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README.md

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+---
+language: sh
+language_name: Serbian (Latin)
+language_family: slavic_south
+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-slavic_south
+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: 4.489
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6562
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-17
+---
+
+# Serbian (Latin) - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Serbian (Latin)** 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** | 3.351x | 3.35 | 0.1426% | 3,710,049 |
+| **16k** | 3.751x | 3.75 | 0.1597% | 3,313,984 |
+| **32k** | 4.136x | 4.14 | 0.1761% | 3,005,948 |
+| **64k** | 4.489x 🏆 | 4.49 | 0.1911% | 2,769,508 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Galbenu ima više značenja: Galbenu, Galbenu Opština Galbenu, Brăila`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gal ben u ▁ima ▁više ▁značenja : ▁gal ben u ... (+12 more)` | 22 |
+| 16k | `▁gal benu ▁ima ▁više ▁značenja : ▁gal benu , ▁gal ... (+8 more)` | 18 |
+| 32k | `▁gal benu ▁ima ▁više ▁značenja : ▁gal benu , ▁gal ... (+7 more)` | 17 |
+| 64k | `▁gal benu ▁ima ▁više ▁značenja : ▁gal benu , ▁gal ... (+6 more)` | 16 |
+
+**Sample 2:** `Molekulska formula se može odnositi na: Deksrazoksan Pentostatin Acetilkarnozin`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁molekulska ▁formula ▁se ▁može ▁odnositi ▁na : ▁de ks ra ... (+12 more)` | 22 |
+| 16k | `▁molekulska ▁formula ▁se ▁može ▁odnositi ▁na : ▁de ks ra ... (+10 more)` | 20 |
+| 32k | `▁molekulska ▁formula ▁se ▁može ▁odnositi ▁na : ▁de ks ra ... (+10 more)` | 20 |
+| 64k | `▁molekulska ▁formula ▁se ▁može ▁odnositi ▁na : ▁de ks ra ... (+8 more)` | 18 |
+
+**Sample 3:** `Marcucci ima više značenja: Marcucci, Lucca Marcucci, Macerata`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mar cu c ci ▁ima ▁više ▁značenja : ▁mar cu ... (+10 more)` | 20 |
+| 16k | `▁mar cu cci ▁ima ▁više ▁značenja : ▁mar cu cci ... (+7 more)` | 17 |
+| 32k | `▁mar cu cci ▁ima ▁više ▁značenja : ▁mar cu cci ... (+7 more)` | 17 |
+| 64k | `▁mar cu cci ▁ima ▁više ▁značenja : ▁mar cu cci ... (+7 more)` | 17 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.489x compression
+- **Lowest UNK Rate:** 8k with 0.1426% 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 | 70,231 | 16.10 | 1,297,205 | 17.5% | 34.2% |
+| **2-gram** | Subword | 308 🏆 | 8.26 | 17,425 | 63.9% | 99.1% |
+| **3-gram** | Word | 71,253 | 16.12 | 1,947,671 | 20.3% | 38.7% |
+| **3-gram** | Subword | 2,856 | 11.48 | 140,131 | 21.8% | 67.0% |
+| **4-gram** | Word | 77,932 | 16.25 | 3,159,151 | 21.1% | 41.0% |
+| **4-gram** | Subword | 17,451 | 14.09 | 805,205 | 10.3% | 34.9% |
+| **5-gram** | Word | 47,310 | 15.53 | 2,271,520 | 22.4% | 44.2% |
+| **5-gram** | Subword | 72,387 | 16.14 | 2,854,449 | 6.9% | 22.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `vanjske veze` | 365,724 |
+| 2 | `reference literatura` | 253,293 |
+| 3 | `u opštini` | 249,864 |
+| 4 | `literatura vanjske` | 239,013 |
+| 5 | `se nalazi` | 230,020 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `literatura vanjske veze` | 239,012 |
+| 2 | `reference literatura vanjske` | 231,740 |
+| 3 | `nadmorskoj visini od` | 206,227 |
+| 4 | `se nalazi na` | 199,011 |
+| 5 | `na nadmorskoj visini` | 195,919 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `reference literatura vanjske veze` | 231,739 |
+| 2 | `na nadmorskoj visini od` | 195,753 |
+| 3 | `nalazi na nadmorskoj visini` | 194,264 |
+| 4 | `se nalazi na nadmorskoj` | 194,263 |
+| 5 | `naselje se nalazi na` | 176,984 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `se nalazi na nadmorskoj visini` | 194,261 |
+| 2 | `nalazi na nadmorskoj visini od` | 194,261 |
+| 3 | `stanovnika naselje se nalazi na` | 176,911 |
+| 4 | `naselje se nalazi na nadmorskoj` | 176,909 |
+| 5 | `m reference literatura vanjske veze` | 158,235 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 11,868,424 |
+| 2 | `e _` | 11,387,649 |
+| 3 | `i _` | 8,439,887 |
+| 4 | `j e` | 7,898,306 |
