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+---
+language: lg
+language_name: Ganda
+language_family: bantu_eastern
+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_eastern
+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: 4.749
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8731
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Ganda - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ganda** 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** | 3.693x | 3.70 | 0.2870% | 259,571 |
+| **16k** | 4.077x | 4.08 | 0.3168% | 235,148 |
+| **32k** | 4.439x | 4.44 | 0.3449% | 215,974 |
+| **64k** | 4.749x 🏆 | 4.75 | 0.3690% | 201,887 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Kigulu, ekibuga mu Kira Town mu Wakiso mu Yuganda.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ki gulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ... (+2 more)` | 12 |
+| 16k | `▁ki gulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ... (+2 more)` | 12 |
+| 32k | `▁kigulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ▁yuganda ... (+1 more)` | 11 |
+| 64k | `▁kigulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ▁yuganda ... (+1 more)` | 11 |
+
+**Sample 2:** `Kibuku nsi e disitulikit wa Yuganda. Obugazi: 490.2 km². Abantu: 181 700 mu Yuga...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kibu ku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ... (+21 more)` | 31 |
+| 16k | `▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)` | 30 |
+| 32k | `▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)` | 30 |
+| 64k | `▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)` | 30 |
+
+**Sample 3:** `thumbnail Flippy lwe e okuba mu naye nga Happy Tree Friends.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁thumb na il ▁f li pp y ▁lwe ▁e ▁okuba ... (+12 more)` | 22 |
+| 16k | `▁thumb na il ▁f lipp y ▁lwe ▁e ▁okuba ▁mu ... (+8 more)` | 18 |
+| 32k | `▁thumb na il ▁f lipp y ▁lwe ▁e ▁okuba ▁mu ... (+7 more)` | 17 |
+| 64k | `▁thumbnail ▁flippy ▁lwe ▁e ▁okuba ▁mu ▁naye ▁nga ▁ha ppy ... (+3 more)` | 13 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.749x compression
+- **Lowest UNK Rate:** 8k with 0.2870% 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 | 14,199 | 13.79 | 38,632 | 12.7% | 34.6% |
+| **2-gram** | Subword | 219 🏆 | 7.77 | 2,147 | 71.2% | 99.8% |
+| **3-gram** | Word | 26,700 | 14.70 | 56,796 | 9.4% | 25.1% |
+| **3-gram** | Subword | 1,669 | 10.70 | 19,121 | 29.6% | 78.5% |
+| **4-gram** | Word | 70,764 | 16.11 | 118,544 | 6.5% | 15.1% |
+| **4-gram** | Subword | 8,452 | 13.05 | 97,188 | 14.1% | 45.2% |
+| **5-gram** | Word | 61,726 | 15.91 | 94,350 | 7.0% | 14.7% |
+| **5-gram** | Subword | 28,463 | 14.80 | 255,792 | 7.8% | 27.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `okuva mu` | 5,167 |
+| 2 | `mu uganda` | 4,435 |
+| 3 | `y e` | 3,265 |
+| 4 | `ya uganda` | 2,854 |
+| 5 | `mu mwaka` | 2,411 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `disitulikiti y e` | 1,864 |
+| 2 | `mu mwaka gwa` | 1,837 |
+| 3 | `mu disitulikiti y` | 1,235 |
+| 4 | `okuva mu okutuuka` | 888 |
+| 5 | `mu okutuuka mu` | 872 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mu disitulikiti y e` | 1,155 |
+| 2 | `okuva mu okutuuka mu` | 831 |
+| 3 | `mu ssaza ly e` | 811 |
+| 4 | `united states of america` | 742 |
+| 5 | `erisangibwa mu ssaza ly` | 735 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `erisangibwa mu ssaza ly e` | 735 |
+| 2 | `mu nsi ya united states` | 734 |
+| 3 | `states of america g e` | 733 |
+| 4 | `united states of america g` | 733 |
+| 5 | `ya united states of america` | 733 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 510,380 |
+| 2 | `m u` | 198,216 |
+| 3 | `u _` | 192,024 |
+| 4 | `_ e` | 166,670 |
+| 5 | `_ m` | 159,088 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `m u _` | 121,316 |
+| 2 | `_ m u` | 114,905 |
+| 3 | `o k u` | 71,249 |
+| 4 | `w a _` | 70,500 |
+| 5 | `a _ e` | 66,280 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u _` | 89,059 |
+| 2 | `a _ m u` | 52,454 |
