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+---
+language: tay
+language_name: Atayal
+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: 3.937
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6811
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Atayal - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Atayal** 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** | 3.548x | 3.55 | 0.2003% | 384,001 |
+| **16k** | 3.734x | 3.74 | 0.2108% | 364,864 |
+| **32k** | 3.856x | 3.86 | 0.2176% | 353,338 |
+| **64k** | 3.937x 🏆 | 3.94 | 0.2222% | 346,059 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Will Arnett kawas tay ryax sa tay 4 nqu tay 5, Will Arnett, squliq na Bunge’. ci...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁will ▁arn ett ▁kawas ▁tay ▁ryax ▁sa ▁tay ▁ 4 ... (+24 more)` | 34 |
+| 16k | `▁will ▁arn ett ▁kawas ▁tay ▁ryax ▁sa ▁tay ▁ 4 ... (+24 more)` | 34 |
+| 32k | `▁will ▁arnett ▁kawas ▁tay ▁ryax ▁sa ▁tay ▁ 4 ▁nqu ... (+22 more)` | 32 |
+| 64k | `▁will ▁arnett ▁kawas ▁tay ▁ryax ▁sa ▁tay ▁ 4 ▁nqu ... (+22 more)` | 32 |
+
+**Sample 2:** `cingay balay llamu/kinkyalan nya phpah. hoqay su' abaw na phpah qasa lwah. iyat ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁cingay ▁balay ▁llamu / k ink yalan ▁nya ▁phpah . ... (+18 more)` | 28 |
+| 16k | `▁cingay ▁balay ▁llamu / k ink yalan ▁nya ▁phpah . ... (+16 more)` | 26 |
+| 32k | `▁cingay ▁balay ▁llamu / kinkyalan ▁nya ▁phpah . ▁hoqay ▁su ... (+12 more)` | 22 |
+| 64k | `▁cingay ▁balay ▁llamu / kinkyalan ▁nya ▁phpah . ▁hoqay ▁su ... (+12 more)` | 22 |
+
+**Sample 3:** `ksxun (被敬重) Mrhuw Yumimg ka ksxun nha mita kwara maki qalang sami. (由命耆老在我們部落很受人...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ks xun ▁( 被 敬 重 ) ▁mrhuw ▁yu mi ... (+26 more)` | 36 |
+| 16k | `▁ks xun ▁( 被 敬重 ) ▁mrhuw ▁yu mim g ... (+23 more)` | 33 |
+| 32k | `▁ksxun ▁( 被敬重 ) ▁mrhuw ▁yumimg ▁ka ▁ksxun ▁nha ▁mita ... (+10 more)` | 20 |
+| 64k | `▁ksxun ▁( 被敬重 ) ▁mrhuw ▁yumimg ▁ka ▁ksxun ▁nha ▁mita ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.937x compression
+- **Lowest UNK Rate:** 8k with 0.2003% 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 | 3,184 | 11.64 | 13,869 | 25.5% | 63.6% |
+| **2-gram** | Subword | 260 🏆 | 8.02 | 5,715 | 71.6% | 98.1% |
+| **3-gram** | Word | 4,214 | 12.04 | 22,311 | 25.5% | 60.8% |
+| **3-gram** | Subword | 1,646 | 10.68 | 21,057 | 33.3% | 78.1% |
+| **4-gram** | Word | 9,656 | 13.24 | 54,321 | 21.7% | 50.4% |
+| **4-gram** | Subword | 6,466 | 12.66 | 75,451 | 18.1% | 52.9% |
+| **5-gram** | Word | 9,511 | 13.22 | 50,500 | 22.2% | 50.1% |
+| **5-gram** | Subword | 15,348 | 13.91 | 137,181 | 11.7% | 39.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hya ga` | 6,473 |
+| 2 | `s uli` | 2,840 |
+| 3 | `gyencumin ga` | 2,299 |
+| 4 | `uli tayan` | 2,183 |
+| 5 | `pqwasan biru` | 1,860 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s uli tayan` | 2,182 |
+| 2 | `pinspngan gyencumin ga` | 1,473 |
+| 3 | `kwara s uli` | 1,448 |
+| 4 | `hi ku kwara` | 1,445 |
+| 5 | `ku kwara s` | 1,445 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `hi ku kwara s` | 1,445 |
+| 2 | `ku kwara s uli` | 1,445 |
+| 3 | `kwara s uli tayan` | 1,445 |
+| 4 | `sa knita sa brbiru` | 1,401 |
+| 5 | `cinkhulan sa knita sa` | 1,401 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ku kwara s uli tayan` | 1,445 |
+| 2 | `hi ku kwara s uli` | 1,445 |
+| 3 | `cinkhulan sa knita sa brbiru` | 1,401 |
+| 4 | `sa knita sa brbiru lists` | 882 |
+| 5 | `knita sa brbiru lists of` | 882 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 122,942 |
+| 2 | `a n` | 88,116 |
+| 3 | `y a` | 79,076 |
+| 4 | `_ n` | 70,851 |
