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+---
+language: so
+language_name: Somali
+language_family: cushitic
+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-cushitic
+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: 4.804
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8622
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Somali - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Somali** 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** | 3.863x | 3.86 | 0.0648% | 951,080 |
+| **16k** | 4.234x | 4.23 | 0.0710% | 867,649 |
+| **32k** | 4.560x | 4.56 | 0.0765% | 805,556 |
+| **64k** | 4.804x 🏆 | 4.80 | 0.0806% | 764,706 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Korean Broadcasting System (KBS) waa shabakad raadiye iyo telefishan Kuuriyada K...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kor ean ▁bro ad cas ting ▁system ▁( k bs ... (+12 more)` | 22 |
+| 16k | `▁korean ▁broad cas ting ▁system ▁( k bs ) ▁waa ... (+10 more)` | 20 |
+| 32k | `▁korean ▁broadcasting ▁system ▁( k bs ) ▁waa ▁shabakad ▁raadiye ... (+7 more)` | 17 |
+| 64k | `▁korean ▁broadcasting ▁system ▁( kbs ) ▁waa ▁shabakad ▁raadiye ▁iyo ... (+6 more)` | 16 |
+
+**Sample 2:** `Universidade Federal do Recôncavo da Bahia (UFRB) waxa ay ku taala magaalada Cru...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁univer sida de ▁federal ▁do ▁rec ô n ca vo ... (+32 more)` | 42 |
+| 16k | `▁univer sida de ▁federal ▁do ▁rec ô n ca vo ... (+28 more)` | 38 |
+| 32k | `▁universidade ▁federal ▁do ▁rec ô n ca vo ▁da ▁bahia ... (+21 more)` | 31 |
+| 64k | `▁universidade ▁federal ▁do ▁rec ô n ca vo ▁da ▁bahia ... (+20 more)` | 30 |
+
+**Sample 3:** `Camar bin Hishaam al-Makhzuumi "abuu jahal" waa gaal weyn oo cadaw ku ahaa islaa...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁cam ar ▁bin ▁h ish aam ▁al - ma kh ... (+19 more)` | 29 |
+| 16k | `▁camar ▁bin ▁hishaam ▁al - ma kh z uu mi ... (+15 more)` | 25 |
+| 32k | `▁camar ▁bin ▁hishaam ▁al - ma kh z uu mi ... (+13 more)` | 23 |
+| 64k | `▁camar ▁bin ▁hishaam ▁al - makh z uu mi ▁" ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.804x compression
+- **Lowest UNK Rate:** 8k with 0.0648% 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 | 18,941 | 14.21 | 69,253 | 15.3% | 34.5% |
+| **2-gram** | Subword | 235 🏆 | 7.88 | 6,710 | 73.0% | 98.7% |
+| **3-gram** | Word | 47,961 | 15.55 | 102,689 | 7.8% | 19.8% |
+| **3-gram** | Subword | 1,924 | 10.91 | 42,349 | 30.9% | 75.9% |
+| **4-gram** | Word | 131,970 | 17.01 | 198,378 | 3.3% | 9.4% |
+| **4-gram** | Subword | 10,789 | 13.40 | 193,486 | 14.2% | 43.9% |
+| **5-gram** | Word | 119,528 | 16.87 | 156,118 | 2.1% | 7.4% |
+| **5-gram** | Subword | 39,683 | 15.28 | 478,214 | 7.8% | 26.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ka mid` | 9,808 |
+| 2 | `ah oo` | 8,183 |
+| 3 | `mid ah` | 8,058 |
+| 4 | `waxa uu` | 7,173 |
+| 5 | `sidoo kale` | 6,685 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ka mid ah` | 7,046 |
+| 2 | `oo ay ku` | 1,827 |
+| 3 | `waxaa ka mid` | 1,557 |
+| 4 | `mid ka mid` | 1,546 |
+| 5 | `ka dib markii` | 1,252 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mid ka mid ah` | 1,525 |
+| 2 | `waxaa ka mid ah` | 1,268 |
+| 3 | `oo ay ku jiraan` | 939 |
+| 4 | `oo ka mid ah` | 887 |
+| 5 | `si kastaba ha ahaatee` | 800 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `waa mid ka mid ah` | 381 |
+| 2 | `badan oo ka mid ah` | 232 |
+| 3 | `oo ay ka mid yihiin` | 222 |
+| 4 | `kani waa maqaal ku saabsan` | 204 |
+| 5 | `ah oo ay ku jiraan` | 193 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 785,129 |
+| 2 | `a a` | 551,833 |
+| 3 | `a y` | 314,106 |
+| 4 | `d a` | 311,005 |
+| 5 | `a d` | 306,639 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `k a _` | 191,283 |
+| 2 | `a y _` | 182,234 |
+| 3 | `_ w a` | 154,920 |
+| 4 | `a d a` | 139,571 |
+| 5 | `o o _` | 132,027 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ w a x` | 83,580 |
+| 2 | `_ o o _` | 75,106 |
