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+---
+language: xh
+language_name: Xhosa
+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: 4.929
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8914
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Xhosa - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Xhosa** 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** | 3.679x | 3.68 | 0.2207% | 429,593 |
+| **16k** | 4.111x | 4.11 | 0.2466% | 384,442 |
+| **32k** | 4.548x | 4.55 | 0.2728% | 347,524 |
+| **64k** | 4.929x 🏆 | 4.93 | 0.2956% | 320,656 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `I-Orta Nova (kude kube ebizwa ngokuba yi-Orta) ngumasipala wase-Italiya onabemi ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁i - or ta ▁nova ▁( kude ▁kube ▁ebizwa ▁ngokuba ... (+16 more)` | 26 |
+| 16k | `▁i - or ta ▁nova ▁( kude ▁kube ▁ebizwa ▁ngokuba ... (+15 more)` | 25 |
+| 32k | `▁i - orta ▁nova ▁( kude ▁kube ▁ebizwa ▁ngokuba ▁yi ... (+13 more)` | 23 |
+| 64k | `▁i - orta ▁nova ▁( kude ▁kube ▁ebizwa ▁ngokuba ▁yi ... (+13 more)` | 23 |
+
+**Sample 2:** `Icawa yindawo yokuhlanganisana yamaKristu, nokuba angamaKatolika, amaOthodoki ok...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁icawa ▁yindawo ▁yoku hlang ani sana ▁yama kristu , ▁nokuba ... (+13 more)` | 23 |
+| 16k | `▁icawa ▁yindawo ▁yoku hlangani sana ▁yama kristu , ▁nokuba ▁angama ... (+11 more)` | 21 |
+| 32k | `▁icawa ▁yindawo ▁yoku hlanganisana ▁yamakristu , ▁nokuba ▁angama katolika , ... (+5 more)` | 15 |
+| 64k | `▁icawa ▁yindawo ▁yoku hlanganisana ▁yamakristu , ▁nokuba ▁angama katolika , ... (+3 more)` | 13 |
+
+**Sample 3:** `IDaouche yilali kunye nendawo yasemaphandleni eNiger. Ukusukela ibinabemi Iimbek...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ida o u che ▁yilali ▁kunye ▁nendawo ▁yasemaphandleni ▁eniger . ... (+6 more)` | 16 |
+| 16k | `▁ida o u che ▁yilali ▁kunye ▁nendawo ▁yasemaphandleni ▁eniger . ... (+4 more)` | 14 |
+| 32k | `▁ida ouche ▁yilali ▁kunye ▁nendawo ▁yasemaphandleni ▁eniger . ▁ukusukela ▁ibinabemi ... (+2 more)` | 12 |
+| 64k | `▁idaouche ▁yilali ▁kunye ▁nendawo ▁yasemaphandleni ▁eniger . ▁ukusukela ▁ibinabemi ▁iimbekiselo ... (+1 more)` | 11 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.929x compression
+- **Lowest UNK Rate:** 8k with 0.2207% 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 | 3,253 | 11.67 | 5,073 | 16.6% | 52.9% |
+| **2-gram** | Subword | 259 🏆 | 8.02 | 2,144 | 68.4% | 99.5% |
+| **3-gram** | Word | 3,451 | 11.75 | 5,094 | 16.6% | 50.4% |
+| **3-gram** | Subword | 2,203 | 11.11 | 15,967 | 24.4% | 72.7% |
+| **4-gram** | Word | 9,133 | 13.16 | 12,576 | 11.1% | 29.3% |
+| **4-gram** | Subword | 12,328 | 13.59 | 78,348 | 10.9% | 38.3% |
+| **5-gram** | Word | 7,660 | 12.90 | 10,427 | 12.5% | 30.1% |
+| **5-gram** | Subword | 39,954 | 15.29 | 185,127 | 6.4% | 23.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kunye ne` | 613 |
+| 2 | `emzantsi afrika` | 405 |
+| 3 | `of the` | 341 |
+| 4 | `ngokuba yi` | 328 |
+| 5 | `emva koko` | 192 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `iimbekiselo amakhonkco angaphandle` | 97 |
+| 2 | `c eyona nyanga` | 78 |
+| 3 | `cc by post` | 76 |
+| 4 | `org cc by` | 76 |
+| 5 | `sa geonames org` | 76 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sa geonames org cc` | 76 |
+| 2 | `org cc by post` | 76 |
+| 3 | `geonames org cc by` | 76 |
+| 4 | `updated database download sa` | 76 |
+| 5 | `post updated database download` | 76 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sa geonames org cc by` | 76 |
