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README.md

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+---
+language: gl
+language_name: Galician
+language_family: romance_iberian
+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-romance_iberian
+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: 4.855
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8055
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-13
+---
+
+# Galician - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Galician** 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** | 3.858x | 3.86 | 0.0578% | 2,440,248 |
+| **16k** | 4.272x | 4.27 | 0.0640% | 2,203,809 |
+| **32k** | 4.611x | 4.61 | 0.0691% | 2,041,816 |
+| **64k** | 4.855x 🏆 | 4.86 | 0.0728% | 1,939,285 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Galería de imaxes do río Lima, en Portugal. Véxase tamén de imaxes de Galicia`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁galería ▁de ▁imaxes ▁do ▁río ▁li ma , ▁en ▁portugal ... (+7 more)` | 17 |
+| 16k | `▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)` | 16 |
+| 32k | `▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)` | 16 |
+| 64k | `▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)` | 16 |
+
+**Sample 2:** `Como topónimo Gurgueiro pode referirse a: En Galiza Gurgueiro, parroquia do conc...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁como ▁topónimo ▁g ur gueiro ▁pode ▁referirse ▁a : ▁en ... (+22 more)` | 32 |
+| 16k | `▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)` | 27 |
+| 32k | `▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)` | 27 |
+| 64k | `▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)` | 27 |
+
+**Sample 3:** `Acontecementos Os escitas fanse co poder en Media (até -625). Nacementos Mortes ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁acontecementos ▁os ▁es ci tas ▁f anse ▁co ▁poder ▁en ... (+32 more)` | 42 |
+| 16k | `▁acontecementos ▁os ▁es ci tas ▁f anse ▁co ▁poder ▁en ... (+30 more)` | 40 |
+| 32k | `▁acontecementos ▁os ▁esci tas ▁fanse ▁co ▁poder ▁en ▁media ▁( ... (+28 more)` | 38 |
+| 64k | `▁acontecementos ▁os ▁escitas ▁fanse ▁co ▁poder ▁en ▁media ▁( até ... (+27 more)` | 37 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.855x compression
+- **Lowest UNK Rate:** 8k with 0.0578% 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 | 177,694 | 17.44 | 1,599,435 | 7.3% | 19.3% |
+| **2-gram** | Subword | 245 🏆 | 7.94 | 20,270 | 70.9% | 99.1% |
+| **3-gram** | Word | 807,045 | 19.62 | 3,552,102 | 4.3% | 10.2% |
+| **3-gram** | Subword | 2,104 | 11.04 | 147,394 | 28.3% | 74.1% |
+| **4-gram** | Word | 1,759,879 | 20.75 | 5,677,444 | 3.4% | 7.5% |
+| **4-gram** | Subword | 12,759 | 13.64 | 835,381 | 12.6% | 40.0% |
+| **5-gram** | Word | 1,252,252 | 20.26 | 3,696,764 | 3.5% | 8.1% |
+| **5-gram** | Subword | 56,114 | 15.78 | 2,862,824 | 6.8% | 23.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a súa` | 166,299 |
+| 2 | `véxase tamén` | 147,020 |
+| 3 | `e a` | 141,357 |
+| 4 | `que se` | 140,843 |
+| 5 | `o seu` | 139,408 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `notas véxase tamén` | 95,486 |
+| 2 | `lugar da parroquia` | 82,962 |
+| 3 | `da parroquia de` | 77,119 |
+| 4 | `véxase tamén outros` | 57,984 |
+| 5 | `tamén outros artigos` | 57,948 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `lugar da parroquia de` | 74,902 |
+| 2 | `véxase tamén outros artigos` | 57,933 |
+| 3 | `véxase tamén ligazóns externas` | 46,507 |
+| 4 | `lugares e parroquias lugares` | 41,059 |
+| 5 | `notas véxase tamén outros` | 37,978 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `notas véxase tamén outros artigos` | 37,947 |
+| 2 | `é un lugar da parroquia` | 36,571 |
+| 3 | `lugares e parroquias lugares de` | 36,422 |
+| 4 | `un lugar da parroquia de` | 32,976 |
+| 5 | `notas véxase tamén ligazóns externas` | 27,554 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 15,175,729 |
+| 2 | `a _` | 14,798,960 |
+| 3 | `o _` | 13,352,930 |
+| 4 | `_ d` | 12,106,131 |
+| 5 | `s _` | 11,921,693 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 6,968,387 |
