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README.md

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+---
+language: ki
+language_name: Kikuyu
+language_family: bantu_central
+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_central
+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: 4.761
+  - name: best_isotropy
+    type: isotropy
+    value: 0.3640
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kikuyu - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kikuyu** 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** | 3.740x | 3.76 | 0.1464% | 56,680 |
+| **16k** | 4.204x | 4.22 | 0.1646% | 50,431 |
+| **32k** | 4.604x | 4.63 | 0.1802% | 46,049 |
+| **64k** | 4.761x 🏆 | 4.78 | 0.1864% | 44,531 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Altay City irĩa nene ya China. Altay City irĩ igũrũ mũno ta 887 m. cia China`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁al ta y ▁city ▁irĩa ▁nene ▁ya ▁china . ▁al ... (+15 more)` | 25 |
+| 16k | `▁altay ▁city ▁irĩa ▁nene ▁ya ▁china . ▁altay ▁city ▁irĩ ... (+11 more)` | 21 |
+| 32k | `▁altay ▁city ▁irĩa ▁nene ▁ya ▁china . ▁altay ▁city ▁irĩ ... (+11 more)` | 21 |
+| 64k | `▁altay ▁city ▁irĩa ▁nene ▁ya ▁china . ▁altay ▁city ▁irĩ ... (+11 more)` | 21 |
+
+**Sample 2:** `Ziyodin city irĩa nene ya Uzbekistan. City ya Ziyodin irĩ igũrũ mũno ta 395 m. c...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁zi yo din ▁city ▁irĩa ▁nene ▁ya ▁uzbekistan . ▁city ... (+16 more)` | 26 |
+| 16k | `▁ziyodin ▁city ▁irĩa ▁nene ▁ya ▁uzbekistan . ▁city ▁ya ▁ziyodin ... (+12 more)` | 22 |
+| 32k | `▁ziyodin ▁city ▁irĩa ▁nene ▁ya ▁uzbekistan . ▁city ▁ya ▁ziyodin ... (+12 more)` | 22 |
+| 64k | `▁ziyodin ▁city ▁irĩa ▁nene ▁ya ▁uzbekistan . ▁city ▁ya ▁ziyodin ... (+12 more)` | 22 |
+
+**Sample 3:** `Matekinoronjĩsti me ngumo Bill Gates Everett Rogers Genrich Altshuller Henry For...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mate kinoronjĩ sti ▁me ▁ngumo ▁bill ▁gates ▁e vere tt ... (+26 more)` | 36 |
+| 16k | `▁mate kinoronjĩ sti ▁me ▁ngumo ▁bill ▁gates ▁everett ▁rogers ▁genrich ... (+13 more)` | 23 |
+| 32k | `▁mate kinoronjĩ sti ▁me ▁ngumo ▁bill ▁gates ▁everett ▁rogers ▁genrich ... (+13 more)` | 23 |
+| 64k | `▁mate kinoronjĩsti ▁me ▁ngumo ▁bill ▁gates ▁everett ▁rogers ▁genrich ▁altshuller ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.761x compression
+- **Lowest UNK Rate:** 8k with 0.1464% 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 | 1,695 | 10.73 | 3,484 | 29.8% | 67.3% |
+| **2-gram** | Subword | 221 🏆 | 7.79 | 1,640 | 72.6% | 99.5% |
+| **3-gram** | Word | 2,343 | 11.19 | 4,922 | 26.6% | 51.7% |
+| **3-gram** | Subword | 1,638 | 10.68 | 10,992 | 32.8% | 77.3% |
+| **4-gram** | Word | 10,195 | 13.32 | 14,421 | 11.0% | 21.2% |
+| **4-gram** | Subword | 8,170 | 13.00 | 46,210 | 15.8% | 47.0% |
+| **5-gram** | Word | 9,790 | 13.26 | 12,205 | 8.8% | 19.4% |
