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+---
+language: rw
+language_name: Kinyarwanda
+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: 4.330
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8846
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kinyarwanda - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kinyarwanda** 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** | 3.440x | 3.44 | 0.7778% | 231,049 |
+| **16k** | 3.758x | 3.76 | 0.8498% | 211,461 |
+| **32k** | 4.054x | 4.06 | 0.9168% | 196,016 |
+| **64k** | 4.330x 🏆 | 4.34 | 0.9791% | 183,531 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Jibuti (izina mu cyarabu : جيبوتي ‎ ; izina mu gifaransa : Djibouti ) n’igihugu ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁j ibu ti ▁( izina ▁mu ▁cyarabu ▁: ▁ج ي ... (+20 more)` | 30 |
+| 16k | `▁jibu ti ▁( izina ▁mu ▁cyarabu ▁: ▁ج ي ب ... (+19 more)` | 29 |
+| 32k | `▁jibuti ▁( izina ▁mu ▁cyarabu ▁: ▁ج ي بو ت ... (+17 more)` | 27 |
+| 64k | `▁jibuti ▁( izina ▁mu ▁cyarabu ▁: ▁ج ي بو تي ... (+16 more)` | 26 |
+
+**Sample 2:** `Bizimana Abdu uzwi nka Bekeni wari umutoza wa Etincelles FC, akayibera umuyobozi...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bi zimana ▁abdu ▁uzwi ▁nka ▁be ke ni ▁wari ▁umutoza ... (+18 more)` | 28 |
+| 16k | `▁bizimana ▁abdu ▁uzwi ▁nka ▁be ke ni ▁wari ▁umutoza ▁wa ... (+15 more)` | 25 |
+| 32k | `▁bizimana ▁abdu ▁uzwi ▁nka ▁be ke ni ▁wari ▁umutoza ▁wa ... (+12 more)` | 22 |
+| 64k | `▁bizimana ▁abdu ▁uzwi ▁nka ▁be ke ni ▁wari ▁umutoza ▁wa ... (+12 more)` | 22 |
+
+**Sample 3:** `thumb Umusigiti wa mukuru muri Dubai (izina mu cyarabu: مسجد دبي الكبير) ni umus...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁thumb ▁umusigiti ▁wa ▁mukuru ▁muri ▁du bai ▁( izina ▁mu ... (+29 more)` | 39 |
+| 16k | `▁thumb ▁umusigiti ▁wa ▁mukuru ▁muri ▁dubai ▁( izina ▁mu ▁cyarabu ... (+24 more)` | 34 |
+| 32k | `▁thumb ▁umusigiti ▁wa ▁mukuru ▁muri ▁dubai ▁( izina ▁mu ▁cyarabu ... (+19 more)` | 29 |
+| 64k | `▁thumb ▁umusigiti ▁wa ▁mukuru ▁muri ▁dubai ▁( izina ▁mu ▁cyarabu ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.330x compression
+- **Lowest UNK Rate:** 8k with 0.7778% 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 | 18,987 | 14.21 | 53,674 | 10.8% | 31.0% |
+| **2-gram** | Subword | 211 🏆 | 7.72 | 3,711 | 74.0% | 99.6% |
+| **3-gram** | Word | 34,739 | 15.08 | 72,467 | 7.4% | 22.2% |
+| **3-gram** | Subword | 1,627 | 10.67 | 26,865 | 29.4% | 80.4% |
+| **4-gram** | Word | 91,637 | 16.48 | 141,823 | 3.7% | 12.2% |
+| **4-gram** | Subword | 8,816 | 13.11 | 136,860 | 12.5% | 45.0% |
+| **5-gram** | Word | 80,915 | 16.30 | 108,276 | 3.1% | 10.7% |
+| **5-gram** | Subword | 31,538 | 14.94 | 354,876 | 6.7% | 26.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mu rwanda` | 5,463 |
+| 2 | `u rwanda` | 4,916 |
+| 3 | `ku ya` | 3,826 |
+| 4 | `mu mwaka` | 3,035 |
+| 5 | `ndetse n` | 2,749 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mu mwaka wa` | 2,056 |
+| 2 | `y u rwanda` | 1,711 |
+| 3 | `mu karere ka` | 1,652 |
+| 4 | `umupira w amaguru` | 1,081 |
+| 5 | `ihuza ryo hanze` | 917 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `repubulika iharanira demokarasi ya` | 738 |
+| 2 | `iharanira demokarasi ya kongo` | 731 |
+| 3 | `reba ihuza ryo hanze` | 632 |
+| 4 | `muri afurika y epfo` | 365 |
+| 5 | `ukina umupira w amaguru` | 346 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `repubulika iharanira demokarasi ya kongo` | 686 |
+| 2 | `umukinnyi ukina umupira w amaguru` | 271 |
+| 3 | `ni umukinnyi ukina umupira w` | 248 |
+| 4 | `izina ry ubumenyi mu kilatini` | 240 |
+| 5 | `leta zunze ubumwe z amerika` | 203 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 625,374 |
+| 2 | `e _` | 368,094 |
+| 3 | `i _` | 293,023 |
+| 4 | `a n` | 290,094 |
+| 5 | `o _` | 286,827 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u` | 160,364 |
+| 2 | `r i _` | 100,543 |
+| 3 | `_ k u` | 99,022 |
+| 4 | `r a _` | 93,428 |
