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README.md

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+---
+language: ts
+language_name: Tsonga
+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: 4.757
+  - name: best_isotropy
+    type: isotropy
+    value: 0.4521
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tsonga - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tsonga** 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** | 4.083x | 4.09 | 0.1543% | 377,103 |
+| **16k** | 4.448x | 4.45 | 0.1681% | 346,189 |
+| **32k** | 4.757x 🏆 | 4.76 | 0.1798% | 323,678 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ostraliya (Xinghezi: Australia) i tiko ra Oxiyeniya.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ostraliya ▁( xing hezi : ▁australia ) ▁i ▁tiko ▁ra ... (+2 more)` | 12 |
+| 16k | `▁ostraliya ▁( xinghezi : ▁australia ) ▁i ▁tiko ▁ra ▁oxiyeniya ... (+1 more)` | 11 |
+| 32k | `▁ostraliya ▁( xinghezi : ▁australia ) ▁i ▁tiko ▁ra ▁oxiyeniya ... (+1 more)` | 11 |
+
+**Sample 2:** `E ndhau leyinga le Soweto la ku ngava ni Soweto uprising.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁e ▁ndha u ▁leyin ga ▁le ▁soweto ▁la ▁ku ▁ngava ... (+6 more)` | 16 |
+| 16k | `▁e ▁ndha u ▁leyinga ▁le ▁soweto ▁la ▁ku ▁ngava ▁ni ... (+5 more)` | 15 |
+| 32k | `▁e ▁ndhau ▁leyinga ▁le ▁soweto ▁la ▁ku ▁ngava ▁ni ▁soweto ... (+2 more)` | 12 |
+
+**Sample 3:** `+Jamhuri ya Kenya 125px 125px (Flag) (Coat of Arms) <small></big> 300px Kenya i ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁+ ja m huri ▁ya ▁kenya ▁ 1 2 5 ... (+30 more)` | 40 |
+| 16k | `▁+ jamhuri ▁ya ▁kenya ▁ 1 2 5 px ▁ ... (+28 more)` | 38 |
+| 32k | `▁+ jamhuri ▁ya ▁kenya ▁ 1 2 5 px ▁ ... (+28 more)` | 38 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.757x compression
+- **Lowest UNK Rate:** 8k with 0.1543% 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 | 3,674 | 11.84 | 6,696 | 17.5% | 52.9% |
+| **2-gram** | Subword | 203 🏆 | 7.66 | 1,426 | 73.8% | 99.8% |
+| **3-gram** | Word | 5,300 | 12.37 | 7,866 | 12.8% | 41.5% |
+| **3-gram** | Subword | 1,457 | 10.51 | 10,147 | 33.7% | 80.2% |
+| **4-gram** | Word | 9,675 | 13.24 | 12,992 | 9.7% | 28.5% |
+| **4-gram** | Subword | 6,717 | 12.71 | 42,330 | 16.4% | 49.9% |
+| **5-gram** | Word | 6,251 | 12.61 | 8,596 | 13.3% | 34.1% |
+| **5-gram** | Subword | 18,231 | 14.15 | 82,731 | 8.9% | 32.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hi ku` | 816 |
+| 2 | `na ku` | 518 |
+| 3 | `tani hi` | 433 |
+| 4 | `lembe ra` | 416 |
+| 5 | `hi lembe` | 403 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hi lembe ra` | 342 |
+| 2 | `hi siku leri` | 165 |
+| 3 | `a ku ri` | 136 |
+| 4 | `member of the` | 119 |
+| 5 | `ku sukela hi` | 105 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `add text add text` | 103 |
+| 2 | `text add text add` | 81 |
+| 3 | `flag coat of arms` | 70 |
+| 4 | `coat of arms small` | 67 |
+| 5 | `hi ku ya hi` | 66 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `add text add text add` | 81 |
+| 2 | `text add text add text` | 81 |
+| 3 | `flag coat of arms small` | 67 |
+| 4 | `life of a south african` | 65 |
+| 5 | `of a south african tribe` | 65 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 92,010 |
+| 2 | `i _` | 42,197 |
+| 3 | `a n` | 25,791 |
+| 4 | `_ n` | 22,549 |
+| 5 | `u _` | 22,416 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a _` | 15,155 |
+| 2 | `k a _` | 14,105 |
+| 3 | `_ k u` | 11,845 |
+| 4 | `w a _` | 11,809 |
+| 5 | `y a _` | 10,410 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k u _` | 7,542 |
+| 2 | `_ y a _` | 7,064 |
+| 3 | `_ h i _` | 6,736 |
+| 4 | `_ n a _` | 6,398 |
+| 5 | `_ w a _` | 5,407 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ e k a _` | 3,747 |
+| 2 | `a _ k u _` | 3,382 |