+| 2 | `org cc by post updated` | 76 |
+| 3 | `cc by post updated database` | 76 |
+| 4 | `by post updated database download` | 76 |
+| 5 | `post updated database download sa` | 76 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 100,386 |
+| 2 | `e _` | 62,380 |
+| 3 | `a n` | 57,095 |
+| 4 | `o _` | 53,048 |
+| 5 | `n g` | 49,243 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a _` | 21,271 |
+| 2 | `_ n g` | 19,972 |
+| 3 | `_ k w` | 17,850 |
+| 4 | `_ k u` | 17,761 |
+| 5 | `a _ k` | 15,793 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n y e _` | 11,326 |
+| 2 | `e l a _` | 8,721 |
+| 3 | `_ u k u` | 8,570 |
+| 4 | `a _ n g` | 8,421 |
+| 5 | `_ n g o` | 8,259 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `k u b a _` | 5,628 |
+| 2 | `u n y e _` | 5,544 |
+| 3 | `k u n y e` | 5,475 |
+| 4 | `n y e _ n` | 5,432 |
+| 5 | `_ k u n y` | 5,381 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 259
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% 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 | 0.6070 | 1.523 | 3.17 | 104,417 | 39.3% |
+| **1** | Subword | 1.1180 | 2.171 | 9.35 | 521 | 0.0% |
+| **2** | Word | 0.1066 | 1.077 | 1.18 | 329,356 | 89.3% |
+| **2** | Subword | 1.0676 | 2.096 | 6.29 | 4,869 | 0.0% |
+| **3** | Word | 0.0246 | 1.017 | 1.03 | 387,463 | 97.5% |
+| **3** | Subword | 0.9182 | 1.890 | 4.35 | 30,613 | 8.2% |
+| **4** | Word | 0.0088 🏆 | 1.006 | 1.01 | 398,295 | 99.1% |
+| **4** | Subword | 0.7004 | 1.625 | 2.83 | 133,109 | 30.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `i multibit ye analog computer ngomzila wefowuni kuquka i eccentric kwaye iza wamkele ukristu bc nang...`
+2. `kunye newololo music education act isikolo samagriqua phesheya kwenciba nakumaxesha angaphambili kun...`
+3. `kwaye inomsebenzi wokutyumba oosompempe ukuba bamthabathe ngokwegqwirha elikhwela esinga ejongise ng...`
+
+**Context Size 2:**
+
+1. `kunye ne 8 500 bc ngexesha lestone age ukuya ekupheleni kwekhulu le 19 pos iqela pld w`
+2. `of the bhacas from earliest times to doctoral dissertation university of natal after he bought a sto...`
+3. `ngokuba yi alchemy nangona kunjalo waqhubeka wasebenza kuguqulo lwendumasiso lwenoveli yodidi engumz...`
+
+**Context Size 3:**
+
+1. `iimbekiselo amakhonkco angaphandle indawo esemthethweni ngesiphuthukezi baseroraima`
+2. `c eyona nyanga ishushu ngujulayi nge c kwaye eyona ngqele kafebruwari ngo c umyinge wokuna kwemvula ...`
+3. `cc by post updated database download sa ime kumasipala wasekalix kommun kunye nephondo lasenorbotten...`
+
+**Context Size 4:**
+
+1. `by post updated database download sa ifumaneka kwiphondo leprovincia di foggia kunye nommandla wepug...`
+2. `sa geonames org cc by post updated database download sa ifumaneka kummandla wezoqoqosho weylä savo k...`
+3. `cc by post updated database download sa ifumaneka kwiphondo leprovincia di verona kunye nommandla we...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ku_nazo_eay’uth`
+2. `abekwhut_(yose_:`
+3. `esisi_jekwabamba`
+
+**Context Size 2:**
+
+1. `a_es._kwagom_hays`
+2. `e_ngozabonfer,_ic`
+3. `ano_yelo_ye_ic_ek`
+
+**Context Size 3:**
+
+1. `la_wenziswengokwen`
+2. `_ngoxa_popolophu._`
+3. `_kwimi_eli_uba_uku`
+
+**Context Size 4:**
+
+1. `nye_la_confederano,`
+2. `ela_lwaseshumi_amaq`
+3. `_ukuze_sifumandeley`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (133,109 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 | 35,909 |
+| Total Tokens | 362,403 |
+| Mean Frequency | 10.09 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 60.47 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | i | 5,328 |
