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README.md

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+---
+language: glk
+language_name: Gilaki
+language_family: iranian_western
+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-iranian_western
+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: 3.924
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7395
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-09
+---
+
+# Gilaki - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Gilaki** 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** | 3.040x | 3.04 | 0.8455% | 224,022 |
+| **16k** | 3.382x | 3.39 | 0.9407% | 201,331 |
+| **32k** | 3.692x | 3.70 | 1.0270% | 184,426 |
+| **64k** | 3.924x 🏆 | 3.93 | 1.0915% | 173,524 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `تولد من هست در جریان باشید😅 ایتفاقان تولدان مرگان توشکه رده : سیا ما روزان رده:ت...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁تول د ▁من ▁هست ▁در ▁ج ریان ▁باش ید 😅 ... (+16 more)` | 26 |
+| 16k | `▁تولد ▁من ▁هست ▁در ▁ج ریان ▁باش ید 😅 ▁ایتفاقان ... (+15 more)` | 25 |
+| 32k | `▁تولد ▁من ▁هست ▁در ▁جریان ▁باشید 😅 ▁ایتفاقان ▁تولدان ▁مرگان ... (+13 more)` | 23 |
+| 64k | `▁تولد ▁من ▁هست ▁در ▁جریان ▁باشید 😅 ▁ایتفاقان ▁تولدان ▁مرگان ... (+13 more)` | 23 |
+
+**Sample 2:** `کیاسرا ایسم ایته جی روستاهان لفمجان دهستان ، لاجان شهرستان مرکزی بخش ایسه اوستان...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁کی اسرا ▁ایسم ▁ایته ▁جی ▁روستاهان ▁لفمجان ▁دهستان ▁، ▁لاجان ... (+11 more)` | 21 |
+| 16k | `▁کی اسرا ▁ایسم ▁ایته ▁جی ▁روستاهان ▁لفمجان ▁دهستان ▁، ▁لاجان ... (+11 more)` | 21 |
+| 32k | `▁کی اسرا ▁ایسم ▁ایته ▁جی ▁روستاهان ▁لفمجان ▁دهستان ▁، ▁لاجان ... (+11 more)` | 21 |
+| 64k | `▁کیاسرا ▁ایسم ▁ایته ▁جی ▁روستاهان ▁لفمجان ▁دهستان ▁، ▁لاجان ▁شهرستان ... (+10 more)` | 20 |
+
+**Sample 3:** `بیلاژ محله ایسم ایته جی روستاهان آهندان دهستان ، لاجان شهرستان مرکزی بخش ایسه او...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁بی لا ژ ▁محله ▁ایسم ▁ایته ▁جی ▁روستاهان ▁آهندان ▁دهستان ... (+13 more)` | 23 |
+| 16k | `▁بی لا ژ ▁محله ▁ایسم ▁ایته ▁جی ▁روستاهان ▁آهندان ▁دهستان ... (+13 more)` | 23 |
+| 32k | `▁بیلا ژ ▁محله ▁ایسم ▁ا��ته ▁جی ▁روستاهان ▁آهندان ▁دهستان ▁، ... (+12 more)` | 22 |
+| 64k | `▁بیلا ژ ▁محله ▁ایسم ▁ایته ▁جی ▁روستاهان ▁آهندان ▁دهستان ▁، ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.924x compression
+- **Lowest UNK Rate:** 8k with 0.8455% 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 | 452 | 8.82 | 22,372 | 68.3% | 84.5% |
+| **2-gram** | Subword | 289 🏆 | 8.17 | 5,138 | 70.6% | 97.4% |
+| **3-gram** | Word | 859 | 9.75 | 38,047 | 59.7% | 79.2% |
+| **3-gram** | Subword | 1,264 | 10.30 | 39,676 | 47.4% | 79.1% |
