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+---
+language: fj
+language_name: Fijian
+language_family: austronesian_oceanic_other
+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_oceanic_other
+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: 4.558
+  - name: best_isotropy
+    type: isotropy
+    value: 0.3441
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Fijian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Fijian** 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** | 4.221x | 4.23 | 0.2173% | 149,069 |
+| **16k** | 4.473x | 4.48 | 0.2303% | 140,670 |
+| **32k** | 4.558x 🏆 | 4.57 | 0.2348% | 138,019 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Shenzhen na siti mai Guagdog, Jaina. 10,78 milioni 'ei wilika kai (Jaina)`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁shenz hen ▁na ▁siti ▁mai ▁gua g dog , ▁jaina ... (+15 more)` | 25 |
+| 16k | `▁shenzhen ▁na ▁siti ▁mai ▁guagdog , ▁jaina . ▁ 1 ... (+12 more)` | 22 |
+| 32k | `▁shenzhen ▁na ▁siti ▁mai ▁guagdog , ▁jaina . ▁ 1 ... (+12 more)` | 22 |
+
+**Sample 2:** `Beijigi na Samoa Solomon Islands mai Jaina. (Jaina)`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁beiji gi ▁na ▁samoa ▁solo mon ▁islands ▁mai ▁jaina . ... (+3 more)` | 13 |
+| 16k | `▁beijigi ▁na ▁samoa ▁solomon ▁islands ▁mai ▁jaina . ▁( jaina ... (+1 more)` | 11 |
+| 32k | `▁beijigi ▁na ▁samoa ▁solomon ▁islands ▁mai ▁jaina . ▁( jaina ... (+1 more)` | 11 |
+
+**Sample 3:** `O Niukaseli ena Tyne () e dua na siti kei Vualiku Tokalau Igiladi. Sega ni veile...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁o ▁niukaseli ▁ena ▁ty ne ▁() ▁e ▁dua ▁na ▁siti ... (+16 more)` | 26 |
+| 16k | `▁o ▁niukaseli ▁ena ▁tyne ▁() ▁e ▁dua ▁na ▁siti ▁kei ... (+15 more)` | 25 |
+| 32k | `▁o ▁niukaseli ▁ena ▁tyne ▁() ▁e ▁dua ▁na ▁siti ▁kei ... (+15 more)` | 25 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.558x compression
+- **Lowest UNK Rate:** 8k with 0.2173% 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 | 1,320 | 10.37 | 3,772 | 35.4% | 72.8% |
+| **2-gram** | Subword | 135 🏆 | 7.08 | 1,021 | 82.8% | 100.0% |
+| **3-gram** | Word | 2,319 | 11.18 | 5,139 | 26.7% | 58.4% |
+| **3-gram** | Subword | 762 | 9.57 | 6,635 | 47.4% | 88.0% |
+| **4-gram** | Word | 4,567 | 12.16 | 6,660 | 15.3% | 41.6% |
+| **4-gram** | Subword | 2,839 | 11.47 | 23,852 | 29.5% | 65.9% |
+| **5-gram** | Word | 2,249 | 11.14 | 2,889 | 17.2% | 54.1% |
+| **5-gram** | Subword | 6,549 | 12.68 | 40,232 | 20.3% | 51.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dua na` | 2,793 |
+| 2 | `e dua` | 1,910 |
+| 3 | `kei na` | 1,768 |
+| 4 | `na kena` | 1,059 |
+| 5 | `mai na` | 875 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e dua na` | 1,710 |
+| 2 | `me vaka na` | 432 |
+| 3 | `ena dua na` | 337 |
+| 4 | `e rawa ni` | 325 |
+| 5 | `me baleta na` | 289 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kina e dua na` | 157 |
+| 2 | `me vaka e dua` | 128 |
+| 3 | `vaka e dua na` | 126 |
+| 4 | `kei na dua na` | 94 |
+| 5 | `dua vei ira na` | 94 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `me vaka e dua na` | 118 |
+| 2 | `tiko kina e dua na` | 81 |
+| 3 | `e tiko kina e dua` | 61 |
+| 4 | `e dua vei ira na` | 61 |
+| 5 | `e dua na vanua ni` | 30 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 58,970 |
+| 2 | `i _` | 29,549 |
+| 3 | `n a` | 29,239 |
+| 4 | `_ n` | 27,570 |
+| 5 | `k a` | 21,085 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a _` | 27,144 |
+| 2 | `_ n a` | 17,261 |
+| 3 | `a _ n` | 13,306 |
+| 4 | `a k a` | 12,166 |
+| 5 | `n i _` | 10,459 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a _` | 16,868 |
+| 2 | `_ n i _` | 9,009 |
+| 3 | `v a k a` | 8,886 |
+| 4 | `a _ n a` | 8,331 |
+| 5 | `_ v a k` | 7,039 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ n a _` | 8,200 |
+| 2 | `_ v a k a` | 6,983 |
+| 3 | `i _ n a _` | 4,445 |
+| 4 | `a _ n i _` | 4,282 |
+| 5 | `_ e n a _` | 4,212 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 135
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~51% 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 | 0.7768 | 1.713 | 4.51 | 11,967 | 22.3% |
+| **1** | Subword | 0.9522 | 1.935 | 6.63 | 397 | 4.8% |
+| **2** | Word | 0.3443 | 1.270 | 1.87 | 53,271 | 65.6% |
+| **2** | Subword | 0.9798 | 1.972 | 5.10 | 2,632 | 2.0% |
+| **3** | Word | 0.1340 | 1.097 | 1.26 | 98,857 | 86.6% |
+| **3** | Subword | 0.7901 | 1.729 | 3.31 | 13,399 | 21.0% |
+| **4** | Word | 0.0539 🏆 | 1.038 | 1.08 | 123,164 | 94.6% |
+| **4** | Subword | 0.5048 | 1.419 | 2.10 | 44,342 | 49.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `na veikau se kavu epinephelus macrospilos epinephelus howlandi epinephelus species ni rerevaka na ma...`
