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+---
+language: nr
+language_name: South Ndebele
+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: 6.115
+  - name: best_isotropy
+    type: isotropy
+    value: 0.4750
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# South Ndebele - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **South Ndebele** 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** | 4.500x | 4.50 | 0.2494% | 232,512 |
+| **16k** | 5.097x | 5.10 | 0.2826% | 205,268 |
+| **32k** | 5.669x | 5.67 | 0.3143% | 184,546 |
+| **64k** | 6.115x 🏆 | 6.12 | 0.3390% | 171,093 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `UJoe Sibanyoni ungusomarhwebo no mphathi omkhulu matekisi, ohlala eKwaggafontein...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁u jo e ▁si ban yoni ▁ungu soma rhwebo ▁no ... (+13 more)` | 23 |
+| 16k | `▁u joe ▁si ban yoni ▁ungu somarhwebo ▁no ▁mphathi ▁omkhulu ... (+9 more)` | 19 |
+| 32k | `▁ujoe ▁sibanyoni ▁ungu somarhwebo ▁no ▁mphathi ▁omkhulu ▁matekisi , ▁ohlala ... (+3 more)` | 13 |
+| 64k | `▁ujoe ▁sibanyoni ▁ungusomarhwebo ▁no ▁mphathi ▁omkhulu ▁matekisi , ▁ohlala ▁ekwaggafontein ... (+2 more)` | 12 |
+
+**Sample 2:** `UJabu Mahlangu obuye aziwe ngo Jabu Pule wayengumdlai wecembe lebhola i Kaizer C...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁u ja bu ▁mahlangu ▁obu ye ▁azi we ▁ngo ▁ja ... (+21 more)` | 31 |
+| 16k | `▁uja bu ▁mahlangu ▁obu ye ▁aziwe ▁ngo ▁ja bu ▁pu ... (+17 more)` | 27 |
+| 32k | `▁uja bu ▁mahlangu ▁obu ye ▁aziwe ▁ngo ▁jabu ▁pu le ... (+11 more)` | 21 |
+| 64k | `▁ujabu ▁mahlangu ▁obuye ▁aziwe ▁ngo ▁jabu ▁pule ▁wayengumdlai ▁wecembe ▁lebhola ... (+7 more)` | 17 |
+
+**Sample 3:** `iSiyabuswa yilokishi lakwaNdebele, eSewula Afrika.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁isi yabuswa ▁yi lokishi ▁la kwandebele , ▁esewula ▁afrika .` | 10 |
+| 16k | `▁isi yabuswa ▁yilokishi ▁la kwandebele , ▁esewula ▁afrika .` | 9 |
+| 32k | `▁isiyabuswa ▁yilokishi ▁la kwandebele , ▁esewula ▁afrika .` | 8 |
+| 64k | `▁isiyabuswa ▁yilokishi ▁lakwandebele , ▁esewula ▁afrika .` | 7 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 6.115x compression
+- **Lowest UNK Rate:** 8k with 0.2494% 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 | 887 | 9.79 | 1,218 | 30.9% | 91.1% |
+| **2-gram** | Subword | 215 🏆 | 7.75 | 1,135 | 73.9% | 99.9% |
+| **3-gram** | Word | 874 | 9.77 | 1,068 | 26.9% | 95.6% |
+| **3-gram** | Subword | 1,524 | 10.57 | 8,047 | 29.1% | 80.7% |
+| **4-gram** | Word | 3,504 | 11.77 | 3,737 | 8.1% | 34.6% |
+| **4-gram** | Subword | 6,910 | 12.75 | 33,324 | 13.8% | 47.8% |
+| **5-gram** | Word | 3,016 | 11.56 | 3,094 | 6.8% | 37.0% |
+| **5-gram** | Subword | 18,833 | 14.20 | 66,076 | 8.5% | 30.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `esewula afrika` | 202 |
+| 2 | `south africa` | 125 |
+| 3 | `wesewula afrika` | 101 |
+| 4 | `kanye ne` | 98 |
+| 5 | `of the` | 90 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `retrieved from retrieved` | 50 |
+| 2 | `from retrieved on` | 49 |
