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README.md

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+---
+language: mrj
+language_name: Western Mari
+language_family: uralic_volgaic
+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-uralic_volgaic
+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: 4.191
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6197
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Western Mari - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Western Mari** 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** | 3.098x | 3.10 | 0.0903% | 303,340 |
+| **16k** | 3.510x | 3.51 | 0.1023% | 267,730 |
+| **32k** | 3.895x | 3.90 | 0.1135% | 241,309 |
+| **64k** | 4.191x 🏆 | 4.20 | 0.1222% | 224,266 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Билимби () — Oxalidaceae йыхыш пырышы фруктан пушӓнгӹ.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁б или м би ▁() ▁— ▁ox al id aceae ... (+5 more)` | 15 |
+| 16k | `▁б или м би ▁() ▁— ▁ox al id aceae ... (+5 more)` | 15 |
+| 32k | `▁били мби ▁() ▁— ▁ox al idaceae ▁йыхыш ▁пырышы ▁фруктан ... (+2 more)` | 12 |
+| 64k | `▁били мби ▁() ▁— ▁oxalidaceae ▁йыхыш ▁пырышы ▁фруктан ▁пушӓнгӹ .` | 10 |
+
+**Sample 2:** `Арлекин той шылдыран кӓдӹ () — кӓдӹ йишвлӓн йыхыш пырышы кечӹвӓлвел Австралиштӹ ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ар л ек ин ▁той ▁шылдыран ▁кӓдӹ ▁() ▁— ▁кӓдӹ ... (+17 more)` | 27 |
+| 16k | `▁ар л екин ▁той ▁шылдыран ▁кӓдӹ ▁() ▁— ▁кӓдӹ ▁йишвлӓн ... (+16 more)` | 26 |
+| 32k | `▁ар лекин ▁той ▁шылдыран ▁кӓдӹ ▁() ▁— ▁кӓдӹ ▁йишвлӓн ▁йыхыш ... (+15 more)` | 25 |
+| 64k | `▁арлекин ▁той ▁шылдыран ▁кӓдӹ ▁() ▁— ▁кӓдӹ ▁йишвлӓн ▁йыхыш ▁пырышы ... (+14 more)` | 24 |
+
+**Sample 3:** `Зичиуйфалу () — Венгриштӹ, Фейер медьежӹштӹ сола. Кымдецшӹ 10.82 км². ин тӹштӹ 9...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁зи чи уй ф ал у ▁() ▁— ▁венгри штӹ ... (+28 more)` | 38 |
+| 16k | `▁зи чи уй фал у ▁() ▁— ▁венгриштӹ , ▁ф ... (+26 more)` | 36 |
+| 32k | `▁зи чи уй фал у ▁() ▁— ▁венгриштӹ , ▁фей ... (+24 more)` | 34 |
+| 64k | `▁зи чиуйфалу ▁() ▁— ▁венгриштӹ , ▁фейер ▁медье жӹштӹ ▁сола ... (+20 more)` | 30 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.191x compression
+- **Lowest UNK Rate:** 8k with 0.0903% 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 | 3,063 | 11.58 | 9,727 | 28.9% | 58.6% |
+| **2-gram** | Subword | 730 🏆 | 9.51 | 3,875 | 39.1% | 95.0% |
+| **3-gram** | Word | 4,248 | 12.05 | 14,627 | 27.5% | 53.4% |
+| **3-gram** | Subword | 6,232 | 12.61 | 33,794 | 13.3% | 47.7% |
+| **4-gram** | Word | 12,214 | 13.58 | 35,262 | 19.5% | 37.0% |
+| **4-gram** | Subword | 28,212 | 14.78 | 162,256 | 7.9% | 28.6% |
+| **5-gram** | Word | 11,612 | 13.50 | 30,530 | 19.7% | 35.2% |
+| **5-gram** | Subword | 63,396 | 15.95 | 327,943 | 6.3% | 23.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `эдем ӹлен` | 2,392 |
+| 2 | `ин тӹштӹ` | 2,202 |
+| 3 | `йыхыш пырышы` | 2,165 |
+| 4 | `официал сайтшы` | 1,847 |
