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README.md

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+---
+language: hsb
+language_name: Upper Sorbian
+language_family: slavic_west
+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_west
+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: 4.478
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8367
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Upper Sorbian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Upper Sorbian** 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** | 3.442x | 3.44 | 0.2404% | 413,488 |
+| **16k** | 3.823x | 3.83 | 0.2670% | 372,334 |
+| **32k** | 4.173x | 4.18 | 0.2914% | 341,064 |
+| **64k** | 4.478x 🏆 | 4.48 | 0.3127% | 317,901 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Das Karpatenblatt su němskorěčne nowiny za němsku mjeńšinu (něhdźe 5.500 ludźi) ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁das ▁kar pat en bla tt ▁su ▁němskorěč ne ▁nowiny ... (+20 more)` | 30 |
+| 16k | `▁das ▁karpat en blatt ▁su ▁němskorěč ne ▁nowiny ▁za ▁němsku ... (+15 more)` | 25 |
+| 32k | `▁das ▁karpat en blatt ▁su ▁němskorěčne ▁nowiny ▁za ▁němsku ▁mjeńšinu ... (+13 more)` | 23 |
+| 64k | `▁das ▁karpat en blatt ▁su ▁němskorěčne ▁nowiny ▁za ▁němsku ▁mjeńšinu ... (+13 more)` | 23 |
+
+**Sample 2:** `Gotho je asteroid, kotryž ma přeměr 58 km a kotryž wotkry Karl Wilhelm Reinmuth ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁got ho ▁je ▁asteroid , ▁kotryž ▁ma ▁přeměr ▁ 5 ... (+15 more)` | 25 |
+| 16k | `▁got ho ▁je ▁asteroid , ▁kotryž ▁ma ▁přeměr ▁ 5 ... (+13 more)` | 23 |
+| 32k | `▁got ho ▁je ▁asteroid , ▁kotryž ▁ma ▁přeměr ▁ 5 ... (+13 more)` | 23 |
+| 64k | `▁got ho ▁je ▁asteroid , ▁kotryž ▁ma ▁přeměr ▁ 5 ... (+13 more)` | 23 |
+
+**Sample 3:** `Tha abo sa (arab. ثاء‎‎) je štwórty pismik arabskeho alfabeta a woznamjenja zwuk...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tha ▁abo ▁sa ▁( arab . ▁ ث ا ء ... (+31 more)` | 41 |
+| 16k | `▁tha ▁abo ▁sa ▁( arab . ▁ ث ا ء ... (+24 more)` | 34 |
+| 32k | `▁tha ▁abo ▁sa ▁( arab . ▁ ث ا ء ... (+24 more)` | 34 |
+| 64k | `▁tha ▁abo ▁sa ▁( arab . ▁ ث ا ء ... (+24 more)` | 34 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.478x compression
+- **Lowest UNK Rate:** 8k with 0.2404% 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 | 7,472 | 12.87 | 36,994 | 24.4% | 49.2% |
+| **2-gram** | Subword | 432 🏆 | 8.75 | 5,177 | 55.1% | 97.8% |
+| **3-gram** | Word | 7,937 | 12.95 | 54,451 | 29.3% | 49.8% |
+| **3-gram** | Subword | 3,653 | 11.83 | 38,557 | 19.7% | 60.6% |
+| **4-gram** | Word | 10,360 | 13.34 | 90,527 | 31.2% | 48.3% |
+| **4-gram** | Subword | 17,324 | 14.08 | 184,751 | 9.7% | 35.1% |
+| **5-gram** | Word | 6,543 | 12.68 | 69,594 | 36.6% | 53.8% |
+| **5-gram** | Subword | 48,335 | 15.56 | 450,452 | 6.5% | 26.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `w lěće` | 6,496 |
+| 2 | `hač do` | 4,857 |
+| 3 | `ze swójby` | 3,718 |
+| 4 | `wot lěta` | 3,612 |
+| 5 | `so w` | 3,439 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `słownik hornjołužiskeje rěče` | 2,945 |
+| 2 | `serbski wšowědny słownik` | 2,828 |