+| 3 | `ku ifunyenwe ngomhlaka` | 48 |
+| 4 | `of south africa` | 41 |
+| 5 | `in south africa` | 36 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `retrieved from retrieved on` | 44 |
+| 2 | `litholakala ku lifunyenwe ngomhlaka` | 33 |
+| 3 | `litholakala ku ifunyenwe ngomhlaka` | 27 |
+| 4 | `eenhlokwaneni ezilandelako sizokutjheja bonyana` | 16 |
+| 5 | `itholakala ku ifunyenwe ngomhlaka` | 16 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ku ifunyenwe ngomhlaka 24 kunobayeni` | 12 |
+| 2 | `litholakala ku ifunyenwe ngomhlaka 24` | 12 |
+| 3 | `u s department of energy` | 10 |
+| 4 | `litholakala ku lifunyenwe ngomhlaka 19` | 8 |
+| 5 | `website retrieved from retrieved on` | 8 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 34,916 |
+| 2 | `a n` | 21,629 |
+| 3 | `n g` | 17,440 |
+| 4 | `l a` | 16,021 |
+| 5 | `i _` | 15,755 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a _` | 7,746 |
+| 2 | `l a _` | 6,995 |
+| 3 | `_ n g` | 6,159 |
+| 4 | `n g a` | 5,962 |
+| 5 | `a _ n` | 5,787 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n a _` | 4,951 |
+| 2 | `_ u k u` | 3,681 |
+| 3 | `a n g a` | 2,726 |
+| 4 | `a _ n g` | 2,689 |
+| 5 | `e n i _` | 2,674 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ u k u` | 1,464 |
+| 2 | `a b a n t` | 1,460 |
+| 3 | `l a n g a` | 1,382 |
+| 4 | `k h u l u` | 1,293 |
+| 5 | `_ n g o k` | 1,293 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 215
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% 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 | 0.5928 | 1.508 | 2.90 | 34,363 | 40.7% |
+| **1** | Subword | 1.2345 | 2.353 | 11.18 | 185 | 0.0% |
+| **2** | Word | 0.1023 | 1.073 | 1.16 | 99,105 | 89.8% |
+| **2** | Subword | 1.3055 | 2.472 | 7.07 | 2,065 | 0.0% |
+| **3** | Word | 0.0231 | 1.016 | 1.03 | 114,483 | 97.7% |
+| **3** | Subword | 0.9022 | 1.869 | 3.87 | 14,600 | 9.8% |
+| **4** | Word | 0.0074 🏆 | 1.005 | 1.01 | 117,392 | 99.3% |
+| **4** | Subword | 0.5974 | 1.513 | 2.39 | 56,446 | 40.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `begodu yamenyezelwa njengezakhamuzi zabantu zinikela amathuba alinganako nofana anganasithunzi bese ...`
+2. `i cape ne oukwanyama iinkomba zephasi namhlanje abentwana abanengi bakhethe ukufudukela emadorobheni...`
+3. `bona unepilo begodu inemingcele yelwandle asekuthomeni kwelwandle lapho akhethwa khona nesiqhema sez...`
+
+**Context Size 2:**
+
+1. `esewula afrika idorojaneli litholakala ngemva kwamakhilomitha ama 53 esewula yedorobha i middleburg ...`
+2. `south africa studia historiae ecclesiasticae 48 1 pp 30 55 ilimi lisetjenziswa ngokufanako kodwana i...`
+3. `wesewula afrika kanye ne ciskei ngomrhayili may nokho aba khona amalungiselelo enziwako kodwana ukut...`
+
+**Context Size 3:**
+
+1. `retrieved from retrieved on umtjhagalo wabomma umnqopho omkhulu wombuso webandlululo bekukuhlukanisa...`
+2. `from retrieved on indlela iintjhijilwezi ezingararululwa ngayo urhulumende kufuze wandise amahlelo w...`
+3. `ku ifunyenwe ngomhlaka 24 kunobayeni ihlathulule ilimi njengehlelo elihlelekileko lezokuthintana lel...`
