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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*
diff --git a/eu_morph_tokenizer.json b/eu_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..4f2d9ed18991cfd75230f85cc19ca4b779cc55d1
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+.»	-29364
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+▁azokan	-35305
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+▁belgik	-35307
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+▁lerrat	-35317
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+▁urkixo	-35327
+▁varyko	-35328
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+▁jazarri	-35337
+▁kamerun	-35338
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+▁pseudom	-35341
+▁savigny	-35342
+▁sengoku	-35343
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+▁bulegoko	-35349
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+▁hautesle	-35355
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+▁intsektuen	-35385
+▁irizpideak	-35386
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+▁paraleloki	-35388
+▁picturesek	-35389
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+▁protestant	-35391
+▁txikiagoan	-35392
+▁bucerotidae	-35393
+▁sukaldariak	-35394
+▁trainerilla	-35395
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+▁pachycephala	-35398
+▁pelhřimoveko	-35399
+▁basamortuaren	-35400
+▁gainontzekoak	-35401
+▁heriotzarekin	-35402
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+▁erabiltzaileek	-35406
+▁menditsuenetan	-35407
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+▁dorrean	-35509
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+▁ikaragarria	-35570
+▁kaltegarria	-35571
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+▁geometrikoak	-35575
+▁hareharrizko	-35576
+▁kolonizazioa	-35577
+▁rhinobatidae	-35578
+▁itsasartearen	-35579
+▁plataformaren	-35580
+▁sindikalistak	-35581
+▁birlandaturiko	-35582
+▁birlandatutako	-35583
+▁konpainiarekin	-35584
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+▁pachycephalidae	-35586
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+▁nieu	-35635
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+▁ahari	-35647
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+▁blade	-35652
+▁chall	-35653
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+▁despu	-35655
+▁diary	-35656
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+▁gazia	-35659
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+▁lioph	-35666
+▁macon	-35667
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+▁natxo	-35671
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+eroaren	-35680
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+▁avilés	-35685
+▁beatty	-35686
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+aquilako	-35699
+azkoitia	-35700
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+▁barclay	-35702
+▁calahor	-35703
+▁charlot	-35704
+▁confess	-35705
+▁devario	-35706
+▁eslalom	-35707
+▁florete	-35708
+▁gaixoen	-35709
+▁maillot	-35710
+▁maskota	-35711
+▁nerbioi	-35712
+▁ontzien	-35713
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+▁eskuekin	-35730
+▁genevako	-35731
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+▁hormaren	-35734
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+▁klaustro	-35740
+▁koadroak	-35741
+▁lorezain	-35742
+▁springer	-35743
+▁thornton	-35744
+▁townsend	-35745
+▁zergatia	-35746
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+▁arabiarra	-35748
+▁argibidea	-35749
+▁basilikan	-35750
+▁berriekin	-35751
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+▁diskograf	-35753
+▁economics	-35754
+▁kamerungo	-35755
+▁paraleloa	-35756
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+▁zalantzak	-35759
+▁zikloaren	-35760
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+▁proiektuen	-35772
