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+---
+language: sn
+language_name: Shona
+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: 5.104
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8867
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Shona - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Shona** 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** | 3.894x | 3.90 | 0.0419% | 276,797 |
+| **16k** | 4.324x | 4.33 | 0.0465% | 249,296 |
+| **32k** | 4.723x | 4.73 | 0.0508% | 228,204 |
+| **64k** | 5.104x 🏆 | 5.11 | 0.0549% | 211,184 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Mukanyiwa (n. Dough). Kuvanga (v. Knead) kureva kukanya. Mitauro yeAfrika Lubwis...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mu kan yiwa ▁( n . ▁do ugh ). ▁ku ... (+32 more)` | 42 |
+| 16k | `▁mu kan yiwa ▁( n . ▁do ugh ). ▁ku ... (+31 more)` | 41 |
+| 32k | `▁mu kan yiwa ▁( n . ▁dough ). ▁ku vanga ... (+27 more)` | 37 |
+| 64k | `▁mukanyiwa ▁( n . ▁dough ). ▁kuvanga ▁( v . ... (+20 more)` | 30 |
+
+**Sample 2:** `Dhuri munhu anofarira zvekurwa zvibhakerera. Mitauro yeAfrika Dinka vanoti dhuur...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁d hu ri ▁munhu ▁ano farira ▁zveku rwa ▁zvi bha ... (+27 more)` | 37 |
+| 16k | `▁dhu ri ▁munhu ▁ano farira ▁zveku rwa ▁zvi bha ke ... (+23 more)` | 33 |
+| 32k | `▁dhu ri ▁munhu ▁anofarira ▁zveku rwa ▁zvi bha kerera . ... (+19 more)` | 29 |
+| 64k | `▁dhu ri ▁munhu ▁anofarira ▁zveku rwa ▁zvi bha kerera . ... (+18 more)` | 28 |
+
+**Sample 3:** `Mukoko wenyuchi (beehive). Mukoko sezita (a family name).`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁muko ko ▁wen yuchi ▁( bee hi ve ). ▁muko ... (+7 more)` | 17 |
+| 16k | `▁mukoko ▁wen yuchi ▁( bee hi ve ). ▁mukoko ▁sezita ... (+5 more)` | 15 |
+| 32k | `▁mukoko ▁wenyuchi ▁( beehive ). ▁mukoko ▁sezita ▁( a ▁family ... (+2 more)` | 12 |
+| 64k | `▁mukoko ▁wenyuchi ▁( beehive ). ▁mukoko ▁sezita ▁( a ▁family ... (+2 more)` | 12 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.104x compression
+- **Lowest UNK Rate:** 8k with 0.0419% 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 | 2,979 | 11.54 | 21,483 | 40.5% | 58.1% |
+| **2-gram** | Subword | 240 🏆 | 7.91 | 3,490 | 69.2% | 99.6% |
+| **3-gram** | Word | 2,908 | 11.51 | 32,746 | 46.6% | 57.5% |
+| **3-gram** | Subword | 1,817 | 10.83 | 20,969 | 30.6% | 75.4% |
+| **4-gram** | Word | 9,269 | 13.18 | 84,970 | 37.5% | 44.8% |
+| **4-gram** | Subword | 9,071 | 13.15 | 105,458 | 17.8% | 46.9% |
+| **5-gram** | Word | 7,269 | 12.83 | 73,171 | 40.2% | 47.4% |
+| **5-gram** | Subword | 28,891 | 14.82 | 288,731 | 12.7% | 34.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mamwe mazwi` | 7,603 |
+| 2 | `ari pedyo` | 5,393 |
+| 3 | `mazwi ari` | 5,117 |
+| 4 | `mitauro yebantu` | 4,853 |
+| 5 | `ari pasi` | 4,571 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mazwi ari pedyo` | 5,088 |
+| 2 | `pano panyorwa mazwi` | 4,522 |
+| 3 | `zvachose nemazwi ari` | 4,521 |
+| 4 | `nemazwi ari pasi` | 4,521 |
+| 5 | `kusiyana zvachose nemazwi` | 4,521 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `zvachose nemazwi ari pasi` | 4,521 |
+| 2 | `kusiyana zvachose nemazwi ari` | 4,521 |