+| 5 | `g a` | 62,384 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 44,559 |
+| 2 | `_ n a` | 40,951 |
+| 3 | `n a _` | 34,903 |
+| 4 | `n g _` | 30,103 |
+| 5 | `_ g a` | 29,840 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a _` | 32,577 |
+| 2 | `_ g a _` | 21,648 |
+| 3 | `_ t a y` | 16,975 |
+| 4 | `t a y _` | 12,076 |
+| 5 | `a n g _` | 11,989 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t a y _` | 10,709 |
+| 2 | `k a w a s` | 8,363 |
+| 3 | `_ k a w a` | 7,754 |
+| 4 | `a w a s _` | 7,042 |
+| 5 | `y a ’ _ g` | 6,882 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 260
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~40% 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 | 0.6602 | 1.580 | 4.61 | 39,339 | 34.0% |
+| **1** | Subword | 1.7512 | 3.366 | 12.08 | 3,139 | 0.0% |
+| **2** | Word | 0.2844 | 1.218 | 1.71 | 181,030 | 71.6% |
+| **2** | Subword | 0.4330 | 1.350 | 2.34 | 37,911 | 56.7% |
+| **3** | Word | 0.1014 | 1.073 | 1.19 | 308,446 | 89.9% |
+| **3** | Subword | 0.3425 | 1.268 | 2.04 | 88,638 | 65.8% |
+| **4** | Word | 0.0422 🏆 | 1.030 | 1.08 | 365,569 | 95.8% |
+| **4** | Subword | 0.3240 | 1.252 | 1.82 | 180,359 | 67.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `na spyang maki yow na linhuyan gyencumin ga 68 buwan 292 buwan nya kwara s uli`
+2. `ga 107 kg banggo na holi na sbunaw wal mhuqil sraral mbuwah nuway ay hya ga`
+3. `sa bleqaw ta mlahang sali buwan nya skwan biru laqi cinkhulan sa zik na qalang myan`
+
+**Context Size 2:**
+
+1. `hya ga nakahama go kwara sali buwan nya ga cingay bes nya jeraldine 杰拉爾丁 musa chicago mlahang`
+2. `s uli 2 maki qu ngasal bziran ngasal psatu tegami ru pqniqan iyu rhzyal kki an tay`
+3. `gyencumin ga 10 kyan ku 175 hi binah ga yat kahun sku pinspngan gyencumin ga 88 kyan`
+
+**Context Size 3:**
+
+1. `s uli tayan s uli tayan pinspngan gyencumin ga 3 kyan ku 15 hi nya pinspung na linhuyan`
+2. `pinspngan gyencumin ga 84 kyan ku 227 hi binah ga yat kahun sku pinspngan gyencumin ga 32 kyan`
+3. `kwara s uli tayan pinspngan gyencumin ga 88 kyan ku 830 hi binah ga yat kahun sku pinspngan`
+
+**Context Size 4:**
+
+1. `ku kwara s uli tayan s uli tayan pinspngan gyencumin ga 70 kyan ku 1 961 hi nya pinspung`
+2. `hi ku kwara s uli tayan s uli tayan pinspngan gyencumin ga 67 kyan ku 191 hi binah ga`
+3. `kwara s uli tayan s uli tayan pinspngan gyencumin ga 72 kyan ku 154 hi binah ga yat kahun`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ta’uyu’ul_roket`
+2. `alppcinokup’,_s_`
+3. `n、ci’_micirun_’u`
+
+**Context Size 2:**
+
+1. `a_si_qqmuchaw_psi`
+2. `an_sa_shingiqutu_`
+3. `yan._qwas_natjan_`
+
+**Context Size 3:**
+
+1. `an_ga,_syo._rhzyal`
+2. `_nah_na_ga_pinliw_`
+3. `na_pqwas,_ru_mimal`
+
+**Context Size 4:**
+
+1. `_na_te_ru_beinango,`
+2. `_ga_bqanux_balay_te`
+3. `_tay_9_byacing_sazi`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (180,359 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 | 17,362 |
+| Total Tokens | 611,143 |
+| Mean Frequency | 35.20 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 414.96 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | na | 32,851 |
+| 2 | ga | 27,245 |
+| 3 | sa | 11,539 |
+| 4 | tay | 10,733 |
+| 5 | nya | 8,397 |
+| 6 | qu | 8,173 |
+| 7 | kawas | 8,159 |
+| 8 | ru | 7,855 |
+| 9 | hya | 7,019 |
+| 10 | maki | 6,131 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | nyut | 2 |
+| 2 | qnsun | 2 |
+| 3 | mtlu | 2 |
+| 4 | sayat | 2 |
+| 5 | 泰雅族女用名 | 2 |
+| 6 | rimuy是女子名 | 2 |
+| 7 | 有思念之意 | 2 |
+| 8 | 也有愉悅的情境 | 2 |
+| 9 | 父母命名子女 | 2 |
+| 10 | 期望快樂成長 | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2513 |
