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README.md

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+---
+language: sl
+language_name: Slovenian
+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: 4.308
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7907
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-17
+---
+
+# Slovenian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Slovenian** 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** | 3.326x | 3.33 | 0.6705% | 1,060,027 |
+| **16k** | 3.677x | 3.68 | 0.7414% | 958,786 |
+| **32k** | 4.017x | 4.02 | 0.8100% | 877,496 |
+| **64k** | 4.308x 🏆 | 4.31 | 0.8686% | 818,369 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Tinja je lahko: Tinja Donja (Bosna in Hercegovina) Tinja Gornja (Bosna in Herceg...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tin ja ▁je ▁lahko : ▁tin ja ▁do nja ▁( ... (+27 more)` | 37 |
+| 16k | `▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+22 more)` | 32 |
+| 32k | `▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+21 more)` | 31 |
+| 64k | `▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+21 more)` | 31 |
+
+**Sample 2:** `Škrabčeva ulica je lahko naziv več ulic: Škrabčeva ulica, Ljubljana Škrabčeva ul...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁š kra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁u ... (+17 more)` | 27 |
+| 16k | `▁š kra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic ... (+16 more)` | 26 |
+| 32k | `▁škra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic : ... (+13 more)` | 23 |
+| 64k | `▁škrab čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic : ▁škrab ... (+10 more)` | 20 |
+
+**Sample 3:** `Kreševo je lahko: Kreševo, Bosna in Hercegovina Kreševo, Hrvaška glej tudi Kruše...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kre še vo ▁je ▁lahko : ▁kre še vo , ... (+13 more)` | 23 |
+| 16k | `▁kre še vo ▁je ▁lahko : ▁kre še vo , ... (+13 more)` | 23 |
+| 32k | `▁kre ševo ▁je ▁lahko : ▁kre ševo , ▁bosna ▁in ... (+9 more)` | 19 |
+| 64k | `▁kre ševo ▁je ▁lahko : ▁kre ševo , ▁bosna ▁in ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.308x compression
+- **Lowest UNK Rate:** 8k with 0.6705% 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 | 230,065 | 17.81 | 1,580,681 | 8.6% | 18.2% |
+| **2-gram** | Subword | 307 🏆 | 8.26 | 19,173 | 64.1% | 99.0% |
+| **3-gram** | Word | 628,655 | 19.26 | 2,417,119 | 4.1% | 11.2% |
+| **3-gram** | Subword | 3,012 | 11.56 | 154,341 | 21.4% | 65.5% |
+| **4-gram** | Word | 1,262,619 | 20.27 | 3,613,803 | 3.4% | 8.8% |
+| **4-gram** | Subword | 20,514 | 14.32 | 898,859 | 9.3% | 30.9% |
+| **5-gram** | Word | 764,287 | 19.54 | 2,274,837 | 4.9% | 11.6% |
+| **5-gram** | Subword | 99,057 | 16.60 | 3,187,233 | 4.7% | 16.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `je bil` | 242,649 |
+| 2 | `se je` | 241,005 |
+| 3 | `ki je` | 174,450 |
+| 4 | `je bila` | 164,565 |
+| 5 | `ki so` | 110,725 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `glej tudi seznam` | 56,792 |
+| 2 | `pr n št` | 34,003 |
+| 3 | `ki ga je` | 25,570 |
+| 4 | `sklici zunanje povezave` | 21,261 |
+| 5 | `opombe glej tudi` | 21,007 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `glej tudi seznam naselij` | 20,688 |
+| 2 | `in opombe glej tudi` | 20,455 |
+| 3 | `opombe glej tudi seznam` | 19,924 |
+| 4 | `viri in opombe glej` | 16,449 |
+| 5 | `ki upravno spada pod` | 15,090 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `in opombe glej tudi seznam` | 19,642 |
+| 2 | `opombe glej tudi seznam naselij` | 16,702 |
+| 3 | `viri in opombe glej tudi` | 16,449 |
+| 4 | `glej tudi seznam naselij v` | 10,861 |
+| 5 | `glej tudi seznam naselij na` | 9,825 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 11,379,031 |
+| 2 | `e _` | 9,711,848 |
+| 3 | `i _` | 7,963,628 |
+| 4 | `_ p` | 7,248,467 |
+| 5 | `_ s` | 7,019,681 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `j e _` | 4,141,312 |
+| 2 | `_ j e` | 2,970,998 |
+| 3 | `_ p o` | 2,883,740 |