+| 2 | kunye | 5,290 |
+| 3 | kwaye | 2,522 |
+| 4 | ukuba | 2,013 |
+| 5 | okanye | 1,987 |
+| 6 | 1 | 1,832 |
+| 7 | the | 1,804 |
+| 8 | of | 1,523 |
+| 9 | kwi | 1,513 |
+| 10 | ke | 1,364 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | okuthengiswa | 2 |
+| 2 | esitalatweni | 2 |
+| 3 | ezitalatweni | 2 |
+| 4 | kwesitalato | 2 |
+| 5 | pilibhit | 2 |
+| 6 | ezifundo | 2 |
+| 7 | nenkubazeko | 2 |
+| 8 | yaseluthere | 2 |
+| 9 | ceulji | 2 |
+| 10 | kwesport | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8870 |
+| R² (Goodness of Fit) | 0.995256 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 21.1% |
+| Top 1,000 | 46.3% |
+| Top 5,000 | 69.4% |
+| Top 10,000 | 80.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9953 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 21.1% of corpus
+- **Long Tail:** 25,909 words needed for remaining 19.9% 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 | 0.8914 | 0.2946 | N/A | N/A |
+| **mono_64d** | 64 | 0.6652 | 0.2434 | N/A | N/A |
+| **mono_128d** | 128 | 0.1559 | 0.2440 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8914 🏆 | 0.2952 | 0.0360 | 0.2160 |
+| **aligned_64d** | 64 | 0.6652 | 0.2484 | 0.0540 | 0.2700 |
+| **aligned_128d** | 128 | 0.1559 | 0.2308 | 0.0880 | 0.3480 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8914 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2594. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.8% 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 | **0.310** | 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` | inyongo, itshintshile, iimitha |
+| `-e` | ehleli, elected, esebenzayo |
+| `-u` | umbane, umtu, ubunkokeli |
+| `-a` | abathunywa, amabanga, arlington |
+| `-n` | ngowayesakuba, njengeempawu, netherland |
+| `-ne` | netherland, neutron, nelungelo |
+| `-s` | steatorrhea, scored, sant |
+| `-ku` | kubanjelwa, kunokwenzeka, kusenziwa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | lokubhala, ngowayesakuba, wamaza |
+| `-o` | inyongo, ngenyawo, kwintetho |
+| `-i` | yabancinci, ngeentombi, ehleli |
+| `-e` | itshintshile, glucose, umbane |
+| `-la` | lokubhala, elivuselela, bawela |
+| `-wa` | kubanjelwa, abathunywa, kwaqhutywa |
+| `-ni` | ekujonganeni, empumelelweni, udlamini |
+| `-yo` | esebenzayo, ukwaziyo, elichaseneyo |
+
+### 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 |
+|------|----------|------------------|----------|
+| `khul` | 2.03x | 88 contexts | khulu, akhule, ekhula |
+| `enzi` | 2.13x | 60 contexts | menzi, enzima, enziwa |
+| `heth` | 2.04x | 68 contexts | khetha, khetho, utheth |
+| `aban` | 1.89x | 70 contexts | abane, abanye, abanga |
+| `okub` | 1.86x | 55 contexts | okuba, nokuba, sokuba |
+| `ezin` | 1.88x | 52 contexts | ezine, ezinde, ezinee |
+| `ants` | 2.26x | 23 contexts | gantsa, nantso, plants |
+| `andl` | 1.90x | 41 contexts | mandla, sandla, imandla |
+| `ngen` | 1.58x | 82 contexts | ingene, ongena, angena |
+| `ndle` | 1.83x | 41 contexts | endle, bundle, ndlebe |
+| `hulu` | 1.94x | 32 contexts | khulu, akhulu, ikhulu |
+| `bant` | 2.19x | 21 contexts | bantu, abantu, ubantu |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-n` | `-a` | 291 words | njengomasipala, nechina |
+| `-u` | `-a` | 256 words | ukuchazwa, unobhala |
+| `-n` | `-o` | 226 words | nkonzo, nenkathalo |
+| `-e` | `-a` | 216 words | ephesheya, entshwana |
+| `-i` | `-a` | 203 words | ingenziwa, ingena |
+| `-n` | `-i` | 179 words | ngamagqabi, neegesi |
+| `-e` | `-o` | 171 words | ebamako, ezichaphazelekayo |