+| **5-gram** | Subword | 23,535 | 14.52 | 90,045 | 8.8% | 30.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nene ya` | 634 |
+| 2 | `irĩa nene` | 619 |
+| 3 | `city irĩa` | 611 |
+| 4 | `mũno ta` | 563 |
+| 5 | `igũrũ mũno` | 558 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `irĩa nene ya` | 618 |
+| 2 | `city irĩa nene` | 611 |
+| 3 | `igũrũ mũno ta` | 554 |
+| 4 | `irĩ igũrũ mũno` | 554 |
+| 5 | `nene ya china` | 269 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `city irĩa nene ya` | 611 |
+| 2 | `irĩ igũrũ mũno ta` | 554 |
+| 3 | `irĩa nene ya china` | 268 |
+| 4 | `ya china city ya` | 253 |
+| 5 | `nene ya china city` | 253 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `city irĩa nene ya china` | 268 |
+| 2 | `nene ya china city ya` | 253 |
+| 3 | `irĩa nene ya china city` | 252 |
+| 4 | `city irĩa nene ya uzbekistan` | 151 |
+| 5 | `nene ya uzbekistan city ya` | 103 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 72,286 |
+| 2 | `_ m` | 27,852 |
+| 3 | `_ n` | 24,566 |
+| 4 | `_ k` | 21,508 |
+| 5 | `o _` | 20,719 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a _` | 13,618 |
+| 2 | `a _ m` | 12,680 |
+| 3 | `a _ k` | 9,647 |
+| 4 | `i a _` | 9,237 |
+| 5 | `a _ n` | 8,811 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a _` | 7,688 |
+| 2 | `_ w a _` | 7,106 |
+| 3 | `n d ũ _` | 4,669 |
+| 4 | `_ n ĩ _` | 4,466 |
+| 5 | `r ĩ a _` | 4,311 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ c i a _` | 2,410 |
+| 2 | `a _ w a _` | 2,350 |
+| 3 | `ũ n d ũ _` | 2,291 |
+| 4 | `k a n a _` | 2,253 |
+| 5 | `_ k a n a` | 2,082 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 221
+- **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 | 0.5880 | 1.503 | 3.26 | 36,290 | 41.2% |
+| **1** | Subword | 1.1410 | 2.205 | 8.50 | 464 | 0.0% |
+| **2** | Word | 0.1749 | 1.129 | 1.35 | 117,531 | 82.5% |
+| **2** | Subword | 1.0027 | 2.004 | 5.54 | 3,943 | 0.0% |
+| **3** | Word | 0.0512 | 1.036 | 1.07 | 157,775 | 94.9% |
+| **3** | Subword | 0.8396 | 1.790 | 3.66 | 21,830 | 16.0% |
+| **4** | Word | 0.0195 🏆 | 1.014 | 1.03 | 168,145 | 98.0% |
+| **4** | Subword | 0.6140 | 1.530 | 2.39 | 79,815 | 38.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `na njĩra ya thĩĩ handũ na indo ugĩciganagĩrĩra handu hatugĩru na kĩngeretha concision moigaga atĩ nĩ`
+2. `wa mundu e heggy discovery of the anatomy of odinani nĩ ya cinda nĩ maũndũ mothe`
+3. `nĩ kĩaringire gĩkaru kĩa njata kana ndamathia apartheid ya kũhũrwo ndwara thita cia mĩhĩrĩga ya keny...`
+
+**Context Size 2:**
+
+1. `nene ya uzbekistan city ya karachi irĩ igũrũ mũno ta 1 270 m cia china`
+2. `irĩa nene ya uzbekistan city ya liuyang irĩ igũrũ mũno ta 162 279 m links poznań cia`
+3. `city irĩa nene ya uzbekistan city ya malindi irĩ igũrũ mũno ta 12 0 m 39 4`
+
+**Context Size 3:**
+
+1. `irĩa nene ya china city ya guigang irĩ igũrũ mũno ta 1 779 m cia china`