+| 4 | `_ p r` | 2,720,662 |
+| 5 | `_ n a` | 2,547,716 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ j e _` | 2,761,955 |
+| 2 | `_ i n _` | 2,026,152 |
+| 3 | `_ n a _` | 1,018,082 |
+| 4 | `_ p r e` | 991,101 |
+| 5 | `e g a _` | 882,400 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ j e _` | 682,064 |
+| 2 | `, _ k i _` | 656,040 |
+| 3 | `_ l e t a` | 563,205 |
+| 4 | `_ j e _ b` | 529,211 |
+| 5 | `j e _ b i` | 504,001 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 307
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~17% 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 | 1.0363 | 2.051 | 12.35 | 1,898,880 | 0.0% |
+| **1** | Subword | 0.7016 | 1.626 | 5.31 | 18,900 | 29.8% |
+| **2** | Word | 0.3404 | 1.266 | 2.08 | 23,427,544 | 66.0% |
+| **2** | Subword | 0.5506 | 1.465 | 3.88 | 100,242 | 44.9% |
+| **3** | Word | 0.1238 | 1.090 | 1.26 | 48,734,695 | 87.6% |
+| **3** | Subword | 0.7077 | 1.633 | 4.16 | 388,929 | 29.2% |
+| **4** | Word | 0.0464 🏆 | 1.033 | 1.08 | 61,326,075 | 95.4% |
+| **4** | Subword | 0.7050 | 1.630 | 3.60 | 1,617,081 | 29.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `je leta drugi strani gore budizma na sinaju pa mohor bogataj obrobje lokovške planote ima v`
+2. `in nkoma kmalu zatem poslala s katero ima toplo hrano kristersson kandiderar till holger in long`
+3. `v kombinaciji piracetama ni usmerjen v očetovi smrti ljubljane upravno spada pod konradom auffenstei...`
+
+**Context Size 2:**
+
+1. `je bil dirkalnik konkurenčen in ickx vztrajala da cape financira svoje zadeve anonymous 24 januar go...`
+2. `se je letalo prehitelo ekspresni vlak za florido glej tudi seznam francoskih skladateljev g rafael g...`
+3. `ki je proučeval razmerja med svojo razgradnjo produktov oksalna kislina je zelo jezen ker je imelo z...`
+
+**Context Size 3:**
+
+1. `glej tudi seznam naselij v črni gori ki upravno spada pod občino čoka slednja pa je del šumadijskega`
+2. `pr n št či leta 357 pr n št posvetili cesarju avgustu sklici viri viri ljudstva`
+3. `ki ga je po podatkih statističnega urada republike slovenije na pobudo prekmurskega društva general ...`
+
+**Context Size 4:**
+
+1. `glej tudi seznam naselij v srbiji raškega upravnega okraja`
+2. `in opombe glej tudi čini slovenske vojske čini ustanovljeni leta čini ukinjeni leta`
+3. `opombe glej tudi seznam naselij v srbiji zlatiborskega upravnega okraja`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dedna_bhe_ka._(`
+2. `ahodnizart;_go_p`
+3. `eštske'ijem_z_zo`
+
+**Context Size 2:**
+
+1. `a_gle_nači_zereva`
+2. `e_vršče,_cepubrek`
+3. `i_pozem_ske_merib`
+
+**Context Size 3:**
+
+1. `je_hočenisoke_dogs`
+2. `_je_tren._podatki_`
+3. `_potekajo_zgodklon`
+
+**Context Size 4:**
+
+1. `_je_po_znan_pedagog`
+2. `_in_renesisteinhart`
+3. `_na_polkovniških_od`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,617,081 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 | 897,122 |
+| Total Tokens | 72,710,721 |
+| Mean Frequency | 81.05 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4937.29 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | je | 2,786,159 |
+| 2 | in | 2,040,383 |
+| 3 | v | 2,029,005 |
+| 4 | na | 1,031,510 |
+| 5 | so | 822,699 |
+| 6 | se | 719,502 |
+| 7 | ki | 679,959 |
+| 8 | za | 672,987 |
+| 9 | leta | 518,963 |
+| 10 | z | 469,417 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | landesgericht | 2 |
+| 2 | súkup | 2 |
+| 3 | rozec | 2 |
+| 4 | malkolma | 2 |
+| 5 | sumate | 2 |
+| 6 | chamlanga | 2 |
+| 7 | presmučala | 2 |
+| 8 | luzejem | 2 |
+| 9 | edrika | 2 |
+| 10 | sigefertovo | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8973 |
+| R² (Goodness of Fit) | 0.998517 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 30.5% |
+| Top 1,000 | 47.9% |
+| Top 5,000 | 64.2% |
+| Top 10,000 | 71.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9985 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 30.5% of corpus
+- **Long Tail:** 887,122 words needed for remaining 28.3% 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 | 0.7907 | 0.3497 | N/A | N/A |