+▁villaverde	-35773
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+▁turismoaren	-35781
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+▁paralepididae	-35790
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+▁txikiarekin	-47385
+▁antolamendua	-47386
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+▁linfield	-48123
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+▁miresten	-48125
+▁mogurnda	-48126
+▁nabearen	-48127
+▁onartuak	-48128
+▁ordizian	-48129
+▁parasito	-48130
+▁petrogra	-48131
+▁pinellas	-48132
+▁question	-48133
+▁sintaxia	-48134
+▁sortzean	-48135
+▁ugandako	-48136
+▁zelulosa	-48137
+▁zenerako	-48138
+▁zigiluak	-48139
+▁žalgiris	-48140
+▁atlantako	-48141
+▁atxagaren	-48142
+▁bailarako	-48143
+▁benetakoa	-48144
+▁borrokari	-48145
+▁breakfast	-48146
+▁brigadier	-48147
+▁deigarria	-48148
+▁desegiten	-48149
+▁dubrovnik	-48150
+▁edukitzen	-48151
+▁egiptotik	-48152
+▁erasoaldi	-48153
+▁erreinuko	-48154
+▁erreminta	-48155
+▁eskulturg	-48156
+▁gorputzez	-48157
+▁hendricks	-48158
+▁heroiaren	-48159
+▁historiaz	-48160
+▁hondatuta	-48161
+▁huelvatik	-48162
+▁hypoplect	-48163
+▁ikasturte	-48164
+▁jeitsiera	-48165
+▁larunbata	-48166
+▁maratoian	-48167
+▁ozeaniako	-48168
+▁publikatu	-48169
+▁sendotzen	-48170
+▁sinpleena	-48171
+▁symphysan	-48172
+▁tentsioak	-48173
+▁underwood	-48174
+▁zelaiaren	-48175
+▁zentzurik	-48176
+▁zuberotar	-48177
+▁adostasuna	-48178
+▁antzerkiak	-48179
+▁basogintza	-48180
+▁bideokonts	-48181
+▁borrokatuz	-48182
+▁bucconidae	-48183
+▁corrientes	-48184
+▁dipluraren	-48185
+▁diruarekin	-48186
+▁elezaharra	-48187
+▁erlatiboki	-48188
+▁erromantiz	-48189
+▁exekutatua	-48190
+▁finalerako	-48191
+▁gizarteari	-48192
+▁gorostiaga	-48193
+▁inguruneko	-48194
+▁irisgarria	-48195
+▁istanbulen	-48196
+▁jarraikako	-48197
+▁lehentasun	-48198
+▁lurgaineko	-48199
+▁magikoaren	-48200
+▁mirandésko	-48201
+▁musikarako	-48202
+▁paisaiaren	-48203
+▁patrimonio	-48204
+▁sagamihara	-48205
+▁tenislaria	-48206
+▁altxatutako	-48207
+▁astronomoek	-48208
+▁aurkituriko	-48209
+▁barietateak	-48210
+▁ekoizlearen	-48211
+▁enpresetako	-48212
+▁errepresent	-48213
+▁eskuliburua	-48214
+▁estadística	-48215
+▁estatuburua	-48216
+▁haurrentzat	-48217
+▁influentzia	-48218
+▁irratsaioak	-48219
+▁konposatzen	-48220
+▁lehenagokoa	-48221
+▁mendialdean	-48222
+▁mitologikoa	-48223
+▁onuragarria	-48224
+▁renaissance	-48225
+▁terrorismoa	-48226
+▁troglodytes	-48227
+▁txinatarren	-48228
+▁uztaritzeko	-48229
+▁aurkikuntzen	-48230
+▁bathypterois	-48231
+▁egiaztatzeko	-48232
+▁eraikuntzako	-48233
+▁sakrifizioak	-48234
+▁synanceiidae	-48235
+▁valenciennes	-48236
+▁ezinbestekoak	-48237
+▁harrapatutako	-48238
+▁kontabilitate	-48239
+▁ordenagailuen	-48240
+▁portugaldarra	-48241
+▁probintziekin	-48242
+▁tropidonophis	-48243
+▁tropidophorus	-48244
+▁antzekotasunak	-48245
+▁azpieuskalkian	-48246
+▁dasyuromorphia	-48247
+▁ezagutarazteko	-48248
+▁konputagailuen	-48249
+▁laborategietan	-48250
+▁esanguratsuenak	-48251
+▁kolonizazioaren	-48252
+▁kontserbadoreko	-48253
+%)	-48254
+afa	-48255
+fio	-48256
+rna	-48257
+tál	-48258
+uva	-48259
+xon	-48260
+zew	-48261
+öld	-48262
+úko	-48263
+оро	-48264
+▁mt	-48265
+▁ns	-48266
+abek	-48267
+adah	-48268
+adok	-48269
+agio	-48270
+ahol	-48271
+bosc	-48272
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+cran	-48274
+duen	-48275
+eeko	-48276
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+lurr	-48279
+mori	-48280
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+phyl	-48284
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+ubil	-48289
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+urit	-48293
+urso	-48294
+utis	-48295
+utxi	-48296
+ziri	-48297
+▁aes	-48298
+▁bmg	-48299
+▁bye	-48300
+▁fei	-48301
+▁gao	-48302
+▁mme	-48303
+▁myc	-48304
+▁mön	-48305
+▁nef	-48306
+▁ors	-48307
+▁ovi	-48308
+▁peb	-48309
+▁vag	-48310
+angan	-48311
+devil	-48312
