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README.md

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+---
+language: zu
+language_name: Zulu
+language_family: bantu_southern
+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-bantu_southern
+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: 5.059
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7797
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Zulu - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Zulu** 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** | 3.796x | 3.80 | 0.4092% | 301,785 |
+| **16k** | 4.244x | 4.25 | 0.4575% | 269,929 |
+| **32k** | 4.672x | 4.68 | 0.5037% | 245,198 |
+| **64k** | 5.059x 🏆 | 5.06 | 0.5454% | 226,437 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `I-Ouled Ahmed Timmi ngumasipala futhi yidolobha elikwisifundazwe se Adrar, e-Alj...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁i - ouled ▁ah med ▁ti m mi ▁ngumasipala ▁futhi ... (+15 more)` | 25 |
+| 16k | `▁i - ouled ▁ahmed ▁ti m mi ▁ngumasipala ▁futhi ▁yidolobha ... (+14 more)` | 24 |
+| 32k | `▁i - ouled ▁ahmed ▁ti mmi ▁ngumasipala ▁futhi ▁yidolobha ▁eli ... (+13 more)` | 23 |
+| 64k | `▁i - ouled ▁ahmed ▁ti mmi ▁ngumasipala ▁futhi ▁yidolobha ▁eli ... (+13 more)` | 23 |
+
+**Sample 2:** `I-Umm Bel yidolobha elikwisifundazwe se South Kordofan, eSudan. Imithombo ase Su...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁i - um m ▁bel ▁yidolobha ▁eli kwisifundazwe ▁se ▁south ... (+7 more)` | 17 |
+| 16k | `▁i - um m ▁bel ▁yidolobha ▁eli kwisifundazwe ▁se ▁south ... (+7 more)` | 17 |
+| 32k | `▁i - umm ▁bel ▁yidolobha ▁eli kwisifundazwe ▁se ▁south ▁kordofan ... (+6 more)` | 16 |
+| 64k | `▁i - umm ▁bel ▁yidolobha ▁eli kwisifundazwe ▁se ▁south ▁kordofan ... (+6 more)` | 16 |
+
+**Sample 3:** `ISousse yisifundazwe sase Thuniziya. Imithombo zase Thuniziya`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁iso us se ▁yisifundazwe ▁sase ▁thuniziya . ▁imithombo ▁zase ▁thuniziya` | 10 |
+| 16k | `▁isousse ▁yisifundazwe ▁sase ▁thuniziya . ▁imithombo ▁zase ▁thuniziya` | 8 |
+| 32k | `▁isousse ▁yisifundazwe ▁sase ▁thuniziya . ▁imithombo ▁zase ▁thuniziya` | 8 |
+| 64k | `▁isousse ▁yisifundazwe ▁sase ▁thuniziya . ▁imithombo ▁zase ▁thuniziya` | 8 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.059x compression
+- **Lowest UNK Rate:** 8k with 0.4092% 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 | 3,031 | 11.57 | 11,107 | 29.9% | 59.1% |
+| **2-gram** | Subword | 252 🏆 | 7.97 | 2,750 | 69.7% | 99.6% |
+| **3-gram** | Word | 2,282 | 11.16 | 10,014 | 34.2% | 65.5% |
+| **3-gram** | Subword | 2,028 | 10.99 | 20,811 | 25.8% | 75.4% |
+| **4-gram** | Word | 7,169 | 12.81 | 29,569 | 24.8% | 48.3% |
+| **4-gram** | Subword | 10,632 | 13.38 | 107,674 | 12.5% | 42.2% |
+| **5-gram** | Word | 6,986 | 12.77 | 25,819 | 24.3% | 47.4% |
+| **5-gram** | Subword | 33,821 | 15.05 | 270,035 | 8.3% | 27.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kwesifundazwe se` | 3,204 |
+| 2 | `imithombo ase` | 3,075 |
+| 3 | `imithombo zase` | 2,993 |
+| 4 | `kulandwe ngo` | 2,897 |
