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+---
+language: pwn
+language_name: Paiwan
+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: 4.197
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2318
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Paiwan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Paiwan** 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** | 3.662x | 3.66 | 0.7466% | 241,760 |
+| **16k** | 3.933x | 3.94 | 0.8020% | 225,069 |
+| **32k** | 4.197x 🏆 | 4.20 | 0.8558% | 210,910 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `aicu a qalici (陰莖) kinacavacavan nua uqaljai, tua sinipukelang nua naqemati tu u...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁aicu ▁a ▁qali ci ▁( 陰 莖 ) ▁kinacavacavan ▁nua ... (+11 more)` | 21 |
+| 16k | `▁aicu ▁a ▁qalici ▁( 陰 莖 ) ▁kinacavacavan ▁nua ▁uqaljai ... (+10 more)` | 20 |
+| 32k | `▁aicu ▁a ▁qalici ▁( 陰 莖 ) ▁kinacavacavan ▁nua ▁uqaljai ... (+8 more)` | 18 |
+
+**Sample 2:** `kivecik(紋身) aicu a titjen a payuan kivecik a vavayan a pitalima. 排灣族來義鄉傳統手紋`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kivecik ( 紋 身 ) ▁aicu ▁a ▁titjen ▁a ▁payuan ... (+16 more)` | 26 |
+| 16k | `▁kivecik ( 紋 身 ) ▁aicu ▁a ▁titjen ▁a ▁payuan ... (+10 more)` | 20 |
+| 32k | `▁kivecik ( 紋身 ) ▁aicu ▁a ▁titjen ▁a ▁payuan ▁kivecik ... (+6 more)` | 16 |
+
+**Sample 3:** `Pucevuljan(煙起的地方) avan tiribi dorama i taiwan. inalang tua tiribi na kacalisian....`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pucev uljan ( 煙 起 的 地方 ) ▁avan ▁tiribi ... (+26 more)` | 36 |
+| 16k | `▁pucevuljan ( 煙起的地方 ) ▁avan ▁tiribi ▁dorama ▁i ▁taiwan . ... (+18 more)` | 28 |
+| 32k | `▁pucevuljan ( 煙起的地方 ) ▁avan ▁tiribi ▁dorama ▁i ▁taiwan . ... (+17 more)` | 27 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.197x compression
+- **Lowest UNK Rate:** 8k with 0.7466% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 1,599 | 10.64 | 3,476 | 30.6% | 70.4% |
+| **2-gram** | Subword | 175 🏆 | 7.45 | 2,439 | 79.5% | 98.6% |
+| **3-gram** | Word | 2,724 | 11.41 | 4,579 | 19.2% | 57.3% |
+| **3-gram** | Subword | 1,042 | 10.03 | 9,633 | 41.2% | 85.4% |
+| **4-gram** | Word | 4,987 | 12.28 | 7,623 | 13.9% | 41.7% |
+| **4-gram** | Subword | 4,257 | 12.06 | 30,586 | 22.0% | 60.0% |
+| **5-gram** | Word | 3,658 | 11.84 | 5,388 | 15.3% | 44.9% |
+| **5-gram** | Subword | 10,340 | 13.34 | 49,675 | 13.3% | 42.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `aicu a` | 1,188 |
+| 2 | `a cavilj` | 821 |
+| 3 | `a caucau` | 748 |
+| 4 | `a a` | 732 |
+| 5 | `ka a` | 570 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a a` | 530 |
+| 2 | `ka a cavilj` | 413 |
+| 3 | `palidring a djalan` | 222 |
+| 4 | `a djalan na` | 167 |
+| 5 | `a palidring a` | 164 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a a a` | 514 |
+| 2 | `a palidring a djalan` | 164 |
+| 3 | `palidring a djalan na` | 143 |
+| 4 | `a djalan na taiwan` | 63 |
+| 5 | `gaku na kukumin a` | 62 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a a a a` | 500 |
+| 2 | `a palidring a djalan na` | 130 |
+| 3 | `palidring a djalan na taiwan` | 62 |
+| 4 | `venecikan na takakudan a umaq` | 41 |
+| 5 | `a venecikan na takakudan a` | 39 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 54,084 |
+| 2 | `a n` | 30,246 |
+| 3 | `_ a` | 28,919 |
+| 4 | `n _` | 16,909 |
+| 5 | `k a` | 16,220 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a _` | 22,599 |
+| 2 | `a n _` | 14,379 |
+| 3 | `_ k a` | 8,495 |
+| 4 | `u a _` | 8,199 |
+| 5 | `a _ k` | 6,913 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ a _` | 4,611 |
+| 2 | `n _ a _` | 4,406 |
+| 3 | `a n _ a` | 4,391 |
+| 4 | `u _ a _` | 3,968 |
+| 5 | `a n g a` | 3,863 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _ a _` | 3,756 |
+| 2 | `_ t u a _` | 2,908 |
+| 3 | `_ a _ c a` | 2,118 |
+| 4 | `k a t a _` | 2,100 |