+2. `ni percussion ni tauyavutaki ena dua na koro levu ni potukali puerto la voui karisito oqo`
+3. `e kune ena rawa e dua na kawatamata kena vakayagataki me vaka axial 82 23 mi`
+
+**Context Size 2:**
+
+1. `dua na tabana ka rawa me wainimate igu ia e dau yaco na veikau na uca kei`
+2. `e dua na vanua e vakavuna na vakayalo ni veika vulavula e dua na lali e tu`
+3. `kei na vakayagataki vakalevu me maroroi tikoga na kisi kei galileo ena lomanibai oqo e sega ni`
+
+**Context Size 3:**
+
+1. `e dua na sikorere ena matavuvale artamidae e sa vakaiyacaga sara ki na vuqa na itutu taudaku ni`
+2. `me vaka na ena vuku ni dredre ni kena vakamacalataki na veimataqali vakasama ni bibi e tiko na`
+3. `ena dua na koniteina vakauyaya ni sega ni tiko manumanu nodra sui e tiko ena yanuyanu o vanua`
+
+**Context Size 4:**
+
+1. `kina e dua na balavu ni ivakatagedegede e 2 7 ki 3 1 na gauna na kena titobu na`
+2. `me vaka e dua na vanua ni wai ka drodro yani ena dela ni qele se na boto wasawasa`
+3. `vaka e dua na droini mai vei rembrandt e dua na bula vakailavo e dua na vanua e 28`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `aki)_nuy_me_dreg`
+2. `_enataromau_sita`
+3. `iva_da,_raluqope`
+
+**Context Size 2:**
+
+1. `a_oqo_takarauta_y`
+2. `i_ena_na_na_ni_ca`
+3. `na_me_tuidini_va_`
+
+**Context Size 3:**
+
+1. `na_raraiti_e_dua_k`
+2. `_na_vakasir_franx_`
+3. `a_na_uciwaseinamat`
+
+**Context Size 4:**
+
+1. `_na_veiwale_e_vura_`
+2. `_ni_sa_vakarai_na_t`
+3. `vakaya_na_kai._rist`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (44,342 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 | 5,184 |
+| Total Tokens | 136,582 |
+| Mean Frequency | 26.35 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 317.63 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | na | 17,507 |
+| 2 | ni | 9,047 |
+| 3 | e | 7,598 |
+| 4 | ena | 4,309 |
+| 5 | kei | 3,339 |
+| 6 | dua | 3,228 |
+| 7 | me | 2,753 |
+| 8 | ka | 2,414 |
+| 9 | kena | 1,753 |
+| 10 | mai | 1,556 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | pumona | 2 |
+| 2 | ilatilati | 2 |
+| 3 | cervix | 2 |
+| 4 | movement | 2 |
+| 5 | citations | 2 |
+| 6 | translation | 2 |
+| 7 | feminisimi | 2 |
+| 8 | vakademografi | 2 |
+| 9 | iceruniduka | 2 |
+| 10 | balisi | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1690 |
+| R² (Goodness of Fit) | 0.991059 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 63.9% |
+| Top 1,000 | 88.2% |
+| Top 5,000 | 99.7% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9911 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 63.9% of corpus
+- **Long Tail:** -4,816 words needed for remaining 100.0% 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 | 0.3441 🏆 | 0.5376 | N/A | N/A |
+| **mono_64d** | 64 | 0.0516 | 0.5508 | N/A | N/A |
+| **mono_128d** | 128 | 0.0106 | 0.5737 | N/A | N/A |
+| **aligned_32d** | 32 | 0.3441 | 0.5520 | 0.0100 | 0.1140 |
+| **aligned_64d** | 64 | 0.0516 | 0.5538 | 0.0100 | 0.0700 |
+| **aligned_128d** | 128 | 0.0106 | 0.5479 | 0.0100 | 0.0600 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.3441 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.5526. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.0% 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 | **0.058** | 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 |
+|--------|----------|
+| `-va` | vakavuniwai, vakabauti, vakasamataki |
+| `-vak` | vakavuniwai, vakabauti, vakasamataki |
+| `-vaka` | vakavuniwai, vakabauti, vakasamataki |
+| `-ve` | veikau, veitokoni, veibasai |
+| `-vei` | veikau, veitokoni, veibasai |
+| `-ma` | malea, makawa, matasawa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | ikoya, ijipita, república |
+| `-i` | enijilisi, itaviqaravi, piqi |
+| `-ki` | vakasamataki, vakayaloqaqataki, daramaki |
+| `-aki` | vakasamataki, vakayaloqaqataki, daramaki |
+| `-ka` | rawarataka, lifuka, taqomaka |
+| `-taki` | vakasamataki, vakayaloqaqataki, yalataki |
+| `-ni` | lesoni, sakini, nabavuni |
+| `-aka` | rawarataka, taqomaka, marautaka |
+
+### 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 |
+|------|----------|------------------|----------|
+| `atak` | 1.45x | 18 contexts | mataka, vuataka, muataka |
+| `akat` | 1.41x | 18 contexts | jakata, vakatui, vakatau |
+| `kata` | 1.34x | 15 contexts | jakata, vakatau, vakatani |
+| `itak` | 1.41x | 12 contexts | nuitaki, beitaki, kuitaki |
+| `akar` | 1.34x | 12 contexts | vakaro, jakarta, vakarua |
+| `veiv` | 1.45x | 9 contexts | veivala, veivola, veivula |
+| `eiva` | 1.50x | 8 contexts | veivala, teivaka, teivaki |
+| `akav` | 1.36x | 10 contexts | cakava, vakavo, rakavi |
+| `akac` | 1.47x | 8 contexts | vakaca, vakacava, vakacegu |
+| `ivak` | 1.43x | 8 contexts | teivaka, teivaki, ivakaro |