+| 3 | `němsko serbski wšowědny` | 2,828 |
+| 4 | `wšowědny słownik hornjołužiskeje` | 2,827 |
+| 5 | `hornjołužiskeje rěče donnerhak` | 2,815 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `němsko serbski wšowědny słownik` | 2,828 |
+| 2 | `wšowědny słownik hornjołužiskeje rěče` | 2,827 |
+| 3 | `serbski wšowědny słownik hornjołužiskeje` | 2,827 |
+| 4 | `słownik hornjołužiskeje rěče donnerhak` | 2,815 |
+| 5 | `filip němsko serbski wšowědny` | 2,814 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `němsko serbski wšowědny słownik hornjołužiskeje` | 2,827 |
+| 2 | `serbski wšowědny słownik hornjołužiskeje rěče` | 2,827 |
+| 3 | `wšowědny słownik hornjołužiskeje rěče donnerhak` | 2,815 |
+| 4 | `filip němsko serbski wšowědny słownik` | 2,814 |
+| 5 | `słownik hornjołužiskeje rěče donnerhak budyšin` | 2,811 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 280,688 |
+| 2 | `e _` | 244,497 |
+| 3 | `j e` | 227,943 |
+| 4 | `_ w` | 207,965 |
+| 5 | `_ s` | 178,194 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `j e _` | 95,450 |
+| 2 | `_ w o` | 74,945 |
+| 3 | `s k e` | 65,512 |
+| 4 | `s k i` | 54,698 |
+| 5 | `n a _` | 54,030 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s k i _` | 31,701 |
+| 2 | `_ w o t` | 30,128 |
+| 3 | `s k e j` | 29,139 |
+| 4 | `n j e _` | 28,786 |
+| 5 | `_ j e _` | 27,490 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s e r b s` | 24,332 |
+| 2 | `e r b s k` | 23,519 |
+| 3 | `_ s e r b` | 19,511 |
+| 4 | `_ r o s t` | 18,011 |
+| 5 | `s t l i n` | 17,455 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 432
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.7366 | 1.666 | 4.48 | 174,223 | 26.3% |
+| **1** | Subword | 1.1524 | 2.223 | 9.47 | 1,407 | 0.0% |
+| **2** | Word | 0.2172 | 1.163 | 1.50 | 778,883 | 78.3% |
+| **2** | Subword | 0.9361 | 1.913 | 5.84 | 13,316 | 6.4% |
+| **3** | Word | 0.0830 | 1.059 | 1.15 | 1,160,767 | 91.7% |
+| **3** | Subword | 0.7921 | 1.732 | 4.05 | 77,743 | 20.8% |
+| **4** | Word | 0.0410 🏆 | 1.029 | 1.07 | 1,326,579 | 95.9% |
+| **4** | Subword | 0.6449 | 1.564 | 2.82 | 315,129 | 35.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a wopytowaše wot lěta podawki narodniny 15 septembra w lěće jako tajki z konjom karl wilhelm`
+2. `w hornjołužiskim budyskim wokrjesu w europskim dźělu oceana na cd rom rěčny centrum witaj wudaće za`
+3. `je rostlina je wjesna gmejna słuša w oktobrje samsneho lěta za 239 30 nowembra železniska čara`
+
+**Context Size 2:**
+
+1. `w lěće je zmóžnjene wopisanje twarske a stawizniske drobnostki kulturneho pomnika lisćina kulturnych...`
+2. `hač do nektara docpěć za čas ndr bě nimo toho je wón pohibowansku załožbu awstriska bewegungsstiftun...`
+3. `ze swójby rupikowych rostlinow saxifragaceae dalše serbske mjeno pochadźa z lěta ditmarus miles de z...`
+
+**Context Size 3:**
+
+1. `słownik hornjołužiskeje rěče donnerhak budy��in eksterne wotkazy kategorija drapalcowe rostliny`
+2. `serbski wšowědny słownik hornjołužiskeje rěče donnerhak budyšin kategorija wijawkowe rostliny`
+3. `němsko serbski wšowědny słownik hornjołužiskeje rěče donnerhak budyšin eksterne wotkazy rostliny płó...`