+edota	-48313
+ekuen	-48314
+forth	-48315
+hamen	-48316
+iaris	-48317
+ifugu	-48318
+imene	-48319
+ingua	-48320
+instr	-48321
+iomys	-48322
+ision	-48323
+jesús	-48324
+oboth	-48325
+opyga	-48326
+pello	-48327
+plata	-48328
+retta	-48329
+tando	-48330
+tenpl	-48331
+tondo	-48332
+urrio	-48333
+utxoa	-48334
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+▁ahok	-48336
+▁arai	-48337
+▁arbe	-48338
+▁biał	-48339
+▁cair	-48340
+▁copy	-48341
+▁dual	-48342
+▁erru	-48343
+▁ewan	-48344
+▁ezti	-48345
+▁filo	-48346
+▁foss	-48347
+▁gres	-48348
+▁hire	-48349
+▁hojo	-48350
+▁hume	-48351
+▁inon	-48352
+▁kist	-48353
+▁laim	-48354
+▁lice	-48355
+▁lodo	-48356
+▁lulu	-48357
+▁macy	-48358
+▁mele	-48359
+▁nban	-48360
+▁niso	-48361
+▁oost	-48362
+▁pelo	-48363
+▁plug	-48364
+▁priz	-48365
+▁quem	-48366
+▁rigo	-48367
+▁says	-48368
+▁spel	-48369
+▁tash	-48370
+▁txer	-48371
+▁veil	-48372
+adunen	-48373
+agnano	-48374
+alarik	-48375
+alepis	-48376
+beteko	-48377
+buruan	-48378
+chères	-48379
+idesek	-48380
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+omorro	-48383
+ophyma	-48384
+orenak	-48385
+seller	-48386
+udadan	-48387
+vallée	-48388
+yoides	-48389
+▁adria	-48390
+▁afrot	-48391
+▁ambro	-48392
+▁auber	-48393
+▁bario	-48394
+▁birul	-48395
+▁carls	-48396
+▁centu	-48397
+▁chrom	-48398
+▁croce	-48399
+▁darri	-48400
+▁delio	-48401
+▁erlax	-48402
+▁foren	-48403
+▁freak	-48404
+▁irwin	-48405
+▁jilin	-48406
+▁jodie	-48407
+▁kalab	-48408
+▁lland	-48409
+▁locke	-48410
+▁lycod	-48411
+▁maien	-48412
+▁maura	-48413
+▁meets	-48414
+▁munro	-48415
+▁pauen	-48416
+▁peral	-48417
+▁pluto	-48418
+▁pritz	-48419
+▁revel	-48420
+▁saran	-48421
+▁saura	-48422
+▁starg	-48423
+▁strik	-48424
+▁tibur	-48425
+▁unico	-48426
+▁valak	-48427
+andaluz	-48428
+eriatik	-48429
+gilbert	-48430
+hôpital	-48431
+iarchus	-48432
+icional	-48433
+ietatea	-48434
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+tsville	-48437
+▁alkain	-48438
+▁armist	-48439
+▁atkins	-48440
+▁aulkia	-48441
+▁balego	-48442
+▁barlow	-48443
+▁carlow	-48444
+▁casado	-48445
+▁cibona	-48446
+▁cosimo	-48447
+▁deliri	-48448
+▁devito	-48449
+▁domest	-48450
+▁egilek	-48451
+▁eguren	-48452
+▁emaile	-48453
+▁enblem	-48454
+▁enigma	-48455
+▁estepa	-48456
+▁flower	-48457
+▁garaiz	-48458
+▁gaubea	-48459
+▁ghosts	-48460
+▁granit	-48461
+▁haundi	-48462
+▁hauter	-48463
+▁hileko	-48464
+▁igeldo	-48465
+▁jabeek	-48466
+▁klanak	-48467
+▁kubara	-48468
+▁landes	-48469
+▁littor	-48470
+▁marais	-48471
+▁millar	-48472
+▁oldman	-48473
+▁onbera	-48474
+▁otsego	-48475
+▁parano	-48476
+▁piztia	-48477
+▁prinia	-48478
+▁sabela	-48479
+▁should	-48480
+▁tirano	-48481
+▁tumulu	-48482
+▁unetik	-48483
+▁urdail	-48484
+▁urrest	-48485
+▁volver	-48486
+▁within	-48487
+▁zuhaix	-48488
+aktoreak	-48489
+asciurus	-48490
+bildumak	-48491
+chistura	-48492
+▁actress	-48493
+▁airetik	-48494
+▁aldizka	-48495
+▁alegría	-48496
+▁aplonis	-48497
+▁aramaio	-48498
+▁arroken	-48499
+▁bangkok	-48500
+▁belasko	-48501
+▁bisitan	-48502
+▁borisov	-48503
+▁camilla	-48504
+▁chanson	-48505
+▁comédie	-48506
+▁critics	-48507
+▁daukagu	-48508
+▁directo	-48509
+▁edukien	-48510
+▁eirenis	-48511
+▁emblyna	-48512
+▁erbeste	-48513
+▁eroriko	-48514
+▁estelle	-48515
+▁eurobas	-48516
+▁fashion	-48517
+▁fiordoa	-48518
+▁flügels	-48519
+▁haskell	-48520
+▁hizpide	-48521
+▁irentsi	-48522
+▁kostuak	-48523
+▁laboral	-48524
+▁larrien	-48525
+▁laurens	-48526
+▁ledesma	-48527
+▁legends	-48528
+▁llobreg	-48529
+▁mamesen	-48530
+▁melilla	-48531
+▁milagro	-48532
+▁mirafra	-48533
+▁modulua	-48534
+▁muniain	-48535
+▁okerrak	-48536
+▁produkt	-48537
+▁público	-48538
+▁quercus	-48539
+▁saindua	-48540
+▁sormena	-48541
+▁spartan	-48542
+▁trachin	-48543
+▁zatitua	-48544
+▁zentrua	-48545
+▁zulueta	-48546
+barrutiak	-48547
+santurtzi	-48548
+▁agaramon	-48549
+▁aldikako	-48550
+▁alemanak	-48551
+▁antiokia	-48552
+▁arquette	-48553
+▁baiezkoa	-48554