+| 5 | `m u _` | 88,869 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u _` | 84,002 |
+| 2 | `u r i _` | 57,539 |
+| 3 | `a _ m u` | 45,538 |
+| 4 | `_ m u r` | 43,557 |
+| 5 | `m u r i` | 42,556 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m u r i` | 39,861 |
+| 2 | `m u r i _` | 39,743 |
+| 3 | `a _ m u _` | 25,873 |
+| 4 | `e _ m u _` | 22,150 |
+| 5 | `_ m u _ m` | 20,415 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 211
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% 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 | 0.8463 | 1.798 | 6.38 | 163,621 | 15.4% |
+| **1** | Subword | 0.8435 | 1.794 | 6.17 | 1,743 | 15.7% |
+| **2** | Word | 0.2628 | 1.200 | 1.68 | 1,040,519 | 73.7% |
+| **2** | Subword | 0.8424 | 1.793 | 5.13 | 10,741 | 15.8% |
+| **3** | Word | 0.0947 | 1.068 | 1.17 | 1,737,932 | 90.5% |
+| **3** | Subword | 0.8178 | 1.763 | 4.19 | 55,059 | 18.2% |
+| **4** | Word | 0.0364 🏆 | 1.026 | 1.06 | 2,027,315 | 96.4% |
+| **4** | Subword | 0.6852 | 1.608 | 2.97 | 230,427 | 31.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `mu buryo ababukora batabukora neza 2 n umuryango w ukwezi kwa muganga cyangwa bashyizeho amakaro gif...`
+2. `n imwe iyo mibare ari umukunzi we washinze kandi ifite umukara amaguru bakiri mu kugabanya ubutaka`
+3. `muri na dabby chimere muriyi filime nibwo yambitswe ikamba agaciro no muri drc ubuzima n imyumbati`
+
+**Context Size 2:**
+
+1. `mu rwanda nka kaminuza ifunguye mu bwongereza agakingirizo ni uburyo abanyarwanda bo hambere bateka ...`
+2. `u rwanda rugeze rwiyubaka ndetse akishimira icyerekezo igihugu gifite abaturage 6 kwigisha ubuhanga ...`
+3. `ku ya 7 nyakanga itangira ku mwanya wa gatandatu mu mikino olempike mu gihugu akatirwa igifungo kire...`
+
+**Context Size 3:**
+
+1. `mu mwaka wa na padiri hitimana waje kucyirukanwamo bivugwa ko byagizwemo uruhare n uwitwa musonera f...`
+2. `mu karere ka bugesera haboneka inamaz ihariye zirebana no guhangana nicyo kibazo cya abana mu miirya...`
+3. `y u rwanda gutera busongora kuko yatekerezaga ko kamaliza akiri muto agikeneye umuntu ujya mu kimbo ...`
+
+**Context Size 4:**
+
+1. `repubulika iharanira demokarasi ya kongo afite imyaka 13 yaje mu bubiligi bitewe nuko nyina yashakan...`
+2. `iharanira demokarasi ya kongo mu mukino wa gicuti 0 0 imibare reba wa congo`
+3. `reba ihuza ryo hanze u rwanda mu gutanga amasoko atandukanye u rwanda rwabonye amashanyarazi ya mber...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_yangit'd_uge,_n`
+2. `ano_kwa_ranga_gs`
+3. `i_ma_kure_mu_ne_`
+
+**Context Size 2:**
+
+1. `a_a_wuge_yumu_bya`
+2. `e_kwicingarerandi`
+3. `i_ko_(kuza_mya_ir`
+
+**Context Size 3:**
+
+1. `_mu_bara:_munta_ic`
+2. `ri_ebya_kare_bushy`
+3. `_kubakororidageneg`
+
+**Context Size 4:**
+
+1. `_mu_rwanyarwaye_mu_`
+2. `uri_ndijk_africa_pr`
+3. `a_muri_w'ama_imye_a`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (230,427 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 | 71,439 |
+| Total Tokens | 2,285,892 |
+| Mean Frequency | 32.00 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 512.25 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mu | 84,764 |
+| 2 | n | 44,189 |
+| 3 | muri | 39,306 |
+| 4 | y | 32,989 |
+| 5 | ya | 31,781 |
+| 6 | ku | 30,372 |
+| 7 | na | 26,704 |
+| 8 | wa | 20,501 |
+| 9 | ni | 18,825 |
+| 10 | kandi | 14,981 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | faddis | 2 |
+| 2 | twiganye | 2 |
+| 3 | pierson | 2 |
+| 4 | whitehead | 2 |
+| 5 | imposing | 2 |
+| 6 | whirlwind | 2 |
+| 7 | abwire | 2 |
+| 8 | verve | 2 |
+| 9 | bassiste | 2 |
+| 10 | pinderhughes | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1070 |
+| R² (Goodness of Fit) | 0.990401 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 33.8% |
+| Top 1,000 | 62.0% |
+| Top 5,000 | 81.1% |
+| Top 10,000 | 87.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9904 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 33.8% of corpus
