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+---
+language: haw
+language_name: Hawaiian
+language_family: austronesian_polynesian
+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-austronesian_polynesian
+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: 3.473
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6902
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Hawaiian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Hawaiian** 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** | 3.272x | 3.28 | 0.0523% | 86,081 |
+| **16k** | 3.363x | 3.37 | 0.0537% | 83,741 |
+| **32k** | 3.442x | 3.45 | 0.0550% | 81,827 |
+| **64k** | 3.473x 🏆 | 3.49 | 0.0555% | 81,083 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `He aupuni kiwikā ‘o , i ka panalā‘au o Salamanca, ma Castille a Leon, ma Sepania...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 16k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 32k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 64k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+
+**Sample 2:** `He aupuni kiwikā ‘o , i ka panalā‘au o Salamanca, ma Castille a Leon, ma Sepania...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 16k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 32k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 64k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+
+**Sample 3:** `He aupuni kiwikā ‘o , i ka panalā‘au o Burgos, ma Castille a Leon, ma Sepania. o...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 16k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 32k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+| 64k | `▁he ▁aupuni ▁kiwikā ▁‘ o ▁, ▁i ▁ka ▁panalā ‘ ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.473x compression
+- **Lowest UNK Rate:** 8k with 0.0523% 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 | 1,927 | 10.91 | 8,889 | 35.3% | 69.2% |
+| **2-gram** | Subword | 172 🏆 | 7.43 | 2,081 | 78.7% | 99.4% |
+| **3-gram** | Word | 4,988 | 12.28 | 16,613 | 22.9% | 52.1% |
+| **3-gram** | Subword | 1,130 | 10.14 | 13,854 | 42.3% | 82.6% |
+| **4-gram** | Word | 9,312 | 13.18 | 27,880 | 17.9% | 41.4% |
+| **4-gram** | Subword | 4,664 | 12.19 | 56,149 | 24.2% | 60.9% |
+| **5-gram** | Word | 7,445 | 12.86 | 20,542 | 19.1% | 42.5% |
+| **5-gram** | Subword | 11,187 | 13.45 | 104,114 | 16.0% | 46.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i ka` | 7,715 |
+| 2 | `a me` | 4,500 |
+| 3 | `o ka` | 3,392 |
+| 4 | `i nā` | 3,235 |
+| 5 | `ma ka` | 2,812 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i ka makahiki` | 1,458 |
+| 2 | `a me ka` | 1,387 |
+| 3 | `he aupuni kiwikā` | 1,246 |
+| 4 | `castille a leon` | 1,214 |
+| 5 | `aupuni kiwikā o` | 1,117 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `he aupuni kiwikā o` | 1,115 |
+| 2 | `castille a leon ma` | 1,107 |
+| 3 | `ma castille a leon` | 1,107 |
+| 4 | `a leon ma sepania` | 1,106 |
+| 5 | `ka panalā au o` | 1,087 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ma castille a leon ma` | 1,107 |
+| 2 | `castille a leon ma sepania` | 1,106 |
+| 3 | `i ka panalā au o` | 1,058 |
+| 4 | `he aupuni kiwikā o i` | 973 |
+| 5 | `a leon ma sepania o` | 953 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 144,590 |
+| 2 | `_ k` | 78,727 |
+| 3 | `k a` | 68,606 |
+| 4 | `i _` | 64,388 |
+| 5 | `_ m` | 61,725 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a` | 40,157 |
+| 2 | `k a _` | 35,837 |
+| 3 | `_ m a` | 34,601 |
+| 4 | `a _ m` | 27,428 |
+| 5 | `n a _` | 26,431 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a _` | 28,598 |
+| 2 | `a n a _` | 15,773 |
+| 3 | `a _ m a` | 14,949 |
+| 4 | `_ m a _` | 14,806 |
+| 5 | `_ i _ k` | 13,220 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _ k a _` | 10,231 |
+| 2 | `_ h o ʻ o` | 9,268 |
+| 3 | `_ k a _ h` | 8,914 |
+| 4 | `_ i _ k a` | 8,275 |
+| 5 | `_ m e a _` | 7,759 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 172