+
+**Context Size 4:**
+
+1. `němsko serbski wšowědny słownik hornjołužiskeje rěče donnerhak budyšin rostliny rostliny`
+2. `serbski wšowědny słownik hornjołužiskeje rěče donnerhak budyšin eksterne wotkazy rostliny rostliny f...`
+3. `wšowědny słownik hornjołužiskeje rěče donnerhak budyšin kategorija křižnokwětne rostliny kategorija ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_rspodntranar_k_`
+2. `anspitowet_zaron`
+3. `e_homolět_c_stko`
+
+**Context Size 2:**
+
+1. `a_-_mjesej_juho_k`
+2. `e_pryskotryčarij:`
+3. `je_17_a_dle_hronj`
+
+**Context Size 3:**
+
+1. `je_mje._wotesać._c`
+2. `_wot_lěta_frankačk`
+3. `skej_skupuje_flerj`
+
+**Context Size 4:**
+
+1. `ski_gymna_rozšěrjen`
+2. `_wot_a_př._chětrow_`
+3. `nje_k_boliwišerstwj`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (315,129 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 | 74,135 |
+| Total Tokens | 1,812,376 |
+| Mean Frequency | 24.45 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 358.06 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 51,679 |
+| 2 | w | 47,800 |
+| 3 | je | 27,682 |
+| 4 | na | 21,213 |
+| 5 | wot | 17,579 |
+| 6 | so | 17,137 |
+| 7 | z | 16,585 |
+| 8 | do | 13,766 |
+| 9 | za | 8,969 |
+| 10 | po | 8,787 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | kaschcżik | 2 |
+| 2 | rukowom | 2 |
+| 3 | bydlišćemi | 2 |
+| 4 | direktorojo | 2 |
+| 5 | łuchowom | 2 |
+| 6 | groźišćom | 2 |
+| 7 | perfektna | 2 |
+| 8 | herzbergskeho | 2 |
+| 9 | herzbergom | 2 |
+| 10 | jg | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0600 |
+| R² (Goodness of Fit) | 0.996792 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 32.2% |
+| Top 1,000 | 61.3% |
+| Top 5,000 | 77.8% |
+| Top 10,000 | 84.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9968 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 32.2% of corpus
+- **Long Tail:** 64,135 words needed for remaining 15.7% 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 | 0.8367 🏆 | 0.3521 | N/A | N/A |
+| **mono_64d** | 64 | 0.7985 | 0.2796 | N/A | N/A |
+| **mono_128d** | 128 | 0.5471 | 0.2451 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8367 | 0.3504 | 0.0560 | 0.2700 |
+| **aligned_64d** | 64 | 0.7985 | 0.2762 | 0.0740 | 0.3240 |
+| **aligned_128d** | 128 | 0.5471 | 0.2552 | 0.1060 | 0.4020 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8367 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2931. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 10.6% 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 | **1.068** | 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` | skalický, sora, schweiz |
+| `-p` | polygaleae, pokazowaše, phil |
+| `-k` | konfesiji, kawkaski, kóždy |
+| `-b` | beethoven, biotit, botaniskeje |
+| `-m` | morjo, měnjenjach, małoróstna |
+| `-w` | wysokosću, wječorki, wustawowa |
+| `-d` | dresdner, döbeln, dołach |
+| `-a` | asclepias, apple, arndt |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | radomia, rjemjeslnistwa, wustawowa |
+| `-e` | polygaleae, noémie, pokazowaše |
+| `-je` | južnoafriskeje, himalaje, botaniskeje |
+| `-ch` | měnjenjach, kašecach, hustich |