+| 5 | `_ s` | 7,108,466 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `j e _` | 4,332,062 |
+| 2 | `_ n a` | 3,400,063 |
+| 3 | `_ j e` | 2,962,324 |
+| 4 | `_ u _` | 2,805,413 |
+| 5 | `_ p r` | 2,640,480 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ j e _` | 2,500,801 |
+| 2 | `_ n a _` | 1,012,547 |
+| 3 | `_ s e _` | 987,234 |
+| 4 | `_ p r o` | 943,243 |
+| 5 | `e _ n a` | 846,873 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a s e l` | 702,873 |
+| 2 | `_ n a s e` | 702,329 |
+| 3 | `a s e l j` | 701,918 |
+| 4 | `a _ j e _` | 594,535 |
+| 5 | `_ g o d i` | 573,498 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 308
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~22% 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 | 1.0184 | 2.026 | 11.40 | 1,765,275 | 0.0% |
+| **1** | Subword | 1.2791 | 2.427 | 8.32 | 8,241 | 0.0% |
+| **2** | Word | 0.3197 | 1.248 | 2.00 | 20,074,468 | 68.0% |
+| **2** | Subword | 0.7024 | 1.627 | 4.69 | 68,441 | 29.8% |
+| **3** | Word | 0.1128 | 1.081 | 1.23 | 40,113,108 | 88.7% |
+| **3** | Subword | 0.7622 | 1.696 | 4.36 | 320,739 | 23.8% |
+| **4** | Word | 0.0405 🏆 | 1.028 | 1.07 | 49,336,446 | 96.0% |
+| **4** | Subword | 0.7216 | 1.649 | 3.66 | 1,398,820 | 27.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `u opštini querétaro u rumunskom okrugu cvikau nojkirhen vorm springs je bilo je ukinuta četiri temen...`
+2. `je zaštićena kraška vrela na početku svoje karijere ona postala glavnom gradu radom unhcr provodi i`
+3. `i teritorijalnim gubicima mp3 on buddhist art of mathematics logic of roman history italy primary do...`
+
+**Context Size 2:**
+
+1. `vanjske veze boeing com mcdonnell douglas md 80 md 90 s druge strane antiohovi maloazijski posedi po...`
+2. `reference literatura vanjske veze serije star trek deep space nine izvori vanjske veze by the cia fa...`
+3. `u opštini tezontepec de aldama prema proceni iz godine u naselju je živelo 15 stanovnika naselje se`
+
+**Context Size 3:**
+
+1. `literatura vanjske veze by the cia factbook italian railways italian national and regional parks his...`
+2. `reference literatura vanjske veze zvanični sajt opštine nem savezni zavod za statistiku stalna konfe...`
+3. `nadmorskoj visini od m reference literatura vanjske veze u opštini tapalpa halisko`
+
+**Context Size 4:**
+
+1. `reference literatura vanjske veze u opštini tlachichilco verakruz`
+2. `na nadmorskoj visini od m reference literatura vanjske veze by the cia factbook italian railways ita...`
+3. `nalazi na nadmorskoj visini od 753 m reference literatura vanjske veze baza podataka insee cornas na...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_-znove_očkmnaca`
+2. `a_stastorari,_kr`
+3. `i_cizno._ičeći_u`
+
+**Context Size 2:**
+
+1. `a_lianje_bel_poša`
+2. `e_oarem_pro_pozič`
+3. `i_3_(pri_na_jevno`
+
+**Context Size 3:**
+
+1. `je_od_96._-_zapano`
+2. `_nastime_bilantoma`
+3. `_je_odnormaturesut`
+
+**Context Size 4:**
+
+1. `_je_bio_je_nalazima`
+2. `_na_wolfgang_su_dje`
+3. `_se_iznosi_0,36_m._`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,398,820 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 | 831,692 |
+| Total Tokens | 73,187,626 |
+| Mean Frequency | 88.00 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 5458.78 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | u | 2,835,134 |
+| 2 | je | 2,539,203 |
+| 3 | i | 1,828,128 |
+| 4 | na | 1,024,921 |
+| 5 | se | 996,788 |
+| 6 | od | 740,498 |
+| 7 | su | 563,164 |
+| 8 | iz | 480,024 |
+| 9 | godine | 465,533 |
+| 10 | za | 457,009 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | fretzera | 2 |
+| 2 | kartif | 2 |
+| 3 | karnion | 2 |
+| 4 | kartifove | 2 |
+| 5 | trifulgasov | 2 |
+| 6 | rouxa | 2 |
+| 7 | pikrata | 2 |
+| 8 | chancelloru | 2 |
+| 9 | jynxstrop | 2 |
+| 10 | shahristoni | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9867 |
+| R² (Goodness of Fit) | 0.999465 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 35.4% |
+| Top 1,000 | 55.2% |
+| Top 5,000 | 69.8% |
+| Top 10,000 | 76.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9995 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 35.4% of corpus
