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README.md

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+---
+language: knc
+language_name: Central Kanuri
+language_family: african_saharan
+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-african_saharan
+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: 4.582
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7581
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Central Kanuri - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Central Kanuri** 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** | 3.865x | 3.87 | 0.1074% | 347,310 |
+| **16k** | 4.175x | 4.18 | 0.1160% | 321,490 |
+| **32k** | 4.383x | 4.39 | 0.1218% | 306,248 |
+| **64k** | 4.582x 🏆 | 4.59 | 0.1273% | 292,925 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Kǝrmai kǝla lardǝbe dǝ shima kǝla lardǝwa gadebe lan amso-a kuru karewa so-a so-...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kǝrmai ▁kǝla ▁lardǝbe ▁dǝ ▁shima ▁kǝla ▁lardǝwa ▁gadebe ▁lan ▁amso ... (+24 more)` | 34 |
+| 16k | `▁kǝrmai ▁kǝla ▁lardǝbe ▁dǝ ▁shima ▁kǝla ▁lardǝwa ▁gadebe ▁lan ▁amso ... (+23 more)` | 33 |
+| 32k | `▁kǝrmai ▁kǝla ▁lardǝbe ▁dǝ ▁shima ▁kǝla ▁lardǝwa ▁gadebe ▁lan ▁amso ... (+23 more)` | 33 |
+| 64k | `▁kǝrmai ▁kǝla ▁lardǝbe ▁dǝ ▁shima ▁kǝla ▁lardǝwa ▁gadebe ▁lan ▁amso ... (+23 more)` | 33 |
+
+**Sample 2:** `Johnny Cash John R Cash dǝ sha chasambo shi kayama cidi Amerika bǝ kuru kaya ruw...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁john ny ▁c ash ▁john ▁r ▁c ash ▁dǝ ▁sha ... (+19 more)` | 29 |
+| 16k | `▁john ny ▁c ash ▁john ▁r ▁c ash ▁dǝ ▁sha ... (+19 more)` | 29 |
+| 32k | `▁johnny ▁cash ▁john ▁r ▁cash ▁dǝ ▁sha ▁chasambo ▁shi ▁kayama ... (+16 more)` | 26 |
+| 64k | `▁johnny ▁cash ▁john ▁r ▁cash ▁dǝ ▁sha ▁chasambo ▁shi ▁kayama ... (+16 more)` | 26 |
+
+**Sample 3:** `Nasionalism dǝ shima raayi-a letǝgǝ-a do lardǝ-a lardǝ-a kalkalzǝyinma.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁nas ional ism ▁dǝ ▁shima ▁raayi - a ▁letǝgǝ - ... (+12 more)` | 22 |
+| 16k | `▁nas ional ism ▁dǝ ▁shima ▁raayi - a ▁letǝgǝ - ... (+12 more)` | 22 |
+| 32k | `▁nas ional ism ▁dǝ ▁shima ▁raayi - a ▁letǝgǝ - ... (+11 more)` | 21 |
+| 64k | `▁nasionalism ▁dǝ ▁shima ▁raayi - a ▁letǝgǝ - a ▁do ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.582x compression
+- **Lowest UNK Rate:** 8k with 0.1074% 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 | 4,001 | 11.97 | 9,862 | 23.1% | 50.6% |
+| **2-gram** | Subword | 249 🏆 | 7.96 | 1,869 | 69.4% | 99.6% |
+| **3-gram** | Word | 4,817 | 12.23 | 9,468 | 19.5% | 45.2% |
+| **3-gram** | Subword | 1,863 | 10.86 | 14,091 | 29.8% | 74.9% |
+| **4-gram** | Word | 8,323 | 13.02 | 14,018 | 13.9% | 34.8% |
+| **4-gram** | Subword | 8,691 | 13.09 | 63,398 | 15.4% | 45.6% |
+| **5-gram** | Word | 5,619 | 12.46 | 8,921 | 15.5% | 38.8% |
+| **5-gram** | Subword | 24,681 | 14.59 | 137,201 | 10.0% | 30.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `saa lan` | 2,887 |
+| 2 | `suro saa` | 2,636 |
+| 3 | `bǝ lan` | 1,942 |
+| 4 | `a kuru` | 1,725 |
+| 5 | `ye lan` | 1,254 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `suro saa lan` | 836 |
+| 2 | `suro saa yen` | 549 |
+| 3 | `lan suro saa` | 420 |
+| 4 | `duwun yar laarrin` | 401 |
+| 5 | `saa duwun yar` | 373 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `saa duwun yar laarrin` | 356 |
+| 2 | `bǝ lan suro saa` | 289 |
+| 3 | `saa lan səta ro` | 279 |
+| 4 | `lan səta ro saadənan` | 266 |
+| 5 | `suro saa duwun yar` | 259 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `saa lan səta ro saadənan` | 250 |
+| 2 | `suro saa duwun yar laarrin` | 246 |
+| 3 | `lan suro saa duwun yar` | 226 |
+| 4 | `bǝ lan suro saa duwun` | 215 |
+| 5 | `lan sun nzu notuna ma` | 147 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 90,022 |
+| 2 | `_ k` | 60,586 |
+| 3 | `_ s` | 55,437 |
+| 4 | `a n` | 52,900 |
+| 5 | `e _` | 48,288 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `y e _` | 25,215 |