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~47% 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 | 0.7250 | 1.653 | 4.38 | 29,186 | 27.5% |
+| **1** | Subword | 0.9483 | 1.930 | 6.15 | 996 | 5.2% |
+| **2** | Word | 0.3100 | 1.240 | 1.82 | 127,141 | 69.0% |
+| **2** | Subword | 0.8333 | 1.782 | 4.77 | 6,120 | 16.7% |
+| **3** | Word | 0.1613 | 1.118 | 1.32 | 230,368 | 83.9% |
+| **3** | Subword | 0.7682 | 1.703 | 3.53 | 29,158 | 23.2% |
+| **4** | Word | 0.0847 🏆 | 1.060 | 1.14 | 302,875 | 91.5% |
+| **4** | Subword | 0.5617 | 1.476 | 2.33 | 102,840 | 43.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ka poʻe a me anna ma pukalani mea uamaiʻakomi wāʻkalama ka sīuakalāmanāʻmō ka rell no kā`
+2. `i ka makahiki he aupuni kiwikā o kona mau hana ʻana ma kahi e komo mua`
+3. `o nā kūlana makauhale ʻāpana ka iʻa ua hoʻonohonoho ʻo ia i ka hakakā inā kīwaʻma`
+
+**Context Size 2:**
+
+1. `i ka makahiki ua komo ʻo ia iā asta i ka dreamcast i ka makahiki ua hoʻokipa`
+2. `a me zulu he haku mele nā hōʻike i hōʻiliʻili kālā no nā kumuhana eduardo mea i`
+3. `o ka wai gastric mucus loaʻa paha ka ramiro menéndez nā kūmole pāʻoi waho eūmia emānahāua mai`
+
+**Context Size 3:**
+
+1. `i ka makahiki ma mikikana nā hāmeʻa nā hāmeʻa nui linda cardellini lindsay weir john francis daley k...`
+2. `a me ka hōʻailona ʻike nui ʻia kahi i pāʻani ai i ka hoʻonui ʻana i kāna kaʻa`
+3. `he aupuni kiwikā o i ka panalā au o soria ma castille a leon ma sepania o zamora`
+
+**Context Size 4:**
+
+1. `he aupuni kiwikā o i ka panalā au o salamanca ma castille a leon ma sepania o salamanca`
+2. `ma castille a leon ma sepania o zamora`
+3. `castille a leon ma sepania o león`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_hose_ma_i_hi_l_`
+2. `a_kijoke_al_kuau`
+3. `iānaʻomeuhelaʻia`
+
+**Context Size 2:**
+
+1. `a_250px_kino_mela`
+2. `_kulamartona_bowe`
+3. `ka_mana_ka_o_i_ma`
+
+**Context Size 3:**
+
+1. `_kana_o_nū_ʻo_moa_`
+2. `ka_lāhelua_pard_no`
+3. `_ma_a_hoʻokakou_ma`
+
+**Context Size 4:**
+
+1. `_ka_wā_e_kona._i_ka`
+2. `ana_ma_puulena_o_ka`
+3. `a_ma_kahiki_ua_hale`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 91.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (102,840 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 | 12,072 |
+| Total Tokens | 429,287 |
+| Mean Frequency | 35.56 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 472.82 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ka | 28,808 |
+| 2 | i | 21,904 |
+| 3 | o | 15,069 |
+| 4 | ma | 15,022 |
+| 5 | nā | 12,739 |
+| 6 | a | 11,420 |
+| 7 | ʻo | 8,852 |
+| 8 | ke | 8,793 |
+| 9 | mea | 7,931 |
+| 10 | me | 7,613 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | dix | 2 |
+| 2 | kaiaola | 2 |
+| 3 | macke | 2 |
+| 4 | kunst | 2 |
+| 5 | kontext | 2 |
+| 6 | neubrandenburger | 2 |
+| 7 | nr | 2 |
+| 8 | wittenberg | 2 |
+| 9 | rostock | 2 |
+| 10 | ethnographic | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2265 |
+| R² (Goodness of Fit) | 0.995587 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 63.7% |
+| Top 1,000 | 86.4% |
+| Top 5,000 | 95.9% |
+| Top 10,000 | 99.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9956 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 63.7% of corpus
+- **Long Tail:** 2,072 words needed for remaining 1.0% 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 | 0.6902 | 0.3929 | N/A | N/A |
+| **mono_64d** | 64 | 0.3523 | 0.3703 | N/A | N/A |
+| **mono_128d** | 128 | 0.1052 | 0.3418 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6902 🏆 | 0.3809 | 0.0260 | 0.1900 |
+| **aligned_64d** | 64 | 0.3523 | 0.3634 | 0.0440 | 0.2580 |
+| **aligned_128d** | 128 | 0.1052 | 0.3458 | 0.0940 | 0.3380 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6902 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3658. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 9.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.093** | 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 |
