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+---
+language: ty
+language_name: Tahitian
+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: 3.561
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0301
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tahitian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tahitian** 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** | 3.455x | 3.48 | 0.1990% | 40,695 |
+| **16k** | 3.561x 🏆 | 3.59 | 0.2052% | 39,479 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `’O Sant Miquel de Campmajor te hō’ē ’oire iti nō Tatarūnia. mau ’oire iti nō Tat...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁’ o ▁sant ▁miquel ▁de ▁camp major ▁te ▁hō ’ ... (+13 more)` | 23 |
+| 16k | `▁’ o ▁sant ▁miquel ▁de ▁campmajor ▁te ▁hō ’ ē ... (+12 more)` | 22 |
+
+**Sample 2:** `Ò Hakahau te òire rahi aè no Ua Pou i Pōrīnetia farāni. ènata`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ò ▁hakahau ▁te ▁òire ▁rahi ▁aè ▁no ▁ua ▁pou ▁i ... (+4 more)` | 14 |
+| 16k | `▁ò ▁hakahau ▁te ▁òire ▁rahi ▁aè ▁no ▁ua ▁pou ▁i ... (+4 more)` | 14 |
+
+**Sample 3:** `’O te hō’ē ’oire iti nō Soria. mau ’oire iti nō Soria`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁’ o ▁te ▁hō ’ ē ▁’ oire ▁iti ▁nō ... (+8 more)` | 18 |
+| 16k | `▁’ o ▁te ▁hō ’ ē ▁’ oire ▁iti ▁nō ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 16k achieves 3.561x compression
+- **Lowest UNK Rate:** 8k with 0.1990% 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 | 422 | 8.72 | 1,465 | 56.1% | 93.7% |
+| **2-gram** | Subword | 157 🏆 | 7.29 | 1,040 | 80.2% | 99.9% |
+| **3-gram** | Word | 804 | 9.65 | 2,559 | 47.1% | 82.0% |
+| **3-gram** | Subword | 845 | 9.72 | 5,412 | 45.0% | 86.0% |
+| **4-gram** | Word | 1,231 | 10.27 | 4,355 | 43.3% | 70.6% |
+| **4-gram** | Subword | 2,588 | 11.34 | 15,928 | 29.6% | 67.7% |
+| **5-gram** | Word | 874 | 9.77 | 3,200 | 48.9% | 75.5% |
+| **5-gram** | Subword | 4,432 | 12.11 | 22,183 | 24.4% | 57.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i te` | 2,291 |
+| 2 | `te mau` | 1,467 |
+| 3 | `o te` | 1,091 |
+| 4 | `oire iti` | 927 |
+| 5 | `iti nō` | 927 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `oire iti nō` | 927 |
+| 2 | `te hō ē` | 509 |
+| 3 | `hō ē oire` | 499 |
+| 4 | `ē oire iti` | 472 |
+| 5 | `mau oire iti` | 455 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `te hō ē oire` | 499 |
+| 2 | `ē oire iti nō` | 472 |
+| 3 | `hō ē oire iti` | 472 |
+| 4 | `mau oire iti nō` | 455 |
+| 5 | `oire iti nō tatarūnia` | 451 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `te hō ē oire iti` | 472 |
+| 2 | `hō ē oire iti nō` | 472 |
+| 3 | `o te hō ē oire` | 228 |
+| 4 | `mau oire iti nō tatarūnia` | 226 |
+| 5 | `tatarūnia mau oire iti nō` | 225 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 14,698 |
+| 2 | `_ t` | 13,338 |
+| 3 | `a _` | 13,137 |
+| 4 | `t e` | 10,307 |
+| 5 | `i _` | 9,732 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t e` | 8,324 |
+| 2 | `t e _` | 8,198 |
+| 3 | `_ m a` | 4,382 |
+| 4 | `_ i _` | 3,923 |
+| 5 | `i _ t` | 3,482 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t e _` | 7,569 |
+| 2 | `i _ t e` | 2,795 |
+| 3 | `_ i _ t` | 2,691 |
+| 4 | `e _ m a` | 2,393 |
+| 5 | `t e _ m` | 2,331 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _ t e _` | 2,677 |