+| R² (Goodness of Fit) | 0.994822 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 51.8% |
+| Top 1,000 | 82.8% |
+| Top 5,000 | 93.8% |
+| Top 10,000 | 97.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9948 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 51.8% of corpus
+- **Long Tail:** 7,362 words needed for remaining 2.6% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.6811 🏆 | 0.3844 | N/A | N/A |
+| **mono_64d** | 64 | 0.4048 | 0.3600 | N/A | N/A |
+| **mono_128d** | 128 | 0.0450 | 0.3581 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6811 | 0.3751 | 0.0160 | 0.1520 |
+| **aligned_64d** | 64 | 0.4048 | 0.3639 | 0.0340 | 0.1780 |
+| **aligned_128d** | 128 | 0.0450 | 0.3422 | 0.0440 | 0.2260 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6811 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3639. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.257** | 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 |
+|--------|----------|
+| `-m` | mktayax, msurux, mbubu |
+| `-s` | sirasit, syaw, smbes |
+| `-p` | plbit, portugueselinpgan, punu |
+| `-k` | kangcyo, kan, kapang |
+| `-t` | tluhung, tommy, tpuyan |
+| `-b` | blin, brenner, buhari |
+| `-a` | anli, aki, anteng |
+| `-h` | harin, haru, huwa |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | kan, rengan, blin |
+| `-an` | kan, rengan, cinkhulan |
+| `-g` | tluhung, kapang, uwang |
+| `-ng` | tluhung, kapang, uwang |
+| `-a` | kora, rwa, benfica |
+| `-y` | yabay, yngiy, tommy |
+| `-s` | keizarmezs, smbes, hakaparis |
+| `-i` | anli, aki, naui |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ngan` | 1.58x | 66 contexts | pngan, tngan, hngan |
+| `zyuw` | 1.80x | 25 contexts | izyuw, zyuwa, pzyuwi |
+| `qala` | 1.85x | 22 contexts | qalan, qalax, qqala |
+| `inga` | 1.42x | 42 contexts | ingat, singa, kinga |
+| `unga` | 1.58x | 26 contexts | yunga, ungat, lunga |
+| `yuwa` | 1.47x | 33 contexts | yuwaw, zyuwa, yuwan |
+| `ngas` | 1.96x | 13 contexts | langas, ngasan, sangas |
+| `gasa` | 1.96x | 11 contexts | mgasa, ngasan, ngasal |
+| `quli` | 1.48x | 24 contexts | squli, qulih, quliq |
+| `uliq` | 1.57x | 19 contexts | tuliq, culiq, quliq |
+| `inah` | 1.56x | 19 contexts | qinah, binah, mbinah |
+| `rgya` | 1.90x | 9 contexts | rgyas, rgyax, rrgyax |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-n` | 163 words | ppspun, pinsqihan |
+| `-p` | `-an` | 124 words | pinsqihan, pinbuyan |
+| `-k` | `-n` | 97 words | kinyopan, kinsasan |
+| `-k` | `-an` | 77 words | kinyopan, kinsasan |
+| `-s` | `-n` | 65 words | sweden, snyogun |
+| `-m` | `-g` | 52 words | mklahang, mahing |
+| `-m` | `-ng` | 50 words | mklahang, mahing |
+| `-c` | `-n` | 46 words | cmyan, ciyan |
+| `-t` | `-n` | 43 words | timberwolvesginlgan, thyayun |
+| `-k` | `-g` | 43 words | klhangang, khokung |
+
+### 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 |
+|------|-----------------|------------|------|
+| pinthwiru | **`p-in-thwiru`** | 7.5 | `thwiru` |
+| mshayhway | **`mshayh-w-ay`** | 7.5 | `w` |
+| matabalay | **`ma-ta-balay`** | 7.5 | `balay` |
+| msinqutux | **`ms-in-qutux`** | 7.5 | `qutux` |
+| kincingay | **`ki-n-cingay`** | 7.5 | `cingay` |
+| mananigay | **`manani-g-ay`** | 7.5 | `g` |
+| pincyawgan | **`pincyaw-g-an`** | 7.5 | `g` |
+| allenryax | **`allenr-y-ax`** | 7.5 | `y` |
+| cyangcinko | **`cyangci-n-ko`** | 7.5 | `n` |
+| cinbawnan | **`cinbaw-n-an`** | 7.5 | `n` |
+| kinsraral | **`ki-n-sraral`** | 7.5 | `sraral` |
+| sincikusya | **`sinciku-s-ya`** | 7.5 | `s` |
+| pinqzywan | **`pinqzy-w-an`** | 7.5 | `w` |
+| skbalayun | **`s-kbalay-un`** | 6.0 | `kbalay` |