+|--------|----------|
+| `-ho` | hoʻouna, hokkaidō, hoʻohiki |
+| `-ma` | marins, makoto, makemakika |
+| `-ka` | kanu, katsutaro, karla |
+| `-hoʻ` | hoʻouna, hoʻohiki, hoʻāhewa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | niwa, lehua, metala |
+| `-na` | hoʻouna, ʻohana, tobalina |
+| `-ia` | poniʻia, ekalesia, huaʻia |
+| `-er` | vermeer, chapter, cutter |
+| `-la` | metala, hoʻōla, anakola |
+
+### 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 |
+|------|----------|------------------|----------|
+| `hoʻo` | 1.78x | 25 contexts | hoʻonā, nohoʻo, hoʻokō |
+| `oʻol` | 1.88x | 20 contexts | hoʻola, hoʻoleo, hoʻolei |
+| `oʻok` | 1.65x | 26 contexts | hoʻokō, hoʻokū, hoʻokau |
+| `oʻoh` | 1.82x | 12 contexts | hoʻohui, hoʻohou, hoʻohua |
+| `oʻop` | 1.85x | 11 contexts | hoʻopā, hoʻopau, hoʻopio |
+| `aʻal` | 1.64x | 12 contexts | aʻale, kaʻala, maʻalea |
+| `ʻoma` | 1.90x | 8 contexts | ʻomana, hoʻomau, hoʻomalu |
+| `maik` | 1.78x | 9 contexts | maiki, maika, maikai |
+| `naka` | 1.58x | 12 contexts | kānaka, kanaka, tanaka |
+| `akah` | 1.52x | 13 contexts | akahi, kakaha, akahai |
+| `anak` | 1.41x | 15 contexts | tanakh, kanaka, kanakā |
+| `oʻom` | 1.85x | 7 contexts | hoʻomau, hoʻomoe, hoʻomalu |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ho` | `-a` | 80 words | hoʻopukakadokawa, hoʻolauleʻa |
+| `-ka` | `-a` | 78 words | kaopa, kakamora |
+| `-ma` | `-a` | 58 words | makaʻala, mauritiusa |
+| `-ka` | `-na` | 20 words | kamaʻāina, kaumokuʻāina |
+| `-ho` | `-na` | 16 words | hopena, hoʻomana |
+| `-ho` | `-ia` | 14 words | hontoria, hoʻohaumia |
+| `-ma` | `-na` | 14 words | mawaena, mahina |
+| `-ma` | `-la` | 9 words | makaʻala, matilla |
+| `-ma` | `-ia` | 9 words | malaia, maikonesia |
+| `-ka` | `-la` | 9 words | kapitala, karla |
+
+### 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 |
+|------|-----------------|------------|------|
+| wehewehena | **`wehewehe-na`** | 4.5 | `wehewehe` |
+| kuhikuhina | **`kuhikuhi-na`** | 4.5 | `kuhikuhi` |
+| kikokikona | **`kikokiko-na`** | 4.5 | `kikokiko` |
+| kamakakūokalani | **`ka-ma-ka-kūokalani`** | 4.5 | `kūokalani` |
+| kīʻnekelana | **`kīʻneke-la-na`** | 3.0 | `kīʻneke` |
+| makekonia | **`ma-kekon-ia`** | 3.0 | `kekon` |
+| hoʻoukaʻia | **`hoʻ-oukaʻ-ia`** | 3.0 | `oukaʻ` |
+| masedonia | **`ma-sedon-ia`** | 3.0 | `sedon` |
+| hoʻoponoponoʻia | **`hoʻ-oponoponoʻ-ia`** | 3.0 | `oponoponoʻ` |
+| kalekonia | **`ka-lekon-ia`** | 3.0 | `lekon` |
+| kamaʻilio | **`ka-ma-ʻilio`** | 3.0 | `ʻilio` |
+| karipiana | **`ka-ripia-na`** | 3.0 | `ripia` |
+| suazilana | **`suazi-la-na`** | 3.0 | `suazi` |
+| hoʻokaʻina | **`hoʻ-okaʻi-na`** | 3.0 | `okaʻi` |
+| hoʻopilikiaʻia | **`hoʻ-opilikiaʻ-ia`** | 3.0 | `opilikiaʻ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Hawaiian 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 (3.47x) |
+| N-gram | **2-gram** | Lowest perplexity (172) |
+| Markov | **Context-4** | Highest predictability (91.5%) |
+| 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: 2026-01-10 02:13:39*
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+▁kenāhekele	-15497
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+▁kolumebusa	-15499
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+▁galatasaray	-15588
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+▁hawaiiensis	-15590
+▁historylink	-15591
+▁hoolimalima	-15592
+▁huinahalike	-15593
+▁hūnīsamaūsa	-15594
+▁influential	-15595
+▁instruments	-15596
+▁interactive	-15597
+▁interchange	-15598
+▁kaliokalani	-15599
+▁keohokālole	-15600
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+▁lichtenfeld	-15603
+▁linguistika	-15604
+▁mauikeaouli	-15605
+▁mesopotamia	-15606
+▁mississippi	-15607
+▁netherlands	-15608
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+▁tarakanovka	-15622