+| **mono_64d** | 64 | 0.7532 | 0.2937 | N/A | N/A |
+| **mono_128d** | 128 | 0.6831 | 0.2333 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7907 🏆 | 0.3571 | 0.3140 | 0.6660 |
+| **aligned_64d** | 64 | 0.7532 | 0.2862 | 0.5440 | 0.8920 |
+| **aligned_128d** | 128 | 0.6831 | 0.2351 | 0.6300 | 0.8880 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7907 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2925. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 63.0% 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 | **-0.314** | 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` | samoporjavitev, severvzhodni, svilne |
+| `-a` | argonne, aleutskega, aiya |
+| `-ma` | mathur, mashel, maerua |
+| `-m` | mowbed, migracijah, melodramatične |
+| `-k` | kickl, kaškega, koknese |
+| `-p` | picramniales, ptujskega, picinus |
+| `-b` | brelich, berruguete, bandai |
+| `-t` | tragedov, teus, travmatsko |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | zimunya, ptujskega, churchosrednja |
+| `-i` | zaposliti, gallicani, rurikoviči |
+| `-e` | berruguete, melodramatične, svilne |
+| `-o` | porfiriato, travmatsko, fajdo |
+| `-m` | levičarskem, jožefom, utesnjenem |
+| `-ga` | ptujskega, igmanskega, petrarkovega |
+| `-s` | picramniales, chlorotis, teus |
+| `-ih` | znotrajjetrnih, boucherjevih, pozitivističnih |
+
+### 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 |
+|------|----------|------------------|----------|
+| `skeg` | 2.01x | 178 contexts | skega, jskega, muskeg |
+| `enje` | 1.57x | 357 contexts | genje, enjeu, ženje |
+| `ovez` | 2.40x | 45 contexts | povez, poveza, povezi |
+| `orab` | 1.90x | 106 contexts | korab, vorab, porab |
+| `ični` | 1.47x | 356 contexts | lični, vični, nični |
+| `ijsk` | 1.34x | 565 contexts | bijsk, bijsku, krijska |
+| `ranj` | 1.42x | 366 contexts | vranj, ranji, kranj |
+| `tičn` | 1.43x | 329 contexts | tičnik, stični, atične |
+| `ržav` | 2.01x | 54 contexts | držav, državy, državi |
+| `acij` | 1.37x | 302 contexts | lacij, acija, tacij |
+| `nske` | 1.34x | 343 contexts | ønske, unske, sanske |
+| `avlj` | 1.33x | 320 contexts | javlja, lavlje, kavlja |
+
+### 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` | 171 words | pumapunkuja, pahmutova |
+| `-s` | `-a` | 133 words | soteška, suvalščizna |
+| `-p` | `-i` | 120 words | prostorninskimi, prisegami |
+| `-p` | `-e` | 100 words | pionirke, priimkie |
+| `-k` | `-a` | 97 words | kolegialnega, khandrika |
+| `-s` | `-i` | 90 words | sampi, stavkati |
+| `-a` | `-a` | 89 words | alkalna, alkibiadesa |
+| `-p` | `-o` | 84 words | postkolonialno, plješivico |
+| `-b` | `-a` | 83 words | brahmsa, benna |
+| `-s` | `-e` | 76 words | stradanje, seksualizirane |
+
+### 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 |
+|------|-----------------|------------|------|
+| desetstrane | **`desetstr-a-ne`** | 7.5 | `a` |
+| globalizirano | **`globalizir-a-no`** | 7.5 | `a` |
+| craigallian | **`craigalli-a-n`** | 7.5 | `a` |
+| šahrastani | **`šahrast-a-ni`** | 7.5 | `a` |
+| sušilnicah | **`sušilnic-a-h`** | 7.5 | `a` |
+| sirakuzah | **`sirakuz-a-h`** | 7.5 | `a` |
+| grdoselski | **`grdosel-s-ki`** | 7.5 | `s` |
+| spremenljivkama | **`spremenljivka-m-a`** | 7.5 | `m` |
+| krogličar | **`kroglič-a-r`** | 7.5 | `a` |
+| skristaliziral | **`skristalizir-a-l`** | 7.5 | `a` |
+| pritajeno | **`pritaj-e-no`** | 7.5 | `e` |
+| izhodiščnega | **`izhodišč-ne-ga`** | 7.5 | `ne` |
+| lastovsko | **`lastov-s-ko`** | 7.5 | `s` |
+| orbeliani | **`orbeli-a-ni`** | 7.5 | `a` |
+| igralkodallas | **`igralkodall-a-s`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Slovenian 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 (4.31x) |
+| N-gram | **2-gram** | Lowest perplexity (307) |
+| Markov | **Context-4** | Highest predictability (95.4%) |
+| 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: 2026-01-17 08:23:12*

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