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+---
+language: rn
+language_name: Rundi
+language_family: bantu_eastern
+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_eastern
+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: 4.735
+  - name: best_isotropy
+    type: isotropy
+    value: 0.1625
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Rundi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Rundi** 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** | 3.942x | 3.95 | 0.2846% | 143,361 |
+| **16k** | 4.328x | 4.33 | 0.3125% | 130,557 |
+| **32k** | 4.735x 🏆 | 4.74 | 0.3419% | 119,347 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Irepuburika y’Ubutariyano ni igihugu kiri m' Uburaya. Umurwa mukuru: Rome Uburin...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁irepuburika ▁y ’ ubu tariyano ▁ni ▁igihugu ▁kiri ▁m ' ... (+19 more)` | 29 |
+| 16k | `▁irepuburika ▁y ’ ubutariyano ▁ni ▁igihugu ▁kiri ▁m ' ▁uburaya ... (+18 more)` | 28 |
+| 32k | `▁irepuburika ▁y ’ ubutariyano ▁ni ▁igihugu ▁kiri ▁m ' ▁uburaya ... (+18 more)` | 28 |
+
+**Sample 2:** `Ushingiye kuri Bibiliya ni umwana w'Imana. Ko Yesu canke Yezu (Jésus) ari umwana...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁u shingiye ▁kuri ▁bibiliya ▁ni ▁umwana ▁w ' imana . ... (+26 more)` | 36 |
+| 16k | `▁u shingiye ▁kuri ▁bibiliya ▁ni ▁umwana ▁w ' imana . ... (+23 more)` | 33 |
+| 32k | `▁ushingiye ▁kuri ▁bibiliya ▁ni ▁umwana ▁w ' imana . ▁ko ... (+21 more)` | 31 |
+
+**Sample 3:** `Indonyi (Kobus ellipsiprymnus defassa) ni igikoko gifise amahembe maremare kikam...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁indon yi ▁( ko bu s ▁el lip si p ... (+25 more)` | 35 |
+| 16k | `▁indon yi ▁( ko bu s ▁ellip si p ry ... (+24 more)` | 34 |
+| 32k | `▁indonyi ▁( kobus ▁ellipsiprymnus ▁defassa ) ▁ni ▁igikoko ▁gifise ▁amahembe ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.735x compression
+- **Lowest UNK Rate:** 8k with 0.2846% 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 | 1,057 | 10.05 | 1,527 | 29.0% | 84.0% |
+| **2-gram** | Subword | 201 🏆 | 7.65 | 1,104 | 75.0% | 99.9% |
+| **3-gram** | Word | 1,104 | 10.11 | 1,428 | 25.0% | 84.0% |
+| **3-gram** | Subword | 1,386 | 10.44 | 6,340 | 29.6% | 82.8% |
+| **4-gram** | Word | 1,786 | 10.80 | 2,222 | 18.3% | 63.5% |
+| **4-gram** | Subword | 6,484 | 12.66 | 23,552 | 12.6% | 46.7% |
+| **5-gram** | Word | 1,088 | 10.09 | 1,323 | 25.3% | 83.2% |
+| **5-gram** | Subword | 17,832 | 14.12 | 46,096 | 7.0% | 28.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ikigabane ca` | 277 |
+| 2 | `na we` | 171 |
+| 3 | `mu gihugu` | 164 |
+| 4 | `avuga ati` | 123 |
+| 5 | `mu burundi` | 116 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `uburinganire ibirometero kwadarato` | 87 |
+| 2 | `mu ntara ya` | 75 |
+| 3 | `mu gihugu ca` | 61 |
+| 4 | `ni igisagara kiri` | 53 |
+| 5 | `ibintu bifise ubuzima` | 41 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `bw ibintu bifise ubuzima` | 41 |
+| 2 | `zunze ubumwe bwa amerika` | 31 |
+| 3 | `leta zunze ubumwe bwa` | 31 |
+| 4 | `mu gihugu ca kanahani` | 27 |
+| 5 | `ni igisagara kiri muri` | 26 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `leta zunze ubumwe bwa amerika` | 31 |
+| 2 | `z unze ubumwe za amerika` | 25 |
+| 3 | `w ibihumbi bibiri na cumi` | 20 |
+| 4 | `ni gutera abavandimwe aa orchidaceae` | 19 |
+| 5 | `mumwaka w ibihumbi bibiri na` | 18 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 21,858 |
+| 2 | `e _` | 12,255 |
+| 3 | `i _` | 10,392 |
+| 4 | `a n` | 9,486 |
+| 5 | `o _` | 9,301 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u` | 5,020 |
+| 2 | `r a _` | 4,514 |
+| 3 | `a r a` | 3,092 |
+| 4 | `a b a` | 3,082 |
+| 5 | `r i _` | 3,003 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u _` | 2,368 |
+| 2 | `_ u m u` | 1,634 |