+| 3 | `w a x a` | 72,968 |
+| 4 | `a d a _` | 69,414 |
+| 5 | `i y o _` | 65,977 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ w a x a` | 71,618 |
+| 2 | `_ i y o _` | 60,073 |
+| 3 | `w a x a a` | 28,120 |
+| 4 | `w a x a y` | 27,648 |
+| 5 | `a x a y _` | 26,222 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 235
+- **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 | 0.8636 | 1.820 | 6.47 | 196,962 | 13.6% |
+| **1** | Subword | 1.0789 | 2.112 | 6.98 | 3,275 | 0.0% |
+| **2** | Word | 0.2528 | 1.192 | 1.66 | 1,269,511 | 74.7% |
+| **2** | Subword | 0.7113 | 1.637 | 4.28 | 22,823 | 28.9% |
+| **3** | Word | 0.0936 | 1.067 | 1.18 | 2,096,777 | 90.6% |
+| **3** | Subword | 0.6878 | 1.611 | 3.58 | 97,500 | 31.2% |
+| **4** | Word | 0.0360 🏆 | 1.025 | 1.06 | 2,465,103 | 96.4% |
+| **4** | Subword | 0.5986 | 1.514 | 2.75 | 349,170 | 40.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `oo ahaa 165 595 in ay direen askar guutaale abadiaziiz maxamuud yaxye bin cumeyr si walboo`
+2. `ee dibedda soomaaliya siyaasadda siyaasadda codsadayaasha maxaliga ah oo dhanna guurti la silciyey s...`
+3. `iyo kuwa la dhaho wacaysmoge degmada dayniile muqdisho guddoomiyaha ururka waxaa lala yeeshay warbaa...`
+
+**Context Size 2:**
+
+1. `ka mid yihiin marc jacobs hervé léger hugo boss giorgio armani beauty 3 xilli ciyaareed ee royal`
+2. `ah oo la wadaago 70 80 in wakhtigaas ku dhawaaqay inay yihiin qaybo ka mida gobolka jarar`
+3. `mid ah 1dii janaayo bisha janaayo musk wuxuu ku laabtay magrib wuxuu ka kooban yihiin lix milyan`
+
+**Context Size 3:**
+
+1. `ka mid ah ciidankiisa waran sumeysan ibni cumar oo qabay walaashiis safiya bniti cubeyd ayuu u qoray...`
+2. `oo ay ku jiraan majaladda sheekada dodge artful vinyl poetry prairie schooner iyo rhino gabayadeeda ...`
+3. `waxaa ka mid ah geela maraykanka ah oo heesta kana shaqeeysa filimada hindiga waxay ka soo muuqatay ...`
+
+**Context Size 4:**
+
+1. `mid ka mid ah kuwa ugu bandhiga badan hollywood spoto p 221 churchwell pp 61 65 lev p 168`
+2. `waxaa ka mid ah sheikh ibraahim yalale oo xilka xildhibannimo hayay inta u dhexeysay doorkii uu shie...`
+3. `oo ay ku jiraan ashoka arab world africa action sinnaanta hadda golaha la talinta ee sanduuqa caalam...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `aminun_d_btoraqa`
+2. `_u_da_caxigleeri`
+3. `ita_o_gadid_b_1.`
+
+**Context Size 2:**
+
+1. `a_gooxdan_dagu_ta`
+2. `aadkally_gobad_we`
+3. `aysabiiyo_maga_sa`
+
+**Context Size 3:**
+
+1. `ka_ka_ay_qur’aano_`
+2. `ay_waxay_damaada_s`
+3. `_waqooyiga_dhaba._`
+
+**Context Size 4:**
+
+1. `_wax_ka_socota_waxa`
+2. `_oo_ka_oo_maamulka_`
+3. `waxay_u_aroor_ayaa_`
+
+
+### 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 (349,170 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 | 88,887 |
+| Total Tokens | 2,839,359 |
+| Mean Frequency | 31.94 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 606.37 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | oo | 75,907 |
+| 2 | ee | 62,003 |
+| 3 | iyo | 60,594 |
+| 4 | ah | 59,190 |
+| 5 | ka | 58,938 |
+| 6 | ku | 47,129 |
+| 7 | u | 33,969 |
+| 8 | ay | 27,872 |
+| 9 | la | 26,142 |
+| 10 | waxay | 24,810 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | abdulsalam | 2 |
+| 2 | jamilu | 2 |
+| 3 | ruggedman | 2 |
+| 4 | rraz | 2 |
+| 5 | inetimi | 2 |
+| 6 | odon | 2 |
+| 7 | eedris | 2 |
+| 8 | foston | 2 |
+| 9 | lanky | 2 |
+| 10 | rhythmz | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0134 |
+| R² (Goodness of Fit) | 0.995365 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 37.0% |
+| Top 1,000 | 59.8% |
+| Top 5,000 | 77.9% |
+| Top 10,000 | 84.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9954 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 37.0% of corpus
+- **Long Tail:** 78,887 words needed for remaining 15.1% 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 | 0.8622 | 0.3506 | N/A | N/A |
+| **mono_64d** | 64 | 0.8393 | 0.2541 | N/A | N/A |