+| 5 | `esingaphansi kwesifundazwe` | 2,436 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `esingaphansi kwesifundazwe se` | 2,436 |
+| 2 | `yisifunda esingaphansi kwesifundazwe` | 2,424 |
+| 3 | `yidolobha elikwisifundazwe se` | 1,989 |
+| 4 | `kulandwe ngo zibandlela` | 1,191 |
+| 5 | `e aljeriya imithombo` | 1,073 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yisifunda esingaphansi kwesifundazwe se` | 2,424 |
+| 2 | `futhi yidolobha elikwisifundazwe se` | 865 |
+| 3 | `ngumasipala futhi yidolobha elikwisifundazwe` | 778 |
+| 4 | `ethiopia shapefiles ethiopias administrative` | 755 |
+| 5 | `shapefiles ethiopias administrative woredas` | 755 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ngumasipala futhi yidolobha elikwisifundazwe se` | 778 |
+| 2 | `org kulandwe ngo masingana 4` | 755 |
+| 3 | `shapefiles ethiopias administrative woredas africaopendata` | 755 |
+| 4 | `africaopendata org kulandwe ngo masingana` | 755 |
+| 5 | `woredas africaopendata org kulandwe ngo` | 755 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 204,965 |
+| 2 | `e _` | 133,326 |
+| 3 | `n g` | 129,678 |
+| 4 | `a n` | 126,471 |
+| 5 | `i _` | 119,226 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a _` | 40,639 |
+| 2 | `_ n g` | 40,201 |
+| 3 | `n g a` | 38,052 |
+| 4 | `t h i` | 34,873 |
+| 5 | `o k u` | 33,998 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t h i _` | 27,064 |
+| 2 | `_ u k u` | 22,270 |
+| 3 | `_ n g o` | 19,184 |
+| 4 | `u t h i` | 17,337 |
+| 5 | `e l a _` | 17,028 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `u t h i _` | 16,069 |
+| 2 | `i f u n d` | 12,578 |
+| 3 | `f u n d a` | 12,506 |
+| 4 | `s i f u n` | 11,468 |
+| 5 | `t h o m b` | 9,287 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 252
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 0.6996 | 1.624 | 3.77 | 162,102 | 30.0% |
+| **1** | Subword | 0.9455 | 1.926 | 7.47 | 974 | 5.4% |
+| **2** | Word | 0.1187 | 1.086 | 1.21 | 608,961 | 88.1% |
+| **2** | Subword | 0.9300 | 1.905 | 5.59 | 7,271 | 7.0% |
+| **3** | Word | 0.0263 | 1.018 | 1.04 | 733,914 | 97.4% |
+| **3** | Subword | 0.8812 | 1.842 | 4.36 | 40,628 | 11.9% |
+| **4** | Word | 0.0098 🏆 | 1.007 | 1.01 | 758,379 | 99.0% |
+| **4** | Subword | 0.7127 | 1.639 | 2.95 | 176,908 | 28.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `i motha owazalwa umgadli kusukela futhi yidolobha elikwisifundazwe se bizerte north kivu ekhongo bra...`
+2. `futhi abantu abaningi ngokukhipha amasoka awo abizwa i psl onamagoli aphakeme ngonyaka imithombo kap...`
+3. `imithombo zase khongo kinshasa administrative woredas africaopendata org kulandwe ngo masingana 4 fb...`
+
+**Context Size 2:**
+
+1. `kwesifundazwe se somali e itiyopiya imithombo ase khongo kinshasa zase khongo kinshasa zase khongo k...`
+2. `imithombo ase gabhoni zase gabhoni imithombo zase aljeriya amadolobha ase khenya imithombo zase erit...`
+3. `imithombo zase aljeriya ngaphansi kwezifundazwe ezitholakala kuzona census of population okwenziwa y...`
+
+**Context Size 3:**
+
+1. `esingaphansi kwesifundazwe se nabeul ethuniziya imithombo zase thuniziya`
