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README.md

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+---
+language: ss
+language_name: Swati
+language_family: bantu_southern
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-bantu_southern
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 5.616
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6744
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Swati - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Swati** 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** | 4.045x | 4.05 | 0.4074% | 268,766 |
+| **16k** | 4.553x | 4.56 | 0.4586% | 238,783 |
+| **32k** | 5.026x | 5.03 | 0.5062% | 216,332 |
+| **64k** | 5.616x 🏆 | 5.62 | 0.5656% | 193,596 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Kusoka yinchubo lesusa sikhumba sesibeletho esitfweni sangasese semuntfu sangans...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kuso ka ▁yin chubo ▁le susa ▁sikhumba ▁sesi bele tho ... (+11 more)` | 21 |
+| 16k | `▁kuso ka ▁yin chubo ▁le susa ▁sikhumba ▁sesi beletho ▁esitfweni ... (+7 more)` | 17 |
+| 32k | `▁kusoka ▁yinchubo ▁le susa ▁sikhumba ▁sesi beletho ▁esitfweni ▁sangasese ▁semuntfu ... (+3 more)` | 13 |
+| 64k | `▁kusoka ▁yinchubo ▁lesusa ▁sikhumba ▁sesibeletho ▁esitfweni ▁sangasese ▁semuntfu ▁sangansense .` | 10 |
+
+**Sample 2:** `7 Bhimbídvwane. Lilanga 7 enyangeni yeBhimbídvwane.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 7 ▁bhimbídvwane . ▁lilanga ▁ 7 ▁enyangeni ▁yebhimbídvwane .` | 10 |
+| 16k | `▁ 7 ▁bhimbídvwane . ▁lilanga ▁ 7 ▁enyangeni ▁yebhimbídvwane .` | 10 |
+| 32k | `▁ 7 ▁bhimbídvwane . ▁lilanga ▁ 7 ▁enyangeni ▁yebhimbídvwane .` | 10 |
+| 64k | `▁ 7 ▁bhimbídvwane . ▁lilanga ▁ 7 ▁enyangeni ▁yebhimbídvwane .` | 10 |
+
+**Sample 3:** `Wonkhe emalanga enyangeni emnyakeni. |- Kúfúna . Kúbópha The History Channel iwe...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁wonkhe ▁emalanga ▁enyangeni ▁emnyakeni . ▁| - ▁kúfúna ▁. ▁kúbópha ... (+14 more)` | 24 |
+| 16k | `▁wonkhe ▁emalanga ▁enyangeni ▁emnyakeni . ▁| - ▁kúfúna ▁. ▁kúbópha ... (+12 more)` | 22 |
+| 32k | `▁wonkhe ▁emalanga ▁enyangeni ▁emnyakeni . ▁| - ▁kúfúna ▁. ▁kúbópha ... (+12 more)` | 22 |
+| 64k | `▁wonkhe ▁emalanga ▁enyangeni ▁emnyakeni . ▁|- ▁kúfúna ▁. ▁kúbópha ▁the ... (+10 more)` | 20 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.616x compression
+- **Lowest UNK Rate:** 8k with 0.4074% 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 | 1,182 | 10.21 | 1,980 | 31.2% | 76.3% |
+| **2-gram** | Subword | 230 🏆 | 7.84 | 1,614 | 72.8% | 99.6% |
+| **3-gram** | Word | 1,207 | 10.24 | 1,767 | 26.2% | 74.8% |
+| **3-gram** | Subword | 1,732 | 10.76 | 11,292 | 28.6% | 78.3% |
+| **4-gram** | Word | 4,701 | 12.20 | 5,505 | 9.8% | 31.8% |
+| **4-gram** | Subword | 8,252 | 13.01 | 47,059 | 13.6% | 45.5% |
+| **5-gram** | Word | 4,101 | 12.00 | 4,561 | 9.0% | 31.5% |
+| **5-gram** | Subword | 22,502 | 14.46 | 93,259 | 8.5% | 29.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ningizimu afrika` | 321 |
+| 2 | `kanye ne` | 310 |
+| 3 | `ngemnyaka wa` | 303 |
+| 4 | `eningizimu afrika` | 284 |
+| 5 | `kúfúna kúbópha` | 190 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wase ningizimu afrika` | 166 |
+| 2 | `likhodi le inthanethi` | 157 |
+| 3 | `usd likhodi le` | 150 |
+| 4 | `km2 linani bantfu` | 145 |
+| 5 | `pib usd likhodi` | 140 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `usd likhodi le inthanethi` | 150 |
+| 2 | `pib usd likhodi le` | 140 |
+| 3 | `african national congress anc` | 34 |
+| 4 | `ungusopolitiki wase ningizimu afrika` | 20 |
+| 5 | `le african national congress` | 19 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pib usd likhodi le inthanethi` | 140 |
+| 2 | `archived from the original on` | 17 |
+| 3 | `licembu lepolitiki lase ningizimu afrika` | 16 |
+| 4 | `km2 pib usd likhodi le` | 15 |
+| 5 | `yetinkhundla the national physical development` | 15 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 44,148 |
+| 2 | `a n` | 27,375 |
+| 3 | `e _` | 27,173 |
+| 4 | `i _` | 25,993 |