+| 3 | `a _ s w i` | 2,884 |
+| 4 | `a _ h i _` | 2,861 |
+| 5 | `a _ n a _` | 2,635 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 203
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~32% 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 | 0.7323 | 1.661 | 4.40 | 27,675 | 26.8% |
+| **1** | Subword | 1.1293 | 2.187 | 8.40 | 380 | 0.0% |
+| **2** | Word | 0.2742 | 1.209 | 1.61 | 121,281 | 72.6% |
+| **2** | Subword | 1.0411 | 2.058 | 5.99 | 3,190 | 0.0% |
+| **3** | Word | 0.0957 | 1.069 | 1.15 | 195,065 | 90.4% |
+| **3** | Subword | 0.8570 | 1.811 | 3.83 | 19,088 | 14.3% |
+| **4** | Word | 0.0308 🏆 | 1.022 | 1.04 | 223,778 | 96.9% |
+| **4** | Subword | 0.5850 | 1.500 | 2.39 | 72,986 | 41.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ku tiyisisa leswaku mati eka buccaneers loko ku betsa timbalelo u tirhile tanihi ntiro wavutshila mi...`
+2. `ya siku relero e nkaveni kenya i xirho xa mozambhiki ni moya wun we people amp`
+3. `hi maribye ya 70 sangiovese na vasuvuki va a xihlanganisi xa vatsari va minhlangano leyi humesiweke`
+
+**Context Size 2:**
+
+1. `hi ku angarhela va vuriwa vatatana ntirho wa vukorhokeri mati na chukele leswi bakiweke hi mahiselo ...`
+2. `na ku thyakisiwa vito hi nandzu wa ku tihlanganisa na swilo kumbe swiendlakalo swin wana leswi a`
+3. `tani hi psitjemba kambe a va swi dyaka swinene mapa lawa ya nyiketeriweke eka hosi kheto uve`
+
+**Context Size 3:**
+
+1. `hi lembe ra huvo leyi ku hlanganisa na swihlawulekisi swa yona xivutiso lexi saleke hi ta mihleketo ...`
+2. `hi siku leri lava tswariweke hi siku leri nelson mandela khale ka phresidenti ya afrika dzonga hinkw...`
+3. `a ku ri xihaha mpfhuka lexi na xona kutani va famba va cela makhele ehenhla ka xona lomu`
+
+**Context Size 4:**
+
+1. `add text add text add text add text add text add text jkl add text add text add text`
+2. `text add text add text add text add text m add text add text add text xyz add text`
+3. `flag coat of arms small big 300px mauritius i tiko ra afrika leri kumekaka exikhari ka afrika dzonga...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_keka_yowi._nya_`
+2. `awambo_lo_yana_h`
+3. `i_ncavu_mananatl`
+
+**Context Size 2:**
+
+1. `a_mpito_yi_ntso_h`
+2. `i_tioatimisi._mu_`
+3. `anitsof_tiwa_hezi`
+
+**Context Size 3:**
+
+1. `na_le_ealt=blackso`
+2. `ka_mbana_e_tala_nh`
+3. `_ku_andla_kufiketo`
+
+**Context Size 4:**
+
+1. `_ku_tirho_leyintirh`
+2. `_ya_le_makarta)_abu`
+3. `_hi_fambia_nelsprud`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (72,986 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 | 11,975 |
+| Total Tokens | 236,184 |
+| Mean Frequency | 19.72 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 174.31 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ku | 7,660 |
+| 2 | ya | 7,078 |
+| 3 | hi | 6,805 |
+| 4 | na | 6,438 |
+| 5 | wa | 5,468 |
+| 6 | a | 4,111 |
+| 7 | eka | 3,768 |
+| 8 | va | 3,734 |
+| 9 | ka | 3,663 |
+| 10 | ra | 2,819 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | übersetzerinnen | 2 |
+| 2 | übersetzern | 2 |
+| 3 | allgemeinen | 2 |
+| 4 | digitalisierung | 2 |
+| 5 | linguistische | 2 |
+| 6 | übersetzungsdienstleistungen | 2 |
+| 7 | elektronischer | 2 |
+| 8 | literarischen | 2 |
+| 9 | übersetzungssektor | 2 |
+| 10 | erfolgt | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1100 |
+| R² (Goodness of Fit) | 0.990922 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 49.2% |
+| Top 1,000 | 75.0% |
+| Top 5,000 | 92.4% |
+| Top 10,000 | 98.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9909 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 49.2% of corpus
+- **Long Tail:** 1,975 words needed for remaining 1.7% 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 | 0.4521 | 0.4099 | N/A | N/A |
+| **mono_64d** | 64 | 0.0928 | 0.3968 | N/A | N/A |