+| 2 | `r o _` | 22,904 |
+| 3 | `_ l a` | 22,653 |
+| 4 | `l a n` | 19,693 |
+| 5 | `_ k ə` | 17,814 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k u r` | 14,890 |
+| 2 | `_ l a n` | 13,928 |
+| 3 | `_ s h i` | 12,446 |
+| 4 | `l a n _` | 12,391 |
+| 5 | `u r o _` | 10,490 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l a n _` | 10,158 |
+| 2 | `_ k u r u` | 9,916 |
+| 3 | `k u r u _` | 9,220 |
+| 4 | `_ s a a _` | 8,034 |
+| 5 | `_ s u r o` | 7,792 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 249
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~31% 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 | 0.7789 | 1.716 | 4.96 | 48,641 | 22.1% |
+| **1** | Subword | 1.1205 | 2.174 | 7.93 | 642 | 0.0% |
+| **2** | Word | 0.2394 | 1.180 | 1.52 | 239,982 | 76.1% |
+| **2** | Subword | 0.9246 | 1.898 | 5.50 | 5,088 | 7.5% |
+| **3** | Word | 0.0706 | 1.050 | 1.11 | 362,994 | 92.9% |
+| **3** | Subword | 0.8154 | 1.760 | 3.92 | 27,951 | 18.5% |
+| **4** | Word | 0.0242 🏆 | 1.017 | 1.04 | 402,605 | 97.6% |
+| **4** | Subword | 0.6137 | 1.530 | 2.57 | 109,408 | 38.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a lardəwa asiabe sammaso stade mohammed goni shidə təlam romanbe kureye sun tzus military administra...`
+2. `lan shiye bayanna kada ivan dychko a nabtə gomna malorossia ye askərra hutuyedə gozənadə tutsi kada`
+3. `ye fimnzə səraanama zeland a halwa bəlin nankaro bakkada dunya bəlin gartəna shidoni ngawolan səta h...`
+
+**Context Size 2:**
+
+1. `saa lan washington college of the group of interparliamentary relations with the chevalier guard reg...`
+2. `suro saa lan səta kəntawu marchye saa lan cotulowo kəntawu march saa acker yǝ bikke nəm sawa`
+3. `bǝ lan suro nashawa league bǝ manchester city bǝ lan sun nzu notunaman sha chesambo yim fyakkin`
+
+**Context Size 3:**
+
+1. `suro saa lan shiga wakil majalis kuraye ro karrada loktu kərmai nigeria yǝ kən diyau medən gozə kowo...`
+2. `suro saa yen loktu kura lardəye arturo umberto illia futu spanish lan bowotin kito r quechua kitu hu...`
+3. `lan suro saa lan bərnidə wuratə saa woson kashi 11 5 səwandəna 9 futu razəwuye faraktənadən tubman y...`
+
+**Context Size 4:**
+
+1. `saa duwun yar laarrin findin laarrin bǝ lan kǝntawu razab bǝ lan suro saa duwun yar laarrin findin l...`
+2. `bǝ lan suro saa duwun yar laarrin fitulurrin luko uwun bǝ lan sha katambo dekkel baktema cidi urugua...`
+3. `saa lan səta ro saadənan demokradiyamen kura lardəye kartəro a saa 16 ro cidazəna kuru shima kamu ku...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_nandəbə_lainza_`
+2. `awurso_kə_ku_dǝ.`
+3. `niwobewo_sǝruwsə`
+
+**Context Size 2:**
+
+1. `a_ka_ko_aprey-zau`
+2. `_kəriero;_shima_i`
+3. `_surunyakkaradebe`
+
+**Context Size 3:**
+
+1. `ye_fro-a,_diodəna.`
+2. `ro_kada_nəm_greef_`
+3. `_lardero_suro_saa_`
+
+**Context Size 4:**
+
+1. `_kuru_nəmnzə-a_lan_`
+2. `_lan,_bəladiya_lan_`
+3. `_shima_lardə_bəlin_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (109,408 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 | 19,840 |
+| Total Tokens | 415,541 |
+| Mean Frequency | 20.94 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 234.13 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 19,508 |
+| 2 | lan | 13,848 |
+| 3 | ye | 10,032 |
+| 4 | kuru | 9,262 |
+| 5 | saa | 8,070 |
+| 6 | suro | 7,134 |
+| 7 | də | 5,746 |
+| 8 | bǝ | 4,251 |
+| 9 | shima | 3,927 |
+| 10 | ro | 3,601 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | rugbyye | 2 |
+| 2 | brivero | 2 |
+| 3 | chiefs | 2 |
+| 4 | nuala | 2 |
+| 5 | éireann | 2 |
+| 6 | taghmon | 2 |
+| 7 | seán | 2 |
+| 8 | girton | 2 |
+| 9 | ryandə | 2 |
+| 10 | ucd | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1101 |
+| R² (Goodness of Fit) | 0.993201 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 42.1% |
+| Top 1,000 | 70.5% |
+| Top 5,000 | 88.4% |
+| Top 10,000 | 94.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9932 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 42.1% of corpus