+| `-i` | `-o` | 156 words | isibonelelo, ibibalihlazo |
+| `-k` | `-a` | 156 words | kuyakweza, kuyafana |
+| `-l` | `-a` | 153 words | lwama, litsha |
+
+### 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 |
+|------|-----------------|------------|------|
+| kamkhwebane | **`kamkhweb-a-ne`** | 7.5 | `a` |
+| yasentaliyane | **`yasentaliy-a-ne`** | 7.5 | `a` |
+| ubungcali | **`ubungc-a-li`** | 7.5 | `a` |
+| kwiitshaneli | **`kw-i-itshaneli`** | 7.5 | `itshaneli` |
+| nesijamani | **`nesijam-a-ni`** | 7.5 | `a` |
+| ezingabamelwane | **`ezingabamelw-a-ne`** | 7.5 | `a` |
+| uyavakala | **`uyavak-a-la`** | 7.5 | `a` |
+| abafikayo | **`abafik-a-yo`** | 7.5 | `a` |
+| nokudodobala | **`nokudodob-a-la`** | 7.5 | `a` |
+| kwisigwebo | **`kwisig-we-bo`** | 7.5 | `we` |
+| ezimfutshane | **`ezimfutsh-a-ne`** | 7.5 | `a` |
+| uzbekistan | **`uzbekist-a-n`** | 7.5 | `a` |
+| kwiinkulungwane | **`kwiinkulungw-a-ne`** | 7.5 | `a` |
+| sebastian | **`sebasti-a-n`** | 7.5 | `a` |
+| ovuthuzayo | **`ovuthuz-a-yo`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Xhosa 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 (4.93x) |
+| N-gram | **2-gram** | Lowest perplexity (259) |
+| Markov | **Context-4** | Highest predictability (99.1%) |
+| 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 04:59:25*
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+▁yamasilamsi	-43000
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+architectural	-43093
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+bangqongileyo	-43096
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+cyanobacteria	-43100
+gunyazisiweyo	-43101
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+isiqinisekiso	-43106
+isishunqulelo	-43107
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+mathematician	-43110
+mucocutaneous	-43111
+ndingowamazwe	-43112
+ngesifrentshi	-43113
+njengecandelo	-43114
+omlinganiselo	-43115
+perpendicular	-43116
+phakanyiselwe	-43117
+phytoplankton	-43118
+pyrimethamine	-43119
+qhagamshelana	-43120
+quarterfinals	-43121
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+sesesikolweni	-43124
+termaritzburg	-43125
+umlinganiselo	-43126
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+yekaterinburg	-43128
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+▁bamashishini	-43162
+▁basebarletta	-43163
+▁basebotswana	-43164
+▁basecolombia	-43165
+▁basehookland	-43166
+▁basemazantsi	-43167
+▁basembindini	-43168
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+▁língua	-54055
+▁menlyn	-54056
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+canusium	-54062
+eghirren	-54063
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+propathi	-54065
+pyvnytsk	-54066
+trophies	-54067
+▁elibize	-54068
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+▁saseboa	-54070
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+astronomi	-54072
+ekuqaleni	-54073
+ggelberge	-54074
+odebrecht	-54075
+sopotamia	-54076
+uruguayan	-54077
+▁ayiyopor	-54078
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+▁uliliane	-54080
+▁vryheids	-54081
+▁zasenami	-54082
+▁zisekeke	-54083
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+pyvnytskyi	-54085
+▁ababangel	-54086
+▁ayiyoporn	-54087
+▁ekufumani	-54088
+▁esewalmer	-54089
+▁espíndola	-54090
+▁icanusium	-54091
+▁mayerling	-54092
+▁nakumamie	-54093
+▁yeyucatán	-54094
+▁zepyrrhic	-54095
+corinthians	-54096
+correttezze	-54097
+cosmoglotta	-54098
+crustaceans	-54099
+pyhä	-54100
+papahā	-54101
+▁nmgca	-54102
+▁lucena	-54103
+▁nepyhä	-54104
+papahāna	-54105
+▁sitshilo	-54106
+iqhelekile	-54107
+papahānaum	-54108