+| 2 | `_ i _ t e` | 2,345 |
+| 3 | `t e _ m a` | 2,135 |
+| 4 | `_ t e _ m` | 2,091 |
+| 5 | `_ m a u _` | 2,082 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 157
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~58% 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 | 0.5459 | 1.460 | 3.10 | 6,845 | 45.4% |
+| **1** | Subword | 1.3451 | 2.541 | 10.23 | 217 | 0.0% |
+| **2** | Word | 0.2579 | 1.196 | 1.61 | 21,075 | 74.2% |
+| **2** | Subword | 1.0621 | 2.088 | 5.21 | 2,216 | 0.0% |
+| **3** | Word | 0.1372 | 1.100 | 1.25 | 33,605 | 86.3% |
+| **3** | Subword | 0.7095 | 1.635 | 2.86 | 11,525 | 29.1% |
+| **4** | Word | 0.0708 🏆 | 1.050 | 1.11 | 41,713 | 92.9% |
+| **4** | Subword | 0.3985 | 1.318 | 1.79 | 32,904 | 60.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `te matahiti te reo wiwi me te apooraa ua riro oia ana i te fare haapiiraa`
+2. `i ma il sung te repupirita no ghana e te haere oia ei tauturu a era`
+3. `e rave a te matahiti ua huru o te purūmu hātua e rave e tae atu`
+
+**Context Size 2:**
+
+1. `i te 27 no mē tai ivuaro peretiteni o te taata nei e nina atoa hia o`
+2. `te mau mea atoa ta na ïa i rave no te mau tupuna i afa i mai`
+3. `o te repūpirita michael sata 23 no tiurai herēni peretiteni o te papori me te aro o`
+
+**Context Size 3:**
+
+1. `oire iti nō tatarūnia mau oire iti nō tatarūnia mau oire iti nō soria mau oire iti nō`
+2. `te hō ē oire iti nō tatarūnia mau oire iti nō soria mau oire iti nō soria mau`
+3. `hō ē oire iti nō tatarūnia mau oire iti nō fenua marite huira atira 681 090 ta ata`
+
+**Context Size 4:**
+
+1. `te hō ē oire iti nō soria mau oire iti nō soria mau oire iti nō tatarūnia mau oire`
+2. `ē oire iti nō soria mau oire iti nō soria mau oire iti nō tatarūnia mau oire iti nō`
+3. `hō ē oire iti nō tatarūnia mau oire iti nō tatarūnia mau oire iti nō soria mau oire iti`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_fētitoino_nurât`
+2. `a,_tetu_hnafena_`
+3. `i_i_nō_tē_no,_oa`
+
+**Context Size 2:**
+
+1. `e_te_14_nov._utom`
+2. `_te_ia_te_mē_’oia`
+3. `a_ra_faapera,_ó_t`
+
+**Context Size 3:**
+
+1. `_te_aorené_paraa_f`
+2. `te_faata_no_tupu_p`
+3. `_mau_fāna_nei_o_tu`
+
+**Context Size 4:**
+
+1. `_te_mau_poritita_na`
+2. `i_te_repūpirita_mot`
+3. `_i_te_di_rave_rapaa`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 92.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (32,904 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 | 2,668 |
+| Total Tokens | 62,941 |
+| Mean Frequency | 23.59 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 206.25 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | te | 7,849 |
+| 2 | i | 4,463 |
+| 3 | e | 2,642 |
+| 4 | o | 2,217 |
+| 5 | no | 2,165 |
+| 6 | mau | 2,091 |
+| 7 | a | 1,691 |
+| 8 | nō | 1,108 |
+| 9 | oire | 1,030 |
+| 10 | iti | 946 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | antitumor | 2 |
+| 2 | mcgill | 2 |
+| 3 | polanyi | 2 |
+| 4 | stanford | 2 |
+| 5 | lehn | 2 |
+| 6 | uttar | 2 |
+| 7 | pradesh | 2 |
+| 8 | papu | 2 |
+| 9 | tarutaru | 2 |
+| 10 | anavai | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1618 |
+| R² (Goodness of Fit) | 0.985563 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 72.0% |
+| Top 1,000 | 93.5% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9856 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 72.0% of corpus