+| **4-gram** | Word | 1,740 | 10.76 | 75,476 | 51.1% | 71.1% |
+| **4-gram** | Subword | 3,145 | 11.62 | 166,775 | 39.8% | 68.3% |
+| **5-gram** | Word | 2,593 | 11.34 | 80,402 | 46.0% | 65.5% |
+| **5-gram** | Subword | 5,057 | 12.30 | 329,311 | 36.7% | 64.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ٚ مئن` | 95,400 |
+| 2 | `أ شأر` | 62,694 |
+| 3 | `ٚ شأرستان` | 56,570 |
+| 4 | `شأرستان ٚ` | 49,032 |
+| 5 | `ايسه گه` | 41,300 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ٚ مئن نهأ` | 40,846 |
+| 2 | `شأرستان ٚ مئن` | 36,418 |
+| 3 | `ايته جه آمريکا` | 34,104 |
+| 4 | `ٚ شأرستان ٚ` | 33,251 |
+| 5 | `شأران ايسه گه` | 31,561 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `شأرستان ٚ مئن نهأ` | 36,381 |
+| 2 | `ٚ مئن نهأ ؤ` | 31,068 |
+| 3 | `آمريکا آمار ٚ مرکز` | 31,056 |
+| 4 | `ؤ آمريکا آمار ٚ` | 31,054 |
+| 5 | `نفر اعلام بۊگۊده سربس` | 31,054 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ؤ آمريکا آمار ٚ مرکز` | 31,054 |
+| 2 | `نهأ ؤ آمريکا آمار ٚ` | 31,053 |
+| 3 | `ٚ مئن نهأ ؤ آمريکا` | 31,053 |
+| 4 | `شأرستان ٚ مئن نهأ ؤ` | 31,051 |
+| 5 | `مئن نهأ ؤ آمريکا آمار` | 31,051 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ا ن` | 344,074 |
+| 2 | `_ٚ _` | 304,817 |
+| 3 | `ه _` | 274,936 |
+| 4 | `_ ش` | 270,095 |
+| 5 | `_ ا` | 227,254 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ش أ` | 217,137 |
+| 2 | `ش أ ر` | 216,916 |
+| 3 | `س ت ا` | 152,303 |
+| 4 | `_ٚ _ م` | 132,016 |
+| 5 | `ت ا ن` | 125,309 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ش أ ر` | 216,867 |
+| 2 | `س ت ا ن` | 123,160 |
+| 3 | `ا ن _ٚ _` | 107,779 |
+| 4 | `_ م ئ ن` | 103,927 |
+| 5 | `_ٚ _ م ئ` | 95,459 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ٚ _ م ئ ن` | 95,452 |
+| 2 | `ر س ت ا ن` | 92,229 |
+| 3 | `ش أ ر س ت` | 87,042 |
+| 4 | `أ ر س ت ا` | 87,041 |
+| 5 | `_ ش أ ر س` | 87,035 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 289
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~65% 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 | 0.5992 | 1.515 | 3.83 | 109,448 | 40.1% |
+| **1** | Subword | 1.2417 | 2.365 | 11.40 | 995 | 0.0% |
+| **2** | Word | 0.1612 | 1.118 | 1.39 | 416,225 | 83.9% |
+| **2** | Subword | 1.0373 | 2.052 | 6.72 | 11,334 | 0.0% |
+| **3** | Word | 0.0547 | 1.039 | 1.14 | 571,714 | 94.5% |
+| **3** | Subword | 0.7917 | 1.731 | 3.86 | 76,144 | 20.8% |
+| **4** | Word | 0.0260 🏆 | 1.018 | 1.09 | 645,056 | 97.4% |
+| **4** | Subword | 0.5603 | 1.475 | 2.44 | 293,705 | 44.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ٚ سر أ شأر دۊئل ٚ مرکز سال تومامه به اۊ زمت که گرم شاهندشت قلعه`
