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README.md

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+---
+language: vls
+language_name: West Flemish
+language_family: germanic_west_continental
+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-germanic_west_continental
+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: 4.163
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8756
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# West Flemish - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **West Flemish** 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** | 3.334x | 3.34 | 0.0287% | 502,567 |
+| **16k** | 3.665x | 3.67 | 0.0315% | 457,201 |
+| **32k** | 3.934x | 3.94 | 0.0338% | 425,860 |
+| **64k** | 4.163x 🏆 | 4.17 | 0.0358% | 402,499 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Achtntwientig is 't getal 28, e nateurlik getal achter zeevnetwientig en voorn n...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁a chtn tw ientig ▁is ▁' t ▁getal ▁ 2 ... (+18 more)` | 28 |
+| 16k | `▁a chtn tw ientig ▁is ▁' t ▁getal ▁ 2 ... (+18 more)` | 28 |
+| 32k | `▁a chtn twientig ▁is ▁' t ▁getal ▁ 2 8 ... (+13 more)` | 23 |
+| 64k | `▁achtn twientig ▁is ▁' t ▁getal ▁ 2 8 , ... (+12 more)` | 22 |
+
+**Sample 2:** `de volksnoame van de gemêente Ôostrôzebeke e dêelgemêente van Stoan, zie: Rôzebe...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁de ▁volk sn oame ▁van ▁de ▁gemêente ▁ôostrôzebeke ▁e ▁dêelgemêente ... (+10 more)` | 20 |
+| 16k | `▁de ▁volk sn oame ▁van ▁de ▁gemêente ▁ôostrôzebeke ▁e ▁dêelgemêente ... (+10 more)` | 20 |
+| 32k | `▁de ▁volk snoame ▁van ▁de ▁gemêente ▁ôostrôzebeke ▁e ▁dêelgemêente ▁van ... (+8 more)` | 18 |
+| 64k | `▁de ▁volk snoame ▁van ▁de ▁gemêente ▁ôostrôzebeke ▁e ▁dêelgemêente ▁van ... (+8 more)` | 18 |
+
+**Sample 3:** `Paltoga (Russisch: Палтога) is e dorp in Rusland in 't district Vytegorsky (obla...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pal t og a ▁( russisch : ▁ п а ... (+37 more)` | 47 |
+| 16k | `▁pal t og a ▁( russisch : ▁ п а ... (+35 more)` | 45 |
+| 32k | `▁pal t oga ▁( russisch : ▁ п ал то ... (+29 more)` | 39 |
+| 64k | `▁palt oga ▁( russisch : ▁ п ал то г ... (+28 more)` | 38 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.163x compression
+- **Lowest UNK Rate:** 8k with 0.0287% 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 | 12,804 | 13.64 | 41,132 | 15.7% | 36.6% |
+| **2-gram** | Subword | 282 🏆 | 8.14 | 3,241 | 64.7% | 99.2% |
+| **3-gram** | Word | 27,763 | 14.76 | 51,974 | 7.7% | 22.9% |
+| **3-gram** | Subword | 2,519 | 11.30 | 27,863 | 25.7% | 68.5% |
+| **4-gram** | Word | 45,411 | 15.47 | 74,505 | 6.8% | 17.6% |
+| **4-gram** | Subword | 15,236 | 13.90 | 154,373 | 12.4% | 36.1% |
+| **5-gram** | Word | 30,248 | 14.88 | 47,265 | 8.2% | 19.7% |
+| **5-gram** | Subword | 57,965 | 15.82 | 420,619 | 7.2% | 22.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `van de` | 15,489 |
+| 2 | `in de` | 10,285 |
+| 3 | `in t` | 6,874 |
+| 4 | `van t` | 5,995 |
+| 5 | `en de` | 3,723 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `joar in de` | 850 |
+| 2 | `van t joar` | 791 |
+| 3 | `bouwkundig erfgoed in` | 765 |
+| 4 | `in west vloandern` | 742 |
+| 5 | `t joar is` | 714 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t joar is t` | 693 |
+| 2 | `eeuwe volgenst de christelikke` | 526 |
+| 3 | `volgenst de christelikke joartellienge` | 526 |
+| 4 | `noa bouwkundig erfgoed in` | 354 |
+| 5 | `t ende van t` | 337 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `eeuwe volgenst de christelikke joartellienge` | 526 |
+| 2 | `t ende van t joar` | 304 |
+| 3 | `volgenst de christelikke joartellienge gebeurtenissn` | 292 |
+| 4 | `lyste van bouwkundig erfgoed in` | 251 |
+| 5 | `toet t ende van t` | 250 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 399,169 |
+| 2 | `e _` | 395,658 |
+| 3 | `e r` | 217,859 |
+| 4 | `e n` | 214,189 |