+| 3 | `_ o k u` | 49,017 |
+| 4 | `n g a _` | 45,054 |
+| 5 | `b w a _` | 32,976 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ m u _` | 43,937 |
+| 2 | `_ n g a _` | 29,244 |
+| 3 | `a _ o k u` | 23,484 |
+| 4 | `g a n d a` | 23,102 |
+| 5 | `u g a n d` | 22,563 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 219
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.7920 | 1.731 | 5.69 | 115,787 | 20.8% |
+| **1** | Subword | 1.1968 | 2.292 | 11.50 | 360 | 0.0% |
+| **2** | Word | 0.2704 | 1.206 | 1.67 | 657,016 | 73.0% |
+| **2** | Subword | 1.2119 | 2.316 | 7.94 | 4,135 | 0.0% |
+| **3** | Word | 0.1023 | 1.073 | 1.18 | 1,092,887 | 89.8% |
+| **3** | Subword | 0.9620 | 1.948 | 4.72 | 32,782 | 3.8% |
+| **4** | Word | 0.0425 🏆 | 1.030 | 1.06 | 1,288,031 | 95.7% |
+| **4** | Subword | 0.6947 | 1.619 | 3.00 | 154,616 | 30.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `mu mutwe gwayo kwe yali mukiise mu mawanga g e kenya visa pour la masaka n`
+2. `ku modern uganda olw amanyi mu kitundu ekisooka difiiri bw ebyoto ebisatu by ebijanjaalo biwerako er...`
+3. `nga 7 223 530 mu mukundani eyatometa n asuulibwa eddalu lya uganda premier soccer league uganda`
+
+**Context Size 2:**
+
+1. `okuva mu bumannyirivu bwe ne famire ye jyavaamu baddamu ebigambo bye baba balowozaako h wewale okuka...`
+2. `mu uganda judith babirye obuto bwe n okusoma kwe kakoma yazaalibwa mu buganda nga tewanabaawo kyefan...`
+3. `y e makerere ayongerako nti yalina ekisanja kye ekyokubiri nga enkambi yamaje era essomero lino lwal...`
+
+**Context Size 3:**
+
+1. `disitulikiti y e buhweju olukalala lw abakyala abawandiisi mu uganda eka femrite era abadde muwandii...`
+2. `mu mwaka gwa okutuuka mu yakomawo mu uganda mu n alondebwa okuba omusumba mu n awummula mu joseph`
+3. `mu disitulikiti y e kayunga mu paalamenti ey omwenda mwalimu edward katumba wamala yazaalibwa ng enn...`
+
+**Context Size 4:**
+
+1. `mu disitulikiti y e ibanda siniya eyokuna yagimaliririza mu immaculate heart nyakibale secondary sch...`
+2. `okuva mu okutuuka mu oluvannyuma yakola ng omukulu w essomero mu yalondebwa nga ssentebe w ekibiina ...`
+3. `mu ssaza ly e texas mu nsi ya united states of america g e kentucky united states ebisangibwa mu`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ddenndeti_e_ngo`
+2. `a_kulizyokamu_ku`
+3. `erokokin’o_ebake`
+
+**Context Size 2:**
+
+1. `a_n'ebyasootard,_`
+2. `mu_neba_kiri_aba_`
+3. `u_mmu_binovera_co`
+
+**Context Size 3:**
+
+1. `mu_kino_okwe_yatal`
+2. `_mu_ugaziko_emisom`
+3. `okutender,_mu_gulu`
+
+**Context Size 4:**
+
+1. `_mu_by'amateekera_e`
+2. `a_mu_mabuvo_bwe_mu_`
+3. `_okuva_mu_luguumiro`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (154,616 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 | 51,479 |
+| Total Tokens | 1,503,057 |
+| Mean Frequency | 29.20 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 508.71 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mu | 89,527 |
+| 2 | ku | 30,386 |
+| 3 | nga | 29,634 |
+| 4 | n | 29,585 |
+| 5 | uganda | 17,006 |
+| 6 | ne | 15,431 |
+| 7 | era | 14,238 |
+| 8 | y | 12,890 |
+| 9 | e | 10,819 |
+| 10 | ya | 10,765 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | leku | 2 |
+| 2 | agataalimu | 2 |
+| 3 | gyebafuna | 2 |
+| 4 | omuwuubi | 2 |
+| 5 | baakyalira | 2 |
+| 6 | eturude | 2 |
+| 7 | bannanyinimu | 2 |
+| 8 | obusoose | 2 |
+| 9 | abanyanya | 2 |
+| 10 | kalogo | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0794 |
+| R² (Goodness of Fit) | 0.993590 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 36.5% |
+| Top 1,000 | 63.8% |
+| Top 5,000 | 82.3% |
+| Top 10,000 | 88.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9936 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 36.5% of corpus
+- **Long Tail:** 41,479 words needed for remaining 11.1% 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 | 0.8731 | 0.3015 | N/A | N/A |
+| **mono_64d** | 64 | 0.8603 | 0.2214 | N/A | N/A |
+| **mono_128d** | 128 | 0.6513 | 0.2031 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8731 🏆 | 0.2999 | 0.0840 | 0.3380 |