+2. `yisifunda esingaphansi kwesifundazwe se aïn témouchent e aljeriya imithombo zase aljeriya ase aljeri...`
+3. `yidolobha elikwisifundazwe se central eyuganda lesiqhingi singaphansi kwesifunda se mukono imithombo...`
+
+**Context Size 4:**
+
+1. `yisifunda esingaphansi kwesifundazwe se north kivu ekhongo kinshasa administrative zones of the demo...`
+2. `futhi yidolobha elikwisifundazwe se sousse ethuniziya imithombo zase thuniziya`
+3. `ngumasipala futhi yidolobha elikwisifundazwe se tizi ouzou e aljeriya imithombo zase aljeriya ase al...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_-ama_nemebontid`
+2. `a_mila_lople_iny`
+3. `ithis_yo._kaston`
+
+**Context Size 2:**
+
+1. `a_isakhamo_ezisan`
+2. `e_ala_yeyidingo-m`
+3. `ngemhlokotabde_iz`
+
+**Context Size 3:**
+
+1. `la_kwisikhulu._ngo`
+2. `_ngekufund_baphosh`
+3. `nganiselwenkanye_n`
+
+**Context Size 4:**
+
+1. `thi_isixhosa_(12.1%`
+2. `_ukuya_kakhulumeni_`
+3. `_ngokusha_kanyise_u`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (176,908 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 | 62,862 |
+| Total Tokens | 817,095 |
+| Mean Frequency | 13.00 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 110.38 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | i | 10,301 |
+| 2 | futhi | 8,577 |
+| 3 | imithombo | 8,425 |
+| 4 | se | 7,351 |
+| 5 | kanye | 6,221 |
+| 6 | noma | 5,916 |
+| 7 | afrika | 5,345 |
+| 8 | e | 4,772 |
+| 9 | ukuthi | 4,267 |
+| 10 | ngo | 4,178 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | izimboma | 2 |
+| 2 | zokushulubeza | 2 |
+| 3 | miniaturowej | 2 |
+| 4 | sztuki | 2 |
+| 5 | profesjonalnej | 2 |
+| 6 | henryk | 2 |
+| 7 | wideo | 2 |
+| 8 | nietypowe | 2 |
+| 9 | sztalugi | 2 |
+| 10 | zapałek | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9199 |
+| R² (Goodness of Fit) | 0.997336 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 24.1% |
+| Top 1,000 | 47.6% |
+| Top 5,000 | 68.0% |
+| Top 10,000 | 77.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9973 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 24.1% of corpus
+- **Long Tail:** 52,862 words needed for remaining 22.9% 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 | 0.7797 🏆 | 0.2970 | N/A | N/A |
+| **mono_64d** | 64 | 0.7639 | 0.2268 | N/A | N/A |
+| **mono_128d** | 128 | 0.3200 | 0.1959 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7797 | 0.2840 | 0.0420 | 0.2500 |
+| **aligned_64d** | 64 | 0.7639 | 0.2098 | 0.0900 | 0.3460 |
+| **aligned_128d** | 128 | 0.3200 | 0.2045 | 0.1420 | 0.4240 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7797 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2363. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 14.2% 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 | **0.743** | 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 |
+|--------|----------|
+| `-i` | izingcwecwe, ijaji, iron |
+| `-e` | ekhokhelwayo, eziqinisekisiwe, eː |
+| `-u` | uzosiza, ubadide, ukundiyaza |
+| `-a` | akunakwenzeka, akhawunti, abavuthiwe |
+| `-s` | sobukhulu, suite, sicela |