+| 5 | `d e` | 208,906 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 123,266 |
+| 2 | `d e _` | 116,073 |
+| 3 | `a n _` | 97,189 |
+| 4 | `e n _` | 96,860 |
+| 5 | `_ v a` | 80,611 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 90,909 |
+| 2 | `_ v a n` | 76,359 |
+| 3 | `v a n _` | 74,288 |
+| 4 | `_ i n _` | 52,878 |
+| 5 | `n _ d e` | 48,858 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ v a n _` | 73,086 |
+| 2 | `n _ d e _` | 39,289 |
+| 3 | `a n _ d e` | 22,613 |
+| 4 | `v a n _ d` | 21,346 |
+| 5 | `e _ v a n` | 19,923 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 282
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~22% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.8228 | 1.769 | 5.44 | 158,804 | 17.7% |
+| **1** | Subword | 1.2080 | 2.310 | 9.96 | 735 | 0.0% |
+| **2** | Word | 0.2583 | 1.196 | 1.64 | 860,998 | 74.2% |
+| **2** | Subword | 1.0608 | 2.086 | 6.74 | 7,322 | 0.0% |
+| **3** | Word | 0.0895 | 1.064 | 1.15 | 1,409,019 | 91.1% |
+| **3** | Subword | 0.9474 | 1.928 | 4.92 | 49,306 | 5.3% |
+| **4** | Word | 0.0313 🏆 | 1.022 | 1.05 | 1,616,997 | 96.9% |
+| **4** | Subword | 0.7502 | 1.682 | 3.21 | 242,577 | 25.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de wyk van t nôordn gruujn dikkers in kontrast me 3 juli gin êen of mêercellig`
+2. `van yper wunt en nieuw ryk in de kustvlaktn groene bewegienge wordn ze egliek nie kost`
+3. `in ip t volgn nog 293 noa bouwkundig erfgoed bevern en mêer tyd toen ze van`
+
+**Context Size 2:**
+
+1. `van de verênigde stoatn busschn`
+2. `in de dertiende êeuwe dus vès ipgedolvn gebied o den ôostkant van de verênigde stoatn en kanada`
+3. `in t ôostn an ciney in noamn in en je viel italië were binn de stad stroomde`
+
+**Context Size 3:**
+
+1. `joar in de 13e of 14e êeuwe en van de 50 000 en 120 000 beschreevn sôortn varieern`
+2. `van t joar geboorn pontormo gabriel fahrenheit gustaaf flamen emiel lauwers bob dylan gestorvn jozef...`
+3. `bouwkundig erfgoed in tiegem in west vloandern t es eignlyk nen ouden arm van den aa t grenst`
+
+**Context Size 4:**
+
+1. `t joar is t 80e joar in de 10e eeuwe volgenst de christelikke joartellienge mmxii is e schrikkeljoar...`
+2. `volgenst de christelikke joartellienge gebeurtenissn 25 april hertog jan zounder vrêes legt an d ips...`
+3. `eeuwe volgenst de christelikke joartellienge gebeurtenissn april 5 de west vlamsche coureur gaston r...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_scoe_taz,_we_ve`
+2. `e,_scar-êli²_man`
+3. `ndstoone_zogers_`
+
+**Context Size 2:**
+
+1. `n_'t_vroegroudt_a`
+2. `e_priens)_gië_e_s`
+3. `erd_ipparem_moste`
+
+**Context Size 3:**
+
+1. `_de_vanasamuele_(>`
+2. `de_piegouwne_refeu`
+3. `an_beken_deel_rede`
+
+**Context Size 4:**
+
+1. `_de_schopinidad_er_`
+2. `_van_mandsche_kenme`
+3. `van_flandn_ip_ne_bu`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (242,577 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 | 68,458 |
+| Total Tokens | 1,735,026 |
+| Mean Frequency | 25.34 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 600.62 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 93,287 |
+| 2 | van | 73,544 |
+| 3 | in | 53,708 |
+| 4 | en | 49,180 |
+| 5 | t | 45,426 |
+| 6 | e | 21,400 |
+| 7 | is | 17,745 |
+| 8 | zyn | 16,831 |
+| 9 | n | 15,475 |
+| 10 | die | 12,301 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | myzeqe | 2 |
+| 2 | seman | 2 |
+| 3 | rumn | 2 |
+| 4 | peshkopi | 2 |
+| 5 | dibër | 2 |
+| 6 | города | 2 |
+| 7 | uytvoernde | 2 |
+| 8 | stoatssecretoarisn | 2 |
+| 9 | soamnstellinge | 2 |
+| 10 | mph | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0178 |
+| R² (Goodness of Fit) | 0.998718 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 40.8% |
+| Top 1,000 | 63.3% |
+| Top 5,000 | 79.0% |
+| Top 10,000 | 85.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9987 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 40.8% of corpus