+| 5 | `l a` | 25,619 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k u` | 10,337 |
+| 2 | `_ l e` | 10,103 |
+| 3 | `_ n g` | 9,569 |
+| 4 | `l a _` | 9,180 |
+| 5 | `a _ k` | 8,164 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t s i _` | 5,957 |
+| 2 | `_ n g e` | 5,441 |
+| 3 | `a _ n g` | 4,181 |
+| 4 | `n y e _` | 3,934 |
+| 5 | `a _ k u` | 3,702 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `u t s i _` | 3,274 |
+| 2 | `a n y e _` | 2,771 |
+| 3 | `k a n y e` | 2,535 |
+| 4 | `n y e _ n` | 2,524 |
+| 5 | `_ k a n y` | 2,505 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 230
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~29% 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 | 0.6025 | 1.518 | 2.92 | 48,904 | 39.7% |
+| **1** | Subword | 0.9034 | 1.870 | 6.18 | 699 | 9.7% |
+| **2** | Word | 0.1026 | 1.074 | 1.17 | 142,184 | 89.7% |
+| **2** | Subword | 0.9440 | 1.924 | 5.27 | 4,316 | 5.6% |
+| **3** | Word | 0.0243 | 1.017 | 1.03 | 165,786 | 97.6% |
+| **3** | Subword | 0.8350 | 1.784 | 3.69 | 22,731 | 16.5% |
+| **4** | Word | 0.0077 🏆 | 1.005 | 1.01 | 170,336 | 99.2% |
+| **4** | Subword | 0.5985 | 1.514 | 2.40 | 83,771 | 40.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `kanye nanobe jana gana iriphabliki inhlokodolobha basseterre linani bantfu labadzala ngemnyaka wa li...`
+2. `i central police station kanye nesikhatsi sekukhula ngekushesha kantsi yena eduardo mondlane uphuma ...`
+3. `kutsi seyingenta matsandza ngekuba ngumdlali we structural anthropology 33 19 december durban yabese...`
+
+**Context Size 2:**
+
+1. `ningizimu afrika lelisekela ema afrika kanye nembhali wetinkondlo wase iningizimu afrika itfola inku...`
+2. `kanye ne computer science tinhlelo te undergraduate kuphela ema undergraduate majors e bachelor of e...`
+3. `ngemnyaka wa waphindze waba sikhulumi sesigungu savelonkhe tikhatsi letimbili letingachubeki kusukel...`
+
+**Context Size 3:**
+
+1. `wase ningizimu afrika umfundzisi umbhali wemafilimu kanye nemlweli wemalungelo ebantfu ushicilele im...`
+2. `likhodi le inthanethi ba kúfúna kúbópha ibhosinya ne hezegovi ibhulgariya ikhuroshiya shekhi idenima...`
+3. `usd likhodi le inthanethi jm kúfúna kúbópha ijamayikha iwebhusayithi jamayikha`
+
+**Context Size 4:**
+
+1. `usd likhodi le inthanethi fi ax kúfúna kúbópha ifini iwebhusayithi`
+2. `pib usd likhodi le inthanethi si kúfúna kúbópha slovenia si your gateway to information on slovenia ...`
+3. `african national congress anc iminyaka yebuncane nekufundza mantashe watalelwa e eastern cape wakhul...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_isahale_fun._nd`
+2. `aba:_i_emlaku_kh`
+3. `emakúne_liohoneu`
+
+**Context Size 2:**
+
+1. `a_manatinhla-hi_l`
+2. `an_econgopetalovu`
+3. `e_inkhe_bodi,_use`
+
+**Context Size 3:**
+
+1. `_kulwane_yemanzanu`
+2. `_letiseleko_lodzaw`
+3. `_ngal_stemari_nala`
+
+**Context Size 4:**
+
+1. `tsi_sobhuza_ii_lica`
+2. `_ngekubukwentarized`
+3. `a_ngapheli_licembu_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (83,771 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 | 17,583 |
+| Total Tokens | 151,832 |
+| Mean Frequency | 8.64 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 38.49 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | kanye | 2,506 |
+| 2 | i | 1,523 |
+| 3 | afrika | 1,491 |
+| 4 | kutsi | 1,291 |
+| 5 | futsi | 1,060 |
+| 6 | bantfu | 768 |
+| 7 | of | 766 |
+| 8 | e | 678 |
+| 9 | the | 675 |
+| 10 | ne | 670 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | wetisebenti | 2 |
+| 2 | pilibhit | 2 |
+| 3 | ecameroon | 2 |
+| 4 | swift | 2 |
+| 5 | bungcweti | 2 |
+| 6 | etychy | 2 |
+| 7 | wideo | 2 |
+| 8 | nietypowe | 2 |
+| 9 | sztalugi | 2 |
+| 10 | zapałek | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8828 |
+| R² (Goodness of Fit) | 0.992928 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 23.6% |
+| Top 1,000 | 51.9% |
+| Top 5,000 | 77.7% |
+| Top 10,000 | 89.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9929 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 23.6% of corpus
