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README.md

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+---
+language: eu
+language_name: Basque
+language_family: basque
+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-basque
+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: 4.507
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6711
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-12
+---
+
+# Basque - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Basque** 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** | 3.579x | 3.58 | 0.0525% | 2,041,670 |
+| **16k** | 3.957x | 3.96 | 0.0580% | 1,846,361 |
+| **32k** | 4.270x | 4.27 | 0.0626% | 1,711,199 |
+| **64k** | 4.507x 🏆 | 4.51 | 0.0661% | 1,621,038 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `, , Galesko udalerri bat da, Monmouthshire konderrian. Kanpo estekak konderriko ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁, ▁, ▁galesko ▁udalerri ▁bat ▁da , ▁mon mo uth ... (+7 more)` | 17 |
+| 16k | `▁, ▁, ▁galesko ▁udalerri ▁bat ▁da , ▁mon mouth shire ... (+6 more)` | 16 |
+| 32k | `▁, ▁, ▁galesko ▁udalerri ▁bat ▁da , ▁monmouthshire ▁konderrian . ... (+4 more)` | 14 |
+| 64k | `▁, ▁, ▁galesko ▁udalerri ▁bat ▁da , ▁monmouthshire ▁konderrian . ... (+4 more)` | 14 |
+
+**Sample 2:** `, Mexikoko Revillagigedo uhartediako uharte bat da, Ozeano Barean. uhartedia`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁, ▁mexikoko ▁re vill ag ig edo ▁uharte d iako ... (+10 more)` | 20 |
+| 16k | `▁, ▁mexikoko ▁re vill ag ig edo ▁uharted iako ▁uharte ... (+9 more)` | 19 |
+| 32k | `▁, ▁mexikoko ▁re vill ag ig edo ▁uharted iako ▁uharte ... (+7 more)` | 17 |
+| 64k | `▁, ▁mexikoko ▁re vill ag ig edo ▁uharted iako ▁uharte ... (+7 more)` | 17 |
+
+**Sample 3:** `{{mineral infotaula | kategoria silikato mineralak|silikato]]`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ {{ min eral ▁inf ota ula ▁| ▁kategoria ▁silikato ... (+7 more)` | 17 |
+| 16k | `▁ {{ min eral ▁inf ota ula ▁| ▁kategoria ▁silikato ... (+6 more)` | 16 |
+| 32k | `▁ {{ mineral ▁infotaula ▁| ▁kategoria ▁silikato ▁mineralak | s ... (+3 more)` | 13 |
+| 64k | `▁ {{ mineral ▁infotaula ▁| ▁kategoria ▁silikato ▁mineralak | s ... (+2 more)` | 12 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.507x compression
+- **Lowest UNK Rate:** 8k with 0.0525% 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 | 101,400 | 16.63 | 1,518,553 | 10.5% | 31.4% |
+| **2-gram** | Subword | 226 🏆 | 7.82 | 17,699 | 72.3% | 99.5% |
+| **3-gram** | Word | 128,394 | 16.97 | 2,211,893 | 10.6% | 32.1% |
+| **3-gram** | Subword | 1,909 | 10.90 | 132,832 | 27.9% | 76.3% |
+| **4-gram** | Word | 179,917 | 17.46 | 3,667,160 | 11.5% | 30.7% |
+| **4-gram** | Subword | 10,807 | 13.40 | 755,000 | 12.9% | 43.1% |
+| **5-gram** | Word | 134,161 | 17.03 | 2,865,762 | 13.8% | 31.9% |
+| **5-gram** | Subword | 41,735 | 15.35 | 2,680,749 | 7.6% | 27.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kanpo estekak` | 411,094 |
+| 2 | `izan zen` | 219,794 |
+| 3 | `bat da` | 194,039 |
+| 4 | `ziren eta` | 172,147 |
+| 5 | `enpresak ziren` | 157,767 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `erreferentziak kanpo estekak` | 152,739 |
+| 2 | `erreferentziak ikus gainera` | 78,821 |
+| 3 | `ziren horien artean` | 67,157 |
+| 4 | `gertuen dauden herriak` | 66,904 |
+| 5 | `bakarrik bizi ziren` | 64,949 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dauden herriak erakusten ditu` | 33,543 |
+| 2 | `honek gertuen dauden herriak` | 33,541 |
+| 3 | `france par comune frantziako` | 33,541 |
+| 4 | `par comune frantziako udalerri` | 33,541 |
+| 5 | `diagrama honek gertuen dauden` | 33,540 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `france par comune frantziako udalerri` | 33,541 |
+| 2 | `diagrama honek gertuen dauden herriak` | 33,540 |
+| 3 | `honek gertuen dauden herriak erakusten` | 33,540 |
+| 4 | `gertuen dauden herriak erakusten ditu` | 33,540 |
+| 5 | `emploi et population active et` | 33,539 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n` | 16,166,768 |
+| 2 | `a _` | 14,488,655 |
+| 3 | `n _` | 14,293,162 |
+| 4 | `_ e` | 11,880,846 |