+- **Long Tail:** 58,458 words needed for remaining 14.7% 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 | 0.8756 🏆 | 0.3181 | N/A | N/A |
+| **mono_64d** | 64 | 0.8383 | 0.2517 | N/A | N/A |
+| **mono_128d** | 128 | 0.5888 | 0.2007 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8756 | 0.3113 | 0.0840 | 0.3740 |
+| **aligned_64d** | 64 | 0.8383 | 0.2465 | 0.1380 | 0.4500 |
+| **aligned_128d** | 128 | 0.5888 | 0.2020 | 0.2000 | 0.5260 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8756 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2550. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.0% 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 | **-0.109** | 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` | soôrtn, schwaben, schick |
+| `-b` | binnstroomde, bolivië, biezelehe |
+| `-a` | arenaria, addington, amazing |
+| `-ge` | gelanceerd, gezeyd, gevoenn |
+| `-o` | oendregienk, ogtepunt, omwald |
+| `-be` | bewaren, bees, bedek |
+| `-k` | kurs, kommiesje, koopman |
+| `-d` | dié, donetsk, darling |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | underne, binnstroomde, poginge |
+| `-n` | soôrtn, fryslân, hopeweunn |
+| `-s` | zothuus, kurs, cervantes |
+| `-t` | ogtepunt, varlet, capaciteit |
+| `-en` | conservatieven, schwaben, bewaren |
+| `-d` | vervolgd, omwald, tulband |
+| `-ge` | poginge, franstalige, lancerienge |
+| `-r` | elektrotoer, êesteminister, hour |
+
+### 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 |
+|------|----------|------------------|----------|
+| `enge` | 2.33x | 50 contexts | engel, oenger, mengel |
+| `sche` | 1.68x | 141 contexts | schee, asche, vasche |
+| `chte` | 1.60x | 115 contexts | achte, echte, vichte |
+| `fran` | 2.05x | 37 contexts | frank, franz, frang |
+| `schi` | 1.77x | 65 contexts | schip, schie, schid |
+| `icht` | 1.56x | 114 contexts | richt, licht, vicht |
+| `isch` | 1.83x | 51 contexts | ischl, visch, vischn |
+| `hter` | 1.94x | 38 contexts | ahter, echter, achter |
+| `nder` | 1.41x | 150 contexts | ander, under, onder |
+| `elik` | 1.72x | 51 contexts | gelik, tielik, feliks |
+| `oate` | 1.77x | 40 contexts | zoate, oater, moate |
+| `erke` | 1.54x | 66 contexts | kerke, berke, werke |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-e` | 169 words | subklasse, sukerziekte |
+| `-b` | `-e` | 149 words | bulskampstroate, beschoafde |
+| `-s` | `-n` | 125 words | skorsenelen, steeën |
+| `-b` | `-n` | 114 words | blokkn, behunn |
+| `-k` | `-e` | 108 words | kunstacademie, kassie |
+| `-m` | `-e` | 100 words | muuzee, multiple |
+| `-o` | `-n` | 95 words | oafbusschn, ofebrookn |
+| `-o` | `-e` | 91 words | ounbevlekte, omriengende |
+| `-d` | `-e` | 90 words | dagtemprateure, duytstoalige |
+| `-a` | `-e` | 88 words | adresse, ansluutienge |
+
+### 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 |
+|------|-----------------|------------|------|
+| fermenteren | **`fermenter-e-n`** | 7.5 | `e` |
+| benoaderd | **`benoa-de-rd`** | 7.5 | `de` |
+| bruggelingen | **`bruggeling-e-n`** | 7.5 | `e` |
+| romantiek | **`romanti-e-k`** | 7.5 | `e` |
+| vruchtvlees | **`vruchtv-le-es`** | 7.5 | `le` |
+| treuzelen | **`treuze-le-n`** | 7.5 | `le` |
+| vluchters | **`vlucht-e-rs`** | 7.5 | `e` |
+| resources | **`resourc-e-s`** | 7.5 | `e` |
+| splenters | **`splent-e-rs`** | 7.5 | `e` |
+| ipbryngsten | **`ipbryngst-e-n`** | 7.5 | `e` |
+| vienkezetters | **`vienkezett-e-rs`** | 7.5 | `e` |
+| knobbeltjes | **`knobbeltj-e-s`** | 7.5 | `e` |
+| beweegboar | **`beweegbo-a-r`** | 7.5 | `a` |
+| schoonhoven | **`schoonhov-e-n`** | 7.5 | `e` |
+| donspluumtjes | **`donspluumtj-e-s`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language West Flemish 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 (4.16x) |
+| N-gram | **2-gram** | Lowest perplexity (282) |
+| Markov | **Context-4** | Highest predictability (96.9%) |
+| 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: 2026-01-11 03:19:22*

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