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README.md

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+---
+language: hr
+language_name: Croatian
+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: 4.592
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7990
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Croatian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Croatian** 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** | 3.541x | 3.54 | 0.0441% | 1,061,585 |
+| **16k** | 3.929x | 3.93 | 0.0489% | 956,840 |
+| **32k** | 4.292x | 4.29 | 0.0534% | 875,971 |
+| **64k** | 4.592x 🏆 | 4.59 | 0.0572% | 818,812 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `NGC je galaksija u zviježđu Vodenoj zmiji. Izvori Vanjske poveznice NGC`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vode noj ▁z mi ji ... (+5 more)` | 15 |
+| 16k | `▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vode noj ▁z miji . ... (+4 more)` | 14 |
+| 32k | `▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vodenoj ▁zmiji . ▁izvori ▁vanjske ... (+2 more)` | 12 |
+| 64k | `▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vodenoj ▁zmiji . ▁izvori ▁vanjske ... (+2 more)` | 12 |
+
+**Sample 2:** `Hrvatska: Kostadinovac (Križevci), gradsko naselje Križevaca Srbija: Kostadinova...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁grad ... (+24 more)` | 34 |
+| 16k | `▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁gradsko ... (+20 more)` | 30 |
+| 32k | `▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁gradsko ... (+19 more)` | 29 |
+| 64k | `▁hrvatska : ▁kosta di novac ▁( križevci ), ▁gradsko ▁naselje ... (+17 more)` | 27 |
+
+**Sample 3:** `NGC 587 je galaksija u zviježđu Trokut. Izvori Vanjske poveznice NGC`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁troku ... (+6 more)` | 16 |
+| 16k | `▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁troku ... (+6 more)` | 16 |
+| 32k | `▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁trokut ... (+5 more)` | 15 |
+| 64k | `▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁trokut ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.592x compression
+- **Lowest UNK Rate:** 8k with 0.0441% 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 | 267,023 | 18.03 | 1,536,962 | 6.2% | 15.5% |
+| **2-gram** | Subword | 314 🏆 | 8.29 | 17,412 | 63.2% | 99.0% |
+| **3-gram** | Word | 860,543 | 19.71 | 2,568,958 | 2.9% | 8.5% |
+| **3-gram** | Subword | 3,101 | 11.60 | 146,611 | 21.1% | 65.0% |
+| **4-gram** | Word | 2,007,494 | 20.94 | 4,346,865 | 2.5% | 6.6% |
+| **4-gram** | Subword | 21,614 | 14.40 | 870,800 | 8.5% | 30.3% |
+| **5-gram** | Word | 1,554,489 | 20.57 | 3,187,745 | 3.2% | 7.7% |
+| **5-gram** | Subword | 106,845 | 16.71 | 3,145,742 | 3.9% | 15.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `je u` | 105,341 |
+| 2 | `vanjske poveznice` | 93,834 |
+| 3 | `koji je` | 79,115 |
+| 4 | `da je` | 76,085 |
+| 5 | `bio je` | 64,808 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `izvori vanjske poveznice` | 48,503 |
+| 2 | `bosne i hercegovine` | 15,350 |
+| 3 | `0 0 0` | 15,157 |
+| 4 | `prema popisu stanovništva` | 14,804 |
+| 5 | `popisu stanovništva iz` | 14,603 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `prema popisu stanovništva iz` | 13,965 |
+| 2 | `popisu stanovništva iz godine` | 9,055 |
+| 3 | `0 0 0 0` | 7,718 |
+| 4 | `stanovništvo prema popisu stanovništva` | 7,610 |
+| 5 | `u bosni i hercegovini` | 7,346 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `prema popisu stanovništva iz godine` | 8,505 |
+| 2 | `stanovništvo prema popisu stanovništva iz` | 7,504 |
+| 3 | `iz godine naselje je imalo` | 6,432 |
+| 4 | `popisu stanovništva iz godine naselje` | 6,074 |
+| 5 | `klub ut pob ner por` | 6,053 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 11,772,034 |
+| 2 | `e _` | 10,057,232 |
+| 3 | `j e` | 9,032,733 |
+| 4 | `i _` | 7,983,271 |
+| 5 | `_ s` | 7,190,572 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `j e _` | 3,895,077 |
+| 2 | `_ j e` | 2,710,825 |