+| `amat` | 1.46x | 7 contexts | tamata, squamata, matamata |
+| `kara` | 1.40x | 7 contexts | karamu, ankara, vakarau |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-va` | `-i` | 199 words | vakaoqori, vakaduri |
+| `-va` | `-a` | 186 words | vakatubura, vakawasoma |
+| `-ve` | `-i` | 126 words | veitiki, veitauni |
+| `-va` | `-ki` | 79 words | vakaituvakitaki, vakarerevaki |
+| `-va` | `-aki` | 75 words | vakaituvakitaki, vakarerevaki |
+| `-va` | `-taki` | 70 words | vakaituvakitaki, vakamatautaki |
+| `-va` | `-ka` | 58 words | vakatayaloyalotaka, vakasamataka |
+| `-ve` | `-ki` | 52 words | veitiki, veiwalitaki |
+| `-ve` | `-aki` | 48 words | veiwalitaki, veivakabulabulataki |
+| `-ve` | `-a` | 45 words | vekita, venezuela |
+
+### 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 |
+|------|-----------------|------------|------|
+| veivosakitaki | **`vei-vosa-ki-taki`** | 7.5 | `vosa` |
+| vakaikuritaki | **`vaka-ikuri-taki`** | 6.0 | `ikuri` |
+| veivosaki | **`vei-vosa-ki`** | 6.0 | `vosa` |
+| vakatikina | **`vaka-tiki-na`** | 6.0 | `tiki` |
+| vakabulabulataki | **`vaka-bulabula-taki`** | 6.0 | `bulabula` |
+| vakatututaki | **`vaka-tutu-taki`** | 6.0 | `tutu` |
+| vakasucuna | **`vaka-sucu-na`** | 6.0 | `sucu` |
+| veiyasana | **`vei-yasa-na`** | 6.0 | `yasa` |
+| vakagalalataki | **`vaka-galala-taki`** | 6.0 | `galala` |
+| vakalewena | **`vaka-lewe-na`** | 6.0 | `lewe` |
+| vakawaicalataki | **`vaka-waicala-taki`** | 6.0 | `waicala` |
+| vakaduiduitaki | **`vaka-duidui-taki`** | 6.0 | `duidui` |
+| vakadodonutaki | **`vaka-dodonu-taki`** | 6.0 | `dodonu` |
+| veitinani | **`vei-tina-ni`** | 6.0 | `tina` |
+| veitacini | **`vei-taci-ni`** | 6.0 | `taci` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Fijian 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 | **32k BPE** | Best compression (4.56x) |
+| N-gram | **2-gram** | Lowest perplexity (135) |
+| Markov | **Context-4** | Highest predictability (94.6%) |
+| 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: 2026-01-04 14:43:20*
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+▁siglevu	-14381
+▁yucatán	-14382
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+▁lalitpur	-14385
+▁magnetic	-14386
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+▁togatapu	-14389
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+▁algebraic	-14391
+▁behaviour	-14392
+▁fisheries	-14393
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+▁neftchala	-14396
+▁nisaikolo	-14397
+▁palatable	-14398
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+▁hawthorn	-14464
+▁tigrinum	-14465
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+▁wainisiki	-14471
+▁waisiliva	-14472
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+▁yorkshire	-14478
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+▁marriott	-14581
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+▁tongarewa	-14587
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+unibisinisi	-14591
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+▁vakaliu	-14756
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+▁barnacle	-14760
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+▁paradisi	-14764
+▁peatqele	-14765
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+vakadikevi	-14771
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+▁ivakavure	-14775
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+▁birmingham	-14791
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+▁borosiliva	-14794
+▁bosokivaki	-14795
+▁botoniqele	-14796
+▁buketetaki	-14797
+▁bulawaiwai	-14798
+▁cagimaliwa	-14799
+ann	-14800
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+cepti	-14810
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+▁cudrucu	-14858
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+▁daukana	-14860
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+▁palumbus	-14871
+▁wananavu	-14872
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+▁eccentric	-14875
+▁hemolymph	-14876
+▁ikatakata	-14877
+▁mataivaka	-14878
+▁plausible	-14879
+▁subkalasi	-14880
+▁veikoloni	-14881
+▁cakamisini	-14882
+▁calcareous	-14883
+▁calculator	-14884
+▁carotenoid	-14885
+▁cayenensis	-14886
+▁cellulosic	-14887
+▁chaerephon	-14888
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+▁chalybeate	-14890
+▁championed	-14891
+▁charophyta	-14892
+▁chesapeake	-14893
+▁chinachina	-14894
+▁chiroptera	-14895
+▁christiani	-14896
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+▁circumflex	-14898
+▁coagulated	-14899
+nb	-14900
+ryi	-14901
+ryo	-14902
+▁gm	-14903
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+▁constru	-14951
+▁meimami	-14952
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+▁bakelite	-14962