+▁balkoiak	-48555
+▁beharriz	-48556
+▁berliner	-48557
+▁carvajal	-48558
+▁castejon	-48559
+▁communis	-48560
+▁dolittle	-48561
+▁egunetik	-48562
+▁erlojuak	-48563
+▁erruduna	-48564
+▁faltsuak	-48565
+▁familiek	-48566
+▁gertrude	-48567
+▁ikusizko	-48568
+▁ishikawa	-48569
+▁jordania	-48570
+▁karboxil	-48571
+▁kibibyte	-48572
+▁konbinak	-48573
+▁maculata	-48574
+▁marinela	-48575
+▁okupatua	-48576
+▁pedersen	-48577
+▁podarcis	-48578
+▁poecilio	-48579
+▁presents	-48580
+▁progreso	-48581
+▁punikoan	-48582
+▁scarlett	-48583
+▁semanita	-48584
+▁sindarin	-48585
+▁takifugu	-48586
+▁ternuako	-48587
+▁tirokatu	-48588
+▁torrelav	-48589
+▁vinalopó	-48590
+▁zatiaren	-48591
+▁ziburuko	-48592
+▁beginning	-48593
+▁beherakoa	-48594
+▁berritzea	-48595
+▁biziberri	-48596
+▁channidae	-48597
+▁chemistry	-48598
+▁cynoscion	-48599
+▁diagramak	-48600
+▁domingoko	-48601
+▁ezabatzen	-48602
+▁galarraga	-48603
+▁galleneko	-48604
+▁giltzarri	-48605
+▁grabatuta	-48606
+▁hasierara	-48607
+▁igarotako	-48608
+▁igotakoak	-48609
+▁ikasteari	-48610
+▁kanpokoak	-48611
+▁killarney	-48612
+▁lightning	-48613
+▁merindade	-48614
+▁mexikotik	-48615
+▁oxidatzen	-48616
+▁pachycara	-48617
+▁palentzia	-48618
+▁pristurus	-48619
+▁recordsen	-48620
+▁relations	-48621
+▁teleorman	-48622
+▁trikitixa	-48623
+▁tronuaren	-48624
+▁zabalgune	-48625
+arkitektura	-48626
+hauteskunde	-48627
+▁angelesera	-48628
+▁angolensis	-48629
+▁bilakaeran	-48630
+▁burlington	-48631
+▁ekitaldiko	-48632
+▁elizagoien	-48633
+▁elizondoko	-48634
+▁erlijioari	-48635
+▁esaldiaren	-48636
+▁gasolindeg	-48637
+▁generokoak	-48638
+▁glukosaren	-48639
+▁government	-48640
+▁heziketako	-48641
+▁inperioari	-48642
+▁iragarpena	-48643
+▁laboratory	-48644
+▁lehiakidea	-48645
+▁mantenduko	-48646
+▁mazedoniar	-48647
+▁melamphaes	-48648
+▁motatakoak	-48649
+▁nemipterus	-48650
+▁orrialdean	-48651
+▁tanzaniako	-48652
+▁txiletarra	-48653
+▁abstraktuak	-48654
+▁altxorraren	-48655
+▁arrantzalea	-48656
+▁derrigorrez	-48657
+▁familietako	-48658
+▁guztiarekin	-48659
+▁inbertsioak	-48660
+▁kartografia	-48661
+▁lycophidion	-48662
+▁mobilizazio	-48663
+▁nafarroaren	-48664
+▁organismoak	-48665
+▁pittosporum	-48666
+▁portugaleko	-48667
+▁produkzioak	-48668
+▁soldatapeko	-48669
+▁tektonikoak	-48670
+▁tripulazioa	-48671
+▁universitat	-48672
+▁ureztatzeko	-48673
+▁zangozerria	-48674
+▁zarrabeitia	-48675
+▁zehaztasuna	-48676
+txapelketaren	-48677
+▁amateurretan	-48678
+▁arraindegiak	-48679
+▁erromanikoan	-48680
+▁hohenzollern	-48681
+▁oncorhynchus	-48682
+▁philothamnus	-48683
+▁sundasciurus	-48684
+▁tratamenduak	-48685
+▁distantziaren	-48686
+▁garaierarekin	-48687
+▁gehiegikeriak	-48688
+▁larruazalaren	-48689
+▁mikrobiologia	-48690
+▁probintziatik	-48691
+▁pseudorhombus	-48692
+▁gerrhosauridae	-48693
+▁neurtuakdatuak	-48694
+▁zaharberritzea	-48695
+▁administrazioko	-48696
+▁craterocephalus	-48697
+▁komunikazioaren	-48698
+▁zuzendariarekin	-48699
+><	-48700
+bm	-48701
+lí	-48702
+rk	-48703
+xs	-48704
+си	-48705
+–,	-48706
+anu	-48707
+bee	-48708
+cca	-48709
+gek	-48710
+hav	-48711
+izé	-48712
+opp	-48713
+uly	-48714
+yns	-48715
+čka	-48716
+▁ça	-48717
+abus	-48718
+acon	-48719
+agir	-48720
+assi	-48721
+derm	-48722
+eana	-48723
+eham	-48724
+esne	-48725
+exka	-48726
+eyko	-48727
+ezio	-48728
+foss	-48729
+inka	-48730
+ivus	-48731
+nies	-48732
+ombo	-48733
+onym	-48734
+ribe	-48735
+rila	-48736
+spec	-48737
+spey	-48738
+sull	-48739
+unce	-48740
+uska	-48741
+wnee	-48742
+éron	-48743
+▁(*)	-48744
+▁(-)	-48745
+▁(?-	-48746
+▁+/-	-48747
+▁aih	-48748
+▁akh	-48749
+▁arv	-48750
+▁coy	-48751
+▁ean	-48752
+▁eil	-48753
+▁euz	-48754
+▁ghe	-48755
+▁hae	-48756
+▁ila	-48757
+▁nür	-48758
+▁odi	-48759
+▁ome	-48760
+▁tux	-48761
+▁vía	-48762
+▁wie	-48763
+▁woj	-48764
+adort	-48765
+agari	-48766
+alisi	-48767
+ardog	-48768
+assol	-48769
+atino	-48770
+azioz	-48771
+defen	-48772
+esper	-48773
+estus	-48774