+| **mono_128d** | 128 | 0.0116 | 0.4051 | N/A | N/A |
+| **aligned_32d** | 32 | 0.4521 🏆 | 0.3983 | 0.0120 | 0.0800 |
+| **aligned_64d** | 64 | 0.0928 | 0.4034 | 0.0220 | 0.1360 |
+| **aligned_128d** | 128 | 0.0116 | 0.4050 | 0.0220 | 0.1020 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.4521 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4031. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.2% 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 | **0.051** | 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 |
+|--------|----------|
+| `-m` | mavulavulelo, mankweng, mindzhuti |
+| `-ma` | mavulavulelo, mankweng, makumekaka |
+| `-xi` | xitanga, xiendla, xiximiwa |
+| `-s` | swisaka, switereka, siku |
+| `-t` | thoveriwa, tiviwaka, tise |
+| `-ti` | tiviwaka, tise, tikonkulu |
+| `-n` | ntlambi, nwaka, ngoni |
+| `-e` | endliwa, evuhlongeni, endyangwini |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | thoveriwa, rhandziwa, vitana |
+| `-i` | ntlambi, wansati, imini |
+| `-e` | gewünschte, have, compare |
+| `-o` | hikwalaho, mavulavulelo, ko |
+| `-ni` | imini, koroni, evuhlongeni |
+| `-le` | fuwile, uyile, chukele |
+| `-wa` | thoveriwa, rhandziwa, endliwa |
+| `-ka` | nwaka, swisaka, switereka |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ungu` | 1.62x | 54 contexts | hungu, kungu, lungu |
+| `andz` | 1.52x | 54 contexts | andza, pandza, kandza |
+| `hamb` | 1.74x | 27 contexts | hamba, hambi, rhambu |
+| `isiw` | 1.56x | 36 contexts | yisiwa, hisiwa, nwisiwa |
+| `tirh` | 1.54x | 35 contexts | tirha, tirhe, tirhi |
+| `karh` | 1.70x | 24 contexts | karhi, nkarhi, mikarhi |
+| `ngan` | 1.52x | 34 contexts | ngana, ngani, angana |
+| `lela` | 1.62x | 26 contexts | hlela, fulela, leland |
+| `riwa` | 1.61x | 25 contexts | siriwa, mariwa, soriwa |
+| `tson` | 1.68x | 21 contexts | watson, tsongo, tsonga |
+| `arhi` | 1.66x | 16 contexts | harhi, karhi, marhi |
+| `ngul` | 1.66x | 15 contexts | angula, nguluve, sungule |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-t` | `-a` | 253 words | titimela, tivekaka |
+| `-m` | `-a` | 207 words | mfukuzana, mintlwa |
+| `-v` | `-i` | 187 words | vulavisisi, vukarhi |
+| `-m` | `-i` | 177 words | mthombheni, mimiti |
+| `-v` | `-a` | 141 words | vona, vatshila |
+| `-e` | `-i` | 138 words | ematini, enyameni |
+| `-m` | `-o` | 133 words | mikomiso, minxaxamelo |
+| `-t` | `-i` | 129 words | tshuri, tihanyi |
+| `-s` | `-a` | 128 words | swekeriwa, swokoma |
+| `-t` | `-e` | 125 words | tumbuluxe, tshahiwile |
+
+### 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 |
+|------|-----------------|------------|------|
+| tumbuluxiweke | **`tumbuluxiw-e-ke`** | 7.5 | `e` |
+| chavisaka | **`chavis-a-ka`** | 7.5 | `a` |
+| mudyandhzaka | **`mudyandhz-a-ka`** | 7.5 | `a` |
+| swivulavulelo | **`swivulavu-le-lo`** | 7.5 | `le` |
+| xirimbyati | **`xirimby-a-ti`** | 7.5 | `a` |
+| wusunguleke | **`wusungu-le-ke`** | 7.5 | `le` |
+| okmalumkoolkat | **`okmalumkoolk-a-t`** | 7.5 | `a` |
+| nyangweni | **`nyangw-e-ni`** | 7.5 | `e` |
+| fikeleleke | **`fikele-le-ke`** | 7.5 | `le` |
+| xikalanga | **`xi-ka-langa`** | 7.5 | `langa` |
+| nhlengani | **`nhleng-a-ni`** | 7.5 | `a` |
+| leswivulaka | **`leswivu-la-ka`** | 7.5 | `la` |
+| robertson | **`robert-s-on`** | 7.5 | `s` |
+| exihlaleni | **`exihla-le-ni`** | 7.5 | `le` |
+| hlanganani | **`hlangan-a-ni`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tsonga 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 | **32k BPE** | Best compression (4.76x) |
+| N-gram | **2-gram** | Lowest perplexity (203) |
+| Markov | **Context-4** | Highest predictability (96.9%) |
+| 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 01:42:25*

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