+2. `أ ۱ ۲۳۶ نفر مردأکان ۰ خانوار ۶۰۵ نفر اعلام بۊگۊده سربس شأرستان ٚ جمعيت أ`
+3. `مئن clark ايته جه ايصفهان ٚ اۊستان ٚ جمعيت أ شأر لاریمر ٚ مرکز آمار ٚ`
+
+**Context Size 2:**
+
+1. `ٚ مئن نهأ ؤ آمريکا آمار ٚ مرکز سال ٚ مئن farley ايته جه آمريکا شأران ايسه`
+2. `أ شأر ٚ جمعيت ۸ ۷۱۰ نفر ۲ ۹۰۶ خانوار بۊ عنوان نتایج سرشماری عمومی نفوس و`
+3. `ٚ شأرستان آیؤوا شأران en pena pobre puerto rico ايته جه آمريکا شأران ايسه گه نطنز ٚ`
+
+**Context Size 3:**
+
+1. `ٚ مئن نهأ ؤ آمريکا آمار ٚ مرکز أ شأر ٚ جمعيت أ ۳۵۲ نفر اعلام بۊگۊده سربس`
+2. `شأرستان ٚ مئن نهأ ؤ آمريکا آمار ٚ مرکز أ شأر ٚ جمعيت أ نفر اعلام بۊگۊده سربس`
+3. `ايته جه آمريکا شأرستانان ايسه گه اينديانا شينه أ شأر بیر ٚ شأرستان ٚ مئن نهأ ؤ آمريکا`
+
+**Context Size 4:**
+
+1. `شأرستان ٚ مئن نهأ ؤ آمريکا آمار ٚ مرکز أ شأر ٚ جمعيت أ ۷۹ نفر اعلام بۊگۊده سربس`
+2. `ٚ مئن نهأ ؤ آمريکا آمار ٚ مرکز أ شأر ٚ جمعيت أ ۸۹ نفر اعلام بۊگۊده سربس ٚ`
+3. `آمريکا آمار ٚ مرکز سال ٚ مئن أ شأر ٚ جمعيت أ نفر اعلام بۊگۊده سربس ٚ شأرستان اؤکلاهؤما`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ته_آم_گهر_ٚ_(معي`
+2. `ایکز_بای_نه._(اد`
+3. `ن_زوستؤر_زوامیتا`
+
+**Context Size 2:**
+
+1. `ان_(ايسه_ده._سربس`
+2. `_ٚ_مار_ٚ_مئن_گه_گه_`
+3. `ه_کي_شأر_هم_بۊ_ۊ_`
+
+**Context Size 3:**
+
+1. `_شأرستان_ٚ_مرکز،_سا`
+2. `شأرستان_ايته_جه_آم`
+3. `ستان_ٚ_مئن_نهأ_ؤ_آم`
+
+**Context Size 4:**
+
+1. `_شأر_ايشماردن_جه_آم`
+2. `ستان_ٚ_مئن_نهأ_ؤ_آمر`
+3. `ان_ٚ_مئن_نهأ_ؤ_آمريک`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (293,705 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 | 46,285 |
+| Total Tokens | 2,415,643 |
+| Mean Frequency | 52.19 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 1903.39 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ٚ | 304,843 |
+| 2 | أ | 119,882 |
+| 3 | مئن | 103,681 |
+| 4 | شأرستان | 80,615 |
+| 5 | شأر | 66,303 |
+| 6 | آمريکا | 65,573 |
+| 7 | شأران | 63,407 |
+| 8 | ايسه | 56,161 |
+| 9 | جه | 56,023 |
+| 10 | ؤ | 55,643 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | سبزان | 2 |
+| 2 | بۊلغاران | 2 |
+| 3 | استعمارکۊنان | 2 |
+| 4 | بامؤييد | 2 |
+| 5 | آلفؤنسؤ | 2 |
+| 6 | ميناب | 2 |
+| 7 | بحرين | 2 |
+| 8 | قطيف | 2 |
+| 9 | واسکؤ | 2 |
+| 10 | گاما | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0546 |
+| R² (Goodness of Fit) | 0.992625 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 74.1% |
+| Top 1,000 | 84.7% |
+| Top 5,000 | 91.8% |
+| Top 10,000 | 94.8% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9926 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 74.1% of corpus
+- **Long Tail:** 36,285 words needed for remaining 5.2% 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 | 0.7395 | 0.3894 | N/A | N/A |