+etuta	-48775
+gorta	-48776
+iusek	-48777
+kirch	-48778
+menes	-48779
+meren	-48780
+music	-48781
+nuovo	-48782
+oduna	-48783
+proba	-48784
+relli	-48785
+temps	-48786
+teron	-48787
+yeong	-48788
+zanok	-48789
+▁ages	-48790
+▁agus	-48791
+▁akai	-48792
+▁atlè	-48793
+▁blum	-48794
+▁boch	-48795
+▁bogd	-48796
+▁bubo	-48797
+▁caix	-48798
+▁cath	-48799
+▁clap	-48800
+▁conm	-48801
+▁cyps	-48802
+▁dene	-48803
+▁ells	-48804
+▁haun	-48805
+▁hesp	-48806
+▁hirs	-48807
+▁irma	-48808
+▁jock	-48809
+▁lako	-48810
+▁laod	-48811
+▁minh	-48812
+▁moya	-48813
+▁neka	-48814
+▁nils	-48815
+▁ocho	-48816
+▁peup	-48817
+▁pili	-48818
+▁reus	-48819
+▁rios	-48820
+▁souv	-48821
+▁vini	-48822
+▁xehe	-48823
+artzea	-48824
+berten	-48825
+carpus	-48826
+earena	-48827
+franca	-48828
+itions	-48829
+quillo	-48830
+usaren	-48831
+▁alesi	-48832
+▁apart	-48833
+▁artek	-48834
+▁bowen	-48835
+▁brant	-48836
+▁brava	-48837
+▁caixa	-48838
+▁ching	-48839
+▁copad	-48840
+▁divor	-48841
+▁fejer	-48842
+▁garci	-48843
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+▁eszenatoki	-52990
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+▁ezberdinei	-52992
+▁fabrikazio	-52993
+▁harpagifer	-52994
+▁hegazkinek	-52995
+▁hondamenak	-52996
+▁imparfinis	-52997
+▁italiarren	-52998
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+▁laburraren	-53000
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+▁plazaratua	-53008
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+▁portsmouth	-53010
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+▁macropodinae	-53062
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+▁sagardotegia	-53068
+▁saskatchewan	-53069
+▁transformers	-53070
+▁unibertsalak	-53071
+▁zortzimilako	-53072
+▁aztarnategiak	-53073
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+▁estrasburgoko	-53079
+▁eublepharidae	-53080
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+▁merindadearen	-53083
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+▁hymenocephalus	-53089
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+sø	-53095
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+idd	-53100
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+▁ryu	-53151
+▁teo	-53152
+▁thw	-53153
+▁tym	-53154
+accus	-53155
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+▁akan	-53200
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+▁araf	-53203
+▁argu	-53204
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+▁bint	-53207
+▁boon	-53208
+▁boot	-53209
+▁břec	-53210
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+▁diot	-53215
+▁drug	-53216
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+▁josi	-53218
+▁kair	-53219
+▁kune	-53220
+▁lezk	-53221
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+▁mili	-53224
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+imantan	-53321
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+lothian	-53324
+obacter	-53325
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+▁biduze	-53338
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+▁bolbor	-53340
+▁botata	-53341
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+▁bustoa	-53343
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+▁kideko	-53361
+▁kornub	-53362
+▁laukil	-53363
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+▁lászló	-53366
+▁maglia	-53367
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+▁amikuze	-53400
+▁armadek	-53401
+▁arokoak	-53402
+▁arrotza	-53403
+▁aukeran	-53404
+▁babcock	-53405
+▁barents	-53406
+▁boerren	-53407
+▁boletín	-53408
+▁brevica	-53409
+▁brouwer	-53410
+▁cyornis	-53411
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+▁egoteaz	-53413
+▁erregel	-53414
+▁etakide	-53415
+▁eurekin	-53416
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+▁gaiztak	-53419
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+▁harrotu	-53421
+▁hirurek	-53422