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+▁qasecake	-14968
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+▁vosavosa	-14974
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+▁matanikau	-14978
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+▁batibatina	-14980
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+▁conduction	-14985
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+▁conulariid	-14987
+▁convection	-14988
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+▁copernicus	-14990
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+▁cudrucudru	-14992
+▁cyprinidae	-14993
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+▁daunipiano	-14996
+▁dauvakadro	-14997
+▁dauveibuli	-14998
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+'"	-15000
+hr	-15001
+++.	-15002
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+▁draunipepa	-15084
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+▁elastomers	-15090
+▁eliosifera	-15091
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+▁reiverswa	-13783
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+▁sabirabad	-13789
+▁saccharum	-13790
+▁saikoloji	-13791
+▁samoensis	-13792
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+▁scrimshaw	-13794
+▁seemannii	-13795
+▁segapolar	-13796
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+▁senicauca	-13798
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+▁folates	-13860
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+▁botswana	-13869
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+▁kidomoki	-13871
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+▁lunisolar	-13876
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+▁neolithic	-13878
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+▁sesamoidi	-13880
+▁sharpened	-13881
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+▁sikopioni	-13883
+▁similarly	-13884
+▁sintering	-13885
+▁sintetiki	-13886
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+▁solovakia	-13890
+▁solovenia	-13891
+▁spacially	-13892
+▁spirogyra	-13893
+▁stoneking	-13894
+▁structure	-13895
+▁substrate	-13896
+▁subtabana	-13897
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+▁supernova	-13899
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+▁keg	-13908
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+▁merkle	-13951
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+▁parachute	-13978
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+▁tennessee	-13987
+▁terahertz	-13988
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+▁tuamasaga	-13995
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+▁vagumatua	-13997
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+▁vakadaroi	-13999
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+cef	-14001
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+▁vukica	-14054
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+▁penikau	-14061
+▁rukuwai	-14062
+▁seychel	-14063
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+▁mavomavo	-14069
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+▁rataboni	-14071
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+▁vosalevu	-14075
+▁wasaliwa	-14076
+▁anguillid	-14077
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+▁cazabonne	-14079
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+▁marihuano	-14081
+▁okosivote	-14082
+▁undulatus	-14083
+▁vakadokai	-14084
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+▁vakadreta	-14086
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+▁vakaiwase	-14088
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+▁vakalaica	-14090
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+▁vakamaori	-14092
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+▁vakarerei	-14094
+▁vakasamae	-14095
+▁vakasukai	-14096
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+▁vakatawai	-14098
+▁vakavinav	-14099
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+▁tamam	-14140
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+▁wainisiki	-14471
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+▁yanuyanuy	-14476
+▁yekaterin	-14477
+▁yorkshire	-14478
+▁zavodskoi	-14479
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+palestinian	-14494
+quirewithin	-14495
+rhopalocera	-14496
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+stethojulis	-14498
+thuataensis	-14499
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+▁breeding	-14575
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+▁iunifomu	-14580
+▁marriott	-14581
+▁oleander	-14582
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+veiivakarau	-14596
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+▁hij	-14608
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+▁kin	-14610