+▁wezincoko	-54109
+dibanisweni	-54110
+dingazaziyo	-54111
+diophantine	-54112
+dlabhenguza	-54113
+drakensberg	-54114
+dunarodnaja	-54115
+eatmosphere	-54116
+ekwabelwana	-54117
+elalisayaku	-54118
+engcaphephe	-54119
+erteventura	-54120
+esempilweni	-54121
+esikhwenene	-54122
+esingangesi	-54123
+etiological	-54124
+ezichakwayo	-54125
+ezingalweni	-54126
+ezingamanzi	-54127
+fanekisweni	-54128
+fekanganono	-54129
+fikimfelana	-54130
+fotocasting	-54131
+fukuchiyama	-54132
+gantinoibra	-54133
+genasiphelo	-54134
+giungimento	-54135
+guepainette	-54136
+gusodolophu	-54137
+haplodiploi	-54138
+herbivorous	-54139
+hlangemihla	-54140
+hwankathelo	-54141
+hämeenlinna	-54142
+iamazonense	-54143
+icosahedron	-54144
+iirrational	-54145
+ijikelezisa	-54146
+intercostal	-54147
+isidlanyama	-54148
+isityhilelo	-54149
+iyayikhotha	-54150
+izimelabizo	-54151
+karhubusana	-54152
+khalifoniya	-54153
+khanyilanga	-54154
+khayelitsha	-54155
+khompuyutha	-54156
+khukuliseka	-54157
+khunjulwayo	-54158
+khusitomoko	-54159
+knockbuster	-54160
+konakalelwa	-54161
+kumphuthuma	-54162
+kunyaniseka	-54163
+kwakungekho	-54164
+kwaneliseka	-54165
+kwantonjane	-54166
+kwaphicotha	-54167
+kwiidolophu	-54168
+kwilaterano	-54169
+loqhwithela	-54170
+lubhiyozele	-54171
+lwezikhumba	-54172
+mangalelana	-54173
+maphrodites	-54174
+mateurligen	-54175
+mbiaezambia	-54176
+mbiguazione	-54177
+measurement	-54178
+menyezelayo	-54179
+mfanenikiso	-54180
+micronation	-54181
+microregion	-54182
+microscopic	-54183
+microstates	-54184
+miliselweni	-54185
+mitteleurop	-54186
+mkelekileyo	-54187
+mndwendwela	-54188
+mnyanzelisa	-54189
+mockbusters	-54190
+mütlichkeit	-54191
+namentation	-54192
+nationalist	-54193
+naviraiense	-54194
+ndandicinga	-54195
+ngavunyelwa	-54196
+ngeyomqungu	-54197
+ngokoulwimi	-54198
+ngosuleleki	-54199
+khtun	-54200
+▁ndosa	-54201
+▁useppo	-54202
+▁eklerks	-54203
+▁icomune	-54204
+▁pakhtun	-54205
+▁yekhaka	-54206
+ngcipheko	-54207
+ufunyanwe	-54208
+zengqondo	-54209
+▁amateurs	-54210
+▁bolukela	-54211
+▁fastener	-54212
+▁axubeneyo	-54213
+▁bhayoloji	-54214
+▁zingenaso	-54215
+▁zingenawo	-54216
+amgqaliselo	-54217
+komfuziselo	-54218
+kukhuthazwa	-54219
+nickelodeon	-54220
+nikezelwana	-54221
+njengezimvo	-54222
+njengolwimi	-54223
+nonviolence	-54224
+nosographic	-54225
+obuqhetseba	-54226
+occupazione	-54227
+olukwisizwe	-54228
+onchocercia	-54229
+ongezelelek	-54230
+ongqothwane	-54231
+onozakuzaku	-54232
+oomakufanwe	-54233
+ordinamenta	-54234
+orientalist	-54235
+osozimayini	-54236
+owayefudula	-54237
+palestinian	-54238
+paracetamol	-54239
+paromomycin	-54240
+participles	-54241
+pendingbest	-54242
+performance	-54243
+phototrophs	-54244
+pitchblende	-54245
+prefectural	-54246
+progressive	-54247
+proportioni	-54248
+psychopatho	-54249
+pterogorgia	-54250
+qhankqalaza	-54251
+qhankqalaze	-54252
+qhekeziweyo	-54253
+qhwithwanga	-54254
+qulukobhodo	-54255
+rhawuzelele	-54256
+rhwebelwano	-54257
+sambutsutsu	-54258
+sebancinane	-54259
+sebenzisani	-54260
+sebunzimeni	-54261
+sebyzantine	-54262
+sekelezelwe	-54263
+senelspruit	-54264
+settenville	-54265
+shunyayezwe	-54266
+sieningskom	-54267
+sihlongonya	-54268
+sinabahlali	-54269
+singavumeli	-54270
+siteitsvoor	-54271
+spaceflight	-54272
+ssification	-54273
+stantinople	-54274
+stosomiasis	-54275
+sulelekilwe	-54276
+superheroes	-54277
+surrealists	-54278
+synthesizer	-54279
+tehtaankatu	-54280
+telecommuni	-54281
+telenovelas	-54282
+teriormente	-54283
+terpolymers	-54284
+thelevijini	-54285
+thunyelelwe	-54286
+tkhutshelwe	-54287
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