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README.md

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+---
+language: st
+language_name: Southern Sotho
+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: 4.418
+  - name: best_isotropy
+    type: isotropy
+    value: 0.5673
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Southern Sotho - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Southern Sotho** 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** | 3.776x | 3.78 | 0.2714% | 231,037 |
+| **16k** | 4.068x | 4.07 | 0.2923% | 214,484 |
+| **32k** | 4.296x | 4.30 | 0.3087% | 203,079 |
+| **64k** | 4.418x 🏆 | 4.42 | 0.3175% | 197,468 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Siphelele Mthembu (ya hlahileng ka la 15 Phato ke sebapadi sa bolo ya maoto Afri...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁si phe lele ▁mthe mbu ▁( ya ▁hlahileng ▁ka ▁la ... (+24 more)` | 34 |
+| 16k | `▁siphelele ▁mthembu ▁( ya ▁hlahileng ▁ka ▁la ▁ 1 5 ... (+21 more)` | 31 |
+| 32k | `▁siphelele ▁mthembu ▁( ya ▁hlahileng ▁ka ▁la ▁ 1 5 ... (+21 more)` | 31 |
+| 64k | `▁siphelele ▁mthembu ▁( ya ▁hlahileng ▁ka ▁la ▁ 1 5 ... (+21 more)` | 31 |
+
+**Sample 2:** `Rafael José Orozco Maestre (Hlakubele 24, – 11 Phupu ne e le sebini, sengoli sa ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ra fa el ▁jo s é ▁o ro z co ... (+26 more)` | 36 |
+| 16k | `▁rafa el ▁josé ▁oroz co ▁mae st re ▁( hla ... (+21 more)` | 31 |
+| 32k | `▁rafael ▁josé ▁orozco ▁mae st re ▁( hlakubele ▁ 2 ... (+18 more)` | 28 |
+| 64k | `▁rafael ▁josé ▁orozco ▁maestre ▁( hlakubele ▁ 2 4 , ... (+16 more)` | 26 |
+
+**Sample 3:** `Mokwallo ke lekeishene le haufi le Vredefort, ka hare ho Masepala wa Ngwathe, po...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mo kwa llo ▁ke ▁lekeishene ▁le ▁haufi ▁le ▁vrede fort ... (+17 more)` | 27 |
+| 16k | `▁mo kwa llo ▁ke ▁lekeishene ▁le ▁haufi ▁le ▁vredefort , ... (+16 more)` | 26 |
+| 32k | `▁mokwallo ▁ke ▁lekeishene ▁le ▁haufi ▁le ▁vredefort , ▁ka ▁hare ... (+14 more)` | 24 |
+| 64k | `▁mokwallo ▁ke ▁lekeishene ▁le ▁haufi ▁le ▁vredefort , ▁ka ▁hare ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.418x compression
+- **Lowest UNK Rate:** 8k with 0.2714% 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 | 4,147 | 12.02 | 10,524 | 21.0% | 52.2% |
+| **2-gram** | Subword | 184 🏆 | 7.52 | 1,683 | 77.1% | 99.6% |
+| **3-gram** | Word | 6,664 | 12.70 | 14,321 | 16.6% | 41.7% |
+| **3-gram** | Subword | 1,318 | 10.36 | 12,094 | 38.3% | 80.9% |
+| **4-gram** | Word | 13,698 | 13.74 | 22,303 | 10.5% | 28.0% |
+| **4-gram** | Subword | 6,177 | 12.59 | 50,733 | 19.5% | 52.6% |
+| **5-gram** | Word | 10,291 | 13.33 | 14,770 | 10.4% | 28.8% |
+| **5-gram** | Subword | 17,540 | 14.10 | 100,714 | 10.4% | 34.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e le` | 2,604 |
+| 2 | `ile a` | 2,556 |
+| 3 | `o ile` | 2,550 |
+| 4 | `afrika borwa` | 1,822 |
+| 5 | `ka la` | 1,398 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `o ile a` | 2,458 |
+| 2 | `e ne e` | 839 |
+| 3 | `ne e le` | 639 |
+| 4 | `sa afrika borwa` | 459 |
+| 5 | `e ile ya` | 458 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e ne e le` | 633 |
+| 2 | `sa bolo ya maoto` | 249 |
+| 3 | `ka o ile a` | 216 |
+| 4 | `bolo ya maoto sa` | 212 |
+| 5 | `ka moka afrika borwa` | 179 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sa bolo ya maoto sa` | 211 |
+| 2 | `sebapadi sa bolo ya maoto` | 161 |
+| 3 | `bolo ya maoto sa afrika` | 156 |
+| 4 | `ya maoto sa afrika borwa` | 155 |
+| 5 | `ke sebapadi sa bolo ya` | 146 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 129,546 |
+| 2 | `e _` | 80,922 |
+| 3 | `o _` | 53,695 |
+| 4 | `l e` | 48,470 |
+| 5 | `_ l` | 37,957 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l e _` | 26,748 |