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+▁filipai	-14660
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+▁suborder	-14676
+▁vulibula	-14677
+▁belijiume	-14678
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+▁jeropoama	-14680
+▁menopause	-14681
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+▁vakailaka	-14684
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+▁asebaijani	-14698
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+▁paradisi	-14764
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+▁ivakavure	-14775
+▁laberintu	-14776
+▁lainitaki	-14777
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+▁vakaliuca	-14782
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+▁birmingham	-14791
+▁bitokisini	-14792
+▁blastocyst	-14793
+▁borosiliva	-14794
+▁bosokivaki	-14795
+▁botoniqele	-14796
+▁buketetaki	-14797
+▁bulawaiwai	-14798
+▁cagimaliwa	-14799
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+cepti	-14810
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+veivala	-14842
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+▁rourou	-14848
+▁tabace	-14849
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+▁wanana	-14851
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+▁cudrucu	-14858
+▁dainaso	-14859
+▁daukana	-14860
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+▁makuali	-14863
+▁neketai	-14864
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+▁veiyasa	-14868
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+▁palumbus	-14871
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+▁baekeland	-14873
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+▁hemolymph	-14876
+▁ikatakata	-14877
+▁mataivaka	-14878
+▁plausible	-14879
+▁subkalasi	-14880
+▁veikoloni	-14881
+▁cakamisini	-14882
+▁calcareous	-14883
+▁calculator	-14884
+▁carotenoid	-14885
+▁cayenensis	-14886
+▁cellulosic	-14887
+▁chaerephon	-14888
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+▁chalybeate	-14890
+▁championed	-14891
+▁charophyta	-14892
+▁chesapeake	-14893
+▁chinachina	-14894
+▁chiroptera	-14895
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+▁circumflex	-14898
+▁coagulated	-14899
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+ryo	-14902
+▁gm	-14903
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+▁bih	-14905
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+▁rnb	-14910
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+▁constru	-14951
+▁meimami	-14952
+▁redwood	-14953
+▁ripabli	-14954
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+▁veimaui	-14956
+▁veitata	-14957
+▁veiwasa	-14958
+▁yalowai	-14959
+▁yasaiva	-14960
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+▁bakelite	-14962
+▁ciwasaga	-14963
+▁construe	-14964
+▁gondwana	-14965
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+▁qasecake	-14968
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+▁matanikau	-14978
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+▁batibatina	-14980
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+▁colloquial	-14982
+▁compulsory	-14983
+▁conceptual	-14984
+▁conduction	-14985
+▁containeri	-14986
+▁conulariid	-14987
+▁convection	-14988
+▁convective	-14989
+▁copernicus	-14990
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+▁cudrucudru	-14992
+▁cyprinidae	-14993
+▁daucakaiba	-14994
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+▁daunipiano	-14996
+▁dauvakadro	-14997
+▁dauveibuli	-14998
+▁delabulewa	-14999
+'"	-15000
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+++.	-15002
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+▁kakanae	-15057
+▁veisiti	-15058
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+▁veiyaya	-15060
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+▁cokocoko	-15064
+▁cokonaka	-15065
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+▁salasiti	-15068
+▁tarataki	-15069
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+▁drakivaki	-15071
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+▁kuliawaqa	-15075
+▁magmatisi	-15076
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+▁veiliuliu	-15078
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+▁devunivaki	-15081
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+▁domesticus	-15083
+▁draunipepa	-15084
+▁drekeniwai	-15085
+▁drividrivi	-15086
+▁duaduataki	-15087
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+▁elastomers	-15090
+▁eliosifera	-15091
+▁elliptical	-15092
+▁envunivaki	-15093
+▁equatorial	-15094
+▁ethnically	-15095
+▁eukaryotes	-15096
+▁euphoriant	-15097
+▁euryhaline	-15098
+▁excellence	-15099
+%);	-15100
+bit	-15101
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+usk	-15103
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+▁bail	-15122
+▁dusk	-15123