+| 3 | `a _ m u` | 1,523 |
+| 4 | `i r a _` | 1,469 |
+| 5 | `_ n a _` | 1,159 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i h u g u` | 754 |
+| 2 | `a _ m u _` | 747 |
+| 3 | `g i h u g` | 681 |
+| 4 | `_ m u r i` | 654 |
+| 5 | `r u n d i` | 653 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 201
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.6054 | 1.521 | 3.16 | 18,741 | 39.5% |
+| **1** | Subword | 1.1432 | 2.209 | 8.47 | 307 | 0.0% |
+| **2** | Word | 0.1529 | 1.112 | 1.26 | 58,782 | 84.7% |
+| **2** | Subword | 0.9964 | 1.995 | 5.12 | 2,593 | 0.4% |
+| **3** | Word | 0.0459 | 1.032 | 1.06 | 73,578 | 95.4% |
+| **3** | Subword | 0.7791 | 1.716 | 3.27 | 13,247 | 22.1% |
+| **4** | Word | 0.0181 🏆 | 1.013 | 1.02 | 77,704 | 98.2% |
+| **4** | Subword | 0.5566 | 1.471 | 2.27 | 43,142 | 44.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `mu gihugu cawe c inyoni zose ziguruka imwimwe ku matwi yakobo 29 uwu nyene imbere y`
+2. `n ubusho nshoreye n iterambere ry igisagara kiri muri rig veda ibihimbano byatangiye hagati yibiro 4...`
+3. `ni gutera abavandimwe acampe nyassana acampe intermedia acampe praemorsa ni we tudaharuye abagore n ...`
+
+**Context Size 2:**
+
+1. `ikigabane ca 21 ikigabane ca 7 ikigabane ca 18 ikigabane ca 11 ikigabane ca 23 ikigabane ca`
+2. `na we avyara tubari kayini yari nahama 23 rameki abwira abagore biwe babiri umukuru w umugambwe cndd`
+3. `mu gihugu benewabo na yozefu ati ehe umuntu yabaye nk umwe mu bantu b urwo rugo yari`
+
+**Context Size 3:**
+
+1. `uburinganire ibirometero kwadarato 840 abanyagihugu 829 677 circus`
+2. `mu ntara ya ngozi komine kiremba mu burundi akabizi ni uruzi ruri mu ntara ya makamba mu buseruko`
+3. `mu gihugu ca kanahani 19 rabani yari yagiye kumwa ubwoya ubusho bwiwe igihe rakeri yiba ibishusho vy...`
+
+**Context Size 4:**
+
+1. `leta zunze ubumwe bwa amerika abaserukizi 435 34 umuserukizi ashika muri sentare house representativ...`
+2. `zunze ubumwe bwa amerika uhimbazwa ryari mu kw indwi mukakaro itariki zine july 4th 10 mur ukwo kwik...`
+3. `mu gihugu ca kanahani i kiriyati areba ari ho heburoni aho aburahamu na izahaki bari barabaye 28 imi...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_risiri_kakagi:_`
+2. `a_nginko_be_n'in`
+3. `isusezi,nandi)_m`
+
+**Context Size 2:**
+
+1. `a_rwo_bangwara_no`
+2. `e_17_izi_atai_imb`
+3. `i_n'inira_ne_muso`
+
+**Context Size 3:**
+
+1. `_mu_gihe_biwe_ikid`
+2. `ra_icendera_cfc1v_`
+3. `araso_nimwaka_ngin`
+
+**Context Size 4:**
+
+1. `_mu_nzu_rero_c'aban`
+2. `_umunani_gusa_bwint`
+3. `a_mu_gushika_iyo_ri`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (43,142 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 | 6,649 |
+| Total Tokens | 72,643 |
+| Mean Frequency | 10.93 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 52.83 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mu | 2,396 |
+| 2 | n | 1,861 |
+| 3 | ni | 1,183 |
+| 4 | na | 1,162 |
+| 5 | y | 780 |
+| 6 | ya | 714 |
+| 7 | w | 652 |
+| 8 | muri | 611 |
+| 9 | ca | 548 |
+| 10 | ku | 530 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | umusalaba | 2 |
+| 2 | ubutwari | 2 |
+| 3 | umukardinali | 2 |
+| 4 | bonaventura | 2 |
+| 5 | akhenaton | 2 |
+| 6 | umukatorika | 2 |
+| 7 | ruanda | 2 |
+| 8 | stanley | 2 |
+| 9 | kirisese | 2 |
+| 10 | inyungu | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9786 |
+| R² (Goodness of Fit) | 0.986220 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 37.3% |
+| Top 1,000 | 72.6% |
+| Top 5,000 | 95.5% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9862 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 37.3% of corpus
+- **Long Tail:** -3,351 words needed for remaining 100.0% 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 | 0.1625 | 0.5214 | N/A | N/A |