+
+**Context Size 4:**
+
+1. `retrieved from retrieved on ekulumenakhe ayethula ngesikhathi athumba unongorwana uthi lokhu kungikh...`
+2. `litholakala ku lifunyenwe ngomhlaka 7 kutjhirhweni ikhotho le ukuze iragele phambili nokulalelwa kwe...`
+3. `litholakala ku ifunyenwe ngomhlaka 24 kunobayeni ngokufanako umtjhini nanyana isithuthi esisebenzisa...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ani_si_nizisi_et`
+2. `_okundema_athizi`
+3. `ekweko_dii_u_a-_`
+
+**Context Size 2:**
+
+1. `a_kos_moyo_elalan`
+2. `anyenya_wisinika_`
+3. `ngemvunengokubo_k`
+
+**Context Size 3:**
+
+1. `na_ball_stransvaal`
+2. `la_ephatho_-_ecamo`
+3. `_ngokwana_begaza_e`
+
+**Context Size 4:**
+
+1. `ana_adlalo_yase_emq`
+2. `_ukuze_umvuzo_yesay`
+3. `anga,_esele_isifo_s`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (56,446 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 | 12,308 |
+| Total Tokens | 101,917 |
+| Mean Frequency | 8.28 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 31.87 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | begodu | 1,170 |
+| 2 | i | 1,079 |
+| 3 | bona | 1,057 |
+| 4 | u | 804 |
+| 5 | afrika | 717 |
+| 6 | abantu | 666 |
+| 7 | of | 666 |
+| 8 | nanyana | 584 |
+| 9 | kanye | 582 |
+| 10 | and | 563 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | isiqundo | 2 |
+| 2 | nkabinde | 2 |
+| 3 | wamajuda | 2 |
+| 4 | polotiki | 2 |
+| 5 | progressive | 2 |
+| 6 | lunga | 2 |
+| 7 | ngokwehlukana | 2 |
+| 8 | enjalo | 2 |
+| 9 | affairs | 2 |
+| 10 | isithunywa | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8997 |
+| R² (Goodness of Fit) | 0.988207 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 25.9% |
+| Top 1,000 | 55.8% |
+| Top 5,000 | 83.1% |
+| Top 10,000 | 95.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9882 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 25.9% of corpus
+- **Long Tail:** 2,308 words needed for remaining 4.5% 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 | 0.4750 | 0.3518 | N/A | N/A |
+| **mono_64d** | 64 | 0.1080 | 0.3618 | N/A | N/A |
+| **mono_128d** | 128 | 0.0129 | 0.3564 | N/A | N/A |
+| **aligned_32d** | 32 | 0.4750 🏆 | 0.3688 | 0.0020 | 0.1080 |
+| **aligned_64d** | 64 | 0.1080 | 0.3760 | 0.0120 | 0.1800 |
+| **aligned_128d** | 128 | 0.0129 | 0.3745 | 0.0280 | 0.2020 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.4750 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3649. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.8% 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 | **0.224** | 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 |
+|--------|----------|
+| `-e` | ezidla, ekufanele, ezincane |
+| `-i` | iinhluthu, improving, isuka |
+| `-a` | awukhulumi, abalimunyileko, about |
+| `-u` | ukutlhaga, ukusela, ukugula |
+| `-n` | nelutjha, nangokuthi, ngokudluleleko |
+| `-ku` | kuzokuba, kunobayeni, kukhukhulamungu |
+| `-s` | sociology, sihlukaniswa, sekhukhune |