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+▁namu	-15125
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+togani	-15133
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+▁meiji	-15138
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+▁spines	-15164
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+▁gusudra	-15170
+▁lalaiua	-15171
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+▁bulavota	-15174
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+▁nabouwalu	-15191
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+uml	-15200
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+▁vakilainika	-17345
+▁vaunafalawa	-17346
+▁veibaqasaqa	-17347
+▁veibasogana	-17348
+▁veibolebole	-17349
+▁veibutakoci	-17350
+▁veicavilaki	-17351
+▁veidigidigi	-17352
+▁veikaukauwa	-17353
+▁veikitikina	-17354
+▁veilasutaki	-17355
+▁veilawalawa	-17356
+▁veimatasere	-17357
+▁veimauilavo	-17358
+▁veiseletaki	-17359
+▁veisivisivi	-17360
+▁veisokoyaki	-17361
+▁veisolitaki	-17362
+▁veisoliyaka	-17363
+▁veitataqati	-17364
+▁veitokiyaki	-17365
+▁veitusaqati	-17366
+▁veivakubeci	-17367
+▁veivaletabu	-17368
+▁veivinakati	-17369
+▁veivolitaki	-17370
+▁veivukayaki	-17371
+▁veivuketaka	-17372
+▁veivukevuke	-17373
+▁veivukiyaki	-17374
+▁veivurevure	-17375
+▁veivuvutaki	-17376
+▁vuaviritaki	-17377
+▁vulavulaika	-17378
+▁waikaukauwa	-17379
+▁weatherbase	-17380
+▁werenivaini	-17381
+cephalopholis	-17382
+daunibisinisi	-17383
+dauniserekali	-17384
+heiligenstadt	-17385
+inquirewithin	-17386
+intergalactic	-17387
+sagavulukadua	-17388
+sagavulukaono	-17389
+trigonometiri	-17390
+vakaiyalayala	-17391
+veisosorataki	-17392
+▁abbreviation	-17393
+▁abstractions	-17394
+▁accumulating	-17395
+▁acknowledged	-17396
+▁adjudication	-17397
+▁administered	-17398
+▁amilopektini	-17399
+▁anatomically	-17400
+▁angustifolia	-17401
+▁announcement	-17402
+▁antananarivo	-17403
+▁anthropology	-17404
+▁archboldiana	-17405
+▁artsfiorello	-17406
+▁astronautika	-17407
+▁auranticavus	-17408
+▁balisitopora	-17409
+▁bekasigasiga	-17410
+▁bouvetisland	-17411
+▁broadcasting	-17412
+▁bulayalataki	-17413
+▁cagivakaseua	-17414
+▁cakacakavaki	-17415
+▁cakavakalotu	-17416
+▁carbohydrate	-17417
+▁cecereduadua	-17418
+▁cenderawasih	-17419
+▁chloroplasts	-17420
+▁christianity	-17421
+▁christiansen	-17422
+▁ciwasagavulu	-17423
+▁combustibles	-17424
+▁condensation	-17425
+▁conglomerate	-17426
+▁cryptography	-17427
+▁cyclogenesis	-17428
+▁daivakaraica	-17429
+▁dauastroloji	-17430
+▁dauivurevure	-17431
+▁dautabaivola	-17432
+▁dautabapiano	-17433
+▁dependencies	-17434
+▁donueuropean	-17435
+▁drakidrakita	-17436
+▁drauniuludra	-17437
+▁dumudumukuro	-17438
+▁echolocation	-17439
+▁encyclopædia	-17440
+▁equivalently	-17441
+▁examinations	-17442
+▁federalisimi	-17443
+▁flotabilidad	-17444
+▁fotosintesis	-17445
+▁francophonie	-17446
+▁galikosidiki	-17447
+▁gesellschaft	-17448
+▁haidrokaboni	-17449
+▁hedlandensis	-17450
+▁homoniukilia	-17451
+▁hydrological	-17452
+▁hydrospheric	-17453
+▁ikaduanadrau	-17454
+▁ikatinikarua	-17455
+▁incineration	-17456
+▁independence	-17457
+▁intraorbital	-17458
+▁ivakatakatai	-17459
+▁iytuvatuvani	-17460
+▁jacksonville	-17461
+▁jiosaenitisi	-17462
+▁jurisdiction	-17463
+▁kakanamamada	-17464
+▁kalokalocavu	-17465
+▁kasakisitani	-17466
+▁kasalaninubu	-17467
+▁kaukauwavale	-17468
+▁kilaideivaki	-17469
+▁kompiutataki	-17470
+▁kompiutavaki	-17471
+▁konungariket	-17472
+▁kunekunetaki	-17473
+▁kurukurucava	-17474
+▁lavelavetaki	-17475
+▁lewemanumanu	-17476
+▁lewenisoqoni	-17477
+▁loganberries	-17478
+▁lomaivakarau	-17479
+▁lomaleqataka	-17480
+▁lomaqaseqase	-17481
+▁lomatarotaro	-17482
+▁madagasikara	-17483
+▁maliwalalaka	-17484
+▁manufactured	-17485
+▁matanikatuba	-17486
+▁matanitutaki	-17487
+▁mataqiriqiri	-17488
+▁mekenikitaki	-17489
+▁melevolavola	-17490
+▁mesolecitali	-17491
+▁metamorphose	-17492
+▁metaphysical	-17493
+▁michelangelo	-17494
+▁morphography	-17495
+▁multiplicand	-17496
+▁munisipaliti	-17497
+▁musikfreunde	-17498
+▁nationalized	-17499
+▁nevertheless	-17500
+▁nicakaudrove	-17501
+▁niufauniladi	-17502
+▁oncorhynchus	-17503
+▁osascobrazil	-17504
+▁ositeokalasi	-17505
+▁osolocapital	-17506
+▁ossification	-17507
+▁palaeolithic	-17508
+▁paramilitary	-17509
+▁pathogenesis	-17510
+▁perturbation	-17511
+▁peterborough	-17512
+▁philadelphia	-17513
+▁phylogenetic	-17514
+▁polisakaridi	-17515
+▁polisitireni	-17516
+▁polyisoprene	-17517
+▁purposefully	-17518
+▁qaliyalatina	-17519
+▁rakarakavaki	-17520
+▁rereganivatu	-17521
+▁sasakurekure	-17522
+▁senikawakawa	-17523
+▁senilagakali	-17524
+▁seninawanawa	-17525
+▁seniwijivaki	-17526
+▁shippensburg	-17527
+▁sikendinavia	-17528
+▁siterioisoma	-17529
+▁solomoniyatu	-17530
+▁sosowiriwiri	-17531
+▁southeastern	-17532
+▁spermatozoon	-17533