+- **Long Tail:** -7,332 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 | 0.0301 | 0.6381 | N/A | N/A |
+| **mono_64d** | 64 | 0.0049 | 0.6224 | N/A | N/A |
+| **mono_128d** | 128 | 0.0009 | 0.6655 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0301 🏆 | 0.6684 | 0.0028 | 0.0499 |
+| **aligned_64d** | 64 | 0.0049 | 0.6410 | 0.0028 | 0.0748 |
+| **aligned_128d** | 128 | 0.0009 | 0.6513 | 0.0055 | 0.0914 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.0301 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.6478. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 0.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.313** | High formulaic/idiomatic 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 |
+|--------|----------|
+| `-t` | tau, tauaparauraa, tapearaa |
+| `-a` | anuanua, apooraa, agnes |
+| `-m` | mori, mesia, mǎta |
+| `-p` | pou, piahi, ph |
+| `-ta` | tau, tauaparauraa, tapearaa |
+| `-ma` | maha, maurice, maoro |
+| `-fa` | farii, fakarava, faaoreraa |
+| `-pa` | paari, paradisiaca, paturaa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | fakarava, anuanua, apooraa |
+| `-e` | òe, grace, ne |
+| `-ia` | mesia, citrifolia, māìtihia |
+| `-i` | fifi, farii, mori |
+| `-aa` | apooraa, oraraa, itiraa |
+| `-ra` | atira, mētera, tera |
+| `-na` | ghana, taina, raihana |
+| `-ta` | mǎta, poritita, rekoata |
+
+### 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 |
+|------|----------|------------------|----------|
+| `anga` | 1.54x | 13 contexts | hangai, whanga, umanga |
+| `ahit` | 1.37x | 7 contexts | tahiti, mahiti, tahito |
+| `faah` | 1.39x | 6 contexts | faahi, faaho, faahou |
+| `tira` | 1.37x | 6 contexts | atira, itiraa, raatira |
+| `aama` | 1.36x | 5 contexts | raama, haamau, haamata |
+| `haam` | 1.36x | 4 contexts | haamo, haamau, haamou |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-t` | `-a` | 68 words | tauaparauraa, tapearaa |
+| `-m` | `-a` | 49 words | mesia, mǎta |
+| `-a` | `-a` | 44 words | anuanua, apooraa |
+| `-fa` | `-a` | 41 words | fakarava, faaoreraa |
+| `-p` | `-a` | 41 words | poritita, paradisiaca |
+| `-fa` | `-aa` | 21 words | faaoreraa, faaotiraa |
+| `-t` | `-aa` | 19 words | tauaparauraa, tapearaa |
+| `-t` | `-ia` | 18 words | torovenia, tureia |
+| `-t` | `-i` | 16 words | tauatini, tieti |
+| `-p` | `-ia` | 15 words | pipiria, punaauia |
+
+### 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 |
+|------|-----------------|------------|------|
+| tauaparauraa | **`tauaparaur-a-a`** | 7.5 | `a` |
+| faaoreraa | **`faaorer-a-a`** | 7.5 | `a` |
+| faaotiraa | **`faaotir-a-a`** | 7.5 | `a` |
+| faaohiparaa | **`faaohipar-a-a`** | 7.5 | `a` |
+| feruriraa | **`ferurir-a-a`** | 7.5 | `a` |
+| faanavairaa | **`faanavair-a-a`** | 7.5 | `a` |
+| boraginaceae | **`boraginace-a-e`** | 7.5 | `a` |
+| faaûruraa | **`faaûrur-a-a`** | 7.5 | `a` |
+| haaparuparu | **`haaparup-a-ru`** | 7.5 | `a` |
+| haapiiraa | **`haapiir-a-a`** | 7.5 | `a` |
+| faahororaa | **`faahoror-a-a`** | 7.5 | `a` |
+| misionare | **`mision-a-re`** | 7.5 | `a` |
+| faaineineraa | **`faaineiner-a-a`** | 7.5 | `a` |
+| rapaauraa | **`rapaaur-a-a`** | 7.5 | `a` |