+| 3 | `zvigona kusiyana zvachose nemazwi` | 4,520 |
+| 4 | `asi zvinoreva zita zvigona` | 4,519 |
+| 5 | `zvinoreva zita zvigona kusiyana` | 4,519 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kusiyana zvachose nemazwi ari pasi` | 4,521 |
+| 2 | `zvigona kusiyana zvachose nemazwi ari` | 4,520 |
+| 3 | `asi zvinoreva zita zvigona kusiyana` | 4,519 |
+| 4 | `zvinoreva zita zvigona kusiyana zvachose` | 4,519 |
+| 5 | `zita zvigona kusiyana zvachose nemazwi` | 4,519 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 324,415 |
+| 2 | `a n` | 217,120 |
+| 3 | `i _` | 172,321 |
+| 4 | `_ m` | 155,472 |
+| 5 | `v a` | 151,544 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k u` | 96,240 |
+| 2 | `_ v a` | 65,419 |
+| 3 | `_ m a` | 59,080 |
+| 4 | `t i _` | 56,775 |
+| 5 | `c h i` | 55,374 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ v a n` | 40,523 |
+| 2 | `r e v a` | 40,340 |
+| 3 | `e v a _` | 39,808 |
+| 4 | `n o t i` | 39,798 |
+| 5 | `o t i _` | 39,573 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `r e v a _` | 39,628 |
+| 2 | `n o t i _` | 39,196 |
+| 3 | `_ v a n o` | 28,589 |
+| 4 | `v a n o t` | 26,619 |
+| 5 | `a n o t i` | 26,589 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 240
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~34% 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 | 0.7188 | 1.646 | 4.18 | 152,645 | 28.1% |
+| **1** | Subword | 0.8114 | 1.755 | 5.35 | 2,484 | 18.9% |
+| **2** | Word | 0.1584 | 1.116 | 1.34 | 634,035 | 84.2% |
+| **2** | Subword | 0.6907 | 1.614 | 3.98 | 13,296 | 30.9% |
+| **3** | Word | 0.0530 | 1.037 | 1.09 | 844,134 | 94.7% |
+| **3** | Subword | 0.6592 | 1.579 | 3.47 | 52,844 | 34.1% |
+| **4** | Word | 0.0249 🏆 | 1.017 | 1.04 | 908,458 | 97.5% |
+| **4** | Subword | 0.6401 | 1.558 | 2.87 | 183,409 | 36.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `vanoti dukne v reward evanhu muzimbabwe vanhu vane zita zvigona kusiyana zvachose nemazwi ari pedyo ...`
+2. `kureva mutezo wemhuka tsumo emubhaibheri ra hadhisoni inotora ruwa runonzi murombedzi sezita mitauro...`
+3. `n cloth and twining plant fananidzai nokuti ichirikadzi a blow sokuti vanorarodutira doro nokugadzir...`
+
+**Context Size 2:**
+
+1. `mamwe mazwi mazulu vanoti tanda n middle mid centre kureva pakati gikyode vanoti karawoo n breakable...`
+2. `ari pedyo nezita iri kune mimwe mitauro yeafrica asi zvinoreva zita zvigona kusiyana zvachose nemazw...`
+3. `mazwi ari pedyo nezita iri kune mimwe mitauro yeafrica asi zvinoreva zita zvigona kusiyana zvachose ...`
+
+**Context Size 3:**
+
+1. `mazwi ari pedyo nezita iri kune mimwe mitauro yeafrika asi zvinoreva zita zvigona kusiyana zvachose ...`
+2. `pano panyorwa mazwi ari pedyo nezita iri kune mimwe mitauro yechibantu asi zvinoreva zita zvigona ku...`
+3. `kusiyana zvachose nemazwi ari pasi aya chinangwa ndechekutsvaga zvinoreva mazita zvikurusei kana maz...`
+
+**Context Size 4:**
+
+1. `kusiyana zvachose nemazwi ari pasi aya pane tarisiro yekuti mitauro yeafrika inotodzana mazwi xitson...`
+2. `zvachose nemazwi ari pasi aya nyanja inoti tewa kana tiwa adj flat stoop down kureva mushevedzeri ma...`