+▁stratosphere	-17534
+▁subakatakata	-17535
+▁subilawalawa	-17536
+▁substraction	-17537
+▁subveiyasana	-17538
+▁superciliosa	-17539
+▁suwitisaladi	-17540
+▁synchronised	-17541
+▁tabacenitoga	-17542
+▁tavutonotono	-17543
+▁tawacakacaka	-17544
+▁tawamosimosi	-17545
+▁testosterone	-17546
+▁thanksgiving	-17547
+▁thoroughfare	-17548
+▁tikobulabula	-17549
+▁tolusagavulu	-17550
+▁transactions	-17551
+▁transcending	-17552
+▁translucence	-17553
+▁trichospadix	-17554
+▁trigliseridi	-17555
+▁trimaculatus	-17556
+▁triphosphate	-17557
+▁turkmenistan	-17558
+▁tuvalaqalaqa	-17559
+▁unforgivable	-17560
+▁unvosacataka	-17561
+▁vakabekataki	-17562
+▁vakacavacava	-17563
+▁vakacokotaki	-17564
+▁vakadagataki	-17565
+▁vakadenimake	-17566
+▁vakadomobula	-17567
+▁vakafikataki	-17568
+▁vakaitalanoa	-17569
+▁vakaiulubale	-17570
+▁vakakarisito	-17571
+▁vakalasitika	-17572
+▁vakalatilati	-17573
+▁vakalawataka	-17574
+▁vakalewelevu	-17575
+▁vakamacalata	-17576
+▁vakamatavulo	-17577
+▁vakamatemate	-17578
+▁vakanadakuya	-17579
+▁vakanunutaka	-17580
+▁vakapotukali	-17581
+▁vakarogotaka	-17582
+▁vakasokomuni	-17583
+▁vakasokumuni	-17584
+▁vakasosataki	-17585
+▁vakataganeki	-17586
+▁vakatagitaka	-17587
+▁vakatakikoya	-17588
+▁vakataotioti	-17589
+▁vakatasausau	-17590
+▁vakatavulici	-17591
+▁vakatavuvuli	-17592
+▁vakatopoloji	-17593
+▁vakatubukawa	-17594
+▁vakavakadewa	-17595
+▁vakavakasala	-17596
+▁vakavakasama	-17597
+▁vakavakilani	-17598
+▁vakavaletaki	-17599
+▁vakaveidabui	-17600
+▁vakavuqataki	-17601
+▁vakayaloyalo	-17602
+▁vakayavalati	-17603
+▁vakayavutaki	-17604
+▁vakolonitaki	-17605
+▁vakomunisere	-17606
+▁vaqaqacotaka	-17607
+▁veielektroma	-17608
+▁veimatanikau	-17609
+▁veitabaivola	-17610
+▁veivakarerei	-17611
+▁veivakatabui	-17612
+▁veivuravuras	-17613
+▁volatukutuku	-17614
+▁vosavakaviti	-17615
+▁vuravuravaki	-17616
+▁wikiespecies	-17617
+▁yalosavasava	-17618
+▁yasawasawasa	-17619
+administration	-17620
+chaetodontidae	-17621
+coralcoastfiji	-17622
+dauvakaraitaki	-17623
+giellaregional	-17624
+tetraodontidae	-17625
+transliterated	-17626
+vakabalavutaki	-17627
+vakadodonutaki	-17628
+▁aesthetically	-17629
+▁alternatively	-17630
+▁amplexicaulis	-17631
+▁andropogoneae	-17632
+▁biokemisitiri	-17633
+▁bulumakautoba	-17634
+▁calcification	-17635
+▁cephalothorax	-17636
+▁certification	-17637
+▁circumstellar	-17638
+▁collaborative	-17639
+▁columbiformes	-17640
+▁commemoration	-17641
+▁configuration	-17642
+▁consecutively	-17643
+▁controversial	-17644
+▁cryptographic	-17645
+▁daunibisinisi	-17646
+▁daunitukutuku	-17647
+▁dauvakadrukai	-17648
+▁dauvakapapeti	-17649
+▁dauvakarorogo	-17650
+▁dauvakasoqoni	-17651
+▁dauvakatamata	-17652
+▁dauvakatitiqa	-17653
+▁dauvakavulica	-17654
+▁dauveivakawai	-17655
+▁decomposition	-17656
+▁denimakeokati	-17657
+▁dioscoreaceae	-17658
+▁documentation	-17659
+▁fotosintetiki	-17660
+▁fuscoguttatus	-17661
+▁heteronukilia	-17662
+▁hydroelectric	-17663
+▁ibulubulubulu	-17664
+▁ikanakanataki	-17665
+▁ikatinikaciwa	-17666
+▁improvisation	-17667
+▁inivarakitesi	-17668
+▁installations	-17669
+▁interactively	-17670
+▁intercalating	-17671
+▁isosokalokalo	-17672
+▁ivakacavacava	-17673
+▁ivakatagitaki	-17674
+▁ivakatikitiki	-17675
+▁ivakavakarewa	-17676
+▁ivalavalataki	-17677
+▁ivukivukitaki	-17678
+▁iyalayalataki	-17679
+▁iyaloyalotaki	-17680
+▁jiokemisitiri	-17681
+▁kleinschmidti	-17682
+▁kronecurrency	-17683
+▁lomalagilaini	-17684
+▁macrolecithal	-17685
+▁makenetasfera	-17686
+▁manufacturers	-17687
+▁matadredredre	-17688
+▁mataivakataba	-17689
+▁mataivaluiwai	-17690
+▁mathuataensis	-17691
+▁metropolitana	-17692
+▁monosakaraidi	-17693
+▁multicellular	-17694
+▁naemballonura	-17695
+▁opportunities	-17696
+▁organizatione	-17697
+▁pallidiflorum	-17698
+▁parliamentary	-17699
+▁parofisaitaka	-17700
+▁participatory	-17701
+▁pathognomonic	-17702
+▁penisilivenia	-17703
+▁petrokemikali	-17704
+▁philosophical	-17705
+▁physiological	-17706
+▁platyrhynchos	-17707
+▁poliocephalus	-17708
+▁polisakaraiti	-17709
+▁pronunciation	-17710
+▁raitayalotaki	-17711
+▁reiverswatina	-17712
+▁saikolajitaki	-17713
+▁sedimentology	-17714
+▁selachimorpha	-17715
+▁sharecroppers	-17716
+▁sitibatitabua	-17717
+▁structuralist	-17718
+▁subtractively	-17719
+▁surveypacific	-17720
+▁systematizing	-17721
+▁taudamunimata	-17722
+▁taxonomically	-17723
+▁thermonuclear	-17724
+▁tovolealuluqa	-17725
+▁traditionally	-17726
+▁trigonometric	-17727
+▁turidrabalavu	-17728
+▁ucacevatalutu	-17729
+▁ultralokaloka	-17730
+▁understanding	-17731
+▁univakatakila	-17732
+▁vakacuruilavo	-17733
+▁vakadrodrowai	-17734
+▁vakafeperueri	-17735
+▁vakaidabedabe	-17736
+▁vakaikuritaka	-17737
+▁vakailavotaka	-17738
+▁vakaisoqosoqo	-17739
+▁vakaitabagone	-17740
+▁vakaitovotaki	-17741
+▁vakaivalavala	-17742
+▁vakaivotavota	-17743
+▁vakaiwasewase	-17744