+| kakawasan | **`ka-kawas-an`** | 6.0 | `kawas` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Atayal 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 (3.94x) |
+| N-gram | **2-gram** | Lowest perplexity (260) |
+| Markov | **Context-4** | Highest predictability (95.8%) |
+| 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 00:23:22*
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diff --git a/models/tokenizer/tay_tokenizer_32k.model b/models/tokenizer/tay_tokenizer_32k.model
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diff --git a/models/tokenizer/tay_tokenizer_32k.vocab b/models/tokenizer/tay_tokenizer_32k.vocab
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index 0000000000000000000000000000000000000000..77a70fdcadeb8fc147fa9f9761659126e0859e87
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+而旅遊業和金	-21971
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+紐約市喜劇俱樂部	-29586
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+如此一來大家都不會	-33792
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+它是屬雜食性的魚類	-33794
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+張起來找人前來救助	-33888
+待會要去她阿姨的家	-33889
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+性比蘆葦草還要好又	-33891
+我們父母長輩大都習	-33892
+所以他生氣就不給了	-33893
+所以打獵時發生什麼	-33894
+所以獵人至少兩個人	-33895
+所的長官並邀集所有	-33896
+按照自己的意念來行	-33897
+搭乘台鐵火車北迴線	-33898
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+族人獵魚都到河流或	-33993
+早期的原住民獵人多	-33994
+是一種葉片鋒利的草	-33995
+是體輕型小且飛行快	-33996
+時代上溪口放了一只	-33997
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+會說我們是同一家人	-33999
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+有二間比較大的教會	-34087
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+桃山部落上方老地名	-34090
+此時部落的年輕人期	-34091
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+殺死一隻熊就像殺人	-34094
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+比蘆葦草還要高很多	-34096
+沒有清洗乾淨醃漬後	-34097
+泰雅傳統樂器口簧琴	-34098
+流傳下來的生活智慧	-34099
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+站在上方處與他對看	-34193
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+與半鍋清水同煮到滾	-34195
+葉子與清水煮滾熱後	-34196
+許多原住民族的部落	-34197
+誰釣得多大家就會說	-34198
+論何時來做毒魚文化	-34199
+▁dc	-34200
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+這時男生如有喜歡的	-34223
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+關西鎮公所網頁介紹	-34228
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+▁recordruw	-34622
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+▁something	-34664
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+▁taytapang	-34680
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+▁tensinkoy	-34682
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+▁zgorzelec	-34710
+▁şükrüpaşa	-34711
+▁วชิรวิชญ์	-34712
+▁也有的女方家長比較	-34713
+▁以前跟父親到塔曼溪	-34714
+▁傳說以前為了不讓吊	-34715
+▁加利福尼亞州伯班克	-34716
+▁各鄉鎮客家人口統計	-34717
+▁同一個血源的意思是	-34718
+▁在以前我們有種稻子	-34719
+▁如果雙方都互相喜歡	-34720
+▁媽媽正在菜園邊澆菜	-34721
+▁就像前述的故事一樣	-34722
+▁從前的族人沒有釣具	-34723
+▁搗爛葉子後敷於蟲咬	-34724
+▁教育部各級學校名錄	-34725
+▁是個很有智慧的長者	-34726
+▁有一個泰雅青年名叫	-34727
+▁每一戶都會發放一把	-34728
+▁泰雅人對家的家看法	-34729
+▁牧師參演黃志堅牧師	-34730
+▁牧師與幾個部落青年	-34731
+▁的意思是年輕的男女	-34732
+▁的社會裡都存有我們	-34733
+▁相邀到河裡潛水射魚	-34734
+▁第二大點之第一小點	-34735
+▁糾紛是為了吃的東西	-34736
+▁花蓮在地文化記憶庫	-34737
+▁街和阿姆斯特丹大道	-34738
+▁這件事被調皮的青年	-34739
+▁部落遷徙上巴陵部落	-34740
+▁高雄市那瑪夏區公所	-34741
+▁高麗菜正在被我澆水	-34742
+一直到比亞外對面的山	-34743
+他一生的傳奇事件到此	-34744
+他們又回到上巴陵繼續	-34745
+他到深山捕抓老鼠的故	-34746
+他嚇得很想跑但底下是	-34747
+但在發音部份不太一樣	-34748
+但如果他連一點關心別	-34749
+但想一想多一個人工資	-34750
+你和打帕絲的親屬關係	-34751
+你為什麼做這些事情呢	-34752
+依山邊直走及到達上村	-34753
+便以醃漬方式保存食物	-34754
+內布拉斯加州的奧馬哈	-34755
+其他族的現況也是如此	-34756
+具不能隨意放置紡織機	-34757
+出發到達前山的比雅山	-34758
+到了隔日早上男生必會	-34759
+加利福尼亞的托盧卡湖	-34760
+勤勞工作幸運得到福報	-34761
+原來是一隻毛被燒光的	-34762
+取名自陽光照射會閃閃	-34763
+名字取自起火燒材用的	-34764
+因此這位失明的母親能	-34765
+因為腹部是平的像葉片	-34766
+因為離家太遠無人聽到	-34767