+| **mono_128d** | 128 | 0.8150 | 0.1899 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8622 🏆 | 0.3423 | 0.0460 | 0.2720 |
+| **aligned_64d** | 64 | 0.8393 | 0.2570 | 0.0880 | 0.3940 |
+| **aligned_128d** | 128 | 0.8150 | 0.1956 | 0.1480 | 0.4820 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8622 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2649. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 14.8% 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 | **-0.717** | 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` | ankka, a6, aruuriyeen |
+| `-s` | simay, sharab, sucuudiyyah |
+| `-ma` | markiisii, masaxaya, markaas |
+| `-ال` | التربية, المستطاب, الخندق |
+| `-m` | markiisii, masaxaya, muxadis |
+| `-d` | dayi, dhadhanku, doobka |
+| `-b` | beyoncés, buuloburde, bulshadan |
+| `-ba` | badbaadiyo, baasna, baangad |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | tula, qaahira, masaxaya |
+| `-n` | kirsten, concepción, nasreen |
+| `-da` | cabbirkeeda, metelida, hijrada |
+| `-i` | markiisii, lari, dayi |
+| `-an` | bulshadan, laaban, aaadan |
+| `-o` | istuudiyoo, dhaqasho, amico |
+| `-y` | yaqanay, simay, wacdiyay |
+| `-ii` | markiisii, halkoodii, khaliifkii |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ooyi` | 2.19x | 64 contexts | mooyi, woqooyi, waqooyi |
+| `iisa` | 2.08x | 69 contexts | hiisa, xiisa, ciisa |
+| `aank` | 1.83x | 108 contexts | aanku, aanka, baanka |
+| `yaas` | 2.16x | 47 contexts | iyaas, yaase, ilyaas |
+| `agaa` | 1.76x | 114 contexts | dagaa, lagaa, tagaa |
+| `eeya` | 1.68x | 136 contexts | geeya, geeyay, beeyay |
+| `eeda` | 1.99x | 61 contexts | eeday, teeda, keeda |
+| `aara` | 1.49x | 206 contexts | aaran, baara, faara |
+| `alka` | 1.69x | 109 contexts | halka, jalka, xalka |
+| `soom` | 2.59x | 20 contexts | soomi, soomo, sooma |
+| `ooma` | 1.94x | 57 contexts | rooma, looma, nooma |
+| `rkii` | 1.76x | 72 contexts | uurkii, jirkii, markii |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-d` | `-a` | 218 words | dhinaciisa, dhigma |
+| `-s` | `-a` | 172 words | shaqaynaya, sperma |
+| `-a` | `-a` | 129 words | arrintiina, aadaya |
+| `-b` | `-a` | 125 words | bataaxa, balaraba |
+| `-k` | `-a` | 123 words | kaashanaysaa, koofiga |
+| `-ma` | `-a` | 99 words | majaajiliistayaasha, maqaarka |
+| `-d` | `-n` | 90 words | dhacsan, daadejin |
+| `-s` | `-n` | 70 words | soojireen, suuxdin |
+| `-d` | `-o` | 69 words | dhawaaqo, duqeymo |
+| `-m` | `-a` | 68 words | midigta, moodaa |
+
+### 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 |
+|------|-----------------|------------|------|
+| martigeliyaan | **`martigeliy-a-an`** | 7.5 | `a` |
+| fadhiistaa | **`fadhiist-a-a`** | 7.5 | `a` |
+| diiddanaa | **`diiddan-a-a`** | 7.5 | `a` |
+| wakiiladu | **`wakiil-a-du`** | 7.5 | `a` |
+| itoobiyada | **`itoobiy-a-da`** | 7.5 | `a` |
+| amxaarada | **`amxaar-a-da`** | 7.5 | `a` |
+| nimankani | **`niman-ka-ni`** | 7.5 | `ka` |
+| filosofiyada | **`filosofiy-a-da`** | 7.5 | `a` |
+| afduubeen | **`afduub-e-en`** | 7.5 | `e` |
+| ilaahaaga | **`ilaaha-a-ga`** | 7.5 | `a` |
+| aqoontaas | **`aqoonta-a-s`** | 7.5 | `a` |
+| kumbuyuutar | **`kumbuyuut-a-r`** | 7.5 | `a` |
+| ceelxagar | **`ceelxag-a-r`** | 7.5 | `a` |
+| hadalkisii | **`hadalki-s-ii`** | 7.5 | `s` |
+| diidnimada | **`diidnim-a-da`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Somali 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 (4.80x) |
+| N-gram | **2-gram** | Lowest perplexity (235) |
+| 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 21:47:10*
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diff --git a/models/tokenizer/so_tokenizer_64k.vocab b/models/tokenizer/so_tokenizer_64k.vocab
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diff --git a/models/tokenizer/so_tokenizer_8k.model b/models/tokenizer/so_tokenizer_8k.model
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diff --git a/models/tokenizer/so_tokenizer_8k.vocab b/models/tokenizer/so_tokenizer_8k.vocab
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