,266 |
| 3 | `من مواليد` | 829,316 |
| 4 | `مواليد يوم` | 809,154 |
| 5 | `الاستوا السماوى` | 668,876 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `لينكات برانيه مصادر` | 1,164,637 |
| 2 | `من مواليد يوم` | 809,006 |
| 3 | `خط الاستوا السماوى` | 630,228 |
| 4 | `الساعيه لجرم سماوى` | 445,892 |
| 5 | `الدايره الساعيه لجرم` | 445,892 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `الدايره الساعيه لجرم سماوى` | 445,892 |
| 2 | `السماوى تكون قيمة بعده` | 445,860 |
| 3 | `الاستوا السماوى تكون قيمة` | 445,860 |
| 4 | `خط الاستوا السماوى تكون` | 445,860 |
| 5 | `لينكات برانيه مصادر من` | 320,790 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `خط الاستوا السماوى تكون قيمة` | 445,860 |
| 2 | `الاستوا السماوى تكون قيمة بعده` | 445,860 |
| 3 | `لستة اكبر بحيرات العالم حسب` | 255,463 |
| 4 | `السماويه اللى المجره جزء منها` | 222,981 |
| 5 | `صوره و هيا مجال الكره` | 222,975 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ا` | 31,094,853 |
| 2 | `ا ل` | 30,178,157 |
| 3 | `ه _` | 17,208,514 |
| 4 | `_ م` | 13,583,995 |
| 5 | `ى _` | 11,832,103 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ا ل` | 25,055,980 |
| 2 | `ي ه _` | 6,400,461 |
| 3 | `ه _ ا` | 6,229,523 |
| 4 | `ا ل م` | 5,957,557 |
| 5 | `_ م ن` | 4,545,069 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ا ل م` | 5,209,448 |
| 2 | `ه _ ا ل` | 5,178,964 |
| 3 | `_ ف ى _` | 4,259,956 |
| 4 | `_ م ن _` | 3,913,053 |
| 5 | `_ ا ل ا` | 3,581,934 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ م ن _ ا` | 1,823,528 |
| 2 | `ر ه _ ا ل` | 1,712,451 |
| 3 | `م ص ا د ر` | 1,614,472 |
| 4 | `_ م ص ا د` | 1,612,850 |
| 5 | `_ ل ي ن ك` | 1,400,053 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 317
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~49% 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 | 1.2202 | 2.330 | 9.16 | 1,361,925 | 0.0% |
| **1** | Subword | 1.0545 | 2.077 | 8.26 | 5,787 | 0.0% |
| **2** | Word | 0.3640 | 1.287 | 1.91 | 12,454,727 | 63.6% |
| **2** | Subword | 0.7835 | 1.721 | 5.53 | 47,806 | 21.7% |
| **3** | Word | 0.1137 | 1.082 | 1.27 | 23,730,854 | 88.6% |
| **3** | Subword | 0.7666 | 1.701 | 4.73 | 264,404 | 23.3% |
| **4** | Word | 0.0623 🏆 | 1.044 | 1.17 | 30,143,409 | 93.8% |
| **4** | Subword | 0.7433 | 1.674 | 3.81 | 1,249,901 | 25.7% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `فى مرصد لويل للتدوير عن تشغيلها willer trains wales police beats and diocesan links milwaukee holy`
2. `من امستردام 16 اكتوبر فى مركز الكواكب الصغيره مصادر من النجوم اللى جايه لينا من البرتغال`
3. `و بكده عملية فى الحزب الديمقراطى المسيحى اشتغل فى ابوت توريبيو الكوليا مساحتها 4 سبتمبر سنة`

**Context Size 2:**

1. `لينكات برانيه مصادر اليمن يمنيه`
2. `برانيه مصادر صدرى من المملكه المتحده عضو برلمان المملكه المتحده حياته نيل ماثيوز ميك ديسبوروج ريس تش...`
3. `من مواليد يوم 12 يونيه فى لوس انجليس اغانى اغانى نيو ويڤ جوايز لينكات برانيه مصادر من`

**Context Size 3:**

1. `لينكات برانيه مصادر من النرويج فى جامعة كوبينهاجين و جامعة جوتينجن و جامعة زيورخ و المعهد الفدرالى ا...`
2. `من مواليد يوم 3 يناير فى تارنوف مات فى 16 يناير الحياه العمليه كان عضو فى academic division`
3. `خط الاستوا السماوى تكون قيمة بعده بالسالب مصادر مايور 2ماس`

**Context Size 4:**

1. `الدايره الساعيه لجرم سماوى و الدايره الساعيه لنقطة الاعتدال الربيعى المطلع المستقيم ممكن يتقاس بقوس ...`
2. `الاستوا السماوى تكون قيمة بعده بالموجب و لو النجم جنوب خط الاستوا السماوى تكون قيمة بعده بالموجب و ل...`
3. `السماوى تكون قيمة بعده بالسالب مصادر مايور 2ماس`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_اعاده_اكلمطقص_ا`
2. `انجنجويناتيلودره`
3. `ل_اوانيره._حيا_ا`