+▁honorio	-53423
+▁kamtxat	-53424
+▁katrina	-53425
+▁kuliska	-53426
+▁lerratu	-53427
+▁maisuen	-53428
+▁mapetan	-53429
+▁marisol	-53430
+▁mcgrath	-53431
+▁muinoen	-53432
+▁pottoka	-53433
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+▁saginaw	-53435
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+▁vertigo	-53440
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+▁enpresei	-53457
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+▁eragitea	-53459
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+▁ezkutuak	-53463
+▁fosfolip	-53464
+▁grisaxka	-53465
+▁hatsheps	-53466
+▁ilunaren	-53467
+▁iparreko	-53468
+▁kantaren	-53469
+▁kolonian	-53470
+▁korearen	-53471
+▁laurendi	-53472
+▁lurrazal	-53473
+▁marineen	-53474
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+▁osasunan	-53481
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+▁prosymna	-53483
+▁sagardoa	-53484
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+▁sculptor	-53486
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+▁semetzat	-53488
+▁sentipen	-53489
+▁talentua	-53490
+▁txanpona	-53491
+▁txantrea	-53492
+▁vallecas	-53493
+▁villette	-53494
+▁zuloetan	-53495
+alberdania	-53496
+groeneveld	-53497
+▁aireontzi	-53498
+▁alterazio	-53499
+▁amurrioko	-53500
+▁anestesia	-53501
+▁autoimmun	-53502
+▁baliogabe	-53503
+▁barbastro	-53504
+▁behaketak	-53505
+▁behealdea	-53506
+▁bertakoen	-53507
+▁butterfly	-53508
+▁collegeko	-53509
+▁cryptotis	-53510
+▁edalontzi	-53511
+▁ederraren	-53512
+▁estibaliz	-53513
+▁etxegoien	-53514
+▁ezkontzan	-53515
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+▁gramática	-53518
+▁göttingen	-53519
+▁haizetara	-53520
+▁harrelson	-53521
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+▁itxaroten	-53527
+▁kagoshima	-53528
+▁kalifaren	-53529
+▁katharina	-53530
+▁klasearen	-53531
+▁komertzio	-53532
+▁laginketa	-53533
+▁laniarius	-53534
+▁lantaldea	-53535
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+▁marseille	-53537
+▁mondadori	-53538
+▁narratibo	-53539
+▁neurketak	-53540
+▁paraguain	-53541
+▁petrogale	-53542
+▁piskanaka	-53543
+▁platyceps	-53544
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+▁salbatzen	-53548
+▁savoiakoa	-53549
+▁sostengua	-53550
+▁tafallako	-53551
+▁telletxea	-53552
+▁tomicodon	-53553
+▁torquatus	-53554
+▁abadetxeak	-53555
+▁aintzirara	-53556
+▁almorabide	-53557
+▁andalustar	-53558
+▁arkitekton	-53559
+▁aulonocara	-53560
+▁bakezaleak	-53561
+▁barietatea	-53562
+▁barraskilo	-53563
+▁bertikalki	-53564
+▁beteranoen	-53565
+▁bizikletaz	-53566
+▁canadensis	-53567
+▁connection	-53568
+▁dipodillus	-53569
+▁efektuaren	-53570
+▁eskoletako	-53571
+▁eskumikatu	-53572
+▁fejervarya	-53573
+▁garaitzeko	-53574
+▁gazteluari	-53575
+▁geografoak	-53576
+▁hamarkadaz	-53577
+▁hilabetero	-53578
+▁hockenheim	-53579
+▁isolaturik	-53580
+▁izendatuko	-53581
+▁kapituluak	-53582
+▁kiroletako	-53583
+▁kosmonauta	-53584
+▁mikrobioen	-53585
+▁mitxelenak	-53586
+▁oblastaren	-53587
+▁ottensheim	-53588
+▁overijssel	-53589
+▁parasitoak	-53590
+▁partiturak	-53591
+▁pizkundean	-53592
+▁salazarrek	-53593
+▁tipografia	-53594
+▁tiratutako	-53595
+▁zonosaurus	-53596
+▁abiaduraren	-53597
+▁alderdikide	-53598
+▁anabantidae	-53599
+▁argudiatzen	-53600
+▁arrebarekin	-53601
+▁ballesteros	-53602
+▁belarjaleak	-53603
+▁ctenogobius	-53604
+▁ekologikoak	-53605
+▁errefrakzio	-53606
+▁fotosintesi	-53607
+▁garaikideen	-53608
+▁goraipatzen	-53609
+▁horrengatik	-53610
+▁infekziosoa	-53611
+▁información	-53612
+▁inkisizioak	-53613
+▁izendapenaz	-53614
+▁jatetxearen	-53615
+▁jatorrikoak	-53616
+▁luzapenaren	-53617
+▁microglanis	-53618
+▁milurtekoan	-53619
+▁palmaresean	-53620
+▁pentsatzeko	-53621
+▁politikorik	-53622
+▁todiramphus	-53623
+▁txulapaingo	-53624
+▁villafranca	-53625
+▁xantusiidae	-53626
+cephalichthys	-53627
+▁ahalbidetuko	-53628
+▁basiprionota	-53629
+▁danimarkaren	-53630