+| 5 | `a r` | 11,450,376 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n _` | 8,377,081 |
+| 2 | `k o _` | 5,400,285 |
+| 3 | `e t a` | 5,000,482 |
+| 4 | `r e n` | 4,339,867 |
+| 5 | `a k _` | 4,189,214 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e t a _` | 3,251,221 |
+| 2 | `_ e t a` | 3,085,028 |
+| 3 | `r e n _` | 2,969,339 |
+| 4 | `a k o _` | 2,216,397 |
+| 5 | `a r e n` | 2,019,670 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ e t a _` | 2,973,733 |
+| 2 | `a r e n _` | 1,944,215 |
+| 3 | `_ z i r e` | 942,772 |
+| 4 | `z i r e n` | 928,644 |
+| 5 | `t z e n _` | 881,836 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 226
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 0.9814 | 1.974 | 11.89 | 2,034,056 | 1.9% |
+| **1** | Subword | 1.0508 | 2.072 | 6.91 | 11,299 | 0.0% |
+| **2** | Word | 0.3086 | 1.238 | 1.95 | 24,154,380 | 69.1% |
+| **2** | Subword | 0.6282 | 1.546 | 4.22 | 78,093 | 37.2% |
+| **3** | Word | 0.1002 | 1.072 | 1.21 | 46,969,150 | 90.0% |
+| **3** | Subword | 0.6997 | 1.624 | 4.08 | 329,201 | 30.0% |
+| **4** | Word | 0.0366 🏆 | 1.026 | 1.07 | 56,781,994 | 96.3% |
+| **4** | Subword | 0.6958 | 1.620 | 3.58 | 1,344,420 | 30.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `eta 20 emakume aktoreak mikel jondoni joanes leizarraga izendatu zuten azpian 99 lanean hasi zen urt...`
+2. `da horretan zangozako merindadean sartu zen 2 lizeo teknologiko asko horietako bi pertsona bakoitzek...`
+3. `zen bertako zuzendaritzarekin doktoretza osatu zuen rayuela eleberri hauek erdialdeko asian dub duba...`
+
+**Context Size 2:**
+
+1. `kanpo estekak monasterioak arkitektura erromanikoa du iurreko amabirjina xii xiii orrialdeak jatorri...`
+2. `izan zen 2 altzari dendak 1 altzari denda zen 1 liburu denda batean lan egiten zuen oso`
+3. `bat da horn barrutian azken zentsuaren arabera hart udalerriak 823 etxebizitza zeuden 667 hektarea e...`
+
+**Context Size 3:**
+
+1. `erreferentziak kanpo estekak kategoria departamenduko kantonamenduak santuen lurraldea`
+2. `erreferentziak ikus gainera porichthys batrachoididae kanpo estekak fishbase org arrainak golkoko ar...`
+3. `ziren horien artean 39 aktiboak ziren eta 255 apartamentuak ziren 375 etxebizitza nagusietatik 310 b...`
+
+**Context Size 4:**
+
+1. `dauden herriak erakusten ditu batzuen distantzia eta kokapen erlatiboa erreferentziak kanpo estekak ...`
+2. `par comune frantziako udalerri guztietako datu zehatzak mapa baten bitartez eskuragarri udalerriak o...`
+3. `france par comune frantziako udalerri guztietako datu zehatzak mapa baten bitartez eskuragarri udale...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dolaugetudagu-f`
+2. `a_eahaianak_grel`
+3. `e_mpeldo_seaskos`
+
+**Context Size 2:**
+
+1. `enpon_emailerako_`
+2. `a_soa_danibola_bu`
+3. `n_etak),_sa_caler`
+
+**Context Size 3:**
+
+1. `en_batua_utz_estek`
+2. `ko_eta_gazioa_bili`
+3. `eta_mota_apolibre_`
+
+**Context Size 4:**
+
+1. `eta_badituzten_adin`
+2. `_eta_liburu_da,_adi`
+3. `ren_aranoaren_kondu`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,344,420 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 | 925,645 |
+| Total Tokens | 82,551,722 |
+| Mean Frequency | 89.18 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4334.93 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | eta | 3,064,757 |
+| 2 | da | 1,077,465 |
+| 3 | zen | 1,014,999 |
+| 4 | ziren | 906,527 |
+| 5 | bat | 694,872 |
+| 6 | zuen | 667,830 |
+| 7 | izan | 539,156 |
+| 8 | zeuden | 442,816 |
+| 9 | kanpo | 430,370 |
+| 10 | 1 | 427,974 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | pveducation | 2 |
+| 2 | chillijchi | 2 |
+| 3 | gaureguneko | 2 |
+| 4 | cupla | 2 |
+| 5 | marwareraren | 2 |
+| 6 | vaṇī | 2 |
+| 7 | antarātmā | 2 |
+| 8 | ले | 2 |
+| 9 | ओमन | 2 |
+| 10 | barbajuan | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0446 |
+| R² (Goodness of Fit) | 0.993920 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 27.0% |
+| Top 1,000 | 53.3% |
+| Top 5,000 | 70.7% |
+| Top 10,000 | 77.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9939 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 27.0% of corpus
