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+---
+language: szy
+language_name: Sakizaya
+language_family: austronesian_formosan
+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_formosan
+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: 3.882
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7206
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Sakizaya - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sakizaya** 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** | 3.383x | 3.39 | 0.1851% | 601,273 |
+| **16k** | 3.613x | 3.61 | 0.1977% | 563,108 |
+| **32k** | 3.789x | 3.79 | 0.2073% | 536,850 |
+| **64k** | 3.882x 🏆 | 3.88 | 0.2124% | 524,017 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `(kamu nu hulam：照顧) diput tu babalaki. 照顧老人。 malalitin tu ihekalay atu zumaay a n...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁( kamu ▁nu ▁hulam : 照 顧 ) ▁d iput ... (+16 more)` | 26 |
+| 16k | `▁( kamu ▁nu ▁hulam : 照顧 ) ▁d iput ▁tu ... (+14 more)` | 24 |
+| 32k | `▁( kamu ▁nu ▁hulam : 照顧 ) ▁diput ▁tu ▁babalaki ... (+12 more)` | 22 |
+| 64k | `▁( kamu ▁nu ▁hulam : 照顧 ) ▁diput ▁tu ▁babalaki ... (+11 more)` | 21 |
+
+**Sample 2:** `(kasatubangan:u kamu nu Hulam:被殖民、被奴隸 pasatubangan:讓他做奴隸)`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+17 more)` | 27 |
+| 16k | `▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+17 more)` | 27 |
+| 32k | `▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+16 more)` | 26 |
+| 64k | `▁( kas atubangan : u ▁kamu ▁nu ▁hulam : 被 ... (+9 more)` | 19 |
+
+**Sample 3:** `kamu nu hulam：掉下 tinaku a kamu mihetik 掉下 mihetik kaku tu kalisiw i ginko. 我去銀行提...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+29 more)` | 39 |
+| 16k | `▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+26 more)` | 36 |
+| 32k | `▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+26 more)` | 36 |
+| 64k | `▁kamu ▁nu ▁hulam : 掉下 ▁tinaku ▁a ▁kamu ▁mihetik ▁ ... (+21 more)` | 31 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.882x compression
+- **Lowest UNK Rate:** 8k with 0.1851% 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 | 8,778 | 13.10 | 36,425 | 17.4% | 45.6% |
+| **2-gram** | Subword | 254 🏆 | 7.99 | 27,613 | 77.3% | 95.0% |
+| **3-gram** | Word | 11,965 | 13.55 | 51,761 | 13.4% | 44.7% |
+| **3-gram** | Subword | 1,471 | 10.52 | 60,255 | 37.3% | 81.6% |
+| **4-gram** | Word | 18,427 | 14.17 | 98,389 | 13.3% | 43.1% |
+| **4-gram** | Subword | 6,740 | 12.72 | 170,144 | 17.8% | 54.2% |
+| **5-gram** | Word | 13,641 | 13.74 | 78,197 | 15.0% | 47.2% |
+| **5-gram** | Subword | 20,122 | 14.30 | 280,627 | 10.5% | 36.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a tademaw` | 9,781 |
+| 2 | `a mihcaan` | 6,305 |
+| 3 | `sa u` | 4,975 |
+| 4 | `idaw ku` | 4,643 |
+| 5 | `ku tademaw` | 4,369 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kamu nu hulam` | 1,808 |
+| 2 | `nasulitan nasakamuan atu` | 1,789 |
+| 3 | `namakayniay a nasulitan` | 1,789 |
+| 4 | `a nasulitan nasakamuan` | 1,789 |
+| 5 | `nasakamuan atu natinengan` | 1,757 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a nasulitan nasakamuan atu` | 1,789 |