+▁fusilatutako	-53631
+▁ghostbusters	-53632
+▁goleatzailea	-53633
+▁harreragilea	-53634
+▁hegoaldekoak	-53635
+▁hirugarrenak	-53636
+▁jacksonville	-53637
+▁karlistaldia	-53638
+▁konpetentzia	-53639
+▁kontratatzen	-53640
+▁oxyurichthys	-53641
+▁parodontidae	-53642
+▁phyllomedusa	-53643
+▁tachyoryctes	-53644
+▁uhartearekin	-53645
+▁urraulgoitin	-53646
+▁acropomatidae	-53647
+▁berritzaileak	-53648
+▁erakusketetan	-53649
+▁erregeorderri	-53650
+▁erresistentea	-53651
+▁erresumarekin	-53652
+▁erretratugile	-53653
+▁frantziarekin	-53654
+▁kontakizunean	-53655
+▁kontsonanteak	-53656
+▁entziklopediak	-53657
+▁internationale	-53658
+▁neolissochilus	-53659
+▁proiektuarekin	-53660
+▁margolaritzaren	-53661
+▁michelangeloren	-53662
+jí	-53663
+abc	-53664
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+ijs	-53667
+jek	-53668
+kau	-53669
+kub	-53670
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+udy	-53676
+voz	-53677
+vra	-53678
+wrc	-53679
+éto	-53680
+ška	-53681
+▁[-	-53682
+▁dd	-53683
+▁gc	-53684
+▁hü	-53685
+▁pw	-53686
+▁sł	-53687
+▁ре	-53688
+abio	-53689
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+ikin	-53701
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+oyah	-53711
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+rade	-53713
+rami	-53714
+ritz	-53715
+skin	-53716
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+thor	-53718
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+ului	-53720
+urai	-53721
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+▁ami	-53724
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+▁gop	-53727
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+▁océ	-53731
+▁sín	-53732
+▁tae	-53733
+▁vys	-53734
+aedia	-53735
+aldai	-53736
+aldas	-53737
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+azita	-53739
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+cante	-53741
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+endel	-53746
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+iyuan	-53759
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+litas	-53762
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+omini	-53765
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+perio	-53767
+quién	-53768
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+setín	-53770
+skita	-53771
+uzkoa	-53772
+woman	-53773
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+▁buis	-53778
+▁chew	-53779
+▁ciao	-53780
+▁comb	-53781
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+▁eaen	-53783
+▁fars	-53784
+▁fein	-53785
+▁hako	-53786
+▁hana	-53787
+▁inqu	-53788
+▁kmra	-53789
+▁kord	-53790
+▁llib	-53791
+▁loto	-53792
+▁luen	-53793
+▁mish	-53794
+▁noct	-53795
+▁ollo	-53796
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+▁opio	-53798
+▁osce	-53799
+▁sage	-53800
+▁stet	-53801
+▁udko	-53802
+▁umil	-53803
+▁univ	-53804
+▁uruk	-53805
+▁vals	-53806
+▁verv	-53807
+▁vico	-53808
+▁ving	-53809
+▁zhou	-53810
+▁zigo	-53811
+▁zoko	-53812
+apurna	-53813
+arrisk	-53814
+aubert	-53815
+balioa	-53816
+castel	-53817
+chneri	-53818
+cières	-53819
+dellín	-53820
+endaal	-53821
+ezkeen	-53822
+flower	-53823
+imanus	-53824
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+inière	-53826
+maroko	-53827
+munich	-53828
+philip	-53829
+soides	-53830
+ugarte	-53831
+worthy	-53832
+ylepis	-53833
+yongia	-53834
+▁aezko	-53835
+▁allig	-53836
+▁almat	-53837
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+▁andui	-53840
+▁aruna	-53841
+▁ateko	-53842
+▁bahra	-53843
+▁balne	-53844
+▁behav	-53845
+▁betsy	-53846
+▁boots	-53847
+▁brugu	-53848
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+▁escob	-53854
+▁estip	-53855
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+▁helga	-53858
+▁jolie	-53859
+▁klabe	-53860
+▁kraus	-53861
+▁kurts	-53862
+▁labea	-53863
+▁lando	-53864
+▁lartz	-53865
+▁leach	-53866
+▁lhota	-53867
+▁lotto	-53868