+| **mono_64d** | 64 | 0.5770 | 0.3519 | N/A | N/A |
+| **mono_128d** | 128 | 0.2174 | 0.3507 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7395 🏆 | 0.3921 | 0.0080 | 0.0960 |
+| **aligned_64d** | 64 | 0.5770 | 0.3499 | 0.0280 | 0.2120 |
+| **aligned_128d** | 128 | 0.2174 | 0.3609 | 0.0600 | 0.2880 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7395 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3658. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.0% 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 | **0.010** | 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 |
+|--------|----------|
+| `-ان` | أتابکيان, کوهبنان, دوشمنان |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ستان` | 1.38x | 183 contexts | آستان, دستان, استان |
+| `یران` | 1.53x | 48 contexts | هیران, میران, ایران |
+| `وستا` | 1.47x | 46 contexts | کوستا, اوستا, روستا |
+| `رستا` | 1.36x | 61 contexts | رستاق, پرستان, رستاقˇ |
+| `انان` | 1.58x | 29 contexts | سانان, بانان, خانان |
+| `روست` | 1.53x | 17 contexts | مروست, روستا, بروستر |
+| `اوست` | 1.66x | 13 contexts | اوستا, اوستاد, اوستان |
+| `ۊستا` | 1.35x | 23 contexts | اۊستا, رۊستا, گۊستاو |
+| `انوا` | 1.63x | 12 contexts | انواع, انوارˇ, خانوار |
+| `ايال` | 1.64x | 8 contexts | ايالت, ايالات, ايالته |
+| `رۊست` | 1.54x | 9 contexts | رۊستا, رۊستم, برۊستن |
+| `يالت` | 1.69x | 7 contexts | ايالت, ايالته, ايالتˇ |
+
+### 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 |
+|------|-----------------|------------|------|
+| وایکینگان | **`وایکینگ-ان`** | 4.5 | `وایکینگ` |
+| بازيکؤنان | **`بازيکؤن-ان`** | 4.5 | `بازيکؤن` |
+| هخامنشيان | **`هخامنشي-ان`** | 4.5 | `هخامنشي` |
+| ویراستاران | **`ویراستار-ان`** | 4.5 | `ویراستار` |
+| استانداردان | **`استاندارد-ان`** | 4.5 | `استاندارد` |
+| کیشاورزان | **`کیشاورز-ان`** | 4.5 | `کیشاورز` |
+| انقلابیان | **`انقلابی-ان`** | 4.5 | `انقلابی` |
+| خاندنکسان | **`خاندنکس-ان`** | 4.5 | `خاندنکس` |
+| دموکراتان | **`دموکرات-ان`** | 4.5 | `دموکرات` |
+| دانشجویان | **`دانشجوی-ان`** | 4.5 | `دانشجوی` |
+| اؤتريشيان | **`اؤتريشي-ان`** | 4.5 | `اؤتريشي` |
+| هونرمندان | **`هونرمند-ان`** | 4.5 | `هونرمند` |
+| کامپیوتران | **`کامپیوتر-ان`** | 4.5 | `کامپیوتر` |
+| موهاجرتان | **`موهاجرت-ان`** | 4.5 | `موهاجرت` |
+| بیمارستانان | **`بیمارست-ان-ان`** | 3.0 | `بیمارست` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Gilaki 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 (3.92x) |
+| N-gram | **2-gram** | Lowest perplexity (289) |
+| Markov | **Context-4** | Highest predictability (97.4%) |
+| 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: 2026-01-09 23:47:34*

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