+| 2 | `namakayniay a nasulitan nasakamuan` | 1,778 |
+| 3 | `nasulitan nasakamuan atu natinengan` | 1,755 |
+| 4 | `atu zumaay a natinengan` | 1,673 |
+| 5 | `tu ihekalay atu zumaay` | 1,466 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `namakayniay a nasulitan nasakamuan atu` | 1,778 |
+| 2 | `a nasulitan nasakamuan atu natinengan` | 1,755 |
+| 3 | `tu ihekalay atu zumaay a` | 1,465 |
+| 4 | `malalitin tu ihekalay atu zumaay` | 1,463 |
+| 5 | `ihekalay atu zumaay a natinengan` | 1,462 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `u _` | 357,853 |
+| 2 | `a n` | 299,562 |
+| 3 | `a _` | 290,493 |
+| 4 | `a y` | 241,409 |
+| 5 | `_ a` | 215,000 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a y _` | 143,914 |
+| 2 | `_ a _` | 137,006 |
+| 3 | `a n _` | 126,871 |
+| 4 | `t u _` | 101,083 |
+| 5 | `_ s a` | 100,121 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n u _` | 84,566 |
+| 2 | `_ t u _` | 65,522 |
+| 3 | `_ k u _` | 59,832 |
+| 4 | `a y _ a` | 54,817 |
+| 5 | `y _ a _` | 47,865 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a y _ a _` | 47,058 |
+| 2 | `_ a t u _` | 22,206 |
+| 3 | `t a d e m` | 21,403 |
+| 4 | `a d e m a` | 21,335 |
+| 5 | `d e m a w` | 21,328 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 254
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~36% 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 | 0.4793 | 1.394 | 3.89 | 158,896 | 52.1% |
+| **1** | Subword | 2.1979 | 4.588 | 29.06 | 6,068 | 0.0% |
+| **2** | Word | 0.2677 | 1.204 | 1.80 | 616,064 | 73.2% |
+| **2** | Subword | 0.5459 | 1.460 | 2.59 | 176,243 | 45.4% |
+| **3** | Word | 0.1031 | 1.074 | 1.20 | 1,105,652 | 89.7% |
+| **3** | Subword | 0.2326 | 1.175 | 1.58 | 456,451 | 76.7% |
+| **4** | Word | 0.0342 🏆 | 1.024 | 1.06 | 1,321,192 | 96.6% |
+| **4** | Subword | 0.1897 | 1.141 | 1.47 | 718,822 | 81.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a kamu nu sakizaya 940 sejek 9 位由執政黨與反對黨分別任命之參議員組成 任期五年 每五年舉行一次普選 malawi sa cacay ademiad mapatay im...`
+2. `nu u miliyaway a cidekay 南島語族 saan ya a kawaw panay有專屬的工作`
+3. `tu 報刊會涼 u siwkay nu sakizaya 鄒族 cou uici itan 卑南 triyatriyaran 阿美 bu a sapaluma`
+
+**Context Size 2:**
+
+1. `a tademaw silecaday a lalangawan lisin kamu atu kabanaan si kalilidan tumuk saca babalaki mililid tu...`
+2. `a mihcaan u nananuman nikaidaw atu sapatakekal hamin i cung ku u pu se su wi alesen`
+3. `sa u moyan putiput tina dadiw sa nasulitan ni tuku sayun nay pabalucu ay a cidekay ku`
+
+**Context Size 3:**
+
+1. `kamu nu hulam a pu ha ce a kakitidaan atu nu sakay kinkuay i paris 巴黎 kina i`
+2. `a nasulitan nasakamuan atu natinengan lists of national basketball association sapuyu en nba u amis ...`
+3. `nasulitan nasakamuan atu natinengan 參考來源 ː malaalitin tu i hekalay atu zumaay a natinengan list of c...`
+
+**Context Size 4:**
+
+1. `a nasulitan nasakamuan atu natinengan lists of national basketball association players alvan adams 阿...`
+2. `namakayniay a nasulitan nasakamuan atu natinengan 撒奇萊雅族語詞典 原住民族委員會線上字詞典 花蓮縣政府`
+3. `nasulitan nasakamuan atu natinengan 中國高等植物資料庫全庫 中國科學院微生物研究所 行政院原住民族委員會 原住民族藥用植物 花序數位典藏國家型科技計畫 應用服務分項...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `abu_mit_in._iw-b`