+在長輩面前做飯的時候	-34768
+大多果實常在未成熟時	-34769
+天主教不管信仰哪一種	-34770
+女兒能如百合美麗潔淨	-34771
+女生他們就會拿起隨身	-34772
+她的房間裡會有好幾個	-34773
+孩子如松木不被惡靈捉	-34774
+對於黑熊的感情是十分	-34775
+就不會有不好的事情了	-34776
+希望大哥能像早晨一樣	-34777
+帶領著弟弟妹妹一大早	-34778
+弗蘭克斯頓布魯斯男隊	-34779
+後代子孫從三光鐵立庫	-34780
+從南投瑞岩部落遷徙後	-34781
+我們心平氣和的來協調	-34782
+我們玩笑說得太過頭了	-34783
+所以我們稱這個地方為	-34784
+所以父母期望女兒如麻	-34785
+才慢慢地遷移到中巴陵	-34786
+才會有很多好笑的故事	-34787
+打獵有他的一個規範在	-34788
+拉喬納里馬曼皮亞尼納	-34789
+搭東部幹線鐵路於南澳	-34790
+是個泰雅族原住民部落	-34791
+是烏來保有最傳統泰雅	-34792
+最早期的房子是茅草屋	-34793
+有潛水打魚的看家本領	-34794
+有的是一個家各自進行	-34795
+本單元僅以製作醃魚的	-34796
+此名長輩取自男子耳飾	-34797
+沒有種植任何農作物了	-34798
+泰雅祖先流傳的古訓有	-34799
+漁網或先進的漁獵工具	-34800
+為能保存食物不會壞掉	-34801
+為部落的主要經濟來源	-34802
+父母希望兒子在此標章	-34803
+父親會將虎頭蜂的頭部	-34804
+用它來洗身體減輕皮膚	-34805
+用浸泡過的熱水讓產婦	-34806
+由於木屋製作起來很繁	-34807
+男女雙方發生關係的機	-34808
+當櫻花盛開時會有許多	-34809
+的是今日桃園市復興區	-34810
+的是他們一直無法生育	-34811
+穿上自己織了一年最尊	-34812
+結婚儀式中必備的條件	-34813
+絕大多數的人口居住於	-34814
+與半鍋清水煮滾後放涼	-34815
+與半鍋清水煮滾成湯汁	-34816
+若懷孕的婦女來不及到	-34817
+若要取得較多的漁獲量	-34818
+觸織布機等任何器具都	-34819
+說今天我們被上部落的	-34820
+身上的配飾一樣也不可	-34821
+身旁也不可以隨意多話	-34822
+轉搭計程車前往金洋村	-34823
+這是違背祖訓規範的事	-34824
+這時少年會請老人家幫	-34825
+部落的耆老共同來調解	-34826
+都會來到河邊聚在一起	-34827
+長輩們會留一間房間做	-34828
+binkganlalu	-34829
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+▁adventures	-34846
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+▁cinderella	-34916
+▁clarksdale	-34917
+▁clipperton	-34918
+▁cocodrilos	-34919
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+▁crocodiles	-34921
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+▁curriculum	-34923
+▁cyolyangci	-34924
+▁dilaverbey	-34925
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+▁eaglebrook	-34927
+▁europeenne	-34928
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+▁federation	-34930
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+▁langkestun	-34963
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+▁leadership	-34965
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+▁lincyalung	-34971
+▁makapahpah	-34972
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+▁mbretneshe	-34979
+▁mckeesport	-34980
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+▁monégasque	-34994
+▁msaysawbih	-34995
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+▁navigating	-34997
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+▁zinthungan	-35096
+▁以前復興鄉山上有很多	-35097
+▁你怎麼把菜全部澆完了	-35098
+▁傳統的溪口部落裡也有	-35099
+▁再一次來到上部落通報	-35100
+▁到家後告訴家人山上有	-35101
+▁原住民人口數統計資料	-35102
+▁取其樹皮約半碗且清洗	-35103
+▁年時與溪口部落的林美	-35104
+▁摘取約一碗的葉片清洗	-35105
+▁昔日泰雅部落生活上並	-35106
+▁時間確定之後部落就會	-35107
+▁晚上出門時我們一定要	-35108
+▁楊勇緯在中華奧林匹克	-35109
+▁泰雅族維基百科姓名學	-35110
+▁產婦產後二日取其樹皮	-35111
+▁當我們到另一個部落時	-35112
+▁聚集的地方是在部落裡	-35113
+▁胃酸時取嫩葉清洗嚼食	-35114
+▁這裡的鬼神擺在第一位	-35115
+▁部落的耆老一定會相約	-35116
+▁隔日清早叔叔帶著一群	-35117
+下游的魚類喝到了毒魚藤	-35118
+他的母親那裡延伸過來的	-35119
+他驚嚇的丟棄香菇跑回家	-35120
+以前沒有電視他們也都會	-35121
+但這是部落雙方耆老同意	-35122
+位於桃園市復興區高義里	-35123
+傳一代傳一代的生活智慧	-35124
+先行直接進入溪口派出所	-35125
+兒子像如此般的幸運網袋	-35126
+其居民以外來工作的單位	-35127
+其過程男子一律不得參與	-35128
+出去竹東記得買一些米哈	-35129
+分享最新消息及族語教材	-35130
+勇於承認並跟部落的人說	-35131
+十一月份果實就會成熟並	-35132
+只見斷了線的繩索還緊緊	-35133
+台北縣海山國民小學教師	-35134
+四季泰雅族中級族語教材	-35135
+因為早期原住民生活不富	-35136
+因為早期的田地就在哪裡	-35137
+在其他的部落也有命名喔	-35138
+女方這裡的血源沒有忘記	-35139
+好的耳飾送給自己的太太	-35140
+尚未掉落的樹皮用火燒後	-35141
+從前族人捕捉老鼠的用意	-35142
+必須學會所有織布的技能	-35143
+怎會有灰色的身影在那邊	-35144
+所以此行的故事告訴他們	-35145
+手禮以取悅長者或老人家	-35146
+掉頭往那風的方向丟去說	-35147
+於是他們一行人認真地祈	-35148
+於是部落的人也會用茅草	-35149
+族人們也只需帶走根部和	-35150
+族人在那裏居住了好多年	-35151
+族人獵魚都到河流或溪流	-35152
+族裡具有重要的社會地位	-35153
+是來自你們已過世的祖父	-35154
+是從後山秀巒村的鎮西堡	-35155
+時節會開出白白的芒草穗	-35156
+曾經在此與太魯閣族發生	-35157
+會發生關係也一定是兩人	-35158
+有很多的觀光客會來觀賞	-35159
+未受到過度的開發與破壞	-35160
+泰雅族培養少年人的習俗	-35161
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+瓦爾卡尼帕羅利的灰姑娘	-35163