+- **Long Tail:** 915,645 words needed for remaining 22.6% 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 | 0.6711 | 0.3672 | N/A | N/A |
+| **mono_64d** | 64 | 0.6503 | 0.2977 | N/A | N/A |
+| **mono_128d** | 128 | 0.5876 | 0.2512 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6711 🏆 | 0.3650 | 0.3080 | 0.7260 |
+| **aligned_64d** | 64 | 0.6503 | 0.3045 | 0.5360 | 0.8520 |
+| **aligned_128d** | 128 | 0.5876 | 0.2534 | 0.6260 | 0.8780 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6711 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3065. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 62.6% 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 | **0.176** | 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` | alienak, alalpardo, azhkhluttach |
+| `-s` | spiritist, stateira, sakanari |
+| `-ma` | maezturekin, malasiako, malaciotis |
+| `-m` | miyashita, mwir, maezturekin |
+| `-e` | eakoak, euskeras, enacryos |
+| `-b` | birjinarenak, budavari, blechnerren |
+| `-ba` | bagoiaren, balazten, banús |
+| `-t` | txingorrigain, tdpm, t280 |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | ultraeskuindarrarekin, blechnerren, borbónen |
+| `-en` | blechnerren, borbónen, aynen |
+| `-a` | miyashita, prestatzera, haparanda |
+| `-k` | eakoak, birjinarenak, paraxialetik |
+| `-o` | villasecako, sakonuneetako, alalpardo |
+| `-ko` | villasecako, sakonuneetako, malasiako |
+| `-ak` | eakoak, birjinarenak, alienak |
+| `-in` | ultraeskuindarrarekin, uzkiarekin, txingorrigain |
+
+### 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 |
+|------|----------|------------------|----------|
+| `rtze` | 1.71x | 538 contexts | artze, ertze, urtze |
+| `tuzt` | 2.70x | 47 contexts | tuzte, dituzt, dtuzte |
+| `ikoa` | 1.67x | 501 contexts | aikoa, oikoa, pikoa |
+| `eude` | 2.63x | 45 contexts | eudes, zeude, eudel |
+| `oare` | 1.66x | 385 contexts | hoare, soare, joare |
+| `anle` | 2.52x | 48 contexts | nanle, anleu, zhanle |
+| `atut` | 1.69x | 284 contexts | matute, batuta, statut |
+| `iare` | 1.49x | 539 contexts | tiare, iaren, iaret |
+| `ntza` | 1.57x | 373 contexts | intza, antza, ontza |
+| `rria` | 1.54x | 343 contexts | irria, erria, orria |
+| `tanl` | 2.47x | 30 contexts | tanlay, stanly, bitanle |
+| `ituz` | 1.75x | 106 contexts | dituz, dituzu, abituz |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-n` | 188 words | ahtren, anbiguoen |
+| `-e` | `-n` | 162 words | epilepsiarekin, ensoren |
+| `-a` | `-a` | 136 words | austfonna, alotropia |
+| `-b` | `-n` | 121 words | bizitasunaren, bayaniren |
+| `-k` | `-n` | 111 words | koltxoiaren, kiroltasunaren |
+| `-s` | `-n` | 105 words | selekzioaren, solasaldien |
+| `-a` | `-k` | 102 words | arrazek, artxuk |
+| `-e` | `-k` | 101 words | eszenaratzeagatik, eskumikaturik |
+| `-e` | `-a` | 99 words | eulychnia, elgetarra |
+| `-p` | `-n` | 97 words | presidenteordetzan, pobrezian |
+
+### 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 |
+|------|-----------------|------------|------|
+| domeinuan | **`domeinu-a-n`** | 7.5 | `a` |
+| aritmometroa | **`aritmometr-o-a`** | 7.5 | `o` |
+| maratoiean | **`maratoi-e-an`** | 7.5 | `e` |
+| goenagari | **`goenag-a-ri`** | 7.5 | `a` |
+| onenerako | **`onener-a-ko`** | 7.5 | `a` |
+| networken | **`networ-k-en`** | 7.5 | `k` |
+| yamatentomon | **`yamatentom-o-n`** | 7.5 | `o` |
+| sulfurozkoa | **`sulfuroz-ko-a`** | 7.5 | `ko` |
+| entzunezkoak | **`entzunez-ko-ak`** | 7.5 | `ko` |
+| esparruetako | **`esparruet-a-ko`** | 7.5 | `a` |
+| ezereztasuna | **`ezereztasu-n-a`** | 7.5 | `n` |
+| mugagabetasuna | **`mugagabetasu-n-a`** | 7.5 | `n` |
+| zutabeari | **`zutabe-a-ri`** | 7.5 | `a` |
+| rouxestevae | **`rouxestev-a-e`** | 7.5 | `a` |
+| karrantzara | **`karrantz-a-ra`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Basque 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 (4.51x) |
+| N-gram | **2-gram** | Lowest perplexity (226) |
+| Markov | **Context-4** | Highest predictability (96.3%) |
+| 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: 2026-01-12 14:02:26*

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