+| faataaraa | **`faataa-ra-a`** | 7.5 | `ra` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tahitian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **16k BPE** | Best compression (3.56x) |
+| N-gram | **2-gram** | Lowest perplexity (157) |
+| Markov | **Context-4** | Highest predictability (92.9%) |
+| 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: 2026-01-11 02:05:21*
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+▁nartey	-8660
+▁pedret	-8661
+▁tabula	-8662
+▁tiairi	-8663
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+aparaura	-8665
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+▁estatut	-8669
+▁jelinko	-8670
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+▁âfirita	-8676
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+▁adoniran	-8684
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+▁akyinbah	-8686
+▁alentejo	-8687
+▁allociné	-8688
+▁amponsah	-8689
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+▁apitihia	-8692
+▁aprucska	-8693
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+▁ardèches	-8696
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+▁aufauraa	-8700
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+▁baseball	-8704
+▁belgrade	-8705
+▁benedict	-8706
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+▁bertaèyn	-8708
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+▁coccinea	-8727
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+▁courrier	-8738
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+▁cultivar	-8740
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+▁desvalls	-8745
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+▁dimádzsá	-8748
+▁dizsánye	-8749
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+▁efigênia	-8755
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+▁emilinha	-8757
+▁escaules	-8758
+▁esclanyà	-8759
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+▁estrelas	-8761
+▁eszencsi	-8762
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+▁faaafaro	-8766
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+▁faaauhia	-8768
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+▁faauehia	-8770
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+▁forecast	-8778
+▁fornells	-8779
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+▁gianfran	-8785
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+▁hanavave	-8791
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+▁hepohepo	-8796
+▁himalaya	-8797
+▁honolulu	-8798
+▁horoahia	-8799
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+▁hrvatska	-8801
+▁huitaata	-8802
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+▁hāatiraa	-8804
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+▁ihifenua	-8806
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+▁iritihia	-8808
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+▁joanetes	-8811
+▁jonquera	-8812
+▁juangroo	-8813
+▁juliette	-8814
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+▁konohete	-8816
+▁kowalski	-8817
+▁kānapera	-8818
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+▁lucilene	-8825
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+▁martinho	-8838
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+▁maìmiraa	-8841
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+▁mkpeiyol	-8848
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+▁nagayama	-8859
