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README.md

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+---
+language: mg
+language_name: Malagasy
+language_family: austronesian_malagasy
+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-austronesian_malagasy
+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: 4.455
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8042
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Malagasy - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Malagasy** 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** | 3.717x | 3.72 | 1.0106% | 763,492 |
+| **16k** | 4.029x | 4.03 | 1.0955% | 704,362 |
+| **32k** | 4.266x | 4.27 | 1.1597% | 665,323 |
+| **64k** | 4.455x 🏆 | 4.46 | 1.2113% | 637,017 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `I Taquaritinga dia kaominina ao , ao amin'i . Jeografia . Ny isam-ponina dia 56....`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁i ▁ta qu arit inga ▁dia ▁kaominina ▁ao ▁, ▁ao ... (+34 more)` | 44 |
+| 16k | `▁i ▁ta qu arit inga ▁dia ▁kaominina ▁ao ▁, ▁ao ... (+34 more)` | 44 |
+| 32k | `▁i ▁ta qu arit inga ▁dia ▁kaominina ▁ao ▁, ▁ao ... (+34 more)` | 44 |
+| 64k | `▁i ▁taqu aritinga ▁dia ▁kaominina ▁ao ▁, ▁ao ▁amin ' ... (+32 more)` | 42 |
+
+**Sample 2:** `Zoltán Stieber dia mpilalao baolina kitra teraka ny 16 Oktobra tao Hongaria Jere...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁z ol t án ▁st i eb er ▁dia ▁mpilalao ... (+16 more)` | 26 |
+| 16k | `▁z olt án ▁st i eber ▁dia ▁mpilalao ▁baolina ▁kitra ... (+13 more)` | 23 |
+| 32k | `▁zoltán ▁st i eber ▁dia ▁mpilalao ▁baolina ▁kitra ▁teraka ▁ny ... (+11 more)` | 21 |
+| 64k | `▁zoltán ▁sti eber ▁dia ▁mpilalao ▁baolina ▁kitra ▁teraka ▁ny ▁ ... (+10 more)` | 20 |
+
+**Sample 3:** `Rutger Backe dia mpilalao baolina kitra mizaka ny zom-pirenen'i Soeda teraka ny ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁r ut ger ▁ba cke ▁dia ▁mpilalao ▁baolina ▁kitra ▁mizaka ... (+18 more)` | 28 |
+| 16k | `▁rut ger ▁ba cke ▁dia ▁mpilalao ▁baolina ▁kitra ▁mizaka ▁ny ... (+17 more)` | 27 |
+| 32k | `▁rut ger ▁ba cke ▁dia ▁mpilalao ▁baolina ▁kitra ▁mizaka ▁ny ... (+17 more)` | 27 |
+| 64k | `▁rut ger ▁ba cke ▁dia ▁mpilalao ▁baolina ▁kitra ▁mizaka ▁ny ... (+17 more)` | 27 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.455x compression
+- **Lowest UNK Rate:** 8k with 1.0106% 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 | 3,283 | 11.68 | 138,920 | 38.8% | 67.4% |
+| **2-gram** | Subword | 188 🏆 | 7.55 | 7,308 | 76.8% | 99.3% |
+| **3-gram** | Word | 6,811 | 12.73 | 327,650 | 31.9% | 62.3% |
+| **3-gram** | Subword | 1,135 | 10.15 | 56,079 | 43.5% | 83.4% |
+| **4-gram** | Word | 13,815 | 13.75 | 695,396 | 27.5% | 57.0% |
+| **4-gram** | Subword | 4,270 | 12.06 | 297,339 | 28.3% | 63.5% |
+| **5-gram** | Word | 15,415 | 13.91 | 666,811 | 25.9% | 55.7% |
+| **5-gram** | Subword | 10,797 | 13.40 | 801,473 | 21.0% | 52.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `amin ny` | 363,322 |
+| 2 | `andro taona` | 205,790 |
+| 3 | `ao amin` | 204,188 |
+| 4 | `au au` | 199,079 |
+| 5 | `au andro` | 199,066 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `au andro taona` | 199,066 |
+| 2 | `au au andro` | 199,066 |
+| 3 | `ao amin ny` | 165,787 |
+| 4 | `tamin ny taona` | 75,724 |
+| 5 | `taona au au` | 52,606 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `au au andro taona` | 199,066 |
+| 2 | `au andro taona au` | 52,606 |
+| 3 | `andro taona au au` | 52,606 |
+| 4 | `taona au au andro` | 52,598 |
+| 5 | `amin ny faritr i` | 42,015 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `au andro taona au au` | 52,606 |
+| 2 | `au au andro taona au` | 52,606 |
+| 3 | `andro taona au au andro` | 52,598 |
+| 4 | `taona au au andro taona` | 52,598 |
+| 5 | `ao amin ny faritr i` | 41,743 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `y _` | 2,972,630 |
+| 2 | `a _` | 2,871,965 |
+| 3 | `a n` | 2,624,146 |
+| 4 | `_ a` | 2,225,943 |