+2. `_uzay_ng”,isasan`
+3. `ude_cihcatu_a_ay`
+
+**Context Size 2:**
+
+1. `u_macay_a_nida_pi`
+2. `anaydaw-mici_paan`
+3. `a_casa_luayinipah`
+
+**Context Size 3:**
+
+1. `ay_izaw_nan_藝術家mis`
+2. `_a_nidaw_masa_mica`
+3. `an_cuduc_tu_pyria_`
+
+**Context Size 4:**
+
+1. `_nu_siyhu_ku_kapah_`
+2. `_tu_takuwanikeliday`
+3. `_ku_akuti’_nu_baluc`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (718,822 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 | 51,046 |
+| Total Tokens | 1,702,988 |
+| Mean Frequency | 33.36 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 928.70 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 138,739 |
+| 2 | nu | 85,232 |
+| 3 | tu | 70,354 |
+| 4 | ku | 61,136 |
+| 5 | u | 60,011 |
+| 6 | sa | 38,061 |
+| 7 | i | 34,413 |
+| 8 | atu | 22,437 |
+| 9 | tademaw | 19,177 |
+| 10 | ci | 13,592 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | lengat | 2 |
+| 2 | 屋頂的裂縫 | 2 |
+| 3 | pulukelin | 2 |
+| 4 | kulisimas | 2 |
+| 5 | pingki | 2 |
+| 6 | matulakay | 2 |
+| 7 | kalimicu | 2 |
+| 8 | 的未來 | 2 |
+| 9 | pisasapi | 2 |
+| 10 | sadihkuay | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1985 |
+| R² (Goodness of Fit) | 0.993933 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 49.3% |
+| Top 1,000 | 75.3% |
+| Top 5,000 | 88.1% |
+| Top 10,000 | 92.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9939 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 49.3% of corpus
+- **Long Tail:** 41,046 words needed for remaining 7.9% 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 | 0.7206 | 0.3585 | N/A | N/A |
+| **mono_64d** | 64 | 0.6971 | 0.2873 | N/A | N/A |
+| **mono_128d** | 128 | 0.4883 | 0.2402 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7206 🏆 | 0.3548 | 0.0300 | 0.1480 |
+| **aligned_64d** | 64 | 0.6971 | 0.2750 | 0.0520 | 0.2520 |
+| **aligned_128d** | 128 | 0.4883 | 0.2443 | 0.0700 | 0.2960 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7206 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2934. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 7.0% 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 | **0.310** | 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 |
+|--------|----------|
+| `-ma` | masakiketay, mabunal, mata目 |
+| `-ka` | kadiceman, kasikawaw, kaniket |
+| `-pa` | pabelien, pakalaliw, pacukeday |
+| `-sa` | saicelangan, sakatu, sakaudipan |
+| `-mi` | mipelu, mipuputay, mingaayay |
+| `-a` | ak, amuawaw, anuyaan |
+| `-s` | saicelangan, sʉhlʉnganʉ, sakatu |
+| `-m` | mipelu, muoli, masakiketay |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | pabelien, saicelangan, anuyaan |
+| `-an` | saicelangan, anuyaan, kadiceman |
+| `-ay` | umahicaay, masakiketay, mipuputay |
+| `-y` | umahicaay, masakiketay, mipuputay |
+| `-a` | yaciyana, yita, esperança |
+| `-ng` | pisasing, ninaimelang, inng |
+| `-g` | pisasing, ninaimelang, inng |
+| `-u` | mipelu, sakatu, swu |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ulit` | 1.96x | 76 contexts | sulit, kulit, asulit |
+| `atin` | 1.96x | 71 contexts | latin, yatin, matin |