+當兵分二路的獵人都回到	-35164
+當她的女兒出嫁到夫家時	-35165
+的時候會有專人去敲十二	-35166
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+走入深山也會看見許多巨	-35168
+這時男子就會在河邊開始	-35169
+這時男生的手拉著口簧琴	-35170
+這樣下雨時也就不會漏水	-35171
+那我們就約在上面的平臺	-35172
+那是要送給媒人的大禮物	-35173
+長輩在坐不可任意在他們	-35174
+長輩面前說話不可太大聲	-35175
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+▁ความตั้งใจเ	-35342
+▁中華民國原住民族委員會	-35343
+▁位於新北市烏來區的德拉	-35344
+▁在溪口部落是一個很正當	-35345
+▁如果雙方看對眼之後家族	-35346
+▁宜蘭縣原住民文教促進會	-35347
+▁收錄許多原住民族委員會	-35348
+▁早期一個部落都是家族性	-35349
+▁是一個用木頭做成的長方	-35350
+▁是個先人佩服的射魚專家	-35351
+▁最喜歡的一件布匹帶回家	-35352
+▁泰雅族語言推動組織提供	-35353
+▁澳花村是由原來的莫瑤社	-35354
+▁的家做事三天來彌補他的	-35355
+▁臺灣原住民族文化知識網	-35356
+▁迎親儀式人越多越有面子	-35357
+▁這一生我是泰雅族的女人	-35358
+▁那上方的高麗菜我會澆水	-35359
+▁部落裡有一間真耶穌教會	-35360
+▁金洋村原來有許多的樟樹	-35361
+▁飯鍋裏的飯盛好了了沒有	-35362
+人住的地方與各部落的分布	-35363
+他是下部落最先居住的長者	-35364
+他會在固定在一個區域生長	-35365
+代表先生對太太情意深厚的	-35366
+兩片木板相接的地方都會有	-35367
+其中還有一個最重要的意義	-35368
+印第安納波利斯奧林匹亞隊	-35369
+命名為巴陵為此部落的地名	-35370
+回娘家時再交給女兒帶回去	-35371
+因此這個地方的族人稱它為	-35372
+婦女也會當場開始煮起魚來	-35373
+山上有種名為烏心石的樹木	-35374
+希望藉助新科技能潛久一點	-35375
+帶著恨意報復的心不歡而散	-35376
+從水中撈上來之後就會斷氣	-35377
+想去尋找卻因太晚無法尋找	-35378
+我們不可忘記傳給後代子孫	-35379
+我們的父母長輩取此名希望	-35380
+所以溪口部落的男子有的人	-35381
+於是這件事就如此完滿解決	-35382
+族人相信虎頭蜂的頭部具有	-35383
+業是巴哈馬主要的經濟活動	-35384
+沒多久傳說新店下游發現一	-35385
+為什麼下半夜只捕到一隻呢	-35386
+父母希望女嬰如虹彩叩頭蟲	-35387
+生活方式及部落文化的部落	-35388
+當時就將這件事圓滿的調解	-35389
+當時的河流魚種多水又清澈	-35390
+的泰雅勇士才得配戴的耳飾	-35391
+相約到台北新店的碧潭射魚	-35392
+織布是泰雅婦女的必要工作	-35393
+胃不舒服的人喝會減輕痛楚	-35394
+著裝滿獵物網袋的男子回家	-35395
+藉由一個晚上以聊天的方式	-35396
+誰會這樣拿狩獵的事開玩笑	-35397
+部落和家人不可織布亦不可	-35398
+長的也比蘆葦草還要高很多	-35399
+filathlitikos	-35400
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+หน้าใสดาวรุ่ง	-35405
+ายแววอนาคตไกล	-35406
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+▁pinslpyungan	-35480
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+▁raygakyakima	-35489
+▁rayhyollpgan	-35490
+▁renfrewshire	-35491
+▁reynoldsburg	-35492
+▁sasilawkahan	-35493
+▁singsingnati	-35494
+▁smithginlgan	-35495
+▁spzyangkrahu	-35496
+▁stagskinmuci	-35497
+▁tinqalangang	-35498
+▁titruyhwosay	-35499
+▁tobagongasal	-35500
+▁traiskirchen	-35501
+▁trangtaygako	-35502
+▁ukrainettman	-35503
+▁uruguayttman	-35504
+▁vermontttman	-35505
+▁västerbotten	-35506
+▁welshlinpgan	-35507
+▁wethyawkkang	-35508
+▁wielkopolski	-35509
+▁yıldırımspor	-35510
+▁นักแสดงหนุ่ม	-35511
+▁不像一班植物到處都看的到	-35512
+▁傳統織布以苧麻為主要材料	-35513
+▁會有人問說從哪個部落來的	-35514
+▁歐洲歷史上不同時期的地圖	-35515
+▁泰雅族共食團體的生活型態	-35516
+▁牧師參演黃志堅牧師資料查	-35517
+▁直到現在在泰雅族的部落裡	-35518
+▁社團法人台灣環境資訊協會	-35519
+▁裡面都是泰雅女子所織的布	-35520
+上部落的族人知道了這件事演	-35521
+他們由溪口下游往上游至烏內	-35522
+他們認為黑熊的習性像人一樣	-35523
+他就到另外一個山谷切腹自殺	-35524
+充滿智慧與口才的派出所主管	-35525
+剛漁撈拿回來的魚要先清除內	-35526
+南澳泰雅人的地理與聚落分布	-35527
+原住民族語言研究發展基金會	-35528
+去河邊放三角網或魚藤捕魚時	-35529
+同時在一間房間進行陪睡文化	-35530
+否則出外狩獵男子會發生意外	-35531
+周邊也種植了許多好看的櫻花	-35532
+和半根老生薑放入備用的涼水	-35533
+問獵了幾隻卻都同樣沒有收穫	-35534
+在泰雅族習俗裡女性不可以在	-35535
+在那裏建立了一個很大的部落	-35536
+如果無意中看到長輩失態之處	-35537
+小米或米飯冷卻後再撒上鹽巴	-35538
+就一定要開始準備製作小米酒	-35539
+年輕的男女們藉由聊天的方式	-35540
+把根部的地方用石頭用力地敲	-35541
+救命但因為離家太遠無人聽到	-35542
+教育部國教司司長和主任秘書	-35543
+是每一個人心目中所屬的地方	-35544
+是泰雅族人日常生活和風習俗	-35545
+有些部落會用洗淨後的石頭推	-35546
+期望女兒遠離惡靈幸運的成長	-35547
+此時整條大漢溪流域人山人海	-35548
+泰雅族住家環境與部落的型態	-35549
+無法容納所有的信徒在教堂裡	-35550
+父親游松平帶我到復興鄉卡拉	-35551
+科樹果子長滿成熟並開始掉落	-35552
+竹腳山就再也沒人敢種植香菇	-35553
+第二次時再打一個結以此類推	-35554
+而是經過雙方長輩同意配對的	-35555
+而當他們知道是同一個血源時	-35556
+這樣才足夠分送給男方的姑姑	-35557
+過去曾是族人經常狩獵的地方	-35558
+達巴陵地區時看到了很多巨木	-35559
+鱸鰻就帶到復興賣掉換取金錢	-35560
+raygakmissouri	-35561
+raygakoklahoma	-35562
+raygakvirginia	-35563
+tayalsexugocyo	-35564
+▁abramovicryax	-35565
+▁aldaelizabeth	-35566
+▁aleksinasryax	-35567
+▁amirikangasal	-35568
+▁arceneauxryax	-35569
+▁arlauckasryax	-35570
+▁aschaffenburg	-35571
+▁baechtoldryax	-35572
+▁baltimoreryax	-35573