+2. `city irĩa nene ya japan city ya sakai irĩ igũrũ mũno ta 757 m cia uzbekistan`
+3. `igũrũ mũno ta 61 m cia uzbekistan`
+
+**Context Size 4:**
+
+1. `city irĩa nene ya uzbekistan cia uzbekistan`
+2. `irĩ igũrũ mũno ta 12 m cia china`
+3. `irĩa nene ya china city ya baotou irĩ igũrũ mũno ta 1 084 m cia china`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ma_gh_rwerĩ_ara`
+2. `a_mwty_rigo_rerî`
+3. `ntha_fegabu_rĩna`
+
+**Context Size 2:**
+
+1. `a_ungĩte_ũgĩthĩ'.`
+2. `_mo_gö_·_agwĩngo-`
+3. `_nĩa_igikamũthead`
+
+**Context Size 3:**
+
+1. `na_kagwo_ata_7.3.2`
+2. `a_mahũ_ya_nĩ_ndu_w`
+3. `a_kũthonal_koretwo`
+
+**Context Size 4:**
+
+1. `_na_kwĩrutaga_rtngt`
+2. `_wa_kũhiti_(deducat`
+3. `ndũ_matho_wa_ũtihoy`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (79,815 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 | 15,538 |
+| Total Tokens | 176,023 |
+| Mean Frequency | 11.33 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 112.81 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | na | 7,738 |
+| 2 | wa | 7,198 |
+| 3 | nĩ | 4,567 |
+| 4 | ya | 4,306 |
+| 5 | cia | 2,416 |
+| 6 | kana | 2,104 |
+| 7 | ta | 1,979 |
+| 8 | inĩ | 1,613 |
+| 9 | kĩa | 1,218 |
+| 10 | city | 1,195 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | bisosa | 2 |
+| 2 | biela | 2 |
+| 3 | nzeba | 2 |
+| 4 | mitshi | 2 |
+| 5 | ikuama | 2 |
+| 6 | bimuma | 2 |
+| 7 | muikale | 2 |
+| 8 | bujima | 2 |
+| 9 | ngondu | 2 |
+| 10 | kumonaye | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9723 |
+| R² (Goodness of Fit) | 0.992255 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 43.1% |
+| Top 1,000 | 67.4% |
+| Top 5,000 | 85.5% |
+| Top 10,000 | 93.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9923 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 43.1% of corpus
+- **Long Tail:** 5,538 words needed for remaining 6.3% 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 | 0.3640 🏆 | 0.4073 | N/A | N/A |
+| **mono_64d** | 64 | 0.0941 | 0.3880 | N/A | N/A |
+| **mono_128d** | 128 | 0.0139 | 0.4127 | N/A | N/A |
+| **aligned_32d** | 32 | 0.3640 | 0.4033 | 0.0120 | 0.0680 |
+| **aligned_64d** | 64 | 0.0941 | 0.3956 | 0.0080 | 0.0980 |
+| **aligned_128d** | 128 | 0.0139 | 0.4268 | 0.0140 | 0.1120 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.3640 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4056. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.4% 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 | **-0.354** | 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` | maarutaga, mahiu, mathondekaga |
+| `-ma` | maarutaga, mahiu, mathondekaga |
+| `-k` | kindũ, kũmuunda, kumenereria |
+| `-kĩ` | kĩhumo, kĩna, kĩũteti |
+| `-n` | nĩũĩ, ndangĩciara, ndĩra |
+| `-a` | athĩni, athĩrĩria, ahingagia |
+| `-t` | tũothe, tehũka, thĩiniĩ |
+| `-g` | gacui, game, gũũcia |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | kũmuunda, maarutaga, bora |
