---
language: cu
language_name: Church Slavic
language_family: slavic_historical
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-slavic_historical
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: 4.940
  - name: best_isotropy
    type: isotropy
    value: 0.2434
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

# Church Slavic - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Church Slavic** 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** | 3.877x | 3.88 | 0.1314% | 107,273 |
| **16k** | 4.367x | 4.37 | 0.1480% | 95,246 |
| **32k** | 4.940x 🏆 | 4.94 | 0.1675% | 84,200 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `Лидьскъ повѣтъ · Бѣла Роусь Лидьскъ повѣтъ · Рѡсїиска їмпєрїꙗ`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁лидьскъ ▁повѣтъ ▁· ▁бѣла ▁роусь ▁лидьскъ ▁повѣтъ ▁· ▁рѡсїиска ▁їмпєрїꙗ` | 10 |
| 16k | `▁лидьскъ ▁повѣтъ ▁· ▁бѣла ▁роусь ▁лидьскъ ▁повѣтъ ▁· ▁рѡсїиска ▁їмпєрїꙗ` | 10 |
| 32k | `▁лидьскъ ▁повѣтъ ▁· ▁бѣла ▁роусь ▁лидьскъ ▁повѣтъ ▁· ▁рѡсїиска ▁їмпєрїꙗ` | 10 |

**Sample 2:** `Оꙁаскоу и · юга Санъ Паоулоу браꙁїльскъ градъ и обьщина ѥстъ ⁙ Людии 718.646 оби...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁о ꙁа скоу ▁и ▁· ▁ю га ▁санъ ▁паоу лоу ... (+24 more)` | 34 |
| 16k | `▁о ꙁа скоу ▁и ▁· ▁ю га ▁санъ ▁паоулоу ▁браꙁїл ... (+23 more)` | 33 |
| 32k | `▁оꙁаскоу ▁и ▁· ▁юга ▁санъ ▁паоулоу ▁браꙁїльскъ ▁градъ ▁и ▁обьщина ... (+19 more)` | 29 |

**Sample 3:** `Октадєканъ и инако н-октадєканъ ѫглѥводородьно вєщьство алканъ рѧдоу ѥстъ ⁙ Ѥгож...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ок тадєканъ ▁и ▁инако ▁н - ок тадєканъ ▁ѫглѥводородьно ▁вєщьство ... (+19 more)` | 29 |
| 16k | `▁октадєканъ ▁и ▁инако ▁н - ок тадєканъ ▁ѫглѥводородьно ▁вєщьство ▁алканъ ... (+17 more)` | 27 |
| 32k | `▁октадєканъ ▁и ▁инако ▁н - октадєканъ ▁ѫглѥводородьно ▁вєщьство ▁алканъ ▁рѧдоу ... (+16 more)` | 26 |