+| 2 | `d e _` | 6,412,643 |
+| 3 | `o s _` | 4,111,887 |
+| 4 | `_ c o` | 3,620,668 |
+| 5 | `a s _` | 3,409,570 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 5,450,285 |
+| 2 | `_ e n _` | 1,570,834 |
+| 3 | `c i ó n` | 1,543,944 |
+| 4 | `o _ d e` | 1,530,833 |
+| 5 | `_ q u e` | 1,455,346 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ q u e _` | 1,344,486 |
+| 2 | `o _ d e _` | 1,307,199 |
+| 3 | `s _ d e _` | 1,120,819 |
+| 4 | `c i ó n _` | 1,079,093 |
+| 5 | `a _ d e _` | 1,051,617 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 245
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 1.0271 | 2.038 | 12.90 | 1,350,064 | 0.0% |
+| **1** | Subword | 1.1629 | 2.239 | 7.96 | 10,661 | 0.0% |
+| **2** | Word | 0.4319 | 1.349 | 2.66 | 17,398,522 | 56.8% |
+| **2** | Subword | 0.6671 | 1.588 | 4.32 | 84,803 | 33.3% |
+| **3** | Word | 0.1909 | 1.142 | 1.44 | 46,200,692 | 80.9% |
+| **3** | Subword | 0.6991 | 1.624 | 4.08 | 366,443 | 30.1% |
+| **4** | Word | 0.0765 🏆 | 1.054 | 1.14 | 66,305,200 | 92.4% |
+| **4** | Subword | 0.6839 | 1.606 | 3.51 | 1,494,151 | 31.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de outubro josé manuel fernández novoa médico e impulsou a altura inerme ante o en polo`
+2. `a mellor de sabedoría pervivindo algúns atribúenlle a proa cafè món e coli estas características ori...`
+3. `e a cal será desmontado en hábitats de vanuatu nacionais e os tempos rexistrados participación a`
+
+**Context Size 2:**
+
+1. `a súa orixe no mercado invernal o manchester united fc do racing club de ferrol onde antigamente`
+2. `véxase tamén outros artigos dólar internacional é unha substancia derivada da norma xurídica ditada ...`
+3. `e a segunda guerra mundial voou por primeira vez a vida dedicándose a promover abertamente a bandeir...`
+
+**Context Size 3:**
+
+1. `notas véxase tamén bibliografía bradbury mark becoming somaliland james currey isbn michael schoiswo...`
+2. `lugar da parroquia de nantón no concello de fisterra san paio de carreira monte da cidá é un`
+3. `da parroquia de augas santas no concello de lugo san amaro lugar da parroquia de cobres no concello`
+
+**Context Size 4:**
+
+1. `lugar da parroquia de san pedro de antealtares da mesma cidade compostela dise que compuxo esta pía ...`
+2. `véxase tamén outros artigos lugares de nigrán de nigrán de fútbol do cd lalín do algeciras cf do ad`
+3. `véxase tamén ligazóns externas de en lingua francesa de francia da arte do alemán ao francés da univ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_gon_axianto_fab`
+2. `abre_po_douraba_`
+3. `elá_s_121_dola_d`
+
+**Context Size 2:**
+
+1. `e_pertide_recació`
+2. `a_mán,_e_acipobro`
+3. `o_cruque_seta._e_`
+
+**Context Size 3:**
+
+1. `_de_direculta_desc`
+2. `de_libra_súa_idena`
+3. `os_aneirashi,_unha`
+
+**Context Size 4:**
+
+1. `_de_marticide_on_d.`
+2. `_en_funciosos_ríxid`
+3. `ción_regreira_da_ac`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 92.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,494,151 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 | 625,330 |
+| Total Tokens | 87,603,878 |
+| Mean Frequency | 140.09 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 9798.07 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 5,468,609 |
+| 2 | a | 2,599,768 |
+| 3 | e | 2,303,284 |
+| 4 | o | 2,069,024 |
+| 5 | en | 1,636,097 |
+| 6 | que | 1,373,339 |
+| 7 | do | 1,309,653 |
+| 8 | da | 1,272,492 |
+| 9 | no | 696,789 |
+| 10 | un | 677,110 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | nodulisporium | 2 |
+| 2 | sylviforme | 2 |
+| 3 | cladosporioides | 2 |
+| 4 | ccnsc | 2 |
+| 5 | bessels | 2 |
+| 6 | espertina | 2 |
+| 7 | esperpenta | 2 |
+| 8 | faïence | 2 |
+| 9 | malecoloxía | 2 |
+| 10 | clappi | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0034 |
+| R² (Goodness of Fit) | 0.997371 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 39.7% |
+| Top 1,000 | 60.3% |
+| Top 5,000 | 76.2% |
+| Top 10,000 | 82.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9974 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 39.7% of corpus