+| `-o` | marotero, hatonyagĩrwo, mĩako |
+| `-e` | ohĩgĩrĩire, game, médiatique |
+| `-ia` | henereria, athĩrĩria, kumenereria |
+| `-wo` | hatonyagĩrwo, gĩakĩtwo, angikorwo |
+| `-i` | hanini, athĩni, woneki |
+| `-ra` | bora, ciura, ndangĩciara |
+| `-re` | ohĩgĩrĩire, ũndũire, inyitanĩire |
+
+### 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 |
+|------|----------|------------------|----------|
+| `gĩrĩ` | 1.60x | 39 contexts | igĩrĩ, ĩgĩrĩ, gĩrĩma |
+| `orag` | 1.77x | 27 contexts | groraga, ĩroraga, űkoragwo |
+| `ĩrĩr` | 1.54x | 44 contexts | kĩrĩrĩ, hĩrĩre, kĩrĩro |
+| `ũthi` | 1.56x | 40 contexts | ũthii, ũthiĩ, ũthiũ |
+| `ithi` | 1.49x | 47 contexts | ithia, nithi, ithii |
+| `gĩth` | 1.57x | 35 contexts | gĩthĩ, gĩthu, gĩthũ |
+| `agwo` | 1.59x | 31 contexts | nagwo, wagwo, magwo |
+| `thia` | 1.45x | 41 contexts | ithia, ethia, athia |
+| `mũth` | 1.67x | 22 contexts | mũthĩ, mũthiu, mũthee |
+| `hũth` | 1.59x | 25 contexts | hũthũ, ũhũthe, hũthia |
+| `math` | 1.57x | 25 contexts | matha, ũmatho, mathaa |
+| `rĩri` | 1.63x | 21 contexts | rĩria, irĩria, arĩria |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-k` | `-a` | 424 words | kũrota, kĩorotaga |
+| `-m` | `-a` | 271 words | mĩanga, matagathira |
+| `-g` | `-a` | 266 words | gĩakinya, gĩrima |
+| `-m` | `-o` | 222 words | mũmero, mehumbĩtwo |
+| `-k` | `-o` | 150 words | kĩroho, kĩnyitithanagio |
+| `-t` | `-a` | 149 words | tga, thĩgia |
+| `-m` | `-e` | 145 words | maruanĩire, mbage |
+| `-k` | `-ia` | 127 words | kũnyiihia, kĩgiragĩrĩria |
+| `-a` | `-a` | 119 words | athamia, arara |
+| `-m` | `-i` | 117 words | mũthũũri, muti |
+
+### 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 |
+|------|-----------------|------------|------|
+| kũgathimĩra | **`kũgathim-ĩ-ra`** | 7.5 | `ĩ` |
+| rĩtingĩrora | **`rĩtingĩr-o-ra`** | 7.5 | `o` |
+| athomeire | **`athome-i-re`** | 7.5 | `i` |
+| uzbekistan | **`uzbekist-a-n`** | 7.5 | `a` |
+| inyanjara | **`inyanj-a-ra`** | 7.5 | `a` |
+| ĩhũthĩkaga | **`ĩhũthĩk-a-ga`** | 7.5 | `a` |
+| ndaragarara | **`ndaragar-a-ra`** | 7.5 | `a` |
+| kũharahara | **`kũharah-a-ra`** | 7.5 | `a` |
+| kĩhũthikaga | **`kĩhũthik-a-ga`** | 7.5 | `a` |
+| ateretaga | **`ateret-a-ga`** | 7.5 | `a` |
+| tengchong | **`tengch-o-ng`** | 7.5 | `o` |
+| mũthigari | **`mũthi-ga-ri`** | 7.5 | `ga` |
+| kĩhũthĩkaga | **`kĩhũthĩk-a-ga`** | 7.5 | `a` |
+| hakundeeru | **`hakunde-e-ru`** | 7.5 | `e` |
+| matikoragwo | **`ma-t-ikoragwo`** | 7.5 | `ikoragwo` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kikuyu 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 (4.76x) |
+| N-gram | **2-gram** | Lowest perplexity (221) |
+| Markov | **Context-4** | Highest predictability (98.0%) |
+| 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 07:41:12*

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