+- **Long Tail:** 7,583 words needed for remaining 10.3% 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 | 0.6744 | 0.3263 | N/A | N/A |
+| **mono_64d** | 64 | 0.1827 | 0.3247 | N/A | N/A |
+| **mono_128d** | 128 | 0.0232 | 0.3346 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6744 🏆 | 0.3287 | 0.0180 | 0.1640 |
+| **aligned_64d** | 64 | 0.1827 | 0.3261 | 0.0480 | 0.2280 |
+| **aligned_128d** | 128 | 0.0232 | 0.3424 | 0.0640 | 0.2540 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6744 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3305. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.4% 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 | **0.078** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-l` | lichazwa, letivela, lisotja |
+| `-ku` | kugcugcutela, kushona, kunetindzawo |
+| `-le` | letivela, lebeyinebantfu, lenkhomo |
+| `-e` | evidence, ebhayi, enkhululekweni |
+| `-a` | angakaze, apple, abengusomabhizinisi |
+| `-i` | indzima, ingabe, imihume |
+| `-s` | sebufati, sidvudvu, sasungulwa |
+| `-n` | nekudla, ngekuntjintja, ngubabe |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | lichazwa, indzima, letivela |
+| `-i` | ebhayi, sebufati, abengusomabhizinisi |
+| `-e` | evidence, timbece, beje |
+| `-o` | lenkhomo, kwawo, letigucukako |
+| `-ni` | enkhululekweni, enkhohlakalweni, elokishini |
+| `-la` | letivela, nekudla, latfola |
+| `-wa` | lichazwa, kwahlanganiswa, sasungulwa |
+| `-le` | apple, lohluphekile, lokwehlukile |
+
+### 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` | 1.63x | 63 contexts | ikhule, akhula, mkhulu |
+| `bant` | 2.00x | 23 contexts | bantu, bantfu, abantu |
+| `anga` | 1.56x | 50 contexts | wanga, banga, yanga |
+| `lang` | 1.65x | 40 contexts | langu, lange, langa |
+| `enti` | 1.72x | 29 contexts | senti, isenti, yentiwa |
+| `hulu` | 1.66x | 32 contexts | mkhulu, sikhulu, omkhulu |
+| `kuts` | 1.74x | 26 contexts | kutsi, kutse, ekutsi |
+| `indz` | 1.49x | 41 contexts | lindza, indzima, indzawo |
+| `etin` | 1.70x | 25 contexts | letine, letinye, letingu |
+| `ndza` | 1.41x | 30 contexts | lindza, ndzawo, indzawo |
+| `antf` | 1.86x | 12 contexts | bantfu, ebantfu, labantfu |
+| `khat` | 1.89x | 9 contexts | khathi, ekhatsi, emakhata |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-l` | `-a` | 490 words | laniketa, lelisempumalanga |
+| `-n` | `-a` | 338 words | nekugcugcutela, nekukhulumisana |
+| `-e` | `-i` | 334 words | ezulwini, entasi |
+| `-l` | `-e` | 317 words | leyehlukahlukene, lesifishane |
+| `-e` | `-ni` | 275 words | ezulwini, ebeleni |
+| `-ku` | `-a` | 255 words | kucaphela, kuwina |
+| `-n` | `-i` | 226 words | ngokuthi, netigidzi |
+| `-l` | `-i` | 205 words | lesisemkhatsini, lasemtsetfweni |
+| `-l` | `-o` | 204 words | lusendvo, libutfo |
+| `-n` | `-o` | 165 words | ngekwenhlalo, ngemalengiso |
+
+### 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 |
+|------|-----------------|------------|------|
+| tfolakele | **`tfolak-e-le`** | 7.5 | `e` |
+| mkhuzweni | **`mkhuz-we-ni`** | 7.5 | `we` |
+| abengusihlalo | **`abengusih-la-lo`** | 7.5 | `la` |
+| bekadlala | **`bekad-la-la`** | 7.5 | `la` |
+| nyamatane | **`nyamat-a-ne`** | 7.5 | `a` |
+| itfolakala | **`itfolak-a-la`** | 7.5 | `a` |
+| impalampala | **`impalamp-a-la`** | 7.5 | `a` |
+| ngesicalo | **`ngesic-a-lo`** | 7.5 | `a` |
+| samasipala | **`samasip-a-la`** | 7.5 | `a` |
+| tinanekwane | **`tinanekw-a-ne`** | 7.5 | `a` |
+| etingijimeni | **`etingijim-e-ni`** | 7.5 | `e` |
+| letinkhulungwane | **`letinkhulung-wa-ne`** | 7.5 | `wa` |
+| lesentasi | **`lesent-a-si`** | 7.5 | `a` |
+| ekuvinjweni | **`ekuvinj-we-ni`** | 7.5 | `we` |
+| labashadene | **`labashad-e-ne`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Swati 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 (5.62x) |
+| N-gram | **2-gram** | Lowest perplexity (230) |
+| Markov | **Context-4** | Highest predictability (99.2%) |
+| 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 22:38:00*

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