+| 5 | `группыш пырышы` | 1,725 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `статистика департаментжӹ эдем` | 1,016 |
+| 2 | `турцин статистика департаментжӹ` | 1,016 |
+| 3 | `департаментжӹ эдем ӹлен` | 1,016 |
+| 4 | `tüi̇k турцин статистика` | 978 |
+| 5 | `район каймакамын официал` | 890 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `турцин статистика департаментжӹ эдем` | 1,016 |
+| 2 | `статистика департаментжӹ эдем ӹлен` | 1,016 |
+| 3 | `tüi̇k турцин статистика департаментжӹ` | 978 |
+| 4 | `официал сайтшы район каймакамын` | 890 |
+| 5 | `сайтшы район каймакамын официал` | 890 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `турцин статистика департаментжӹ эдем ӹлен` | 1,016 |
+| 2 | `tüi̇k турцин статистика департаментжӹ эдем` | 978 |
+| 3 | `сайтшы район каймакамын официал сайтшы` | 890 |
+| 4 | `официал сайтшы район каймакамын официал` | 890 |
+| 5 | `муниципалитетӹн официал сайтшы район каймакамын` | 889 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. _` | 53,850 |
+| 2 | `н _` | 45,770 |
+| 3 | `_ к` | 39,996 |
+| 4 | `_ (` | 37,130 |
+| 5 | `, _` | 34,841 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `в л ӓ` | 28,557 |
+| 2 | `_ — _` | 25,910 |
+| 3 | `л ӓ _` | 14,577 |
+| 4 | `i s _` | 12,190 |
+| 5 | `u s _` | 11,117 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `в л ӓ _` | 13,629 |
+| 2 | `ш т ӹ _` | 7,644 |
+| 3 | `_ д ӓ _` | 7,347 |
+| 4 | `) _ — _` | 7,100 |
+| 5 | `о л о г` | 6,985 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `о л о г .` | 5,559 |
+| 2 | `л о г . _` | 5,557 |
+| 3 | `_ х а л а` | 4,299 |
+| 4 | `ы р ы ш ы` | 4,237 |
+| 5 | `р ы ш ы _` | 4,215 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 730
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% 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 | 0.7074 | 1.633 | 3.61 | 97,412 | 29.3% |
+| **1** | Subword | 1.0661 | 2.094 | 8.57 | 1,022 | 0.0% |
+| **2** | Word | 0.1371 | 1.100 | 1.28 | 349,102 | 86.3% |
+| **2** | Subword | 1.0482 | 2.068 | 6.56 | 8,742 | 0.0% |
+| **3** | Word | 0.0484 | 1.034 | 1.09 | 443,244 | 95.2% |
+| **3** | Subword | 0.9331 | 1.909 | 4.42 | 57,294 | 6.7% |
+| **4** | Word | 0.0268 🏆 | 1.019 | 1.05 | 479,455 | 97.3% |
+| **4** | Subword | 0.6599 | 1.580 | 2.59 | 253,059 | 34.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `дӓ тӹдӹм мам ит попы пилли ри маналтеш котка халан территорижӹ 425 campylocentrum hirtzii luer luer`
+2. `ин тӹштӹ 58 387 сингатока ра ra мериг mérig мере лава méré lava ошмаотывлӓ йӹлмӹвлӓ систематика`
+3. `пырышы кушкыш йишвлӓ bluering angelfish chaetodontoplus niger chan blueface angelfish chaetodontoplu...`
+
+**Context Size 2:**
+
+1. `ин тӹштӹ 50 511 tüi̇k турцин статистика департаментжӹ эдем ӹлен ажедмӓшвлӓ линквлӓ муниципалитетӹн о...`
+2. `йыхыш пырышы пеледшӹ кушкыш америкышты вӓшлиӓлтеш цилӓжӹ 60 йиш тӹрлӹ циприпедиум улы йишвлӓ knodus ...`
+3. `эдем ӹлен лӹмжӹ лӹмӹн этимологижӹ йеди шӹмӹт дон су вӹд шамаквлӓ гӹц лин ажедмӓшвлӓ линквлӓ муниципа...`
+
+**Context Size 3:**
+
+1. `статистика департаментжӹ эдем ӹлен ӹлӹзӹ шот и хала солавлӓ цилӓжӹ 31 581 34 323 65 904 61 561`