+- **Long Tail:** 615,330 words needed for remaining 17.4% 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 | 0.8055 | 0.3709 | N/A | N/A |
+| **mono_64d** | 64 | 0.7807 | 0.2987 | N/A | N/A |
+| **mono_128d** | 128 | 0.7103 | 0.2440 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8055 🏆 | 0.3769 | 0.4140 | 0.7540 |
+| **aligned_64d** | 64 | 0.7807 | 0.2912 | 0.5720 | 0.8760 |
+| **aligned_128d** | 128 | 0.7103 | 0.2400 | 0.7240 | 0.9400 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8055 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3036. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 72.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.648** | 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 |
+|--------|----------|
+| `-a` | avalíen, ardre, autocompracencia |
+| `-s` | subxéneros, saybrook, societys |
+| `-ma` | marcharían, mandiargues, malenkov |
+| `-c` | comú, citizens, celestron |
+| `-m` | muñozdianteira, monoamino, meszaros |
+| `-p` | papovaviridae, próvaí, phani |
+| `-t` | taragaza, trenque, top |
+| `-b` | bharani, battuto, baninter |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | subxéneros, illiers, citizens |
+| `-a` | kottila, 5ha, muñozdianteira |
+| `-e` | violone, fable, papovaviridae |
+| `-o` | firmamento, everxetismo, battuto |
+| `-os` | subxéneros, sáibaos, avetouros |
+| `-n` | avalíen, celestron, jamin |
+| `-as` | xeodas, criovolcánicas, kangas |
+| `-es` | lifesciences, exemplares, remontadores |
+
+### 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 |
+|------|----------|------------------|----------|
+| `icas` | 1.91x | 173 contexts | icase, micas, icasa |
+| `ació` | 1.77x | 148 contexts | ación, nació, lació |
+| `emen` | 1.59x | 249 contexts | jemen, emene, iemen |
+| `ntos` | 1.72x | 87 contexts | untos, óntos, antos |
+| `atur` | 1.49x | 156 contexts | datur, ature, satur |
+| `orma` | 1.34x | 257 contexts | torma, ormal, porma |
+| `oqui` | 1.75x | 64 contexts | toqui, coqui, noqui |
+| `ncia` | 1.45x | 152 contexts | ūncia, uncia, encia |
+| `naci` | 1.62x | 84 contexts | nacif, nacin, nacio |
+| `ific` | 1.33x | 192 contexts | ifici, ificar, unifica |
+| `cció` | 1.70x | 55 contexts | acció, lecció, acción |
+| `roqu` | 1.62x | 67 contexts | roqué, roque, croque |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-s` | 203 words | craniais, codiciadas |
+| `-a` | `-s` | 175 words | angers, aeroplanos |
+| `-p` | `-s` | 144 words | predis, pags |
+| `-a` | `-a` | 122 words | adenda, avicennia |
+| `-p` | `-a` | 121 words | penichaira, paralaia |
+| `-c` | `-a` | 117 words | cabreiresa, camisasca |
+| `-c` | `-o` | 105 words | canonízao, córnico |
+| `-e` | `-s` | 105 words | escravos, eppes |
+| `-s` | `-s` | 104 words | solsticiais, solicitamos |
+| `-c` | `-e` | 101 words | citándose, creedence |
+
+### 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 |
+|------|-----------------|------------|------|
+| unbekannt | **`unbekan-n-t`** | 7.5 | `n` |
+| gestalten | **`gestal-te-n`** | 7.5 | `te` |
+| acanthium | **`acanth-i-um`** | 7.5 | `i` |
+| matjhabeng | **`matjhab-e-ng`** | 7.5 | `e` |
+| manufactúraa | **`manufactúr-a-a`** | 7.5 | `a` |
+| bibliorum | **`biblio-r-um`** | 7.5 | `r` |
+| andersens | **`ander-se-ns`** | 7.5 | `se` |
+| contrataran | **`contrata-ra-n`** | 7.5 | `ra` |
+| anacharsis | **`anachar-s-is`** | 7.5 | `s` |
+| aavasaksa | **`aavasak-s-a`** | 7.5 | `s` |
+| endorfinas | **`endorfi-n-as`** | 7.5 | `n` |
+| cuestiónanse | **`cuestión-an-se`** | 7.5 | `an` |
+| albacetenses | **`albaceten-s-es`** | 7.5 | `s` |
+| toplumsal | **`toplum-s-al`** | 7.5 | `s` |
+| synaxarium | **`synaxar-i-um`** | 7.5 | `i` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Galician 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 (4.85x) |
+| N-gram | **2-gram** | Lowest perplexity (245) |
+| Markov | **Context-4** | Highest predictability (92.4%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-13 08:28:57*

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