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+▁tirthankara	-15624
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+▁universitas	-15626
+▁valmadrigal	-15627
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+▁wiliwiliwai	-15629
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+▁zichyújfalu	-15632
+▁усміхнеться	-15633
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+▁fuentelmonge	-15654
+▁ghostbusters	-15655
+▁hūnōsawīsaza	-15656
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+▁kālaikūlohea	-15658
+▁liliuokalani	-15659
+▁manaiakalani	-15660
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+▁ornithischia	-15664
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+▁sūealanaūawa	-15670
+▁tecnomacumba	-15671
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+▁fuentepinilla	-15682
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+▁kanaonokūmāhā	-15684
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+▁valderrodilla	-15689
+▁zawahasūapala	-15690
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+▁confraternidad	-15694
+▁ekateringofsky	-15695
+▁hūwacīsauōnawa	-15696
+▁internationale	-15697
+▁superampeonato	-15698
+▁transformation	-15699
+▁tunuieaiteatua	-15700
+▁wepimanoamikan	-15701
+iakaikapoliopele	-15702
+iwakāluakūmākahi	-15703
+iwakāluakūmālima	-15704
+iwakāluakūmāwalu	-15705
+▁hūzanīzanawīaza	-15706
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+clc	-15708
+dīm	-15709
+fn̩	-15710
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+jɪt	-15712
+kcb	-15713
+kər	-15714
+lhc	-15715
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+ühm	-15722
+ēmé	-15723
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+ūtā	-15725
+ɛkn	-15726
+ʃɛf	-15727
+ʃɪl	-15728
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+llōn	-15771
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+törü	-15781
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+umün	-15783
+vayê	-15784
+vjɪt	-15785
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+wdry	-15787
+yáng	-15788
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+ágkē	-15790
+érōs	-15791
+ëtze	-15792
+ühru	-15793
+īśrā	-15794
+ɔfn̩	-15795
+ʃɪlɐ	-15796
+ʊvən	-15797
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+mwcw	-15800
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+геев	-15809
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+kaʊɑɪ	-15880
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+kiokē	-15882
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+ksand	-15884
+ktörü	-15885
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+kīasā	-15887
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+mbolê	-15891
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+nazōa	-15898
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+nkhof	-15900
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+nāoua	-15902
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+ronym	-15912
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+ūsima	-16005
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+▁фото	-16012
+▁بيرو	-16013
+▁يسوا	-16014
+aesthe	-16015
+aistos	-16016
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+andare	-16018
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+anismo	-16020
+ankcez	-16021
+anágkē	-16022
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+asōawa	-16024
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+aōsana	-16026
+aūkika	-16027
+badelo	-16028
+bandit	-16029
+banedo	-16030
+baneja	-16031
+banesī	-16032
+belgië	-16033
+billed	-16034
+brexit	-16035
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+bīsawa	-16037
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+chultz	-16040
+cliffe	-16041
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+conies	-16044
+conium	-16045
+crotch	-16046
+dbrain	-16047
+detski	-16048
+dinner	-16049
+dopted	-16050
+dostem	-16051
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+edings	-16053