+▁mainz	-53869
+▁malte	-53870
+▁mccul	-53871
+▁mcint	-53872
+▁mehdi	-53873
+▁monet	-53874
+▁murat	-53875
+▁neure	-53876
+▁noran	-53877
+▁olvid	-53878
+▁opéra	-53879
+▁parva	-53880
+▁pleia	-53881
+▁princ	-53882
+▁proce	-53883
+▁punch	-53884
+▁regen	-53885
+▁salut	-53886
+▁sarek	-53887
+▁sendi	-53888
+▁sigma	-53889
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+▁txundituta	-55990
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+▁atxikimendu	-55998
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+▁capromyidae	-56000
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+▁estreinakoz	-56006
+▁ezinbestean	-56007
+▁futbolarien	-56008
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+▁hypopomidae	-56011
+▁idiazabalgo	-56012
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+▁osasunerako	-56020
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+▁aurrekoetxea	-56027
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+▁lophiidae	-60839
+▁marinelak	-60840
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+▁meropidae	-60843
+▁modernoek	-60844
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+▁sindikatuen	-60959
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+▁xenotilapia	-60962
+▁zeharkatuta	-60963
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+▁artifizialki	-60965
+▁bathyergidae	-60966
+▁batxilergoko	-60967
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+▁dorretxearen	-60970
+▁elortzibarko	-60971
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+▁paralichthys	-60990
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+▁herrialdeekin	-61004
+▁ordezkaritzak	-61005
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+▁ariznab	-62990
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+▁cardoso	-63000
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+▁soldatak	-63153
+▁stargate	-63154
+▁tafallan	-63155
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+▁zurichen	-63163
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+▁abiatzeko	-63168
+▁absolbitu	-63169
+▁acrochord	-63170
+▁aktibismo	-63171
+▁alzheimer	-63172
+▁angelukoa	-63173
+▁anonimoak	-63174
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+▁aurkituak	-63176
+▁berpiztea	-63177
+▁brachirus	-63178
+▁budismoan	-63179
+▁bulebarra	-63180
+▁burusoila	-63181
+▁colobinae	-63182
+▁ctenopoma	-63183
+▁delfoseko	-63184
+▁dezakegun	-63185
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+▁diskoteka	-63187
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+▁emagaldua	-63191
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+▁enpirikoa	-63195
+▁erliebean	-63196
+▁erostarbe	-63197
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+▁gaztañaga	-63199
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+▁gonçalves	-63202
+▁hainbaten	-63203
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+▁hispanian	-63207
+▁hornituak	-63208
+▁indalecio	-63209
+▁isolatzen	-63210
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+▁nabukodon	-63222
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+▁schizodon	-63229
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+▁villamayor	-63309
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+▁desberdinez	-63321
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+▁dicrostonyx	-63323
+▁ekuatoriala	-63324
+▁emakumeekin	-63325
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+▁galiziarrak	-63330
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+▁gehiegikeri	-63333
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+▁kinosternidae	-63403
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+▁kontrajartzen	-63406
+▁meteorologian	-63407
+▁mycobacterium	-63408
+▁nekazaritzari	-63409
+▁paracheilinus	-63410
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+▁(...).	-63798
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