**Context Size 2:**

1. `_الحارض_فراكريفيا`
2. `النظمى_نقطه_الشعا`
3. `ه_الربيس_الداد_(+`

**Context Size 3:**

1. `_الاكتوردشت_كندا._`
2. `يه_لجرم_الحرة._نظا`
3. `ه_المقرا_جبات_فى_م`

**Context Size 4:**

1. `_المتحده_فضاء_منها.`
2. `ه_السكان_سكان_فى_كو`
3. `_فى_مركز_مُدَافِع,_و_ه`


### Key Findings

- **Best Predictability:** Context-4 (word) with 93.8% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (1,249,901 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 | 859,607 |
| Total Tokens | 116,985,057 |
| Mean Frequency | 136.09 |
| Median Frequency | 4 |
| Frequency Std Dev | 9386.65 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | فى | 4,423,347 |
| 2 | من | 3,916,260 |
| 3 | و | 3,516,072 |
| 4 | مصادر | 1,612,738 |
| 5 | لينكات | 1,359,751 |
| 6 | برانيه | 1,299,373 |
| 7 | هيا | 1,062,774 |
| 8 | اللى | 967,317 |
| 9 | يوم | 853,586 |
| 10 | مواليد | 836,389 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ثاكراي | 2 |
| 2 | تشوهاتها | 2 |
| 3 | جبائر | 2 |
| 4 | jesuss | 2 |
| 5 | وأران | 2 |
| 6 | مرثير | 2 |
| 7 | راثماينز | 2 |
| 8 | غرانغغورمان | 2 |
| 9 | grangegorman | 2 |
| 10 | ditsu | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.2584 |
| R² (Goodness of Fit) | 0.994685 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 46.0% |
| Top 1,000 | 76.5% |
| Top 5,000 | 85.8% |
| Top 10,000 | 88.9% |

### Key Findings

- **Zipf Compliance:** R²=0.9947 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 46.0% of corpus
- **Long Tail:** 849,607 words needed for remaining 11.1% 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 | 0.7938 | 0.3446 | N/A | N/A |
| **mono_64d** | 64 | 0.7682 | 0.2977 | N/A | N/A |
| **mono_128d** | 128 | 0.7168 | 0.2564 | N/A | N/A |
| **aligned_32d** | 32 | 0.7938 🏆 | 0.3389 | 0.1080 | 0.4340 |
| **aligned_64d** | 64 | 0.7682 | 0.3004 | 0.2180 | 0.6240 |
| **aligned_128d** | 128 | 0.7168 | 0.2666 | 0.3440 | 0.7120 |

### Key Findings

- **Best Isotropy:** aligned_32d with 0.7938 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3008. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 34.4% 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 | **0.218** | 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 |
|--------|----------|
| `-ال` | الطبقه, التحاقه, التنسيق |
| `-وا` | واعتراف, وازواجها, والسحالى |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ين` | ڤيكيلين, لالغليمين, كورجتچارنين |
| `-ان` | فالسارتان, نيوبان, تيزمان |
| `-ون` | اندريلتون, ازانون, السيويون |

### 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 |
|------|----------|------------------|----------|
| `المج` | 1.77x | 271 contexts | المجن, المجد, المجل |
| `ياته` | 2.08x | 97 contexts | بياته, آياته, عياته |
| `الشع` | 2.04x | 104 contexts | الشعف, الشعر, الشعب |
| `انزي` | 1.84x | 164 contexts | انزيچ, انزيت, انزيغ |
| `الاع` | 1.91x | 107 contexts | الاعمل, الاعدا, الاعيب |
| `لموج` | 2.21x | 48 contexts | لموجة, الموج, الموجة |
| `الاح` | 1.75x | 110 contexts | الاحد, الاحرد, والاحد |
| `مستق` | 1.86x | 81 contexts | مستقر, مستقل, ومستقل |
| `لمجر` | 1.87x | 71 contexts | لمجرى, لمجرم, للمجر |
| `لساع` | 2.28x | 28 contexts | لساعة, الساعى, لساعته |
| `لمطل` | 2.23x | 29 contexts | لمطلع, المطل, المطله |
| `لسما` | 1.60x | 110 contexts | لسماء, للسما, لسماع |

### 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 |
|--------|--------|-----------|----------|
| `-ال` | `-ين` | 42 words | المسؤولين, الهواريين |
| `-ال` | `-ون` | 27 words | الغويلفيون, المراديون |
| `-ال` | `-ان` | 16 words | الشخصان, اليرقان |
| `-وا` | `-ين` | 6 words | والاصلاحيين, والمخبرين |
| `-وا` | `-ان` | 4 words | وايزمان, والغثيان |
| `-وا` | `-ون` | 4 words | واسيون, وايتيلون |

### 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 |
|------|-----------------|------------|------|
| الرومانيتين | **`ال-رومانيت-ين`** | 6.0 | `رومانيت` |
| والمنظمين | **`وا-لمنظم-ين`** | 6.0 | `لمنظم` |
| والخريجون | **`وا-لخريج-ون`** | 6.0 | `لخريج` |
| اوليمبيين | **`اوليمبي-ين`** | 4.5 | `اوليمبي` |
| الفينلاندى | **`ال-فينلاندى`** | 4.5 | `فينلاندى` |
| لوڤتچارنين | **`لوڤتچارن-ين`** | 4.5 | `لوڤتچارن` |
| الرحمانوف | **`ال-رحمانوف`** | 4.5 | `رحمانوف` |
| الإرسالية | **`ال-إرسالية`** | 4.5 | `إرسالية` |
| جيريدهاران | **`جيريدهار-ان`** | 4.5 | `جيريدهار` |
| البرمائيات | **`ال-برمائيات`** | 4.5 | `برمائيات` |
| المتبادلة | **`ال-متبادلة`** | 4.5 | `متبادلة` |
| المستخرجة | **`ال-مستخرجة`** | 4.5 | `مستخرجة` |
| الباراجواى | **`ال-باراجواى`** | 4.5 | `باراجواى` |
| الايرلندى | **`ال-ايرلندى`** | 4.5 | `ايرلندى` |
| التصميمات | **`ال-تصميمات`** | 4.5 | `تصميمات` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Egyptian Arabic 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 (3.90x) |
| N-gram | **2-gram** | Lowest perplexity (317) |
| Markov | **Context-4** | Highest predictability (93.8%) |
| 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: 2026-01-03 20:14:21*