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+feedee	-16055
+fición	-16056
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+gaisha	-16059
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+gfried	-16061
+gynous	-16062
+harmav	-16063
+hōukie	-16064
+ikasor	-16065
+ikēama	-16066
+ilohia	-16067
+imāuni	-16068
+inside	-16069
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+kachan	-16071
+khazia	-16072
+kiwile	-16073
+kiāpia	-16074
+klāhai	-16075
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+krónos	-16077
+ksandr	-16078
+kukuia	-16079
+kyistä	-16080
+kīkēkē	-16081
+kīukie	-16082
+leswig	-16083
+leyend	-16084
+lidays	-16085
+litzer	-16086
+lminha	-16087
+lokehi	-16088
+lomena	-16089
+louise	-16090
+lovdiv	-16091
+lovens	-16092
+lowing	-16093
+makule	-16094
+mbabue	-16095
+mbardo	-16096
+medios	-16097
+mensoo	-16098
+menszo	-16099
+llj	-16100
+icos	-16101
+kēau	-16102
+lljj	-16103
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+corma	-16112
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+kīama	-16115
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+steri	-16121
+thigh	-16122
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+yland	-16126
+áchar	-16127
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+cturne	-16134
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+nināna	-16149
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++,	-23900
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+▁valderrueda	-34723
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+▁villafranca	-34732
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+▁villaquirán	-34738
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+▁vivacissimo	-34741
+▁vladivostok	-34742
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+▁wahinefoxxy	-34744
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+▁wapaunakaia	-34746
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+▁wokelikiāna	-34750
+▁wokelimaiki	-34751
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+▁wolaiakmama	-34753
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+▁ūalanūawala	-34770
+▁калашникова	-34771
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+▁узбекистане	-34773
+▁утверждении	-34774
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+bahamakikīama	-34776
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+kulanakauhale	-34783
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+stegasauridae	-34793
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+umikumamawalu	-34795
+umikumamākolu	-34796
+understanding	-34797
+ākaukarolaina	-34798
+▁accomplished	-34799
+▁agricultural	-34800
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+▁akonimaniala	-34802
+▁allegrissimo	-34803
+▁alpukeikenan	-34804
+▁amarnatureza	-34805
+▁anekantavada	-34806
+▁antananarivo	-34807
+▁anthropologe	-34808
+▁antihydrogen	-34809
+▁aproximación	-34810
+▁aristocracia	-34811
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+▁bloodstained	-34825
+▁brahmacharya	-34826
+▁brasiliensis	-34827
+▁brassicaceae	-34828
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+▁bīuahamākana	-34832
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+▁callophyllis	-34834
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+▁castrocalbón	-34837
+▁castropodame	-34838
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+▁cheeseburger	-34843
+▁chiracmichel	-34844
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+▁thumbgabriel	-35032
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+anekilosauruan	-35081
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+▁cucurbitaceae	-35124
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+▁fuentemolinos	-35142
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+▁ionagunidunan	-35163
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+▁pīanawaīazana	-35209
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+▁pāma	-63063