+▁vakaiyayataka	-17745
+▁vakaiyayataki	-17746
+▁vakalevuvanua	-17747
+▁vakamamautaki	-17748
+▁vakamatautaka	-17749
+▁vakataratarai	-17750
+▁vakatauvatana	-17751
+▁vakatokaivaki	-17752
+▁vakaukauataka	-17753
+▁vakaukauataki	-17754
+▁vakavanuatani	-17755
+▁vakaveitokani	-17756
+▁vakavinavnaka	-17757
+▁vakayalomatua	-17758
+▁valecakaitaba	-17759
+▁vatabautataki	-17760
+▁vatavataitovo	-17761
+▁veiceceretaki	-17762
+▁veidacalataki	-17763
+▁veikaedauyaco	-17764
+▁veikasasotavi	-17765
+▁veikavuravura	-17766
+▁veimatatamata	-17767
+▁veiparokaramu	-17768
+▁veisosomitaki	-17769
+▁veitabanilotu	-17770
+▁veitaratarana	-17771
+▁veivakacudrui	-17772
+▁veivakacurumi	-17773
+▁veivakadodonu	-17774
+▁veivakamacala	-17775
+▁veivakananumi	-17776
+▁veivakatarogi	-17777
+▁veivakatikori	-17778
+▁veivakatulewa	-17779
+▁veivalenivuli	-17780
+▁veivosakitaka	-17781
+▁veivutunitaki	-17782
+▁venezuelatuya	-17783
+▁viribaititaki	-17784
+▁vitrification	-17785
+▁vuravuratarai	-17786
+▁wasewaseataki	-17787
+▁yekaterinburg	-17788
+▁თვითმმართველი	-17789
+ivakatakilakila	-17790
+noweikongeriket	-17791
+▁administrative	-17792
+▁bitunivakatagi	-17793
+▁bulakemisitiri	-17794
+▁cevatavatataki	-17795
+▁chondrichthyes	-17796
+▁cilamatanisiga	-17797
+▁cinematography	-17798
+▁constitutional	-17799
+▁conventionally	-17800
+▁dauniveisereki	-17801
+▁dauveisarasara	-17802
+▁differentiated	-17803
+▁distaleitakaki	-17804
+▁elasmobranchii	-17805
+▁frameworkscath	-17806
+▁generalization	-17807
+▁homogenization	-17808
+▁ilawalawaivesu	-17809
+▁iqaqaloiqaqalo	-17810
+▁kalisifikataki	-17811
+▁lalaivanualala	-17812
+▁lewenimanumanu	-17813
+▁macrovakailavo	-17814
+▁maqosanutrient	-17815
+▁matanivolalevu	-17816
+▁microvakailavo	-17817
+▁mineralization	-17818
+▁nanotekinoloji	-17819
+▁naturalization	-17820
+▁navakayagataki	-17821
+▁nisaikolopedia	-17822
+▁oligosakaraiti	-17823
+▁paralelokaramu	-17824
+▁paramesonifiri	-17825
+▁pentecostalism	-17826
+▁plantaginaceae	-17827
+▁revolutionizes	-17828
+▁rorogovolavola	-17829
+▁sigararamataki	-17830
+▁solidification	-17831
+▁spermatophytes	-17832
+▁stratification	-17833
+▁streptostachys	-17834
+▁supercontinent	-17835
+▁tabanayalataki	-17836
+▁tautauvatataka	-17837
+▁transportation	-17838
+▁vakabalavutaka	-17839
+▁vakabulagitaki	-17840
+▁vakacaucautaki	-17841
+▁vakaceceretaki	-17842
+▁vakadredretaki	-17843
+▁vakalecalecava	-17844
+▁vakamaletitaka	-17845
+▁vakamatailalai	-17846
+▁vakamatatataki	-17847
+▁vakaositerelia	-17848
+▁vakaparokaramu	-17849
+▁vakaperesitedi	-17850
+▁vakasewasewani	-17851
+▁vakatadraicake	-17852
+▁vakatavuvutaki	-17853
+▁vakatekinolaji	-17854
+▁vakatekinoloji	-17855
+▁vakatitiqataka	-17856
+▁vakatitobutaka	-17857
+▁vakatorocaketa	-17858
+▁vakatotolotaki	-17859
+▁vakatuburarawa	-17860
+▁vakauitukutuku	-17861
+▁vakaukauwataka	-17862
+▁vakavolekataka	-17863
+▁vakawabokotaki	-17864
+▁vakawasomataki	-17865
+▁vakayayarataka	-17866
+▁vakidacalataka	-17867
+▁vakidacalataki	-17868
+▁vakusakusataka	-17869
+▁valenisarasara	-17870
+▁veikauniveikau	-17871
+▁veiliutakitaki	-17872
+▁veimataqalikai	-17873
+▁veitaudakutaki	-17874
+▁veivakadrodroi	-17875
+▁vualikutokalau	-17876
+▁vukatautauvata	-17877
+▁vuravurararama	-17878
+▁vutinimanumanu	-17879
+▁yalomalimalumu	-17880
+▁cakacakanivolai	-17881
+▁cakaniiyaloyalo	-17882
+▁crystallization	-17883
+▁crystallography	-17884
+▁daukabukaburaki	-17885
+▁daukovutinivosa	-17886
+▁dauniveiliutaki	-17887
+▁dausaikolajisai	-17888
+▁dauvakasaqaqara	-17889
+▁dauvakasarasara	-17890
+▁dauveivakabulai	-17891
+▁experimentation	-17892
+▁galeocerdonidae	-17893
+▁gônoprojatontrī	-17894
+▁hydroxylapatite	-17895
+▁intensification	-17896
+▁interchangeably	-17897
+▁internationally	-17898
+▁ivakadodorairai	-17899
+▁ivakasaribariba	-17900
+▁kakabohaidreiti	-17901
+▁kawakawairilagi	-17902
+▁levuvakaitamera	-17903
+▁maikorolecitali	-17904
+▁marauveiliutaki	-17905
+▁mediterraneanna	-17906
+▁monosaccharides	-17907
+▁norgekongeriket	-17908
+▁novokuybyshevsk	-17909
+▁optoelectronics	-17910
+▁rabailailaitaki	-17911
+▁saikovakadikevi	-17912
+▁standardization	-17913
+▁stegostomatidae	-17914
+▁tokaruatakarawa	-17915
+▁trichloroethane	-17916
+▁ucacevatatawana	-17917
+▁vakadravudravua	-17918
+▁vakaidinavakani	-17919
+▁vakaisosomitaka	-17920
+▁vakaitamerataki	-17921
+▁vakaituvatuvaki	-17922
+▁vakataucokotaka	-17923
+▁vakaveicakacaka	-17924
+▁veicokovatavaki	-17925
+▁veikavakasaqani	-17926
+▁veisausaumitaka	-17927
+▁veitabacakacaka	-17928
+▁veivakabibitaki	-17929
+▁veivakahomotopi	-17930
+▁veivakatorocake	-17931
+▁veivakatotogani	-17932
+▁veivakatovotovo	-17933
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diff --git a/models/tokenizer/fj_tokenizer_8k.vocab b/models/tokenizer/fj_tokenizer_8k.vocab
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