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+▁nyasvizh	-8866
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+▁nászopky	-8868
+▁ohiparaa	-8869
+▁oleaceae	-8870
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+▁orureraa	-8875
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+▁pahikara	-8877
+▁pandanus	-8878
+▁papeetei	-8879
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+▁paruparu	-8881
+▁patoihia	-8882
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+▁pescador	-8885
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+▁pinaìnaì	-8887
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+▁plinnyer	-8889
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+▁pompidou	-8891
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+▁pukupuku	-8900
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+▁puurautī	-8902
+▁pāpaìhia	-8903
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+▁revardit	-8908
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+▁richeton	-8910
+▁rikjaart	-8911
+▁riudaura	-8912
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+▁salatiel	-8914
+▁santalum	-8915
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+▁scranton	-8917
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+▁sogakofe	-8921
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+▁squamosa	-8924
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+▁strommij	-8926
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+▁taahiraa	-8929
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+▁tehaapao	-8943
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+▁tohutoro	-8951
+▁tokotoru	-8952
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+▁tortellà	-8955
+▁toszkáre	-8956
+▁toulouse	-8957
+▁trending	-8958
+▁tuheiava	-8959
+▁tungsten	-8960
+▁tāpaeraa	-8961
+▁tāàmuhia	-8962
+▁uturaini	-8963
+▁vabariik	-8964
+▁vatitano	-8965
+▁ventalló	-8966
+▁vernyilo	-8967
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+▁vilafant	-8970
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+▁vilanova	-8972
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+▁vonisztá	-8979
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+valevskaya	-9015
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+▁abdelaziz	-9017
+▁aberahama	-9018
+▁acuminata	-9019
+▁adakudugu	-9020
+▁adakugugu	-9021
+▁adulyadej	-9022
+▁afareaitu	-9023
+▁ahuatanga	-9024
+▁aiguaviva	-9025
+▁aleurites	-9026
+▁alexander	-9027
+▁amankwaah	-9028
+▁amsterdam	-9029
+▁aotarāria	-9030
+▁apotarupo	-9031
+▁appointed	-9032
+▁aratairaa	-9033
+▁arecaceae	-9034
+▁attention	-9035
+▁auteaèraa	-9036
+▁avinyonet	-9037
+▁barcelona	-9038
+▁bellcaire	-9039
+▁besztráti	-9040
+▁bienvenue	-9041
+▁bolsonaro	-9042
+▁boycotted	-9043
+▁brodenyij	-9044
+▁buixalleu	-9045
+▁camallera	-9046
+▁cambridge	-9047
+▁campllong	-9048
+▁campmajor	-9049
+▁canailles	-9050
+▁candidate	-9051
+▁cannaceae	-9052
+▁carambola	-9053
+▁castellar	-9054
+▁chinensis	-9055
+▁chocolate	-9056
+▁christine	-9057
+▁colocasia	-9058
+▁commander	-9059
+▁comprends	-9060
+▁cordyline	-9061
+▁csrivnyes	-9062
+▁davenport	-9063
+▁delicious	-9064
+▁detallada	-9065
+▁dimension	-9066
+▁eberhardt	-9067
+▁education	-9068
+▁elizabeth	-9069
+▁esponellà	-9070
+▁etropolar	-9071
+▁faaatiraa	-9072
+▁faainohia	-9073
+▁faaorahia	-9074
+▁faaoreraa	-9075
+▁faariiraa	-9076
+▁faataìraa	-9077