+| 5 | `n y` | 2,058,703 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n y _` | 1,991,132 |
+| 2 | `n a _` | 984,036 |
+| 3 | `_ n y` | 972,813 |
+| 4 | `m i n` | 698,997 |
+| 5 | `a n a` | 687,522 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n y _` | 971,989 |
+| 2 | `a m i n` | 574,682 |
+| 3 | `m i n '` | 543,499 |
+| 4 | `' n y _` | 517,014 |
+| 5 | `n ' n y` | 516,967 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a m i n '` | 543,348 |
+| 2 | `n ' n y _` | 516,918 |
+| 3 | `_ d i a _` | 465,119 |
+| 4 | `_ a m i n` | 438,819 |
+| 5 | `a u ) _ a` | 398,149 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 188
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~52% 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 | 0.6521 | 1.571 | 4.93 | 355,569 | 34.8% |
+| **1** | Subword | 0.6364 | 1.554 | 5.02 | 4,923 | 36.4% |
+| **2** | Word | 0.2834 | 1.217 | 1.88 | 1,748,434 | 71.7% |
+| **2** | Subword | 0.7914 | 1.731 | 4.56 | 24,686 | 20.9% |
+| **3** | Word | 0.1358 | 1.099 | 1.33 | 3,283,841 | 86.4% |
+| **3** | Subword | 0.8149 | 1.759 | 4.15 | 112,579 | 18.5% |
+| **4** | Word | 0.0673 🏆 | 1.048 | 1.15 | 4,348,637 | 93.3% |
+| **4** | Subword | 0.6559 | 1.576 | 3.01 | 467,417 | 34.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ny masoandro mitatao ho an drenirano sy tsy hita eo amin ny fivavahana iraniana manodidina amin`
+2. `dia ary maty tamin ny toerana avo indrindra dia tanàna ao amin ny fehiben ny insee`
+3. `amin ny insee dia degre jereo koa hainkintana zavatra ara daharanjarahasin ilay kaominina ao amin ny`
+
+**Context Size 2:**
+
+1. `amin ny boribory lavoraryizay antsoina koa hoe excentricité amin ny soratra desimaly ny faritr i nou...`
+2. `andro taona au au andro taona karenfletch au au andro taona bomans rg au au andro taona`
+3. `ao amin ny 0 2 0 3 ary manana hafanana eo amin ny fivondronan i guéret ao`
+
+**Context Size 3:**
+
+1. `au andro taona jb13 au au andro taona ja59 au au andro taona xn45 au au andro taona`
+2. `au au andro taona tk27 au au andro taona qj2 au au andro taona au au andro taona`
+3. `ao amin ny vondronosy maley ho aty madagasikara notarihin i roger le goff no ben ny tanàna mandritry`
+
+**Context Size 4:**
+
+1. `au au andro taona oe4 au au andro taona sq3 au au andro taona au au andro taona om23`
+2. `au andro taona au au andro taona au au andro taona wp3 au au andro taona xy4 au au`
+3. `andro taona au au andro taona au au andro taona sg92 au au andro taona au au andro taona`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `asogrewwrau)_any`
+2. `_aom4)_a_eliantr`
+3. `navony_bamiieser`
+
+**Context Size 2:**
+
+1. `y_mats.com-ponial`
+2. `a_dia_sy_hity_sy_`
+3. `andray_fy_ny_ambo`
+
+**Context Size 3:**
+
+1. `ny_ary_olomer_sns.`
+2. `na_rohy_i_juantsah`
+3. `_ny_ham-panodikoro`
+
+**Context Size 4:**
+
+1. `_ny_14_dia_mpilala_`
+2. `amin'_i_bernambaràn`
+3. `min'_ny_faritimes,_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (467,417 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 | 186,416 |
+| Total Tokens | 12,311,117 |
+| Mean Frequency | 66.04 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 4301.01 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ny | 1,518,386 |
+| 2 | dia | 465,913 |
+| 3 | amin | 435,633 |
+| 4 | au | 412,623 |
+| 5 | i | 399,465 |
+| 6 | taona | 314,686 |
+| 7 | ao | 283,721 |
+| 8 | andro | 214,534 |
+| 9 | ary | 149,725 |
+| 10 | tamin | 113,939 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | clavaud | 2 |
+| 2 | holaboay | 2 |
+| 3 | olaboay | 2 |
+| 4 | marggie | 2 |
+| 5 | xiomara | 2 |
+| 6 | tapias | 2 |
+| 7 | firmo | 2 |
+| 8 | gentofte | 2 |
+| 9 | amalienborg | 2 |
+| 10 | vyborg | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2392 |
+| R² (Goodness of Fit) | 0.998231 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 60.0% |
+| Top 1,000 | 81.4% |
+| Top 5,000 | 89.6% |
+| Top 10,000 | 92.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9982 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 60.0% of corpus