+| **mono_64d** | 64 | 0.0296 | 0.5371 | N/A | N/A |
+| **mono_128d** | 128 | 0.0040 | 0.5210 | N/A | N/A |
+| **aligned_32d** | 32 | 0.1625 🏆 | 0.5232 | 0.0183 | 0.0888 |
+| **aligned_64d** | 64 | 0.0296 | 0.5482 | 0.0209 | 0.1384 |
+| **aligned_128d** | 128 | 0.0040 | 0.5338 | 0.0235 | 0.1514 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.1625 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.5308. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.3% 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 | **1.919** | 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 |
+|--------|----------|
+| `-i` | imbabazi, imiringa, ishikanwa |
+| `-a` | asubira, akoresheje, ashira |
+| `-b` | bivugwa, bakomeye, bitungwa |
+| `-ba` | bakomeye, baravuga, bahamagara |
+| `-m` | marin, mbwira, mumakomine |
+| `-mu` | mumakomine, muji, mugitondo |
+| `-n` | nzoyiguha, nabantu, ntare |
+| `-k` | kumugabane, keza, kampala |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | guhindura, asubira, yumva |
+| `-e` | kumugabane, umuhinde, akoresheje |
+| `-ra` | guhindura, asubira, ashira |
+| `-i` | imbabazi, umushatsi, umutamvyi |
+| `-o` | dukoko, ninaho, ivyiyumviro |
+| `-ye` | bakomeye, ibaye, ndayizeye |
+| `-wa` | bivugwa, ishikanwa, atorwa |
+| `-ka` | abasangwabutaka, yubaka, agaruka |
+
+### 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 |
+|------|----------|------------------|----------|
+| `anga` | 1.62x | 32 contexts | nanga, banga, ibanga |
+| `andi` | 1.50x | 23 contexts | bandi, kandi, bandit |
+| `nshi` | 1.59x | 18 contexts | menshi, kenshi, benshi |
+| `fise` | 1.45x | 23 contexts | afise, ufise, mfise |
+| `vuga` | 1.43x | 24 contexts | uvuga, avuga, ivuga |
+| `indi` | 1.46x | 20 contexts | zindi, bindi, rindi |
+| `gira` | 1.32x | 24 contexts | agira, ugira, igira |
+| `kuru` | 1.31x | 21 contexts | nkuru, bikuru, mukuru |
+| `anye` | 1.62x | 11 contexts | azanye, ajanye, bazanye |
+| `bere` | 1.55x | 12 contexts | mbere, mabere, imbere |
+| `mber` | 1.55x | 11 contexts | mbere, ambera, imbere |
+| `agar` | 1.43x | 13 contexts | hagari, agaruka, amagara |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-a` | 289 words | asubira, ashira |
+| `-i` | `-a` | 217 words | imiringa, ishikanwa |
+| `-b` | `-a` | 208 words | bivugwa, bitungwa |
+| `-k` | `-a` | 183 words | keza, kampala |
+| `-i` | `-o` | 152 words | ivyiyumviro, ikirago |
+| `-u` | `-a` | 142 words | umwuga, ushobora |
+| `-u` | `-i` | 117 words | umushatsi, umutamvyi |
+| `-b` | `-e` | 109 words | bakomeye, bahejeje |
+| `-a` | `-ra` | 107 words | asubira, ashira |
+| `-i` | `-e` | 104 words | itikize, ibaye |
+
+### 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 |
+|------|-----------------|------------|------|
+| shineyari | **`shiney-a-ri`** | 7.5 | `a` |
+| bahamagara | **`bahamag-a-ra`** | 7.5 | `a` |
+| iyondwara | **`iyondw-a-ra`** | 7.5 | `a` |
+| umupfakazi | **`umupfa-ka-zi`** | 7.5 | `ka` |
+| yaramuhaye | **`yaramu-ha-ye`** | 7.5 | `ha` |
+| colombiana | **`colombi-a-na`** | 7.5 | `a` |
+| inyambaro | **`inyamb-a-ro`** | 7.5 | `a` |
+| abahanuzi | **`abahan-u-zi`** | 7.5 | `u` |
+| umuganuro | **`umugan-u-ro`** | 7.5 | `u` |
+| ikibiribiri | **`ikibirib-i-ri`** | 7.5 | `i` |
+| ahagaragara | **`ahagarag-a-ra`** | 7.5 | `a` |
+| nyamukuru | **`n-ya-mukuru`** | 7.5 | `mukuru` |
+| yagaragaye | **`yagarag-a-ye`** | 7.5 | `a` |
+| ahamagara | **`ahamag-a-ra`** | 7.5 | `a` |
+| intambara | **`intamb-a-ra`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Rundi 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 | **32k BPE** | Best compression (4.73x) |
+| N-gram | **2-gram** | Lowest perplexity (201) |
+| Markov | **Context-4** | Highest predictability (98.2%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 18:46:39*
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