+| `inen` | 1.96x | 69 contexts | yinen, bineng, tineng |
+| `tade` | 2.10x | 42 contexts | tadek, taden, tadem |
+| `dema` | 2.08x | 40 contexts | demaw, demad, demak |
+| `emia` | 2.16x | 34 contexts | emiad, demia, demiad |
+| `awan` | 1.69x | 92 contexts | tawan, dawan, awang |
+| `tine` | 2.29x | 27 contexts | tineng, atineng, utineng |
+| `demi` | 2.21x | 29 contexts | demia, demied, kudemi |
+| `hcaa` | 2.19x | 28 contexts | ihcaan, mihcaa, mhcaan |
+| `anan` | 1.56x | 108 contexts | canan, nanan, panan |
+| `anat` | 2.28x | 18 contexts | canata, kanatl, kanata |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ma` | `-y` | 218 words | mapasimaay, mapatidengay |
+| `-ma` | `-ay` | 211 words | mapasimaay, mapatidengay |
+| `-ka` | `-n` | 148 words | kasaupuan, kalalulan |
+| `-ka` | `-an` | 141 words | kasaupuan, kalalulan |
+| `-sa` | `-n` | 122 words | sakalihalayan, sakayduhan |
+| `-mi` | `-y` | 120 words | mitatibay, micacuy |
+| `-mi` | `-ay` | 116 words | mitatibay, mibelinay |
+| `-pa` | `-n` | 114 words | pazen, pasilisian |
+| `-sa` | `-an` | 93 words | sakalihalayan, sakayduhan |
+| `-sa` | `-y` | 72 words | sapisahemay, sakasiidaay |
+
+### 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 |
+|------|-----------------|------------|------|
+| nikuwanay | **`nikuw-an-ay`** | 7.5 | `an` |
+| asasemaan | **`asase-ma-an`** | 7.5 | `ma` |
+| maytebanay | **`mayteb-an-ay`** | 7.5 | `an` |
+| sakaputun | **`sakapu-tu-n`** | 7.5 | `tu` |
+| sapaiyuwan | **`sapaiyu-w-an`** | 7.5 | `w` |
+| kasasudang | **`kasasu-da-ng`** | 7.5 | `da` |
+| binacadana | **`binacad-an-a`** | 7.5 | `an` |
+| nipikisaan | **`nipikis-a-an`** | 7.5 | `a` |
+| lalaliyunan | **`lalaliyu-n-an`** | 7.5 | `n` |
+| tadatabaki | **`ta-da-tabaki`** | 7.5 | `tabaki` |
+| namakaadih | **`na-ma-kaadih`** | 7.5 | `kaadih` |
+| amasasetul | **`a-ma-sasetul`** | 7.5 | `sasetul` |
+| mamamelawan | **`ma-ma-melawan`** | 7.5 | `melawan` |
+| tadaadidi | **`ta-da-adidi`** | 7.5 | `adidi` |
+| malalawlaw | **`malalaw-l-aw`** | 7.5 | `l` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sakizaya 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 | **64k BPE** | Best compression (3.88x) |
+| N-gram | **2-gram** | Lowest perplexity (254) |
+| Markov | **Context-4** | Highest predictability (96.6%) |
+| 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: 2026-01-11 00:15:31*
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diff --git a/models/tokenizer/szy_tokenizer_64k.model b/models/tokenizer/szy_tokenizer_64k.model
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diff --git a/models/tokenizer/szy_tokenizer_64k.vocab b/models/tokenizer/szy_tokenizer_64k.vocab
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+▁當你把箭舉起的時侯	-21539
+分佈在台東縣的延平鄉	-21540
+分部於花蓮縣的卓溪鄉	-21541
+嘉蘭村位於太麻里溪畔	-21542
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+然後拉緊緊繩子稻草就綁好了	-29551
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+▁他把麵包樹給砍了	-44510
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+▁只是你不認得他了	-44513
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+▁請站在大眾前演說	-44542
+▁這個布是用在外皮	-44543
+▁通常只用於外來語	-44544
+イゼトベゴヴィッチ	-44545
+也是我小時候的玩具	-44546