### Key Findings

- **Best Compression:** 32k achieves 4.940x compression
- **Lowest UNK Rate:** 8k with 0.1314% 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 | 802 | 9.65 | 1,417 | 38.7% | 88.9% |
| **2-gram** | Subword | 451 🏆 | 8.82 | 2,622 | 56.3% | 95.5% |
| **3-gram** | Word | 965 | 9.91 | 1,734 | 35.4% | 82.3% |
| **3-gram** | Subword | 2,629 | 11.36 | 12,286 | 25.7% | 67.4% |
| **4-gram** | Word | 1,583 | 10.63 | 2,960 | 29.4% | 67.1% |
| **4-gram** | Subword | 8,218 | 13.00 | 33,187 | 16.1% | 45.2% |
| **5-gram** | Word | 1,176 | 10.20 | 2,224 | 32.9% | 74.0% |
| **5-gram** | Subword | 14,289 | 13.80 | 46,031 | 12.7% | 35.8% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ꙁьри такождє` | 432 |
| 2 | `людии обитаѥтъ` | 260 |
| 3 | `ѥстъ людии` | 234 |
| 4 | `градъ ѥстъ` | 230 |
| 5 | `стольнъ градъ` | 186 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ѥстъ людии обитаѥтъ` | 181 |
| 2 | `дрьжавѣ бѣла роусь` | 120 |
| 3 | `въ дрьжавѣ бѣла` | 120 |
| 4 | `градъ ѥстъ людии` | 115 |
| 5 | `бѣла роусь сѣи` | 114 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `въ дрьжавѣ бѣла роусь` | 120 |
| 2 | `дрьжавѣ бѣла роусь сѣи` | 114 |
| 3 | `оудѣлъ въ дрьжавѣ бѣла` | 114 |
| 4 | `ꙁємьскъ оудѣлъ въ дрьжавѣ` | 114 |
| 5 | `бѣла роусь сѣи оудѣлъ` | 114 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `роусь сѣи оудѣлъ бѣ члѣнъ` | 114 |
| 2 | `ꙁємьскъ оудѣлъ въ дрьжавѣ бѣла` | 114 |
| 3 | `оудѣлъ въ дрьжавѣ бѣла роусь` | 114 |
| 4 | `бѣла роусь сѣи оудѣлъ бѣ` | 114 |
| 5 | `дрьжавѣ бѣла роусь сѣи оудѣлъ` | 114 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ъ _` | 17,697 |
| 2 | `и _` | 9,192 |
| 3 | `а _` | 8,589 |
| 4 | `с т` | 8,369 |
| 5 | `_ с` | 6,568 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `т ъ _` | 5,939 |
| 2 | `_ · _` | 4,413 |
| 3 | `ь с к` | 3,883 |
| 4 | `_ ⁙ _` | 3,094 |
| 5 | `с т ъ` | 3,038 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ѥ с т` | 2,895 |
| 2 | `с т ъ _` | 2,876 |
| 3 | `ѥ с т ъ` | 2,698 |
| 4 | `ъ _ ⁙ _` | 1,902 |
| 5 | `т ъ _ ⁙` | 1,813 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ѥ с т ъ` | 2,695 |
| 2 | `ѥ с т ъ _` | 2,559 |
| 3 | `т ъ _ ⁙ _` | 1,796 |
| 4 | `_ г р а д` | 1,425 |
| 5 | `с т ъ _ ⁙` | 1,340 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 451
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~36% 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 | 0.4863 | 1.401 | 2.62 | 18,746 | 51.4% |
| **1** | Subword | 0.9940 | 1.992 | 7.09 | 1,077 | 0.6% |
| **2** | Word | 0.1229 | 1.089 | 1.22 | 48,473 | 87.7% |
| **2** | Subword | 0.8201 | 1.766 | 4.18 | 7,633 | 18.0% |
| **3** | Word | 0.0444 | 1.031 | 1.07 | 58,365 | 95.6% |
| **3** | Subword | 0.5514 | 1.466 | 2.43 | 31,900 | 44.9% |
| **4** | Word | 0.0207 🏆 | 1.014 | 1.03 | 61,255 | 97.9% |
| **4** | Subword | 0.3387 | 1.265 | 1.70 | 77,420 | 66.1% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `и ꙁападьнꙑ дъвинꙑ роуси пьсаниꙗ алєѯандра данїиловища свѣтьлѣиша кънѧꙃа владєнию бѣ съ словѣньскомь ...`
2. `ѥстъ стольнъ градъ ѥстъ нѣмьцкомь єпископомь албєртомь а нꙑнѣ жє носьнꙑи приꙁвѫкъ нє ꙁнаашє ѥдьнъ ис`
3. `лѣта їмпєратѡръ ѥстъ пєроунъ сварогъ ѩꙁꙑчьство`

**Context Size 2:**

1. `ꙁьри такождє обитѣльско напьсаниѥ владиславъ їѡаннъ асєн҄ь а҃ и блъгарїꙗ цѣсарь бѣ їѡанна асєнꙗ а҃ с...`
2. `людии обитаѥтъ 6 9 лєѡ́дръ їсторїꙗ лѣта по нѣмьць ѥдьнѥниꙗ бєрлинъ пакꙑ сталъ ѥстъ ꙁьри такъждє брюѯ...`
3. `ѥстъ людии 2 лєѡдръ обитаѥтъ пакистана дрьжавьнъ ѩꙁꙑкъ тѷрчьскъ ѥстъ їсторїꙗ дѣлꙗ охранꙑ съдравиꙗ лѣ...`

**Context Size 3:**

1. `ѥстъ людии обитаѥтъ 398 и иꙁъ ихъжє мѫжь 175 и жєнъ 223 наибол҄ии числомь народъ роусьсци ѥстъ 99`
2. `въ дрьжавѣ бѣла роусь сѣи оудѣлъ бѣ члѣнъ ѡбласти рѣкома могилєвьска ѡбласть повѣтъ имаѥтъ оурѧдъ рѣ...`
3. `дрьжавѣ бѣла роусь сѣи оудѣлъ бѣ члѣнъ градоу витьбьскъ въ ѡбласти рѣкома мѣньска ѡбласть повѣтъ има...`