+▁bannisterryax	-35574
+▁bapunyocineya	-35575
+▁barksdaleryax	-35576
+▁barnhorstryax	-35577
+▁bartolomeryax	-35578
+▁baumholtzryax	-35579
+▁bennettsville	-35580
+▁bolstorffryax	-35581
+▁bonniwellryax	-35582
+▁borsavageryax	-35583
+▁braveskinmuci	-35584
+▁bridgemanryax	-35585
+▁brightmanryax	-35586
+▁cameroonttman	-35587
+▁cangsyeylyang	-35588
+▁coloradottman	-35589
+▁columbiattman	-35590
+▁cwonfaminkoku	-35591
+▁cyalifuniyaco	-35592
+▁cyataylonniya	-35593
+▁delawarettman	-35594
+▁dioulalinpgan	-35595
+▁dmitrynikolai	-35596
+▁filathlitikos	-35597
+▁germanlinpgan	-35598
+▁germanyngasal	-35599
+▁hintganpzyang	-35600
+▁hwocicinlinpa	-35601
+▁iinlemisanzaw	-35602
+▁iranpcywagunm	-35603
+▁islandsngasal	-35604
+▁khoekhoegowab	-35605
+▁kinmagbatunux	-35606
+▁kinmtkknayril	-35607
+▁knerilbrandon	-35608
+▁kwangsyangcyu	-35609
+▁kwansibuwuchu	-35610
+▁lolunsomakuys	-35611
+▁maibagahungat	-35612
+▁makitennessee	-35613
+▁mechanicsburg	-35614
+▁milatovićlaqi	-35615
+▁moldovangasal	-35616
+▁nyukinblayqan	-35617
+▁pakarumakiyas	-35618
+▁qnxanpinspung	-35619
+▁raygakcollege	-35620
+▁raygakflorida	-35621
+▁raygakoakland	-35622
+▁romanilinpgan	-35623
+▁scotlandttman	-35624
+▁sharecroppers	-35625
+▁sinpulowiteng	-35626
+▁sinpurowitens	-35627
+▁taysazinggluw	-35628
+▁victoriathumb	-35629
+▁williamsville	-35630
+▁zealandngasal	-35631
+▁zhuanglinpgan	-35632
+▁一年才一次有這樣的機會及場	-35633
+▁喝鼠湯吃鼠肉便能很快退燒並	-35634
+▁我已經把湯匙和筷子泡在水裏	-35635
+▁藝術創作展演及學術研究人才	-35636
+▁這也是泰雅族人對食物的尊重	-35637
+▁這樣下雨時房子也就不會漏水	-35638
+▁部落族人也在路邊種植了許多	-35639
+▁開始對著心儀的女生吹了起來	-35640
+▁隔天白天他們先下溪流去釣魚	-35641
+上巴陵部落有二間比較大的教會	-35642
+不可任意放屁必須即刻閃到外面	-35643
+以家的稱呼來分類泰雅族的支群	-35644
+以早期日本學者所作的語言系統	-35645
+任何事都要先想到他人再想自己	-35646
+因此調解的對話就產生了很大的	-35647
+因為在許多原住民的狩獵傳統裡	-35648
+在溪口的溪流就射了五條大鱸鰻	-35649
+夾骨他們會拿來用作飯匙來盛飯	-35650
+她便跟著女兒一起前往新家居住	-35651
+家中只要有婚事就會一起去釣魚	-35652
+已經很久沒有種植任何農作物了	-35653
+帶著後代子孫到達三光流域之後	-35654
+常常在空閒時以射魚來貼補家用	-35655
+或苦花魚醃漬起來以便長期保存	-35656
+所以打獵時發生什麼事都不會講	-35657
+所以獵人至少兩個人相約去打獵	-35658
+新娘子還需準備兩件大件的布匹	-35659
+是從我們的父親那裡延伸過來的	-35660
+是未婚的年輕男女可以互相認識	-35661
+泰雅先人所流傳下來的生活智慧	-35662
+用浸泡過的熱水讓產婦洗滌身體	-35663
+由命耆老在我們部落很受人敬重	-35664
+祖靈祭等特別的日子才準備醃肉	-35665
+福山部落山保有傳統原始的環境	-35666
+老鼠肉為早期泰雅族人的上等菜	-35667
+而是我們這一帶的獵人才會亂說	-35668
+說下部落的族人真的帶刀上來了	-35669
+說今天我們被上部落的人欺負了	-35670
+跟母親或祖母談起女性的事情時	-35671
+這個地方是以前老人家留下來的	-35672
+通常只在祭典或喜慶時才會進行	-35673
+還有一說是最小的女兒也會取名	-35674
+長輩們會留一間房間做陪睡文化	-35675
+cyuokengkyuwsyo	-35676
+framelessdragan	-35677
+▁aguascalientes	-35678
+▁aireskespanish	-35679
+▁anderzunasryax	-35680
+▁argentinattman	-35681
+▁australiattman	-35682
+▁bakeraitisryax	-35683
+▁bellencanpyuta	-35684
+▁berdimuhamedov	-35685
+▁biedenbachryax	-35686
+▁bornheimerryax	-35687
+▁brickowskiryax	-35688
+▁brookfieldryax	-35689
+▁castelmaggiore	-35690
+▁collegeginlgan	-35691
+▁conquistadores	-35692
+▁czechoslovakia	-35693
+▁dnipropetrovsk	-35694
+▁hampshirettman	-35695
+▁illinoismhuqil	-35696
+▁jeffersonville	-35697
+▁jollycolombani	-35698
+▁khürelsükhlaqi	-35699
+▁kurdishlinpgan	-35700
+▁kyoikkenkyoing	-35701
+▁lingalalinpgan	-35702
+▁louiskeenglish	-35703
+▁mgmczyuwawyuaw	-35704
+▁mongoliangasal	-35705
+▁nuggetskinmuci	-35706
+▁nyahildellizza	-35707
+▁occitanlinpgan	-35708
+▁pistonskinmuci	-35709
+▁pqnahansklokah	-35710
+▁puluouesuotung	-35711
+▁raygakarkansas	-35712
+▁raygakhumboldt	-35713
+▁raygakkentucky	-35714
+▁raygakmaryland	-35715
+▁raygakstanford	-35716
+▁revolucionario	-35717
+▁rocketskinmuci	-35718
+▁sunskinblayqan	-35719
+▁székesfehérvár	-35720
+▁tradesomething	-35721
+▁umbundulinpgan	-35722
+▁unionpcywagunm	-35723
+▁wizardskinmuci	-35724
+▁國立政治大學原住民族研究中心	-35725
+▁妹妹先把煮飯鍋拿去洗再去煮飯	-35726
+▁搗爛葉子後敷於蟲咬處很快消腫	-35727
+▁是日文所以族人只好稱之為深潭	-35728
+▁晚上出門時我們一定要帶上拐杖	-35729
+▁溪口部落位於大溪石門水庫上游	-35730