+- **Long Tail:** 821,692 words needed for remaining 23.8% 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 | 0.6562 🏆 | 0.3601 | N/A | N/A |
+| **mono_64d** | 64 | 0.6544 | 0.2950 | N/A | N/A |
+| **mono_128d** | 128 | 0.6020 | 0.2379 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6562 | 0.3559 | 0.2600 | 0.6660 |
+| **aligned_64d** | 64 | 0.6544 | 0.2879 | 0.4620 | 0.8340 |
+| **aligned_128d** | 128 | 0.6020 | 0.2418 | 0.5900 | 0.8740 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6562 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2964. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 59.0% 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 | **0.982** | 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 |
+|--------|----------|
+| `-s` | smatram, sekeré, szucsáva |
+| `-a` | arsenija, amsterdamove, aharski |
+| `-ma` | marshom, malvinu, mashrou |
+| `-m` | marshom, malvinu, mashrou |
+| `-p` | puruborá, prenatalni, prejak |
+| `-k` | kopitarovo, kačketi, kaftarinska |
+| `-b` | bettis, bobovište, belavića |
+| `-d` | drăgești, dekorisani, dobel |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | arsenija, kaftarinska, szucsáva |
+| `-e` | amsterdamove, natpolovične, bobovište |
+| `-i` | prenatalni, zatrudniti, kačketi |
+| `-m` | smatram, marshom, copernicanism |
+| `-u` | malvinu, mashrou, severinsku |
+| `-om` | marshom, migratornom, probuđenom |
+| `-n` | warleggan, wallisian, voisin |
+| `-o` | kopitarovo, eskimsko, afipo |
+
+### 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 |
+|------|----------|------------------|----------|
+| `efer` | 2.11x | 96 contexts | nefer, lefer, hefer |
+| `dmor` | 2.17x | 60 contexts | odmor, edmor, odmoru |
+| `admo` | 2.57x | 31 contexts | kadmo, nadmoć, tadmor |
+| `anjs` | 1.63x | 226 contexts | vanjse, vanjsk, banjsku |
+| `elje` | 1.48x | 378 contexts | relje, celje, kelje |
+| `acij` | 1.51x | 295 contexts | lacij, acija, aciju |
+| `njsk` | 1.56x | 183 contexts | vnjske, vanjsk, banjsku |
+| `alaz` | 1.77x | 92 contexts | zalaz, nalaz, kalaz |
+| `rsko` | 1.36x | 261 contexts | mrsko, drsko, irsko |
+| `ržav` | 1.54x | 130 contexts | držav, kržava, državu |
+| `ocen` | 1.46x | 126 contexts | kocen, ocenu, bocen |
+| `pšti` | 1.82x | 39 contexts | opšti, uopšti, opštim |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-a` | 182 words | prigorja, palmata |
+| `-p` | `-e` | 132 words | poljepšavanje, planiranje |
+| `-s` | `-a` | 126 words | stoogesa, strtenica |
+| `-k` | `-a` | 124 words | korijenja, klericima |
+| `-p` | `-i` | 118 words | puhati, prokoagulansi |
+| `-b` | `-a` | 101 words | bajkerska, brgata |
+| `-d` | `-a` | 89 words | diližansama, došašća |
+| `-s` | `-e` | 87 words | saksofoniste, srednjoafričke |
+| `-a` | `-a` | 82 words | ariola, agatoerga |
+| `-p` | `-m` | 79 words | plimskim, pečuškim |
+
+### 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 |
+|------|-----------------|------------|------|
+| talasemiju | **`talasem-i-ju`** | 7.5 | `i` |
+| šestokraku | **`šestok-ra-ku`** | 7.5 | `ra` |
+| divanhane | **`divanh-a-ne`** | 7.5 | `a` |
+| sadržavat | **`sadržav-a-t`** | 7.5 | `a` |
+| zaštitilo | **`zaštiti-l-o`** | 7.5 | `l` |
+| jednadžbama | **`jednadžb-a-ma`** | 7.5 | `a` |
+| eliminisane | **`eliminis-a-ne`** | 7.5 | `a` |
+| kanalizirane | **`kanalizir-a-ne`** | 7.5 | `a` |
+| kiiyaahaan | **`kiiyaah-a-an`** | 7.5 | `a` |
+| prostirati | **`prostir-a-ti`** | 7.5 | `a` |
+| uranographia | **`uranograph-i-a`** | 7.5 | `i` |
+| nesputane | **`nespu-ta-ne`** | 7.5 | `ta` |
+| asfaltirane | **`asfaltir-a-ne`** | 7.5 | `a` |
+| transalpina | **`transalp-i-na`** | 7.5 | `i` |
+| parametrizovano | **`parametrizov-a-no`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Serbian (Latin) 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 (4.49x) |
+| N-gram | **2-gram** | Lowest perplexity (308) |
+| Markov | **Context-4** | Highest predictability (96.0%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-17 05:35:37*

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+size 179040909