+3. `zvigona kusiyana zvachose nemazwi ari pasi aya pane tarisiro yekuti mitauro yeafrika inotodzana mazw...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_anamurod_ae_je_`
+2. `ana_sev._mbany_-`
+3. `irotulyo_shuho._`
+
+**Context Size 2:**
+
+1. `a_uye_mutersomo_y`
+2. `ani;_dzi_ariremit`
+3. `i_kance)._eva_84_`
+
+**Context Size 3:**
+
+1. `_kufambabwe_mitaur`
+2. `_vano_pass.)_ostii`
+3. `_makwaba_(to_(n._t`
+
+**Context Size 4:**
+
+1. `_vanonzi_chishona_k`
+2. `reva_dze_nhasikana_`
+3. `eva_munhu_mutsetse_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (183,409 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 | 64,427 |
+| Total Tokens | 1,165,961 |
+| Mean Frequency | 18.10 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 259.83 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | vanoti | 26,200 |
+| 2 | kureva | 25,672 |
+| 3 | n | 18,559 |
+| 4 | kana | 17,759 |
+| 5 | mazwi | 15,009 |
+| 6 | mitauro | 13,604 |
+| 7 | to | 12,577 |
+| 8 | inoti | 12,418 |
+| 9 | ari | 10,962 |
+| 10 | iri | 10,923 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | miganho | 2 |
+| 2 | nyasalendi | 2 |
+| 3 | chebhiritani | 2 |
+| 4 | chidzivirwana | 2 |
+| 5 | mvumiwa | 2 |
+| 6 | epuweti | 2 |
+| 7 | rematabelelandi | 2 |
+| 8 | pilibhit | 2 |
+| 9 | ifuleyisitata | 2 |
+| 10 | efreyistata | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9530 |
+| R² (Goodness of Fit) | 0.997578 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 38.1% |
+| Top 1,000 | 57.5% |
+| Top 5,000 | 74.2% |
+| Top 10,000 | 81.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9976 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 38.1% of corpus
+- **Long Tail:** 54,427 words needed for remaining 18.4% 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 | 0.8867 | 0.2895 | N/A | N/A |
+| **mono_64d** | 64 | 0.7943 | 0.2343 | N/A | N/A |
+| **mono_128d** | 128 | 0.3518 | 0.2276 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8867 🏆 | 0.2975 | 0.0380 | 0.2580 |
+| **aligned_64d** | 64 | 0.7943 | 0.2419 | 0.0800 | 0.3400 |
+| **aligned_128d** | 128 | 0.3518 | 0.2227 | 0.1160 | 0.4200 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8867 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2522. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 11.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.377** | 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 |
+|--------|----------|
+| `-ku` | kung, kuvaya, kudenguma |
+| `-mu` | munazarini, mudyo, musipa |
+| `-ma` | maswiswi, masawu, mataya |
+| `-m` | maswiswi, mwi, munazarini |
+| `-n` | nerokuti, natalensis, numeral |
+| `-s` | stuck, sikhatele, sefa |
+| `-ne` | nerokuti, nekumakomo, nemuchero |
+| `-k` | kihanda, kung, kwokunze |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | akataura, ruzhowa, kihanda |
+| `-e` | kwokunze, emergence, chemurume |
+| `-i` | chekunzi, nerokuti, maswiswi |
+| `-wa` | ruzhowa, gadzirwa, mhangwa |
+| `-ra` | akataura, horamvura, yakabundira |
+| `-o` | mudyo, kitendo, kwamashoko |
+| `-ka` | tyinyuka, kika, chikwaka |
+| `-s` | connections, natalensis, chums |
+
+### 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 |
+|------|----------|------------------|----------|