+2. `турцин статистика департаментжӹ эдем ӹлен хала лӹмӹн этимологижӹ дениз тангыж домуз сасна дон ли suf...`
+3. `департаментжӹ эдем ӹлен ажедмӓшвлӓ линквлӓ муниципалитетӹн официал сайтшы район каймакамын официал с...`
+
+**Context Size 4:**
+
+1. `турцин статистика департаментжӹ эдем ӹлен ажедмӓшвлӓ халавлӓ районвлӓ`
+2. `статистика департаментжӹ эдем ӹлен хала лӹмӹн этимологижӹ элма олма дон даг кырык шамаквлӓ гӹц лин ӹ...`
+3. `tüi̇k турцин статистика департаментжӹ эдем ӹлен ажедмӓшвлӓ линквлӓ муниципалитетӹн официал сайтшы ра...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_араверолӹшӹ_кул`
+2. `а_[па-_с_3_(бенн`
+3. `льышлӹш._тиз_rik`
+
+**Context Size 2:**
+
+1. `._va)_—_salopota_`
+2. `н_(johay_clis_kr_`
+3. `_короте_литла_уль`
+
+**Context Size 3:**
+
+1. `_—_асть_—_руш_ӓль_`
+2. `влӓ._336_см_лишнӹ_`
+3. `лӓ_nyalı_paridl.,_`
+
+**Context Size 4:**
+
+1. `влӓ_лин_де_группын_`
+2. `штӹ_дӓ_шылдыр_шӓрӓн`
+3. `_дӓ_аквариум_кӹшкӹж`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (253,059 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 | 43,052 |
+| Total Tokens | 565,174 |
+| Mean Frequency | 13.13 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 89.04 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | дӓ | 7,457 |
+| 2 | ин | 4,224 |
+| 3 | пырышы | 4,065 |
+| 4 | эдем | 3,208 |
+| 5 | йишвлӓ | 2,684 |
+| 6 | гӹц | 2,636 |
+| 7 | вӓшлиӓлтшӹ | 2,633 |
+| 8 | ӹлен | 2,497 |
+| 9 | герпетолог | 2,413 |
+| 10 | тӹштӹ | 2,391 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | сирӹмӓн | 2 |
+| 2 | һәм | 2 |
+| 3 | этвеш | 2 |
+| 4 | лоранд | 2 |
+| 5 | гамбургский | 2 |
+| 6 | муромский | 2 |
+| 7 | данилова | 2 |
+| 8 | кадров | 2 |
+| 9 | охлобыстин | 2 |
+| 10 | aena | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9957 |
+| R² (Goodness of Fit) | 0.994221 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 25.8% |
+| Top 1,000 | 55.8% |
+| Top 5,000 | 75.0% |
+| Top 10,000 | 83.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9942 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 25.8% of corpus
+- **Long Tail:** 33,052 words needed for remaining 16.7% 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 | 0.6197 🏆 | 0.3838 | N/A | N/A |
+| **mono_64d** | 64 | 0.2539 | 0.3763 | N/A | N/A |
+| **mono_128d** | 128 | 0.0468 | 0.3602 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6197 | 0.3829 | 0.0160 | 0.1380 |
+| **aligned_64d** | 64 | 0.2539 | 0.3701 | 0.0220 | 0.1600 |
+| **aligned_128d** | 128 | 0.0468 | 0.3665 | 0.0460 | 0.2140 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6197 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3733. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.504** | 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 |
+|--------|----------|
+| `-к` | кирибатиштӹш, кудымшы, кудвичӹ |
+| `-s` | scopulifera, stolzmann, surdus |
+| `-a` | alacakaya, auricularis, adilcevaz |
+| `-b` | borellii, bendilna, bergh |
+| `-m` | marco, minuticauda, musschenbroekii |