+- **Long Tail:** 176,416 words needed for remaining 7.7% 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 | 0.8042 | 0.3503 | N/A | N/A |
+| **mono_64d** | 64 | 0.7680 | 0.2980 | N/A | N/A |
+| **mono_128d** | 128 | 0.7205 | 0.2509 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8042 🏆 | 0.3596 | 0.0820 | 0.3280 |
+| **aligned_64d** | 64 | 0.7680 | 0.2994 | 0.1540 | 0.4960 |
+| **aligned_128d** | 128 | 0.7205 | 0.2597 | 0.2000 | 0.5660 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8042 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3030. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.0% 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 | **0.070** | 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 |
+|--------|----------|
+| `-s` | stenopetalum, stenay, sumiyoshi |
+| `-a` | andriamarobasy, anggun, aboville |
+| `-r` | reignat, rm97, raty |
+| `-t` | tm67, td34, tp34 |
+| `-c` | christensen, ce6, celentano |
+| `-b` | bakkoury, bev, bakr |
+| `-f` | famantaranavaratra, frisano, fanandraman |
+| `-g` | gp6, gq54, gc61 |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | donnera, kwaśniewska, famantaranavaratra |
+| `-na` | fanasokajiana, andaminana, hampijoroana |
+| `-n` | nosoniavin, anggun, christensen |
+| `-s` | pégairolles, aups, tauxières |
+| `-e` | aboville, bartole, louze |
+| `-y` | andriamarobasy, namitany, stenay |
+| `-o` | frisano, celentano, shapiro |
+| `-i` | oerstedii, sumiyoshi, salviani |
+
+### 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 |
+|------|----------|------------------|----------|
+| `inin` | 2.33x | 55 contexts | minin, vining, jining |
+| `indr` | 1.81x | 124 contexts | indre, indri, indry |
+| `andr` | 1.49x | 336 contexts | andry, andro, andra |
+| `ndra` | 1.67x | 176 contexts | ondra, andra, indra |
+| `itra` | 1.69x | 141 contexts | mitra, ritra, kitra |
+| `iana` | 1.64x | 164 contexts | kiana, riana, niana |
+| `ndri` | 1.63x | 161 contexts | endri, indri, andri |
+| `ants` | 1.70x | 116 contexts | sants, antsa, wants |
+| `ahar` | 1.66x | 111 contexts | nahar, bahar, ahary |
+| `ndro` | 1.58x | 111 contexts | andro, indro, androy |
+| `inta` | 1.74x | 60 contexts | vinta, linta, kinta |
+| `ntan` | 1.49x | 111 contexts | entan, entana, antany |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-a` | 134 words | ansikilika, aminata |
+| `-f` | `-a` | 128 words | fanononana, fanaparitahana |
+| `-f` | `-na` | 105 words | fanononana, fanaparitahana |
+| `-h` | `-a` | 103 words | hetaheta, hamitika |
+| `-t` | `-a` | 77 words | theodosia, tuberifera |
+| `-s` | `-a` | 64 words | serrania, sirasida |
+| `-f` | `-n` | 62 words | furlan, flaxman |
+| `-c` | `-s` | 61 words | citées, cisterciensis |
+| `-a` | `-na` | 56 words | alamàna, andriankotonavalona |
+| `-b` | `-a` | 52 words | bizantioma, botovasoa |
+
+### 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 |
+|------|-----------------|------------|------|
+| miratoerana | **`mi-ra-toerana`** | 7.5 | `toerana` |
+| namoronany | **`namoro-na-ny`** | 7.5 | `na` |
+| fanakanana | **`fanaka-na-na`** | 7.5 | `na` |
+| newfoundland | **`newfoundl-an-d`** | 7.5 | `an` |
+| fampitany | **`fampit-a-ny`** | 7.5 | `a` |
+| firenenena | **`firenen-e-na`** | 7.5 | `e` |
+| holazainao | **`holazai-na-o`** | 7.5 | `na` |
+| boetticher | **`boetti-ch-er`** | 7.5 | `ch` |
+| cucurbiteae | **`cucurbite-a-e`** | 7.5 | `a` |
+| cobergher | **`coberg-h-er`** | 7.5 | `h` |
+| fankanesana | **`fankanes-a-na`** | 7.5 | `a` |
+| fahatoranana | **`fahatora-na-na`** | 7.5 | `na` |
+| nanohanany | **`nanoha-na-ny`** | 7.5 | `na` |
+| anamafana | **`anamaf-a-na`** | 7.5 | `a` |
+| fampirantiana | **`fampiranti-a-na`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Malagasy shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.46x) |
+| N-gram | **2-gram** | Lowest perplexity (188) |
+| Markov | **Context-4** | Highest predictability (93.3%) |
+| 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 12:09:55*

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