+| `evan` | 2.31x | 46 contexts | evans, evana, pevane |
+| `vanh` | 2.44x | 30 contexts | vanhu, evanhu, navanhu |
+| `zvin` | 2.03x | 52 contexts | zvino, zvine, zvina |
+| `chir` | 1.53x | 109 contexts | chira, chiri, chiro |
+| `anhu` | 2.07x | 29 contexts | vanhu, kanhu, sanhu |
+| `itau` | 2.02x | 30 contexts | chitau, mitauto, mitauro |
+| `mamw` | 2.49x | 15 contexts | mamwe, mamwi, emamwe |
+| `ikwa` | 1.88x | 38 contexts | sikwa, tsikwa, abikwa |
+| `taur` | 1.68x | 51 contexts | taura, ataure, taurwa |
+| `vach` | 1.98x | 23 contexts | vacho, tevach, zvacho |
+| `nore` | 1.61x | 40 contexts | snore, ignore, snorer |
+| `orev` | 1.95x | 20 contexts | roreva, yoreva, torevei |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ku` | `-a` | 576 words | kudzamura, kupandana |
+| `-ch` | `-a` | 219 words | chasara, chinogochewa |
+| `-n` | `-a` | 193 words | nunga, nekutaurirana |
+| `-a` | `-a` | 178 words | anoyevedza, amateka |
+| `-ku` | `-ra` | 150 words | kudzamura, kuruara |
+| `-k` | `-a` | 131 words | kolesa, kudzamura |
+| `-mu` | `-a` | 113 words | musasa, mutsuba |
+| `-ma` | `-a` | 104 words | machayina, magombedzanwa |
+| `-mu` | `-i` | 92 words | muvereki, mupinyi |
+| `-n` | `-i` | 80 words | nemasevhisi, ndiamai |
+
+### 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 |
+|------|-----------------|------------|------|
+| nekuvhara | **`nekuvh-a-ra`** | 7.5 | `a` |
+| dindiziri | **`dindiz-i-ri`** | 7.5 | `i` |
+| zveruvara | **`zveruv-a-ra`** | 7.5 | `a` |
+| mafutanhara | **`mafutanh-a-ra`** | 7.5 | `a` |
+| kufuruvara | **`kufuruv-a-ra`** | 7.5 | `a` |
+| mbungamabari | **`mbungamab-a-ri`** | 7.5 | `a` |
+| caribbean | **`caribbe-a-n`** | 7.5 | `a` |
+| runyararo | **`runya-ra-ro`** | 7.5 | `ra` |
+| yechidzimai | **`yechidzim-a-i`** | 7.5 | `a` |
+| yechirwere | **`yechir-we-re`** | 7.5 | `we` |
+| mumashure | **`mu-ma-shure`** | 7.5 | `shure` |
+| musandarara | **`musandar-a-ra`** | 7.5 | `a` |
+| pekuzvara | **`pekuzv-a-ra`** | 7.5 | `a` |
+| kwaanogara | **`kwaanog-a-ra`** | 7.5 | `a` |
+| ruambakare | **`ruamba-ka-re`** | 7.5 | `ka` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Shona 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 (5.10x) |
+| N-gram | **2-gram** | Lowest perplexity (240) |
+| Markov | **Context-4** | Highest predictability (97.5%) |
+| 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 21:39:08*
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+▁senegalensis	-13070
+▁yakaganhurwa	-13071
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+▁cheat	-13325
+▁chiau	-13326
+▁doubt	-13327
+▁dunga	-13328
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+▁gahwi	-13330
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+▁gwiri	-13332
+▁itayi	-13333
+▁jinda	-13334
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+▁kubhe	-13337
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+▁kusve	-13339
+▁lines	-13340
+▁mashi	-13341
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+▁kelvin	-13399
+▁kufuka	-13400
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+▁kuputa	-13408
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+▁kwenda	-13410
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