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README.md

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+---
+language: fy
+language_name: Western Frisian
+language_family: germanic_west_anglofrisian
+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_anglofrisian
+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: 4.585
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8266
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-09
+---
+
+# Western Frisian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Western Frisian** 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** | 3.696x | 3.70 | 0.0789% | 977,187 |
+| **16k** | 4.052x | 4.05 | 0.0865% | 891,334 |
+| **32k** | 4.350x | 4.35 | 0.0929% | 830,096 |
+| **64k** | 4.585x 🏆 | 4.59 | 0.0979% | 787,685 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Samuel Maresius (Frankryk, wie û.o. heechlearaar oan de Universiteit fan Grins. ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁samuel ▁mar es ius ▁( frank ryk , ▁wie ▁û ... (+26 more)` | 36 |
+| 16k | `▁samuel ▁mar es ius ▁( frankryk , ▁wie ▁û . ... (+24 more)` | 34 |
+| 32k | `▁samuel ▁mar es ius ▁( frankryk , ▁wie ▁û . ... (+21 more)` | 31 |
+| 64k | `▁samuel ▁mar es ius ▁( frankryk , ▁wie ▁û . ... (+21 more)` | 31 |
+
+**Sample 2:** `Zwijndrecht (Belgje) - in plak yn de Belgyske provinsje Antwerpen Zwijndrecht (N...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁zw ijn d recht ▁( bel gje ) ▁- ▁in ... (+23 more)` | 33 |
+| 16k | `▁zw ijn d recht ▁( bel gje ) ▁- ▁in ... (+23 more)` | 33 |
+| 32k | `▁zwijndrecht ▁( belgje ) ▁- ▁in ▁plak ▁yn ▁de ▁belgyske ... (+16 more)` | 26 |
+| 64k | `▁zwijndrecht ▁( belgje ) ▁- ▁in ▁plak ▁yn ▁de ▁belgyske ... (+16 more)` | 26 |
+
+**Sample 3:** `Foarfallen Berne Gangulfus, Frankysk hillige († 760) Ferstoarn iuw`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁foarfallen ▁berne ▁g ang ulf us , ▁frank ysk ▁hillige ... (+8 more)` | 18 |
+| 16k | `▁foarfallen ▁berne ▁gang ulf us , ▁frank ysk ▁hillige ▁(† ... (+7 more)` | 17 |
+| 32k | `▁foarfallen ▁berne ▁gang ulfus , ▁frankysk ▁hillige ▁(† ▁ 7 ... (+5 more)` | 15 |
+| 64k | `▁foarfallen ▁berne ▁gangulfus , ▁frankysk ▁hillige ▁(† ▁ 7 6 ... (+4 more)` | 14 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.585x compression
+- **Lowest UNK Rate:** 8k with 0.0789% 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 | 57,203 | 15.80 | 465,510 | 14.1% | 27.9% |
+| **2-gram** | Subword | 266 🏆 | 8.05 | 8,299 | 66.8% | 99.3% |
+| **3-gram** | Word | 299,808 | 18.19 | 933,282 | 3.2% | 10.5% |
+| **3-gram** | Subword | 2,222 | 11.12 | 64,900 | 27.9% | 71.5% |
+| **4-gram** | Word | 693,401 | 19.40 | 1,535,091 | 1.9% | 6.9% |
+| **4-gram** | Subword | 13,001 | 13.67 | 386,948 | 14.2% | 40.4% |
+| **5-gram** | Word | 545,201 | 19.06 | 1,044,881 | 1.9% | 7.3% |
+| **5-gram** | Subword | 54,381 | 15.73 | 1,339,398 | 8.0% | 24.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `fan e` | 141,194 |
+| 2 | `dy t` | 134,819 |
+| 3 | `fan de` | 123,734 |
+| 4 | `yn e` | 98,988 |
+| 5 | `yn de` | 89,074 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dy t yn` | 12,335 |
+| 2 | `dy t de` | 8,917 |
+| 3 | `keppeling om utens` | 7,988 |
+| 4 | `yn stoarn yn` | 7,907 |
+| 5 | `berne yn stoarn` | 7,873 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `berne yn stoarn yn` | 7,871 |
+| 2 | `f kr f kr` | 2,991 |
+| 3 | `yn e feriene steaten` | 2,975 |
+| 4 | `kr f kr f` | 2,776 |
+| 5 | `yn e amerikaanske steat` | 2,575 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kr f kr f kr` | 2,776 |
+| 2 | `f kr f kr f` | 2,776 |
+| 3 | `om utens offisjele webside fan` | 2,314 |
+| 4 | `keppelings om utens offisjele webside` | 2,291 |
+| 5 | `yn e internet movie database` | 1,690 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 4,878,462 |
+| 2 | `e _` | 4,610,980 |
+| 3 | `e n` | 2,898,399 |
+| 4 | `e r` | 2,688,468 |
+| 5 | `t _` | 2,451,793 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n _` | 1,790,859 |
+| 2 | `d e _` | 1,493,107 |