+▁faatiàhia	-9078
+▁faaàpuraa	-9079
+▁faaàtihia	-9080
+▁faaòhuraa	-9081
+▁faaûruhia	-9082
+▁faaûruraa	-9083
+▁faraipāni	-9084
+▁ferdinand	-9085
+▁fernandes	-9086
+▁finestres	-9087
+▁florentin	-9088
+▁francisco	-9089
+▁fulbright	-9090
+▁fāanauhia	-9091
+▁generková	-9092
+▁geodetski	-9093
+▁godofredo	-9094
+▁gonçalves	-9095
+▁gossypium	-9096
+▁guarnieri	-9097
+▁guillermo	-9098
+▁haafaufaa	-9099
+▁haamauhia	-9100
+▁hamanihia	-9101
+▁hammurabi	-9102
+▁hanekeria	-9103
+▁hinengaro	-9104
+▁hiòpoàraa	-9105
+▁hohonuraa	-9106
+▁horizonte	-9107
+▁horomoana	-9108
+▁hortênsia	-9109
+▁hostalets	-9110
+▁hostalric	-9111
+▁huihuinga	-9112
+▁hurufenua	-9113
+▁innisztui	-9114
+▁inocarpus	-9115
+▁inocentes	-9116
+▁invisível	-9117
+▁izdavačko	-9118
+▁jilyngász	-9119
+▁kaohsiung	-9120
+▁kelendáre	-9121
+▁kibingáre	-9122
+▁kilopetri	-9123
+▁kinyáresz	-9124
+▁kukairani	-9125
+▁lappaceum	-9126
+▁lauraceae	-9127
+▁lebecetin	-9128
+▁leboucher	-9129
+▁likomento	-9130
+▁llampaies	-9131
+▁llobregat	-9132
+▁lunnettes	-9133
+▁lászkurtá	-9134
+▁léontieff	-9135
+▁malavella	-9136
+▁mangareva	-9137
+▁mangifera	-9138
+▁manifesto	-9139
+▁margarida	-9140
+▁marialina	-9141
+▁marseille	-9142
+▁mcdannell	-9143
+▁meirelles	-9144
+▁middlesex	-9145
+▁milanović	-9146
+▁moingiter	-9147
+▁molecular	-9148
+▁moliterno	-9149
+▁moluccana	-9150
+▁montblanc	-9151
+▁naissance	-9152
+▁nanotubes	-9153
+▁nationale	-9154
+▁nyevutská	-9155
+▁nyovergij	-9156
+▁padroeira	-9157
+▁pahonoraa	-9158
+▁pallerols	-9159
+▁peletolia	-9160
+▁politique	-9161
+▁polynesia	-9162
+▁porqueres	-9163
+▁preduzeće	-9164
+▁primaries	-9165
+▁programme	-9166
+▁président	-9167
+▁purpurata	-9168
+▁párláming	-9169
+▁périgueux	-9170
+▁radcliffe	-9171
+▁recherche	-9172
+▁reference	-9173
+▁reflètent	-9174
+▁rencontre	-9175
+▁robinette	-9176
+▁rodrigues	-9177
+▁roppelder	-9178
+▁ruhiruhia	-9179
+▁ruthinéia	-9180
+▁saccharum	-9181
+▁salihorsk	-9182
+▁scarecrow	-9183
+▁schwimmer	-9184
+▁sebastião	-9185
+▁socarrats	-9186
+▁southmayd	-9187
+▁sphonjker	-9188
+▁taatoaraa	-9189
+▁tahuamana	-9190
+▁taneterie	-9191
+▁tatararaa	-9192
+▁teriitahi	-9193
+▁tevahitua	-9194
+▁thespesia	-9195
+▁tiliaceus	-9196
+▁tiritiāno	-9197
+▁torovenia	-9198
+▁torovātia	-9199
+▁tāviniraa	-9200
+▁ullastret	-9201
+▁ultramort	-9202
+▁valentina	-9203
+▁variegata	-9204
+▁victoires	-9205
+▁vilacolum	-9206
+▁viladamat	-9207
+▁vilajuïga	-9208
+▁vilamalla	-9209
+▁vilatenim	-9210
+▁vrahovice	-9211
+▁vulpellac	-9212
+▁whakakapi	-9213
+▁whakanuia	-9214
+▁whakaputa	-9215
+▁wikiquote	-9216
+▁yoshihide	-9217
+▁āutaipera	-9218
+▁ūkareinga	-9219
+▁скупштина	-9220
+calophyllum	-9221
+christensen	-9222
+czpospolita	-9223
+estadística	-9224
+kporizányie	-9225
+netherlands	-9226
+tinanatanga	-9227
+tīanianiraa	-9228
+▁abdelmalek	-9229
+▁akanvariba	-9230
+▁akanvariva	-9231
+▁aleksandar	-9232
+▁alexandria	-9233
+▁amphibiens	-9234
+▁argelaguer	-9235
+▁artificial	-9236
+▁artocarpus	-9237
+▁azerbaijan	-9238
+▁balatavila	-9239
+▁basshunter	-9240
+▁bernadette	-9241
+▁berteroana	-9242
+▁biologique	-9243
+▁bouteflika	-9244
+▁brotherson	-9245
+▁cabanelles	-9246
+▁cantallops	-9247
+▁caricaceae	-9248
+▁casuarinas	-9249
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diff --git a/models/tokenizer/ty_tokenizer_8k.vocab b/models/tokenizer/ty_tokenizer_8k.vocab
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