+▁父母希望女兒能如百合美麗潔淨	-35731
+▁牧師與幾個部落青年相約去打獵	-35732
+▁現在我們利用祖先所傳承的領地	-35733
+▁臺灣原住民歷史語言文化大辭典	-35734
+並可尋找未來的另一半很好的機會	-35735
+今天是頭一次看到有這麼長的筷子	-35736
+他們又回到上巴陵繼續生活到至今	-35737
+但想一想多一個人工資就會少一些	-35738
+像雪霧鬧的族人只要到住家附近獵	-35739
+兒子像如此般的幸運網袋滿滿收穫	-35740
+就會留下一些布匹在家裡等新娘子	-35741
+拿香蕉葉或姑婆芋放到甕的平口處	-35742
+搭乘台鐵火車北迴線在南澳站下車	-35743
+沿著金洋村後方的產業道路向上走	-35744
+泰雅族的傳統產業以及工藝的發展	-35745
+當天晚上叔叔未見姪女返家就很驚	-35746
+知道了這件事就連夜到上部落通報	-35747
+織布機的製作修整不經過二位男人	-35748
+這個名字在其他的部落也有命名喔	-35749
+部落的流域範圍順著流域跑來跑去	-35750
+framelesssintuqi	-35751
+▁baskervilleryax	-35752
+▁belaruscanpyuta	-35753
+▁bialosukniaryax	-35754
+▁capitolskinmuci	-35755
+▁ethiopialinpgan	-35756
+▁himarëpcywagunm	-35757
+▁miamikinblayqan	-35758
+▁mitteldeutscher	-35759
+▁nyaaustriabinah	-35760
+▁oregonpcywagunm	-35761
+▁pakalolaynacoli	-35762
+▁queenslandttman	-35763
+▁raygakminnesota	-35764
+▁statespcywagunm	-35765
+▁surentamawsiyen	-35766
+▁syangkongsomrhu	-35767
+▁territoryngasal	-35768
+▁一般醃漬肉類和魚類的方式大同小	-35769
+▁宜蘭澤敖利泰雅族語語言學習教材	-35770
+▁就帶領他的後代子孫從三光鐵立庫	-35771
+▁年時與溪口部落的林美玉女士結婚	-35772
+▁時間確定之後部落就會派人去山裡	-35773
+▁每一個部落進行的方式都不太相同	-35774
+▁泰雅部落裡最早期的房子是茅草屋	-35775
+▁當晚也將刀子放於枕頭下以備所需	-35776
+▁的社會裡都存有我們祖先留下來的	-35777
+▁耳飾為部落男女生晚上的休閒活動	-35778
+因此這位失明的母親能隨著女兒同住	-35779
+團法人原住民族語言研究發展基金會	-35780
+在以前我們也會去拉竹子和背木頭去	-35781
+大大小小的人都會來到河邊聚在一起	-35782
+布農族等都有禁獵黑熊的習俗或禁忌	-35783
+所幸這些人先行直接進入溪口派出所	-35784
+搭東部幹線鐵路於南澳或武搭站下車	-35785
+敘述著父親帶他到深山捕抓老鼠的故	-35786
+此時部落的年輕人期待這個日子很久	-35787
+然而在同一晚上可以有好幾對的男女	-35788
+當櫻花盛開時會有許多觀光客來觀賞	-35789
+絕大多數的人口居住於首都拿索一帶	-35790
+誰釣得多大家就會說他今天的運氣好	-35791
+這是祖先口傳一代傳一代的生活智慧	-35792
+長輩在坐不可任意在他們的前面走動	-35793
+順著大漢溪住著許多原住民族的部落	-35794
+麻器和織布器具由泰雅男子製作之外	-35795
+ุงเ	-35796
+้อย	-35797
+▁սա	-35798
+▁富光	-35799
+一片一	-35800
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+來分類	-35804
+來協調	-35805
+個先人	-35806
+備所需	-35807
+像是地	-35808
+再轉搭	-35809
+加哥小	-35810
+半夜只	-35811
+卡羅來	-35812
+合胃口	-35813
+吉他風	-35814
+吉納法	-35815
+和外婆	-35816
+咚咚咚	-35817
+回憶起	-35818
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+塞拜然	-35821
+壽豐鄉	-35822
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+奈史密	-35824
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+將其嚼	-35827
+專人去	-35828
+山人海	-35829
+彈阿彈	-35830
+很好看	-35831
+德浮史	-35832
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+所傳承	-35834
+所流傳	-35835
+打草驚	-35836
+排好堆	-35837
+有亨社	-35838
+校訓信	-35839
+根咬碎	-35840
+格麗達	-35841
+沿河岸	-35842
+洗再去	-35843
+派人去	-35844
+深厚的	-35845
+澳花村	-35846
+烏心石	-35847
+球到月	-35848
+用麻線	-35849
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+當兵分	-35851
+發放一	-35852
+的來射	-35853
+的前面	-35854
+看看是	-35855
+祭等等	-35856
+福田康	-35857
+種多水	-35858
+立方體	-35859
+笑很傾	-35860
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+節會開	-35862
+納沙泰	-35863
+絕大多	-35864
+統分類	-35865
+絲細雨	-35866
+經種麻	-35867
+者田野	-35868
+自我約	-35869
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+虛地說	-35872
+裡咬碎	-35873
+說了氣	-35874
+起魚來	-35875
+遊手好	-35876
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+道格斯	-35878
+達伊希	-35879
+遠處望	-35880
+邪的功	-35881
+部幹線	-35882
+酸時取	-35883
+里奧社	-35884
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+長新葉	-35886
+阿蘇馬	-35887
+雞尖叫	-35888
+頭往那	-35889
+飲酒作	-35890
+高校生	-35891
+麗莎白	-35892
+黑色斑	-35893
+รุงเ	-35894
+วอนา	-35895
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+▁一億張	-35897
+▁上去把	-35898
+▁主打單	-35899
+▁參考	-35900
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+以此來	-35902
+像雪霧	-35903
+利伯塔	-35904
+利斯奧	-35905
+士頓凱	-35906
+妹妹先	-35907
+姿勢走	-35908
+富里鄉	-35909
+教不管	-35910
+此標章	-35911
+的一件	-35912
+的標章	-35913
+盧卡湖	-35914
+福氣的	-35915
+結以此	-35916
+都看的	-35917
+開心並	-35918
+離開位	-35919
+飛鼠拿	-35920
+驚嚇的	-35921
+魚類和	-35922
+ุ่มว	-35923
+▁nkv	-35924
+▁公尺的	-35925
+▁就用他	-35926
+▁平方公	-35927
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