+| 5 | `_ n u a _` | 1,928 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 175
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~43% 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 | 0.4964 | 1.411 | 3.23 | 22,120 | 50.4% |
+| **1** | Subword | 1.2269 | 2.341 | 6.30 | 2,701 | 0.0% |
+| **2** | Word | 0.2355 | 1.177 | 1.53 | 71,169 | 76.5% |
+| **2** | Subword | 0.4160 | 1.334 | 2.32 | 17,020 | 58.4% |
+| **3** | Word | 0.0941 | 1.067 | 1.15 | 108,439 | 90.6% |
+| **3** | Subword | 0.3831 | 1.304 | 2.05 | 39,422 | 61.7% |
+| **4** | Word | 0.0376 🏆 | 1.026 | 1.05 | 124,759 | 96.2% |
+| **4** | Subword | 0.3237 | 1.252 | 1.72 | 80,954 | 67.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a tja sini pakigaljuanga tua zusi yuli citing a cavilj a tja sicavu tua qinaljan sa`
+2. `i marekacemecemel i vudai izua a 源氏物語 ka a puday ljaceng tua mareka caucau pukeljang a`
+3. `tua cawtun a cavilj sigac masansika drusapuluq sa pitju a cengkung a qemungcuy maqati a tja`
+
+**Context Size 2:**
+
+1. `aicu a ika namakeljang saka aza tjaljanguanguaqan a zuga nu tjapacunan tucu tucu maljian anga zidai ...`
+2. `a cavilj aza cenkungaw a qinaljan a caucau nua cemual nu secevung tua amis a i tjaikacedas`
+3. `a caucau i guan aza na linbien 林邊 pana qapulu kemasi kuljauc pasakaledep a navalj tua taiwan`
+
+**Context Size 3:**
+
+1. `a a a a a a a a a a a a a a a a a a`
+2. `ka a cavilj tjelu a qiljas masansivalj drusa a kuzulj sa alu taiday sa siva a cuacau 3`
+3. `palidring a djalan na qakaw 23px sikamasan pitjulj a palidring a djalan na taiwan paravacan a racev ...`
+
+**Context Size 4:**
+
+1. `a a a a a a a a a a a a a a a a a a a`
+2. `a palidring a djalan na taiwan djalan a pasaviri itua taiwan 省道 23px sikamasan 118 a palidirng a dja...`
+3. `palidring a djalan na taiwan patje dahu gu kata sanwan gu 省道 23px sikamasannemelj a palidring a djal...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `alanasemekin._ke`
+2. `_a_kay_avavan"pa`
+3. `ngaivazua_ilâ-1,`
+
+**Context Size 2:**
+
+1. `a_liljeledasa_pai`
+2. `an_i_nucau,_kak(區`
+3. `_ayalet_of_jilicu`
+
+**Context Size 3:**
+
+1. `_a_drusa_kinalj_i_`
+2. `an_富源森林遊樂區vuy_umin`
+3. `_kata_katj張孝娘(muma`
+
+**Context Size 4:**
+
+1. `a_a_qiljan_niamadju`
+2. `n_a_caviljan_nua_in`
+3. `an_a_hada_kuara_sin`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (80,954 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 | 7,537 |
+| Total Tokens | 130,405 |
+| Mean Frequency | 17.30 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 279.99 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 22,819 |
+| 2 | i | 3,801 |
+| 3 | tua | 2,914 |
+| 4 | ta | 2,856 |
+| 5 | na | 2,750 |
+| 6 | sa | 2,550 |
+| 7 | nua | 1,941 |
+| 8 | kata | 1,767 |
+| 9 | izua | 1,539 |
+| 10 | aicu | 1,375 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tuleken | 2 |
+| 2 | iqecev | 2 |
+| 3 | rigi | 2 |
+| 4 | 新年快樂 | 2 |
+| 5 | kalevay | 2 |
+| 6 | ljavia | 2 |
+| 7 | capelju | 2 |
+| 8 | sanvaljin | 2 |
+| 9 | qazavai | 2 |
+| 10 | sinikamaretimalji | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0332 |
+| R² (Goodness of Fit) | 0.987155 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 56.8% |
+| Top 1,000 | 81.7% |
+| Top 5,000 | 96.1% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9872 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 56.8% of corpus
+- **Long Tail:** -2,463 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 | 0.2318 | 0.4443 | N/A | N/A |
+| **mono_64d** | 64 | 0.0360 | 0.4479 | N/A | N/A |
+| **mono_128d** | 128 | 0.0037 | 0.4516 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2318 🏆 | 0.4503 | 0.0153 | 0.1682 |
+| **aligned_64d** | 64 | 0.0360 | 0.4544 | 0.0612 | 0.2355 |