+| `-n` | nenkonzo, nendodana, ngumholi |
+| `-ku` | kuncike, kuthonywa, kuleminyaka |
+| `-k` | kwizaga, komkhankaso, knuth |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | uzosiza, nendodana, ukundiyaza |
+| `-i` | zamabhaluni, ijaji, ngumholi |
+| `-e` | izingcwecwe, okucacisiwe, ubadide |
+| `-o` | nenkonzo, bebengenawo, ekhokhelwayo |
+| `-la` | indlela, awasungula, ezwela |
+| `-wa` | eyayiqondiswa, ukucekelwa, ethunyelwa |
+| `-ni` | zamabhaluni, zasehlathini, egciwaneni |
+| `-le` | westville, usonhlalakahle, okungalungile |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ifun` | 2.49x | 64 contexts | ifuna, sifuna, zifuna |
+| `khul` | 2.04x | 154 contexts | khula, khulu, ekhula |
+| `unda` | 2.44x | 42 contexts | lunda, undab, funda |
+| `ning` | 2.25x | 57 contexts | mining, iningi, eningi |
+| `sifu` | 2.54x | 34 contexts | sifuna, sifunde, sifunda |
+| `aban` | 1.89x | 96 contexts | abane, abangu, abanzi |
+| `anga` | 1.76x | 132 contexts | tanga, banga, angar |
+| `hulu` | 2.04x | 64 contexts | uhulu, khulu, okhulu |
+| `itho` | 1.90x | 81 contexts | zitho, ithole, isitho |
+| `apha` | 2.02x | 58 contexts | lapha, qapha, ngapha |
+| `kuth` | 1.91x | 68 contexts | ukuth, kuthi, kuthe |
+| `homb` | 2.12x | 42 contexts | ukhomba, ekhomba, akhomba |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-u` | `-a` | 340 words | ukunikeza, ugcina |
+| `-n` | `-a` | 329 words | nokujwayela, nethaba |
+| `-e` | `-a` | 244 words | enakekela, ezizosetshenziswa |
+| `-e` | `-i` | 241 words | ezimbizeni, emlandweni |
+| `-i` | `-a` | 212 words | ichasisa, isasasa |
+| `-n` | `-i` | 186 words | namashumi, nasekuthuthukiseni |
+| `-e` | `-ni` | 182 words | ezimbizeni, emlandweni |
+| `-e` | `-e` | 164 words | evinjelwe, ezimisele |
+| `-k` | `-a` | 155 words | kukajona, kokuvulwa |
+| `-a` | `-a` | 151 words | abrama, abalandelwa |
+
+### 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 |
+|------|-----------------|------------|------|
+| wasejalimane | **`wasejalim-a-ne`** | 7.5 | `a` |
+| continued | **`continu-e-d`** | 7.5 | `e` |
+| ukufudumala | **`ukufudum-a-la`** | 7.5 | `a` |
+| wayengunkosikazi | **`wayengunkosik-a-zi`** | 7.5 | `a` |
+| sikhakhane | **`sikhakh-a-ne`** | 7.5 | `a` |
+| ubuhlengikazi | **`ubuhlengik-a-zi`** | 7.5 | `a` |
+| afghanistani | **`afghanist-a-ni`** | 7.5 | `a` |
+| owayedlalela | **`owayedla-le-la`** | 7.5 | `le` |
+| samasulumane | **`samasulum-a-ne`** | 7.5 | `a` |
+| abancweli | **`abanc-we-li`** | 7.5 | `we` |
+| kwesilandelayo | **`kwesilande-la-yo`** | 7.5 | `la` |
+| abashokobezi | **`abashokob-e-zi`** | 7.5 | `e` |
+| nabwatswana | **`nabwats-wa-na`** | 7.5 | `wa` |
+| ukuhlabeka | **`ukuhlab-e-ka`** | 7.5 | `e` |
+| nezinselele | **`nezinse-le-le`** | 7.5 | `le` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Zulu 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 (5.06x) |
+| N-gram | **2-gram** | Lowest perplexity (252) |
+| Markov | **Context-4** | Highest predictability (99.0%) |
+| 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: 2026-01-11 06:02:31*

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