+| **aligned_64d** | 64 | 0.8603 | 0.2236 | 0.0980 | 0.3860 |
+| **aligned_128d** | 128 | 0.6513 | 0.2073 | 0.1800 | 0.5160 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8731 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2428. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 18.0% 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 | **-0.474** | 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 |
+|--------|----------|
+| `-a` | atuzibwa, abafirosoofa, amazze |
+| `-e` | ekifuula, enschede, ebisonjola |
+| `-ba` | banabyamizannyo, bazanye, babonabona |
+| `-b` | banabyamizannyo, bazanye, braun |
+| `-m` | musambi, miracle, margret |
+| `-k` | kyaleetera, kikungiri, kipande |
+| `-ka` | kabwegyere, kaweefube, kagoma |
+| `-o` | okwefuula, omugate, okulinnyisibwa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | atuzibwa, lwawangula, okwefuula |
+| `-wa` | atuzibwa, okulinnyisibwa, obubwa |
+| `-o` | banabyamizannyo, luweero, ssonko |
+| `-e` | enschede, omugate, kipande |
+| `-ra` | kyaleetera, yakyogera, luddirira |
+| `-i` | musambi, kikungiri, ppulaani |
+| `-u` | ekizungu, ntenjeru, gyawulwamu |
+| `-za` | awezezza, byanjigiriza, kulowooza |
+
+### 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 |
+|------|----------|------------------|----------|
+| `teek` | 2.29x | 104 contexts | teeka, ateeka, eteeka |
+| `tion` | 2.59x | 28 contexts | action, motion, cation |
+| `wang` | 1.81x | 117 contexts | wangu, wangi, lwang |
+| `gand` | 2.28x | 38 contexts | ganda, ugand, nganda |
+| `anny` | 1.99x | 62 contexts | danny, zannya, zannyi |
+| `atio` | 2.45x | 26 contexts | ratio, cation, nation |
+| `embe` | 2.09x | 37 contexts | ember, dembe, ddembe |
+| `ugan` | 1.97x | 46 contexts | ugand, uganda, ugandas |
+| `erez` | 2.01x | 30 contexts | perez, tereza, wereza |
+| `omuk` | 1.90x | 34 contexts | omuko, omuka, omukka |
+| `okus` | 1.82x | 37 contexts | okusa, okussa, okusiba |
+| `okuk` | 1.97x | 26 contexts | okuka, okukka, okukuu |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-o` | `-a` | 720 words | okukiikirirwa, okumusaba |
+| `-e` | `-a` | 516 words | eyonooneddwa, entakyuuka |
+| `-k` | `-a` | 384 words | kuvuganya, kubula |
+| `-a` | `-a` | 376 words | atea, akomererwa |
+| `-e` | `-o` | 230 words | ebipapajjo, ekigattikakibabiro |
+| `-ba` | `-a` | 197 words | bamerika, balumbagana |
+| `-b` | `-a` | 185 words | bamerika, balumbagana |
+| `-o` | `-o` | 147 words | ogwomukwano, okuzaawo |
+| `-e` | `-wa` | 138 words | eyonooneddwa, egizannyirwa |
+| `-o` | `-u` | 116 words | ogusibukamu, ogirimu |
+
+### 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 |
+|------|-----------------|------------|------|
+| baamatwale | **`baamat-wa-le`** | 7.5 | `wa` |
+| ebikwatibwako | **`ebikwatib-wa-ko`** | 7.5 | `wa` |
+| okuyisaawo | **`okuyisa-a-wo`** | 7.5 | `a` |
+| ekisingamu | **`ekising-a-mu`** | 7.5 | `a` |
+| obusingirayo | **`obusingir-a-yo`** | 7.5 | `a` |
+| kumuyamba | **`ku-mu-yamba`** | 7.5 | `yamba` |
+| kwatuukibwako | **`kwatuukib-wa-ko`** | 7.5 | `wa` |
+| mukungaaniramu | **`mukungaanir-a-mu`** | 7.5 | `a` |
+| ekitangaala | **`ekitanga-a-la`** | 7.5 | `a` |
+| batandikawo | **`batandik-a-wo`** | 7.5 | `a` |
+| bannyonyola | **`bannyony-o-la`** | 7.5 | `o` |
+| obunakuwavu | **`obunaku-wa-vu`** | 7.5 | `wa` |
+| akaateekebwawo | **`akaateekeb-wa-wo`** | 7.5 | `wa` |
+| okwetuusaako | **`okwetuusa-a-ko`** | 7.5 | `a` |
+| ekwatibwako | **`ekwatib-wa-ko`** | 7.5 | `wa` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ganda 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 (4.75x) |
+| N-gram | **2-gram** | Lowest perplexity (219) |
+| Markov | **Context-4** | Highest predictability (95.7%) |
+| 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 10:45:52*
diff --git a/lg_morph_tokenizer.json b/lg_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..6f577f5a14cfd0b910d4b041d3f2244e41048bbf
--- /dev/null
+++ b/lg_morph_tokenizer.json
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