+| 3 | `_ p o` | 2,506,868 |
+| 4 | `_ p r` | 2,383,257 |
+| 5 | `_ n a` | 2,336,425 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ j e _` | 2,225,392 |
+| 2 | `_ n a _` | 884,954 |
+| 3 | `_ s e _` | 864,331 |
+| 4 | `_ p r o` | 684,557 |
+| 5 | `_ k o j` | 681,175 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ j e _` | 584,793 |
+| 2 | `o _ j e _` | 536,381 |
+| 3 | `_ g o d i` | 464,832 |
+| 4 | `g o d i n` | 453,046 |
+| 5 | `o d i n e` | 358,859 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 314
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~16% 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 | 1.0357 | 2.050 | 12.27 | 1,815,273 | 0.0% |
+| **1** | Subword | 1.2283 | 2.343 | 8.11 | 7,670 | 0.0% |
+| **2** | Word | 0.3287 | 1.256 | 2.06 | 22,242,688 | 67.1% |
+| **2** | Subword | 0.7670 | 1.702 | 5.14 | 62,088 | 23.3% |
+| **3** | Word | 0.1208 | 1.087 | 1.25 | 45,802,650 | 87.9% |
+| **3** | Subword | 0.8038 | 1.746 | 4.62 | 318,839 | 19.6% |
+| **4** | Word | 0.0449 🏆 | 1.032 | 1.07 | 57,168,259 | 95.5% |
+| **4** | Subword | 0.7427 | 1.673 | 3.77 | 1,471,918 | 25.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `je jedini gol bod1 orijent expressu od do polufinala nastupila je manji zbog toga dragocjena u`
+2. `u 56 km kvadratnih kilometara je postao vodeći u dundu maroju armandu kemičara i bečki i`
+3. `i izraz malo energije na njihovo je također povezivanje svakoga naroda onaj za istraživanje je minog...`
+
+**Context Size 2:**
+
+1. `je u sabirni logor za zarobljene španjolske muškarce i žene koji su bez uspjeha robert lowie je`
+2. `vanjske poveznice hrvatske kazališne manifestacije u hrvatskoj reformsko krilo koje se smatra normal...`
+3. `koji je osvojio pojedinačnu medalju na austrian openu u osmini završnice osam i protjerivan sedam pu...`
+
+**Context Size 3:**
+
+1. `izvori vanjske poveznice hartmut frommert revidirani novi opći katalog eng izvangalaktička baza poda...`
+2. `0 0 0 0 0 4 1 kvalifikacije za afrički kup nacija 08 17 21 lipnja abuja national`
+3. `bosne i hercegovine postao je slobodno područje izabran je za izvanrednog profesora na harvardu te v...`
+
+**Context Size 4:**
+
+1. `prema popisu stanovništva iz godine rajčići su imali 4 stanovnika vanjske poveznice o blažević dolu ...`
+2. `popisu stanovništva iz godine naselje je imalo 0 stanovnikapopis stanovništva www dzs hr te 25 obite...`
+3. `0 0 0 0 0 hispanoamerikanci 4 0 9 12 1 4 ukupno 844 861 vrela vanjske poveznice u`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_poskovopr._vi_d`
+2. `av_jeni_staog_1.`
+3. `ire_zbe._n_pledo`
+
+**Context Size 2:**
+
+1. `a_prednog_reba_me`
+2. `e_urisamom_kakvu.`
+3. `jedina_jensih_fij`
+
+**Context Size 3:**
+
+1. `je_udružen_uglavno`
+2. `_je_je_meki_držana`
+3. `_postavu_i_murski_`
+
+**Context Size 4:**
+
+1. `_je_i_„bijedložili_`
+2. `_na_bio_je_breedler`
+3. `_se_tada_satenu_dat`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,471,918 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 | 865,837 |
+| Total Tokens | 68,760,487 |
+| Mean Frequency | 79.42 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4611.66 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | je | 2,245,537 |
+| 2 | u | 2,108,487 |
+| 3 | i | 2,058,490 |
+| 4 | na | 897,376 |
+| 5 | se | 873,737 |
+| 6 | su | 661,725 |
+| 7 | za | 564,276 |
+| 8 | od | 535,634 |
+| 9 | s | 445,590 |
+| 10 | a | 436,542 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | uerpmann | 2 |
+| 2 | cociancicha | 2 |
+| 3 | fornasari | 2 |
+| 4 | federighi | 2 |
+| 5 | ulanoff | 2 |
+| 6 | svelteov | 2 |
+| 7 | ractive | 2 |
+| 8 | jsdoc | 2 |
+| 9 | vercel | 2 |
+| 10 | onsubmit | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9105 |
+| R² (Goodness of Fit) | 0.998328 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 29.2% |
+| Top 1,000 | 47.5% |
+| Top 5,000 | 64.0% |
+| Top 10,000 | 71.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9983 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 29.2% of corpus