+| `-с` | своей, седӹндонок, северной |
+| `-а` | азбукы, алматы, ариель |
+| `-п` | пётр, пӹрнявлӓжӹм, пайдажым |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | hispida, alacakaya, scopulifera |
+| `-s` | pergracilis, pondicerianus, surdus |
+| `-н` | бодлерӹн, этажан, йыдпелӹн |
+| `-us` | pondicerianus, surdus, cinctus |
+| `-i` | verboonenii, borellii, clarkii |
+| `-is` | pergracilis, auricularis, hillis |
+| `-e` | tsubotae, chippindale, ambroise |
+| `-а` | калеана, момоца, гусянова |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ensi` | 2.15x | 19 contexts | boensis, poensis, obiensis |
+| `ывлӓ` | 1.60x | 43 contexts | озывлӓ, ошывлӓ, пучывлӓ |
+| `anth` | 1.87x | 24 contexts | anthus, fantham, anthony |
+| `кышт` | 1.89x | 18 contexts | кышты, юкышты, рикышты |
+| `олог` | 1.66x | 24 contexts | геолог, биолог, зоолог |
+| `нвлӓ` | 1.56x | 26 contexts | шонвлӓ, данвлӓ, пынвлӓ |
+| `авлӓ` | 1.43x | 29 contexts | аравлӓ, твавлӓ, таравлӓ |
+| `лавл` | 1.63x | 17 contexts | солавла, халавлӓ, солавлӓ |
+| `квлӓ` | 1.48x | 22 contexts | юквлӓ, ӓквлӓ, кеквлӓ |
+| `влӓж` | 1.64x | 15 contexts | ивлӓжӹ, ивлӓжӹн, ивлӓжӹм |
+| `тӹшт` | 1.80x | 11 contexts | тӹштӹ, тӹшты, тӹштӓт |
+| `райо` | 1.81x | 10 contexts | район, районы, района |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-s` | 95 words | cheleensis, catamblyrhynchus |
+| `-p` | `-s` | 84 words | paedocypris, phataginus |
+| `-c` | `-a` | 80 words | chiroptera, chrysochlora |
+| `-a` | `-a` | 77 words | america, arida |
+| `-s` | `-a` | 70 words | sonderiana, sororcula |
+| `-p` | `-a` | 68 words | pachira, parotia |
+| `-m` | `-a` | 67 words | mubuga, multistriata |
+| `-a` | `-s` | 64 words | acridotheres, aggeris |
+| `-к` | `-н` | 59 words | капаен, кырыкын |
+| `-m` | `-s` | 59 words | maculicollis, mishmensis |
+
+### 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 |
+|------|-----------------|------------|------|
+| insidiosa | **`insidio-s-a`** | 7.5 | `s` |
+| uréparapara | **`uréparap-a-ra`** | 7.5 | `a` |
+| robertsii | **`robert-s-ii`** | 7.5 | `s` |
+| гахенгери | **`гахенге-р-и`** | 7.5 | `р` |
+| ventricosa | **`ventrico-s-a`** | 7.5 | `s` |
+| мӱлӓндӹжӹм | **`мӱлӓндӹ-жӹ-м`** | 6.0 | `мӱлӓндӹ` |
+| tristrami | **`tristram-i`** | 4.5 | `tristram` |
+| чонгештӓт | **`чонгештӓ-т`** | 4.5 | `чонгештӓ` |
+| венгришты | **`венгриш-ты`** | 4.5 | `венгриш` |
+| blanfordi | **`blanford-i`** | 4.5 | `blanford` |
+| hamburger | **`hamburg-er`** | 4.5 | `hamburg` |
+| артиствлӓжӹ | **`артиствлӓ-жӹ`** | 4.5 | `артиствлӓ` |
+| элементжӹ | **`элемент-жӹ`** | 4.5 | `элемент` |
+| кудвичӹштӹ | **`кудвичӹш-тӹ`** | 4.5 | `кудвичӹш` |
+| драмывлӓм | **`драмывлӓ-м`** | 4.5 | `драмывлӓ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Western Mari 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 (4.19x) |
+| N-gram | **2-gram** | Lowest perplexity (730) |
+| Markov | **Context-4** | Highest predictability (97.3%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 13:10:29*

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