+| 2 | `_ l e` | 23,579 |
+| 3 | `n g _` | 22,710 |
+| 4 | `k a _` | 18,228 |
+| 5 | `h o _` | 18,075 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l e _` | 15,451 |
+| 2 | `_ h o _` | 13,673 |
+| 3 | `_ k a _` | 12,473 |
+| 4 | `e n g _` | 11,083 |
+| 5 | `_ y a _` | 9,749 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ h o _` | 6,521 |
+| 2 | `_ t s a _` | 5,552 |
+| 3 | `_ t s e _` | 4,528 |
+| 4 | `e _ l e _` | 4,398 |
+| 5 | `a _ l e _` | 4,221 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 184
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.7915 | 1.731 | 4.86 | 30,896 | 20.8% |
+| **1** | Subword | 0.9659 | 1.953 | 8.17 | 449 | 3.4% |
+| **2** | Word | 0.3145 | 1.244 | 1.77 | 149,534 | 68.5% |
+| **2** | Subword | 1.0583 | 2.082 | 6.21 | 3,664 | 0.0% |
+| **3** | Word | 0.1184 | 1.086 | 1.22 | 264,209 | 88.2% |
+| **3** | Subword | 0.8464 | 1.798 | 3.86 | 22,722 | 15.4% |
+| **4** | Word | 0.0501 🏆 | 1.035 | 1.08 | 320,187 | 95.0% |
+| **4** | Subword | 0.5799 | 1.495 | 2.42 | 87,601 | 42.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `le ka setereke provensing ya hae pele a le phahameng sa setjhaba ba hae la sebaka`
+2. `e neng se bapalang e nang le lefapha lefapha bakeng sa bohareng sa boeta pele a`
+3. `ho masepala wa bophelo lisebelisoa tsohle tse ling tsa zone 14 qetellong ya latela mokhatlo o`
+
+**Context Size 2:**
+
+1. `e le puo yaa bahatelli e le toropo ea ypres setsi sa setso sa sekgowa`
+2. `ile a fumana diploma ya hae le ka leboya ho noka ya elands ka histori sebaka sena`
+3. `o ile a khethwa sehlopheng sa gauteng afrika borwa u23 ha a hopola mabaka a mang a`
+
+**Context Size 3:**
+
+1. `o ile a latelwa ke moprofesa daya reddy ka la 13 phuptjane ke senokwane sa afrika borwa dipina`
+2. `e ne e le ya hae ya independence day dipina bahale ba hosane ho hong ho maafrika borwa`
+3. `ne e le karolo ea sehlopha se neng se nahana hore se utlwa likhohlano tsa lelapa le ho`
+
+**Context Size 4:**
+
+1. `e ne e le moruti mme seo sa etsa hore a be le maqhama hodima dijo le meetlo letsatsing`
+2. `sa bolo ya maoto sa afrika borwa se bapalang e le sebapadi sa bohareng ba sehlopha sa ts galaxy`
+3. `ka o ile a hlaha nakong ea papali ea papadi eo afrika borwa e ileng ya e ba ngaka`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_sts'erie_pa_pha`
+2. `a_lesora_me._ya_`
+3. `ent_afumapa_kabi`
+
+**Context Size 2:**
+
+1. `a_tliaha_ka_bo_o_`
+2. `e_mohlo,_tlo_b_'m`
+3. `o_kemini_wa_mang_`
+
+**Context Size 3:**
+
+1. `le_swa_bokgatang_e`
+2. `_le_mabotjoalonyan`
+3. `ng_ba_yuniteremira`
+
+**Context Size 4:**
+
+1. `_le_45_000_ka_e_mpe`
+2. `_ho_bua_kang_jwalo_`
+3. `_ka_nation_boydelli`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (87,601 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 | 14,659 |
+| Total Tokens | 368,067 |
+| Mean Frequency | 25.11 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 312.16 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | le | 15,561 |
+| 2 | e | 14,132 |
+| 3 | ho | 13,814 |
+| 4 | ka | 12,570 |
+| 5 | a | 10,894 |
+| 6 | ya | 10,066 |
+| 7 | ba | 7,883 |
+| 8 | sa | 7,305 |
+| 9 | o | 6,830 |
+| 10 | ea | 5,887 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | baker | 2 |
+| 2 | navorsingsentrum | 2 |
+| 3 | afrikanerbakens | 2 |
+| 4 | federasie | 2 |
+| 5 | kultuurvereniginge | 2 |
+| 6 | 112 | 2 |
+| 7 | ntlokgolo | 2 |
+| 8 | lingoli | 2 |
+| 9 | moiloa | 2 |
+| 10 | trelawny | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1072 |
+| R² (Goodness of Fit) | 0.991733 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 53.1% |
+| Top 1,000 | 76.3% |
+| Top 5,000 | 92.1% |
+| Top 10,000 | 97.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9917 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 53.1% of corpus