**Context Size 4:**

1. `въ дрьжавѣ бѣла роусь сѣи оудѣлъ бѣ члѣнъ ѡбласти рѣкома бєрєстєиска ѡбласть повѣтъ имаѥтъ оурѧдъ рѣ...`
2. `роусь сѣи оудѣлъ бѣ члѣнъ ѡбласти рѣкома мѣньска ѡбласть повѣтъ имаѥтъ оурѧдъ рѣкомъ сєльскъ съвѣтъ ...`
3. `бѣла роусь сѣи оудѣлъ бѣ члѣнъ ѡбласти рѣкома витєбьска ѡбласть повѣтъ имаѥтъ оурѧдъ рѣкомъ сєльскъ ...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_иното_ѥгонокꙑ_ѥ`
2. `а_одаскꙑ_сточлѣс`
3. `орлѩꙁа_гокє_ꙁꙑ_с`

**Context Size 2:**

1. `ъ_обирѡсьскꙑ_рѣвь`
2. `и_•_всєли_·_рѡпьс`
3. `а_посладъпрꙗѥтъ_ꙁ`

**Context Size 3:**

1. `тъ_⁙_глаголєптємпє`
2. `_·_дѣлъ_бѣлороусло`
3. `ьскъ_прьвовец_гора`

**Context Size 4:**

1. `_ѥстъ_·_мїхаилъ_хоу`
2. `стъ_словєниꙗ_мѫжь_с`
3. `ѥстъ_⁙_глагоданьска`


### Key Findings

- **Best Predictability:** Context-4 (word) with 97.9% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (77,420 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 | 6,189 |
| Total Tokens | 62,865 |
| Mean Frequency | 10.16 |
| Median Frequency | 3 |
| Frequency Std Dev | 60.08 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | и | 2,821 |
| 2 | ѥстъ | 2,694 |
| 3 | лѣта | 952 |
| 4 | бѣ | 910 |
| 5 | въ | 842 |
| 6 | градъ | 792 |
| 7 | ꙁьри | 536 |
| 8 | такождє | 533 |
| 9 | жє | 512 |
| 10 | людии | 470 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | катєгорїꙗ | 2 |
| 2 | سخ | 2 |
| 3 | هس | 2 |
| 4 | ش | 2 |
| 5 | ؤخخم | 2 |
| 6 | خىث | 2 |
| 7 | ىعةلاثق | 2 |
| 8 | صشس | 2 |
| 9 | пльсковьская | 2 |
| 10 | маѭтъ | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.9373 |
| R² (Goodness of Fit) | 0.986343 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 41.0% |
| Top 1,000 | 72.8% |
| Top 5,000 | 96.2% |
| Top 10,000 | 0.0% |

### Key Findings

- **Zipf Compliance:** R²=0.9863 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 41.0% of corpus
- **Long Tail:** -3,811 words needed for remaining 100.0% 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 | 0.2434 | 0.4441 | N/A | N/A |
| **mono_64d** | 64 | 0.0769 | 0.4495 | N/A | N/A |
| **mono_128d** | 128 | 0.0128 | 0.4700 | N/A | N/A |
| **aligned_32d** | 32 | 0.2434 🏆 | 0.4485 | 0.0177 | 0.1032 |
| **aligned_64d** | 64 | 0.0769 | 0.4699 | 0.0324 | 0.1475 |
| **aligned_128d** | 128 | 0.0128 | 0.4554 | 0.0442 | 0.1357 |

### Key Findings

- **Best Isotropy:** aligned_32d with 0.2434 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.4562. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 4.4% 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 | **1.066** | 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 |
|--------|----------|
| `-по` | поѩла, погꙑнѫли, польꙃєвати |
| `-пр` | прєждє, придънѣстрии, прасловѣньскъ |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ъ` | въꙁвращєнъ, дѣлъ, ѳєрапѡнтъ |
| `-къ` | липьтьскъ, грьчьскъ, словѣньскъ |
| `-нъ` | въꙁвращєнъ, гла́вьнъ, съꙁиждєнъ |
| `-ка` | кировьска, фроунꙁєньска, видодъска |
| `-скъ` | липьтьскъ, грьчьскъ, словѣньскъ |
| `-ска` | кировьска, фроунꙁєньска, видодъска |
| `-ьска` | кировьска, фроунꙁєньска, городєньска |
| `-кꙑ` | блъгарьскꙑ, хръватьскꙑ, словѣньскꙑ |