+- **Long Tail:** 855,837 words needed for remaining 28.5% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7990 | 0.3752 | N/A | N/A |
+| **mono_64d** | 64 | 0.7419 | 0.2943 | N/A | N/A |
+| **mono_128d** | 128 | 0.6113 | 0.2735 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7990 🏆 | 0.3713 | 0.2440 | 0.6400 |
+| **aligned_64d** | 64 | 0.7419 | 0.2911 | 0.4700 | 0.8320 |
+| **aligned_128d** | 128 | 0.6113 | 0.2771 | 0.6240 | 0.8980 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7990 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3137. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 62.4% 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 | **-0.514** | 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 |
+|--------|----------|
+| `-s` | saccharina, stažem, sieversia |
+| `-a` | appleton, aromatika, antipatros |
+| `-ma` | macv, mahajangu, manfredonija |
+| `-m` | mešetari, midp, megasten |
+| `-k` | konfederacije, kumarom, karlovačku |
+| `-p` | prostalih, portulani, panopticum |
+| `-b` | breviarium, bandašica, botticellija |
+| `-t` | terpenoide, tamnocrvenkast, teregova |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | saccharina, sieversia, premašenima |
+| `-e` | konfederacije, terpenoide, elaboracije |
+| `-i` | portulani, vori, mešetari |
+| `-m` | stažem, panopticum, breviarium |
+| `-u` | nahalu, ikonostasu, karlovačku |
+| `-om` | kumarom, samarom, kokom |
+| `-s` | servas, winos, clupeoides |
+| `-o` | dezorijentirano, dsno, papio |
+
+### 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 |
+|------|----------|------------------|----------|
+| `anov` | 1.67x | 1068 contexts | anove, hanov, banov |
+| `cije` | 2.00x | 238 contexts | cijel, cijev, cijem |
+| `acij` | 1.85x | 273 contexts | lacij, acije, racij |
+| `ijel` | 1.69x | 293 contexts | cijel, ijele, dijel |
+| `ansk` | 1.35x | 1078 contexts | ansko, anski, dansk |
+| `ljen` | 1.42x | 618 contexts | kljen, pljen, ljeni |
+| `avlj` | 1.51x | 394 contexts | javlja, vavlje, lavlji |
+| `elik` | 1.71x | 176 contexts | melik, jelik, çelik |
+| `ijsk` | 1.36x | 538 contexts | hijska, bijsku, kijski |
+| `egov` | 1.60x | 208 contexts | negov, begov, egove |
+| `novn` | 1.84x | 95 contexts | onovno, pnovno, ponovno |
+| `telj` | 1.66x | 146 contexts | atelj, artelj, stelje |
+
+### 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` | 202 words | prekorava, petruša |
+| `-s` | `-a` | 178 words | suverenizma, sritna |
+| `-p` | `-e` | 114 words | produbljavanje, perenense |
+| `-k` | `-a` | 106 words | kanatima, koruška |
+| `-p` | `-i` | 97 words | protoni, poigravati |
+| `-a` | `-a` | 93 words | almanusa, alžirka |
+| `-s` | `-i` | 88 words | svesokolski, saeculi |
+| `-d` | `-a` | 88 words | disonancija, denzimetrija |
+| `-b` | `-a` | 85 words | barista, bhattija |
+| `-p` | `-m` | 85 words | perfectum, punicum |
+
+### 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 |
+|------|-----------------|------------|------|
+| auchenipteridae | **`auchenipterid-a-e`** | 7.5 | `a` |
+| neprikazane | **`neprikaz-a-ne`** | 7.5 | `a` |
+| arunkumar | **`arunkum-a-r`** | 7.5 | `a` |
+| intervjuua | **`intervju-u-a`** | 7.5 | `u` |
+| domeciidae | **`domeciid-a-e`** | 7.5 | `a` |
+| ventricosus | **`ventrico-s-us`** | 7.5 | `s` |
+| codiaceae | **`codiace-a-e`** | 7.5 | `a` |
+| anastasiju | **`anastas-i-ju`** | 7.5 | `i` |
+| sistemsko | **`sistem-s-ko`** | 7.5 | `s` |
+| pattalophyllia | **`pattalophyll-i-a`** | 7.5 | `i` |
+| studenske | **`studen-s-ke`** | 7.5 | `s` |
+| modernizirani | **`modernizir-a-ni`** | 7.5 | `a` |
+| filtrirani | **`filtrir-a-ni`** | 7.5 | `a` |
+| postavljane | **`postavlj-a-ne`** | 7.5 | `a` |
+| coriariaceae | **`coriariace-a-e`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Croatian 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 (4.59x) |
+| N-gram | **2-gram** | Lowest perplexity (314) |
+| Markov | **Context-4** | Highest predictability (95.5%) |
+| 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 10:10:35*

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