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+▁pikku	-63081
+▁seymo	-63082
+▁velik	-63083
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+▁basshu	-63087
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+▁indivis	-63095
+▁missour	-63096
+▁mccullou	-63097
+▁meatball	-63098
+▁melanole	-63099
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+мі	-63101
+ένη	-63102
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+▁asi	-63115
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+▁nāpau	-63168
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+▁yakus	-63172
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+▁laweha	-63186
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+▁meakaia	-63192
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+▁mississi	-63250
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+▁monakona	-63253
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+▁mukherje	-63256
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+▁mānalona	-63260
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+▁nonviole	-63276
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+▁nānahiki	-63279
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+▁osobisto	-63285
+▁palahala	-63286
+▁palaiolo	-63287
+▁palanika	-63288
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+▁parvekke	-63292
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+▁pelanahi	-63295
+▁pelekani	-63296
+▁pelekike	-63297
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+▁perfectt	-63300
+▁peritone	-63301
+▁peteroda	-63302
+▁pełnomoc	-63303
+▁philippi	-63304
+▁phocarct	-63305
+▁photogra	-63306
+▁pikkusis	-63307
+▁pilikinō	-63308
+▁pilinapi	-63309
+▁placeand	-63310
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+▁polychlo	-63314
+▁ponofran	-63315
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+▁presenta	-63317
+▁proclama	-63318
+▁professi	-63319
+▁proskune	-63320
+▁protecti	-63321
+▁protecto	-63322
+▁protesta	-63323
+▁psycholo	-63324
+▁pterosau	-63325
+▁publishi	-63326
+▁pwodiksy	-63327
+▁pālahala	-63328
+▁pōhakukū	-63329
+▁quiñoner	-63330
+▁rakiūawa	-63331
+▁rakétaer	-63332
+▁ramkrish	-63333
+▁rembrand	-63334
+▁repubbli	-63335
+▁restaura	-63336
+▁rodrigue	-63337
+▁rāhuāmai	-63338
+▁schauins	-63339
+▁selfless	-63340
+▁sepaniav	-63341
+▁sepatema	-63342
+▁seregély	-63343
+▁shakespe	-63344
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+▁synchrot	-63355
+▁sīawanai	-63356
+▁sīuakalā	-63357
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+▁therapeu	-63363
+▁thumbpop	-63364
+▁tolmache	-63365
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+▁turkmeni	-63367
+▁tutankha	-63368
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+▁uawaīawa	-63370
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+▁unerzähl	-63372
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+▁vainēawa	-63374
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+▁valdefue	-63376
+▁valdelag	-63377
+▁valdeloi	-63378
+▁valdepié	-63379
+▁valderad	-63380
+▁valderro	-63381
+▁vallecil	-63382
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+▁villaesc	-63387
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+▁villamañ	-63389
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+▁waenakon	-63400
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+▁wahinefo	-63402
+▁wahineka	-63403
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+▁weihnach	-63406
+▁welakiki	-63407
+▁welokira	-63408
+▁wikivers	-63409
+▁wokeleni	-63410
+▁wokelima	-63411
+▁wokeninā	-63412
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+▁yaeyamay	-63416
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+baūanawala	-63430
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+chiosaurus	-63435
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+nuinnaqtun	-63499
+nōsawīsaza	-63500
+occupation	-63501
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