+- **Long Tail:** 61,439 words needed for remaining 12.6% 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 | 0.8846 | 0.3598 | N/A | N/A |
+| **mono_64d** | 64 | 0.8628 | 0.2440 | N/A | N/A |
+| **mono_128d** | 128 | 0.8154 | 0.1705 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8846 🏆 | 0.3561 | 0.0400 | 0.2600 |
+| **aligned_64d** | 64 | 0.8628 | 0.2297 | 0.1180 | 0.3800 |
+| **aligned_128d** | 128 | 0.8154 | 0.1717 | 0.1600 | 0.4620 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8846 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2553. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 16.0% 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 | **-0.590** | 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 |
+|--------|----------|
+| `-a` | azitandukanya, agbada, anitta |
+| `-ba` | bariciwe, batisimu, barware |
+| `-b` | bwakorwaga, bubarizwa, bonnaterre |
+| `-m` | marked, moi, mtb |
+| `-i` | ikurikiyeho, ibyinitse, influenced |
+| `-n` | ninini, ntacyananira, niño |
+| `-s` | strigiformes, santa, slowakiya |
+| `-ma` | marked, malagarasi, makossa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | azitandukanya, agbada, anitta |
+| `-e` | kubagore, tubakunde, rugwe |
+| `-o` | ukorerwamo, cyigo, zubuhumekero |
+| `-ra` | byinzara, ntacyananira, banywera |
+| `-ye` | twakagombye, bikoranye, cyicaye |
+| `-i` | ubukanishi, umusesenguzi, funji |
+| `-wa` | hagashyirwa, bubarizwa, bigikorwa |
+| `-we` | rugwe, abimuwe, bariciwe |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ores` | 2.61x | 62 contexts | forest, flores, scores |
+| `anga` | 1.61x | 275 contexts | banga, langa, zanga |
+| `mber` | 2.03x | 60 contexts | mbera, imber, amber |
+| `atan` | 1.63x | 165 contexts | atanu, zlatan, satani |
+| `ngan` | 1.73x | 120 contexts | ngano, ngange, ungana |
+| `ihug` | 2.53x | 25 contexts | ihugu, bihugu, gihugu |
+| `aban` | 1.54x | 200 contexts | abana, abanu, yabana |
+| `ikor` | 1.64x | 138 contexts | ikora, ikoro, ikore |
+| `amas` | 1.96x | 56 contexts | damas, amaso, amase |
+| `anda` | 1.67x | 114 contexts | andam, randa, panda |
+| `shin` | 1.67x | 112 contexts | shina, oshin, shine |
+| `ubur` | 1.73x | 84 contexts | ubura, uburo, rubura |
+
+### 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` | 297 words | kuzenguruka, kudaha |
+| `-a` | `-a` | 252 words | azerubayija, atarahabwa |
+| `-b` | `-a` | 240 words | bwanwa, baha |
+| `-b` | `-e` | 230 words | bristlecone, barateye |
+| `-i` | `-a` | 226 words | izitera, iyihanganira |
+| `-ba` | `-a` | 170 words | baha, bahashinga |
+| `-i` | `-e` | 136 words | inshinge, ikingiye |
+| `-a` | `-e` | 133 words | abaturajye, ardenne |
+| `-ba` | `-e` | 119 words | barateye, babonye |
+| `-i` | `-o` | 119 words | ihitamo, ikirushijeho |
+
+### 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 |
+|------|-----------------|------------|------|
+| yoherezayo | **`yohereza-y-o`** | 7.5 | `y` |
+| wimibereho | **`wimiber-e-ho`** | 7.5 | `e` |
+| bakamujya | **`ba-ka-mujya`** | 7.5 | `mujya` |
+| porofeseri | **`porofes-e-ri`** | 7.5 | `e` |
+| umurynago | **`umury-na-go`** | 7.5 | `na` |
+| yiyumvagamo | **`yiyumvag-a-mo`** | 7.5 | `a` |
+| byanditseho | **`byandits-e-ho`** | 7.5 | `e` |
+| byakongera | **`byakong-e-ra`** | 7.5 | `e` |
+| karidinari | **`karidin-a-ri`** | 7.5 | `a` |
+| ashushanyijeho | **`ashushanyij-e-ho`** | 7.5 | `e` |
+| accessories | **`accesso-ri-es`** | 7.5 | `ri` |
+| kwerekera | **`kwerek-e-ra`** | 7.5 | `e` |
+| ikigabiro | **`ikigab-i-ro`** | 7.5 | `i` |
+| akanyamasyo | **`akanyamas-y-o`** | 7.5 | `y` |
+| rwagennye | **`rwagen-n-ye`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kinyarwanda 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 (4.33x) |
+| N-gram | **2-gram** | Lowest perplexity (211) |
+| Markov | **Context-4** | Highest predictability (96.4%) |
+| 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 19:15:41*
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