### 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 |
|------|----------|------------------|----------|
| `боук` | 1.89x | 14 contexts | боукꙑ, боуквꙑ, боукъвь |
| `ловѣ` | 1.63x | 18 contexts | словѣ, чловѣкъ, словѣнє |
| `слов` | 1.77x | 14 contexts | слово, словѣ, слова |
| `ласт` | 1.55x | 20 contexts | властъ, власть, власти |
| `ьжав` | 1.75x | 13 contexts | дрьжавꙑ, дрьжавъ, дрьжавѫ |
| `ньск` | 1.65x | 15 contexts | мѣньска, мѣньскъ, жєньскъ |
| `ьска` | 1.64x | 14 contexts | омьска, єстьска, сѣрьска |
| `овѣн` | 1.83x | 10 contexts | словѣнє, словѣнъ, словѣнїꙗ |
| `град` | 1.63x | 13 contexts | градѣ, градъ, гради |
| `блас` | 1.69x | 10 contexts | ѡбласти, области, ѡбласть |
| `ьскъ` | 1.63x | 11 contexts | омьскъ, римьскъ, ꙁємьскъ |
| `рьжа` | 1.69x | 9 contexts | дрьжавꙑ, дрьжавъ, дрьжавѫ |

### 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 |
|--------|--------|-----------|----------|
| `-по` | `-ъ` | 34 words | побѣдъ, помѣновєнъ |
| `-пр` | `-ъ` | 34 words | прьвꙑимъ, проливъ |
| `-по` | `-нъ` | 11 words | помѣновєнъ, посъланъ |
| `-по` | `-ка` | 7 words | подъкарпатьска, по́л̑ьска |
| `-по` | `-къ` | 7 words | подъбрадъкъ, подълѣсьскъ |
| `-по` | `-скъ` | 6 words | подълѣсьскъ, пол҄ьскъ |
| `-пр` | `-нъ` | 6 words | прѣданъ, природьнъ |
| `-пр` | `-къ` | 6 words | приморьскъ, прьвотравєньскъ |
| `-по` | `-ска` | 5 words | подъкарпатьска, по́л̑ьска |
| `-по` | `-ьскъ` | 5 words | подълѣсьскъ, пол҄ьскъ |

### 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 |
|------|-----------------|------------|------|
| гєѡргїиска | **`гєѡргїи-ска`** | 4.5 | `гєѡргїи` |
| посєлѥниѥ | **`по-сєлѥниѥ`** | 4.5 | `сєлѥниѥ` |
| октѡврїиска | **`октѡврїи-ска`** | 4.5 | `октѡврїи` |
| посєлѥниꙗ | **`по-сєлѥниꙗ`** | 4.5 | `сєлѥниꙗ` |
| самостоꙗтєл҄ьна | **`самостоꙗтєл҄ь-на`** | 4.5 | `самостоꙗтєл҄ь` |
| аѵстралїиска | **`аѵстралїи-ска`** | 4.5 | `аѵстралїи` |
| самостоꙗтѣльна | **`самостоꙗтѣль-на`** | 4.5 | `самостоꙗтѣль` |
| аѵстрїискъ | **`аѵстрїи-скъ`** | 4.5 | `аѵстрїи` |
| приморьскъ | **`пр-имор-ьскъ`** | 3.0 | `имор` |
| подольскъ | **`по-доль-скъ`** | 3.0 | `доль` |
| полїтїчьскъ | **`по-лїтїч-ьскъ`** | 3.0 | `лїтїч` |
| подъꙁємьнъ | **`по-дъꙁємь-нъ`** | 3.0 | `дъꙁємь` |
| прѣѥмьникъ | **`пр-ѣѥмьни-къ`** | 3.0 | `ѣѥмьни` |
| потрѣбьна | **`по-трѣбь-на`** | 3.0 | `трѣбь` |
| политическа | **`по-литиче-ска`** | 3.0 | `литиче` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Church Slavic 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 | **32k BPE** | Best compression (4.94x) |
| N-gram | **2-gram** | Lowest perplexity (451) |
| Markov | **Context-4** | Highest predictability (97.9%) |
| 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 20:59:44*