+- **Long Tail:** 9,840 words needed for remaining 5.5% 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 | 0.7581 | 0.3263 | N/A | N/A |
+| **mono_64d** | 64 | 0.3103 | 0.3195 | N/A | N/A |
+| **mono_128d** | 128 | 0.0510 | 0.3146 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7581 🏆 | 0.3414 | 0.0460 | 0.2480 |
+| **aligned_64d** | 64 | 0.3103 | 0.3156 | 0.0720 | 0.3240 |
+| **aligned_128d** | 128 | 0.0510 | 0.3140 | 0.0860 | 0.4020 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7581 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3219. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.6% 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 | **0.268** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | abrahamye, alao, awardsbe |
+| `-s` | speaker, shawayen, saracenic |
+| `-b` | beaumont, b3, bannazəna |
+| `-k` | keryə, kəmbuzayin, kla |
+| `-m` | manitobayen, maud, mukon |
+| `-ma` | manitobayen, maud, magaji |
+| `-d` | duro, dunyabewo, darajatin |
+| `-c` | chaplin, crew, challenger |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | zutəye, abrahamye, ukeje |
+| `-n` | manitobayen, kəmbuzayin, chaplin |
+| `-a` | kla, kəlanza, kaza |
+| `-ə` | zamanbedə, keryə, kəzəkkə |
+| `-be` | awardsbe, afghanistanbe, cathedralbe |
+| `-o` | gowono, kadiwo, alao |
+| `-ye` | zutəye, abrahamye, disembaye |
+| `-də` | zamanbedə, kəradə, matədə |
+
+### 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 |
+|------|----------|------------------|----------|
+| `zəna` | 1.88x | 142 contexts | azəna, zazəna, lazəna |
+| `zana` | 1.90x | 71 contexts | nozana, rozana, rizana |
+| `ardə` | 2.06x | 25 contexts | lardə, gardə, lardəa |
+| `rmai` | 2.13x | 21 contexts | kǝrmai, kirmai, kərmai |
+| `asha` | 1.85x | 33 contexts | jasha, nasha, sasha |
+| `andi` | 1.60x | 43 contexts | sandi, fandi, nandi |
+| `dəna` | 1.70x | 31 contexts | dənan, tədəna, gadəna |
+| `ərma` | 2.00x | 17 contexts | kərma, kərmai, kərmaro |
+| `lard` | 1.74x | 23 contexts | lardə, lardu, larde |
+| `sand` | 1.73x | 22 contexts | sandi, sanda, sandǝ |
+| `ambo` | 1.61x | 21 contexts | tambo, kambo, dambo |
+| `nash` | 1.98x | 11 contexts | nasha, nashaa, nashan |
+
+### 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` | 164 words | karewa, kazadalawa |
+| `-k` | `-e` | 142 words | kasattəbe, kungiyadəye |
+| `-k` | `-n` | 128 words | koktənadən, kǝrǝn |
+| `-k` | `-ə` | 120 words | kərmaitədə, kasuwudə |
+| `-a` | `-e` | 109 words | augustusbe, alcockye |
+| `-s` | `-n` | 96 words | smithsonian, sədin |
+| `-k` | `-o` | 87 words | kərazənaro, karəngaro |
+| `-s` | `-e` | 84 words | saharanye, samiye |
+| `-b` | `-a` | 82 words | bannatəma, bega |
+| `-b` | `-n` | 81 words | bernstein, baditənzən |
+
+### 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 |
+|------|-----------------|------------|------|
+| tǝdǝnaben | **`tǝdǝna-be-n`** | 7.5 | `be` |
+| februaryben | **`february-be-n`** | 7.5 | `be` |
+| generally | **`general-l-y`** | 7.5 | `l` |
+| daurabedə | **`daura-be-də`** | 7.5 | `be` |
+| beakerbedə | **`beaker-be-də`** | 7.5 | `be` |
+| shaizarbedə | **`shaizar-be-də`** | 7.5 | `be` |
+| africaben | **`africa-be-n`** | 7.5 | `be` |
+| professorbero | **`professor-be-ro`** | 7.5 | `be` |
+| kamuwaben | **`kamuwa-be-n`** | 7.5 | `be` |
+| faidatanadə | **`faidata-na-də`** | 7.5 | `na` |
+| kərgənbedə | **`kərgən-be-də`** | 7.5 | `be` |
+| gargammabe | **`gargam-ma-be`** | 7.5 | `ma` |
+| rinderpestbeye | **`rinderpest-be-ye`** | 7.5 | `be` |
+| faidatinmawo | **`faidatin-ma-wo`** | 7.5 | `ma` |
+| turkeyben | **`turkey-be-n`** | 7.5 | `be` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Central Kanuri shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.58x) |
+| N-gram | **2-gram** | Lowest perplexity (249) |
+| Markov | **Context-4** | Highest predictability (97.6%) |
+| 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 08:01:45*

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