+- **Long Tail:** 4,659 words needed for remaining 2.5% 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 | 0.5673 🏆 | 0.3940 | N/A | N/A |
+| **mono_64d** | 64 | 0.1528 | 0.3621 | N/A | N/A |
+| **mono_128d** | 128 | 0.0222 | 0.3760 | N/A | N/A |
+| **aligned_32d** | 32 | 0.5673 | 0.3806 | 0.0140 | 0.2000 |
+| **aligned_64d** | 64 | 0.1528 | 0.3683 | 0.0300 | 0.2140 |
+| **aligned_128d** | 128 | 0.0222 | 0.3775 | 0.0460 | 0.2040 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.5673 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3764. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.169** | 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` | menyabuketso, motorsports, makhooa |
+| `-ma` | makhooa, maiteko, makhadzi |
+| `-s` | sahesu, sammy, silila |
+| `-b` | blaq, bruce, behile |
+| `-mo` | motorsports, mopalami, motona |
+| `-t` | tlalehilwe, toit, tsebahatsoa |
+| `-bo` | bonahetse, bomampodi, bohahlauli |
+| `-di` | diporesente, dikarabello, dienjini |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ng` | iponahatsang, thahasellang, liking |
+| `-a` | ginwala, elella, makhooa |
+| `-e` | tlalehilwe, ujeqe, vlamertinge |
+| `-g` | iponahatsang, thahasellang, liking |
+| `-o` | menyabuketso, pablo, alebamo |
+| `-i` | giovanni, makhadzi, mopalami |
+| `-s` | motorsports, countries, bioethics |
+| `-n` | in, upington, chan |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ilen` | 1.60x | 35 contexts | ileng, bileng, nileng |
+| `tswe` | 1.62x | 27 contexts | etswe, entswe, tswela |
+| `tsoe` | 1.70x | 21 contexts | etsoe, tsoelo, tsoela |
+| `etso` | 1.32x | 45 contexts | ketso, setso, etsoa |
+| `tsen` | 1.63x | 21 contexts | tsena, etseng, itseng |
+| `lang` | 1.46x | 29 contexts | tlang, slang, lange |
+| `elet` | 1.45x | 26 contexts | eletsa, leleti, keletso |
+| `bapa` | 1.77x | 13 contexts | bapapa, bapale, bapala |
+| `etsi` | 1.53x | 17 contexts | wetsi, setsi, metsi |
+| `bets` | 1.58x | 15 contexts | betsa, ebetso, sebetse |
+| `otho` | 1.41x | 20 contexts | motho, botho, sotho |
+| `ehlo` | 1.48x | 14 contexts | lehloyo, lehloeo, lefehlo |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-m` | `-a` | 170 words | maphalla, masilela |
+| `-m` | `-i` | 128 words | multi, moletsi |
+| `-m` | `-e` | 128 words | mohurutshe, millione |
+| `-l` | `-o` | 125 words | lechato, likoloto |
+| `-t` | `-g` | 120 words | tsejweng, tswelang |
+| `-m` | `-o` | 120 words | mosiamo, meipiletso |
+| `-t` | `-ng` | 118 words | tsejweng, tswelang |
+| `-m` | `-g` | 108 words | maropeng, moelelong |
+| `-b` | `-i` | 108 words | babuelli, bolepi |
+| `-m` | `-ng` | 106 words | maropeng, moelelong |
+
+### 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 |
+|------|-----------------|------------|------|
+| lithaoleng | **`lithaol-e-ng`** | 7.5 | `e` |
+| lokolohile | **`lokoloh-i-le`** | 7.5 | `i` |
+| hammanskraal | **`hammanskr-a-al`** | 7.5 | `a` |
+| phetohelo | **`phetoh-e-lo`** | 7.5 | `e` |
+| performing | **`perform-i-ng`** | 7.5 | `i` |
+| matšeliso | **`matše-li-so`** | 7.5 | `li` |
+| mangaliso | **`manga-li-so`** | 7.5 | `li` |
+| tsamaisana | **`tsamais-a-na`** | 7.5 | `a` |
+| nathaniel | **`nathani-e-l`** | 7.5 | `e` |
+| dihlabeng | **`dihlab-e-ng`** | 7.5 | `e` |
+| litlhaselo | **`litlha-se-lo`** | 7.5 | `se` |
+| macroalga | **`macroal-g-a`** | 7.5 | `g` |
+| hlahisang | **`hlahi-sa-ng`** | 7.5 | `sa` |
+| moloisane | **`moloi-sa-ne`** | 7.5 | `sa` |
+| batlileng | **`batli-le-ng`** | 7.5 | `le` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Southern Sotho 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 (4.42x) |
+| N-gram | **2-gram** | Lowest perplexity (184) |
+| Markov | **Context-4** | Highest predictability (95.0%) |
+| 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 22:42:51*

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