+也是許多高科技產業	-44547
+亦可延伸有遷移之意	-44548
+享用我們擺設的物品	-44549
+人類和自然良好共處	-44550
+來賓才會感到被尊重	-44551
+們是怎樣對出下句的	-44552
+全世界都會為你讓路	-44553
+原本住在大甲溪上游	-44554
+又稱肌痛性腦脊髓炎	-44555
+可能會以空腹或運動	-44556
+台東縣金峰鄉新興村	-44557
+台東縣金峰鄉賓茂村	-44558
+台灣能教世界什麼事	-44559
+因為我們有智慧之神	-44560
+壽莊月眉行政區類別	-44561
+它會從天上掉落地面	-44562
+將掛在高高的檳榔葉	-44563
+尖石鄉錦屏村那羅二	-44564
+就已經生活在北美洲	-44565
+就算臺上穿得再入戲	-44566
+峰國家級自然保護區	-44567
+很多可以使用的東西	-44568
+心裡裝著不喜歡的事	-44569
+擁有廣泛的基礎設施	-44570
+是今天裝了明天的事	-44571
+是指在前哥倫布時期	-44572
+最後歸順日本的一族	-44573
+會將人帶至懸崖峭壁	-44574
+有關藥物的服用方法	-44575
+朋友是小學語文老師	-44576
+檢貝殼還有海螺螃蟹	-44577
+歷史語言文化大辭典	-44578
+為族人宣傳部落文化	-44579
+老師說牛決對跑不掉	-44580
+肌肉和靈活關節形成	-44581
+腳與腿須始終保持堅	-44582
+自強新村娜魯灣部落	-44583
+蒼蠅拍使用方便耐用	-44584
+被列為最重要的肌肉	-44585
+裝了好多不該裝的事	-44586
+語維基百科孵育計畫	-44587
+還是早早退休回鄉下	-44588
+都是我們要學習傳承	-44589
+需很長時間才能恢復	-44590
+非常的乾淨潔淨厚實	-44591
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+▁撒奇萊雅族鳥的名稱	-45632
+▁是那張紙自己飛走的	-45633
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+以及血糖恢復時的症狀	-45651
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+承載人體上最重要負荷	-45661
+拉威阿拉伯民主共和國	-45662
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+當時本村名稱為隘寮墾	-45666
+繁育與保護重點實驗室	-45667
+耶穌會聖保祿學院教堂	-45668
+該症狀是否在個案血糖	-45669
+豐滿的胸部大大的屁股	-45670
+這展現出她的騎士美德	-45671
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+▁患有肝病的人皮膚泛黃	-46278
+▁我今天穿綠色的上衣和	-46279
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+▁我就講到這裡謝謝你們	-46283
+▁我已決定了不會再閃動	-46284
+▁有些房子是用木頭蓋的	-46285
+▁煮麵包樹果時要加小魚	-46286
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+▁這個是去園裡的出口處	-46292
+▁這是我建立起來的房子	-46293
+▁那是你對我的天長地久	-46294
+▁領悟生命的莊嚴與尊重	-46295
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+台灣蔬果實用百科第一輯	-46301
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+臺灣鯨魚的觀光資源與鯨	-46311
+記錄古代蒙古語的惠斯陶	-46312
+............	-46313
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+▁布拉達曼特不願殺死手無	-46714
+▁感謝林秀蓮老師協助教學	-46715
+▁我去看你的石牆還在不在	-46716
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+▁我母親送糖果給我的孩子	-46718
+▁我相信沙漠一直都在那裡	-46719
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+▁有些東西看起來更好寶貝	-46721
+▁民國三十九年之前撒布優	-46722
+▁老師鳳林鎮的農地長滿了	-46723
+▁臺灣原住民族資訊資源網	-46724
+▁花蓮縣壽豐鄉月眉村舊名	-46725
+▁開水放著放著會變成冷水	-46726
+一個充滿環保意識順應自然	-46727
+他說現在學生的語文水平連	-46728
+以未罹患糖尿病的成人而言	-46729
+像我們現場所有的貴賓族人	-46730
+勝鍊胰島素濃度等其他檢驗	-46731
+古人喜歡將之鋪在地上坐臥	-46732
+和高度發達的公共運輸系統	-46733
+布農人是台灣的原住民當中	-46734
+帝瓦伊撒耘文化藝術基金會	-46735
+想想以前的老人還真有智慧	-46736
+懂的在艱困中生活延續下去	-46737
+教育部原住民族語維基百科	-46738
+服裝上有口袋後也以此稱之	-46739
+生過一場大病的人都有體會	-46740
+葡萄牙並將這個地方命名為	-46741
+行政院衛生署中醫藥委員會	-46742
+試用的客戶是英超球隊曼聯	-46743
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+鐵又沒有戰鬥力的布魯內洛	-46745
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+▁sakanikauzip	-46887
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+▁新竹縣尖石鄉錦屏村那羅二	-46926
+▁橫倒在馬路上的樹已經沒了	-46927
+▁牛在工作的時候一定要掛牛	-46928
+▁當今基輔是東歐重要的工業	-46929
+▁這就是這個部落復興的原因	-46930
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+可用來測定低血糖發生的原因	-46933
+台灣原住民族維基百科孵育場	-46934
+因此圖瓦人也有回紇人的部落	-46935
+就近鄰近的祭師幫你拍好一點	-46936
+決定了我們人生的姿態與形態	-46937
+瑪家三個鄉深山的原住民移住	-46938
+經村民大會議決改名為三和村	-46939
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+懂得食用這天然植物高蛋白食物	-47043
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