+| **aligned_128d** | 128 | 0.0037 | 0.4558 | 0.0795 | 0.2630 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.2318 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4507. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.0% 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 | **0.051** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | serviciilor, sineqetj, sanparavac |
+| `-ma` | mavananga, marekatalem, masiljid |
+| `-pa` | pacual, paywanzuku, pakan |
+| `-si` | sineqetj, sisupuan, sinikieces |
+| `-ka` | kaku, kabalelradhane, katalemmang |
+| `-t` | tjaljev, tunis, tatun |
+| `-k` | kising, kaku, kusitik |
+| `-ki` | kising, kipusalimaliman, kinanavun |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-an` | sisupuan, pusikingan, sinupuan |
+| `-n` | amen, zunghen, sisupuan |
+| `-a` | numatazuwa, mavananga, alja |
+| `-ng` | kising, wearing, kicaing |
+| `-u` | dukangpu, ninpu, kaku |
+| `-g` | kising, wearing, kicaing |
+| `-lj` | nasetevelj, sikamasantjelulj, cemqalj |
+| `-j` | sineqetj, nasetevelj, sikamasantjelulj |
+
+### 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 |
+|------|----------|------------------|----------|
+| `malj` | 1.43x | 24 contexts | malje, malji, limalj |
+| `alan` | 1.31x | 31 contexts | alang, calan, kalan |
+| `java` | 1.40x | 18 contexts | tjava, kaljava, utjavan |
+| `jalj` | 1.37x | 18 contexts | udjalj, tjalju, tjalja |
+| `kema` | 1.41x | 16 contexts | kemac, kemai, keman |
+| `djal` | 1.41x | 16 contexts | djali, udjalj, djalin |
+| `ljan` | 1.43x | 13 contexts | aljan, iljang, ljangi |
+| `nalj` | 1.69x | 8 contexts | inaljan, naljavek, pinaljak |
+| `tjal` | 1.37x | 12 contexts | tjala, tjalju, tjalja |
+| `ayan` | 1.36x | 11 contexts | ayanga, pavayan, kavayan |
+| `emas` | 1.35x | 11 contexts | cemas, remasi, kemasi |
+| `cavi` | 1.51x | 8 contexts | cavij, cavilj, tucavilj |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-n` | 201 words | sisupuan, sinupuan |
+| `-s` | `-an` | 182 words | sisupuan, sinupuan |
+| `-ka` | `-n` | 145 words | kacilisian, kaljasangasangasan |
+| `-ka` | `-an` | 138 words | kacilisian, kaljasangasangasan |
+| `-k` | `-n` | 127 words | kipusalimaliman, kinanavun |
+| `-t` | `-n` | 126 words | tatun, tjanusun |
+| `-k` | `-an` | 117 words | kipusalimaliman, kinavecikan |
+| `-t` | `-an` | 108 words | taivuan, tjaisangasangasan |
+| `-p` | `-n` | 89 words | pusikingan, pinuvecikan |
+| `-p` | `-an` | 82 words | pusikingan, pinuvecikan |
+
+### 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 |
+|------|-----------------|------------|------|
+| sikudjaljan | **`si-ku-djaljan`** | 7.5 | `djaljan` |
+| sematjaljitiv | **`se-ma-tjaljitiv`** | 7.5 | `tjaljitiv` |
+| sasipavay | **`sa-si-pavay`** | 7.5 | `pavay` |
+| matadrusa | **`ma-ta-drusa`** | 7.5 | `drusa` |
+| kinaqipuan | **`kinaqi-pu-an`** | 7.5 | `pu` |
+| ljivakung | **`ljiva-ku-ng`** | 7.5 | `ku` |
+| sikamasansimuluq | **`si-ka-masansimuluq`** | 7.5 | `masansimuluq` |
+| djadjaljunan | **`djadjalju-n-an`** | 7.5 | `n` |
+| rinipunan | **`rinipu-n-an`** | 7.5 | `n` |
+| sekacedas | **`se-ka-cedas`** | 7.5 | `cedas` |
+| blubluone | **`blubluo-n-e`** | 7.5 | `n` |
+| philippines | **`philippi-n-es`** | 7.5 | `n` |
+| makapalingulj | **`ma-ka-palingulj`** | 7.5 | `palingulj` |
+| kadjunagnan | **`kadjunag-n-an`** | 7.5 | `n` |
+| mapualang | **`ma-pu-alang`** | 7.5 | `alang` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Paiwan shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **32k BPE** | Best compression (4.20x) |
+| N-gram | **2-gram** | Lowest perplexity (175) |
+| Markov | **Context-4** | Highest predictability (96.2%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 18:13:50*
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diff --git a/models/tokenizer/pwn_tokenizer_32k.model b/models/tokenizer/pwn_tokenizer_32k.model
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diff --git a/models/tokenizer/pwn_tokenizer_8k.vocab b/models/tokenizer/pwn_tokenizer_8k.vocab
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