+| 3 | `_ d e` | 1,370,256 |
+| 4 | `a n _` | 1,211,042 |
+| 5 | `_ f a` | 969,260 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 1,176,063 |
+| 2 | `_ f a n` | 872,451 |
+| 3 | `f a n _` | 862,725 |
+| 4 | `_ y n _` | 734,798 |
+| 5 | `_ i t _` | 642,514 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ f a n _` | 852,840 |
+| 2 | `n _ d e _` | 356,953 |
+| 3 | `n _ ' e _` | 267,813 |
+| 4 | `n _ i t _` | 229,400 |
+| 5 | `_ f o a r` | 216,531 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 266
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~24% 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 | 0.9453 | 1.926 | 9.11 | 637,706 | 5.5% |
+| **1** | Subword | 0.9583 | 1.943 | 7.11 | 3,156 | 4.2% |
+| **2** | Word | 0.3681 | 1.291 | 2.23 | 5,803,614 | 63.2% |
+| **2** | Subword | 0.9139 | 1.884 | 5.94 | 22,371 | 8.6% |
+| **3** | Word | 0.1688 | 1.124 | 1.38 | 12,949,659 | 83.1% |
+| **3** | Subword | 0.8088 | 1.752 | 4.68 | 132,865 | 19.1% |
+| **4** | Word | 0.0712 🏆 | 1.051 | 1.12 | 17,845,307 | 92.9% |
+| **4** | Subword | 0.7559 | 1.689 | 3.72 | 621,806 | 24.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de kroanein of aldekleaster wie net doopt op 1 35 5 6 cpn deasketten troch gerardus`
+2. `fan út de gemeente sittard en driuwende boarplatfoarmen hefplatfoarm in kulturele sintra yn in dûnss...`
+3. `yn dizze spoarline oanpast se 1 7 1 jannewaris heart hoewol t it lemma oer langere`
+
+**Context Size 2:**
+
+1. `fan e grutte dobbe besuden teksas folrûn sa ûntstie stadichoan in paleis en de grutte sû oarloch`
+2. `dy t har kearden tsjin e jierren de redaksje fan charles williams transposition and other poems adam`
+3. `fan de stilste song er by tafal of rieden it is letterlik út de earste oerwinning helle`
+
+**Context Size 3:**
+
+1. `dy t yn drylts op 12 febrewaris har partner yn dit konkoers wie e de groot ek de`
+2. `dy t de ferlerne gebieten wer werompakt en as ryksgoaen yn it dútske keizerryk under de weimarrepubl...`
+3. `berne yn stoarn yn stoarn yn de 20e iuw waarden karakterisearre troch tige heech opmakke kapsels guo...`
+
+**Context Size 4:**
+
+1. `f kr f kr f kr f kr f kr f kr f kr sjoch ek iuwskema jierskema deiskema`
+2. `yn e feriene steaten foar opskuor soarge troch him ta de drager fan ien fan harren films spile oare`
+3. `kr f kr f kr f kr f kr f kr f kr f kr f kr f kr`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_wern_imkar_fomi`
+2. `enoaaslat_utwêry`
+3. `ndes_itrenjop_ve`
+
+**Context Size 2:**
+
+1. `n_om_wurde_rov_6e`
+2. `e_ferden_utslânst`
+3. `en_rettliblyk_wrâ`
+
+**Context Size 3:**
+
+1. `en_spedacht_troch_`
+2. `de_tiation_yn_oar_`
+3. `_de_âlderen_(*_-_l`
+
+**Context Size 4:**
+
+1. `_de_wer_de_lân_28_-`
+2. `_fan_'e_lit_einige_`
+3. `fan_de_mandy,_ornar`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 92.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (621,806 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 | 288,790 |
+| Total Tokens | 22,743,254 |
+| Mean Frequency | 78.75 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 3957.81 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 1,204,486 |
+| 2 | fan | 856,838 |
+| 3 | yn | 766,114 |
+| 4 | it | 650,720 |
+| 5 | en | 563,600 |
+| 6 | in | 518,256 |
+| 7 | e | 325,741 |
+| 8 | t | 279,402 |
+| 9 | op | 222,427 |
+| 10 | mei | 208,595 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | paleobiogeografy | 2 |
+| 2 | palaios | 2 |
+| 3 | afrotropis | 2 |
+| 4 | antarktis | 2 |
+| 5 | neärktis | 2 |
+| 6 | neotropis | 2 |
+| 7 | paleärktis | 2 |
+| 8 | νέος | 2 |
+| 9 | tropis | 2 |
+| 10 | sahulplat | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0465 |
+| R² (Goodness of Fit) | 0.997413 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 45.7% |
+| Top 1,000 | 65.1% |
+| Top 5,000 | 79.4% |
+| Top 10,000 | 84.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9974 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 45.7% of corpus
+- **Long Tail:** 278,790 words needed for remaining 15.1% 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 | 0.8266 | 0.3772 | N/A | N/A |