+| `-i` | wječorki, konfesiji, kawkaski |
+| `-y` | goramśicy, ćahawy, kóždy |
+| `-m` | triumfowym, scabrum, tuchwilnym |
+| `-h` | měnjenjach, kašecach, hustich |
+
+### 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 |
+|------|----------|------------------|----------|
+| `skic` | 2.28x | 33 contexts | skica, skicy, skicow |
+| `jenj` | 1.76x | 85 contexts | jenje, rjenje, mjenja |
+| `skej` | 1.69x | 79 contexts | muskej, ošskej, ruskej |
+| `tlin` | 2.28x | 24 contexts | catlin, rostlin, watling |
+| `mjen` | 1.47x | 87 contexts | kmjen, mjena, mjenu |
+| `owan` | 1.39x | 77 contexts | głowan, wowanus, głowana |
+| `iske` | 1.43x | 68 contexts | niske, aziske, bliske |
+| `słow` | 1.79x | 28 contexts | słowo, słowa, słowu |
+| `keje` | 2.10x | 15 contexts | muskeje, małkeje, tajkeje |
+| `stli` | 2.39x | 10 contexts | rostlin, östlich, rostlinu |
+| `erbs` | 1.75x | 22 contexts | verbs, zerbst, herbst |
+| `rbsk` | 1.79x | 20 contexts | srbská, serbske, serbska |
+
+### 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` | `-e` | 144 words | přeněmčenje, priwatnje |
+| `-p` | `-a` | 140 words | paederija, pućowanska |
+| `-w` | `-e` | 123 words | wozrodźenje, watowe |
+| `-s` | `-a` | 120 words | svitava, stachowa |
+| `-s` | `-e` | 107 words | spódnje, shane |
+| `-k` | `-a` | 103 words | kuala, kajkostnika |
+| `-w` | `-a` | 98 words | widźa, wersija |
+| `-m` | `-a` | 78 words | majska, mina |
+| `-b` | `-a` | 76 words | bira, bzeža |
+| `-k` | `-e` | 74 words | kotmarje, knježerstwje |
+
+### 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 |
+|------|-----------------|------------|------|
+| serbskeju | **`serbsk-e-ju`** | 7.5 | `e` |
+| thomaschk | **`thomas-ch-k`** | 7.5 | `ch` |
+| maćičneje | **`maćičn-e-je`** | 7.5 | `e` |
+| přesadźichu | **`přesadźi-ch-u`** | 7.5 | `ch` |
+| bibliotekach | **`bibliotek-a-ch`** | 7.5 | `a` |
+| wjedźechu | **`wjedźe-ch-u`** | 7.5 | `ch` |
+| systematischen | **`systematis-ch-en`** | 7.5 | `ch` |
+| mróčelach | **`mróčel-a-ch`** | 7.5 | `a` |
+| zběhnychu | **`zběhny-ch-u`** | 7.5 | `ch` |
+| handbücher | **`handbü-ch-er`** | 7.5 | `ch` |
+| ćišćaneho | **`ćišćan-e-ho`** | 7.5 | `e` |
+| demokratische | **`demokratis-ch-e`** | 7.5 | `ch` |
+| biographical | **`biographic-a-l`** | 7.5 | `a` |
+| utahensis | **`utahen-s-is`** | 7.5 | `s` |
+| rubježneho | **`rubježn-e-ho`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Upper Sorbian 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 (4.48x) |
+| N-gram | **2-gram** | Lowest perplexity (432) |
+| Markov | **Context-4** | Highest predictability (95.9%) |
+| 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 02:59:16*

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@@ -0,0 +1,17 @@
+{
+  "language": "hsb",
+  "vocabulary_size": 74135,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.09116096193914551,
+    "coverage": {
+      "top_100": 0.3047012043846188,
+      "top_1000": 0.5811977967107517,
+      "top_5000": 0.737216713418157,
+      "top_10000": 0.799248141922362
+    },
+    "hapax_count": 100219,
+    "hapax_ratio": 0.5748018399348452,
+    "total_documents": 14240
+  }
+}