+| `-b` | bekuyindawo, buhlungu, bekuliyunithi |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | wambuza, nelutjha, sihlukaniswa |
+| `-i` | awukhulumi, nangokuthi, bekuliyunithi |
+| `-o` | ngokudluleleko, bekuyindawo, abalimunyileko |
+| `-e` | maqhawe, sekhukhune, ekufanele |
+| `-la` | ezidla, wokuthola, ukusela |
+| `-ni` | ekwabelaneni, kunobayeni, emasikweni |
+| `-wa` | sihlukaniswa, abawa, elidluliselwa |
+| `-ko` | ngokudluleleko, abalimunyileko, ezisetjenziswako |
+
+### 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 |
+|------|----------|------------------|----------|
+| `lang` | 1.86x | 45 contexts | langa, lange, ilanga |
+| `khul` | 1.58x | 60 contexts | khula, khulu, khuli |
+| `benz` | 1.96x | 25 contexts | benze, benza, ebenza |
+| `enzi` | 1.77x | 32 contexts | enzima, enziwe, zenziwa |
+| `aban` | 1.63x | 40 contexts | abane, abanga, abantu |
+| `kuth` | 1.50x | 46 contexts | kuthi, ukuthi, nokuth |
+| `anga` | 1.51x | 39 contexts | langa, abanga, angabi |
+| `hulu` | 1.65x | 24 contexts | khulu, mkhulu, omkhulu |
+| `antu` | 2.05x | 11 contexts | bantu, abantu, ubantu |
+| `hlan` | 1.70x | 19 contexts | hlanu, mhlana, bahlanu |
+| `nyan` | 1.48x | 29 contexts | nyanga, mnyango, bonyana |
+| `hath` | 1.33x | 43 contexts | thathu, uthatha, athathe |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-u` | `-a` | 450 words | umulwana, ukufiphala |
+| `-n` | `-a` | 404 words | ngokufana, ngokusebenzisana |
+| `-e` | `-i` | 344 words | emathuthumbeni, emapholiseni |
+| `-e` | `-ni` | 305 words | emathuthumbeni, emapholiseni |
+| `-n` | `-o` | 254 words | nobunjalo, nekghono |
+| `-n` | `-i` | 214 words | nobudisi, namalori |
+| `-a` | `-a` | 203 words | abelana, akhambisana |
+| `-i` | `-o` | 200 words | iziko, iinqunto |
+| `-e` | `-a` | 198 words | eziphila, eziphikisana |
+| `-i` | `-a` | 187 words | ithelerina, inamandla |
+
+### 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 |
+|------|-----------------|------------|------|
+| batholakala | **`batholak-a-la`** | 7.5 | `a` |
+| nakazithweleko | **`nakazithwe-le-ko`** | 7.5 | `le` |
+| nakafundisako | **`nakafundis-a-ko`** | 7.5 | `a` |
+| ebantwini | **`ebantw-i-ni`** | 7.5 | `i` |
+| abanelwazi | **`abanel-wa-zi`** | 7.5 | `wa` |
+| lokuhlobana | **`lokuhlob-a-na`** | 7.5 | `a` |
+| zahlukana | **`zahluk-a-na`** | 7.5 | `a` |
+| ezizumako | **`ezizum-a-ko`** | 7.5 | `a` |
+| emahlubini | **`emahlub-i-ni`** | 7.5 | `i` |
+| ubuntazana | **`ubuntaz-a-na`** | 7.5 | `a` |
+| elakhiweko | **`elakhiw-e-ko`** | 7.5 | `e` |
+| lobulondolwazi | **`lobulondol-wa-zi`** | 7.5 | `wa` |
+| emkhandlwini | **`emkhandlw-i-ni`** | 7.5 | `i` |
+| ikohlakalo | **`ikohlak-a-lo`** | 7.5 | `a` |
+| okhanyisako | **`okhanyis-a-ko`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language South Ndebele 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 (6.11x) |
+| N-gram | **2-gram** | Lowest perplexity (215) |
+| Markov | **Context-4** | Highest predictability (99.3%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 16:03:31*
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