+| **mono_64d** | 64 | 0.7657 | 0.3036 | N/A | N/A |
+| **mono_128d** | 128 | 0.7103 | 0.2325 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8266 🏆 | 0.3840 | 0.2540 | 0.6200 |
+| **aligned_64d** | 64 | 0.7657 | 0.3009 | 0.4000 | 0.7340 |
+| **aligned_128d** | 128 | 0.7103 | 0.2303 | 0.4360 | 0.7600 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8266 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3048. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 43.6% 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 | **-0.699** | 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` | sâltmar, súdamerikaansk, stanwyck |
+| `-a` | aue, ayanna, audiïnsjes |
+| `-b` | baggeljen, bertken, bijlmermeer |
+| `-k` | kleanmakkerssit, klazien, koloanisearre |
+| `-ma` | maslup, mawr, maltesen |
+| `-t` | tsjoch, trommelet, thessalonika |
+| `-m` | maslup, mikroplestiks, museumkolleksje |
+| `-be` | bertken, beblette, bevensen |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | aue, lânsearre, strange |
+| `-en` | baggeljen, eksportearjen, bertken |
+| `-n` | baggeljen, eksportearjen, bertken |
+| `-s` | mikroplestiks, konkwistadores, myrtillus |
+| `-er` | snuggerder, rossacher, wrakseler |
+| `-r` | sâltmar, snuggerder, rossacher |
+| `-t` | elektrisiteitsnet, ranft, kleanmakkerssit |
+| `-ng` | minachting, 2kyung, stroomsteuring |
+
+### 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 |
+|------|----------|------------------|----------|
+| `tter` | 1.65x | 304 contexts | atter, utter, etter |
+| `nnen` | 1.64x | 135 contexts | onnen, annen, innen |
+| `arre` | 1.50x | 211 contexts | oarre, farre, harre |
+| `nder` | 1.33x | 419 contexts | ûnder, ender, únder |
+| `erke` | 1.55x | 177 contexts | erken, erkel, ierke |
+| `rden` | 1.61x | 145 contexts | arden, orden, erden |
+| `chte` | 1.44x | 247 contexts | èchte, echte, achte |
+| `aste` | 1.47x | 207 contexts | laste, paste, gaste |
+| `asje` | 1.83x | 55 contexts | aasje, tasje, pasje |
+| `joch` | 1.56x | 101 contexts | rjoch, jocht, sjoch |
+| `urde` | 1.87x | 45 contexts | wurde, murde, burde |
+| `nske` | 1.65x | 72 contexts | ynske, anske, munske |
+
+### 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` | 172 words | swiniastate, slokke |
+| `-s` | `-n` | 164 words | sisyljen, sprektalen |
+| `-s` | `-en` | 129 words | sisyljen, sprektalen |
+| `-b` | `-n` | 118 words | bestriden, bistehûden |
+| `-s` | `-s` | 113 words | söss, sebaldus |
+| `-b` | `-e` | 97 words | buchverlage, bungle |
+| `-k` | `-e` | 89 words | konvensjonele, kommee |
+| `-k` | `-n` | 88 words | kaishakunin, konventuelen |
+| `-a` | `-e` | 85 words | arsjitektuerskoalle, awardnominearreynternetbabe |
+| `-p` | `-e` | 84 words | protohistoarje, psoolme |
+
+### 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 |
+|------|-----------------|------------|------|
+| observeum | **`observ-e-um`** | 7.5 | `e` |
+| karavanen | **`karava-n-en`** | 7.5 | `n` |
+| yndustriegebiet | **`yndustriegebi-e-t`** | 7.5 | `e` |
+| belenenses | **`belenens-e-s`** | 7.5 | `e` |
+| constantina | **`constanti-n-a`** | 7.5 | `n` |
+| trewantsjes | **`trewantsj-e-s`** | 7.5 | `e` |
+| diktegroei | **`diktegro-e-i`** | 7.5 | `e` |
+| moeremans | **`moerema-n-s`** | 7.5 | `n` |
+| feangeniet | **`feangeni-e-t`** | 7.5 | `e` |
+| nederrynsk | **`nederry-n-sk`** | 7.5 | `n` |
+| diagnostiek | **`diagnosti-e-k`** | 7.5 | `e` |
+| praelectiones | **`praelection-e-s`** | 7.5 | `e` |
+| hulstreed | **`hulstre-e-d`** | 7.5 | `e` |
+| suderseedunen | **`suderseedu-n-en`** | 7.5 | `n` |
+| maskerdokes | **`maskerdok-e-s`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Western Frisian 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 (4.58x) |
+| N-gram | **2-gram** | Lowest perplexity (266) |
+| Markov | **Context-4** | Highest predictability (92.9%) |
+| 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: 2026-01-09 23:41:39*

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