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+---
+language: pam
+language_name: Pampanga
+language_family: austronesian_philippine_northern
+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_philippine_northern
+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: 4.758
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8287
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Pampanga - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Pampanga** 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** | 3.876x | 3.88 | 0.0164% | 341,122 |
+| **16k** | 4.216x | 4.22 | 0.0179% | 313,678 |
+| **32k** | 4.511x | 4.51 | 0.0191% | 293,138 |
+| **64k** | 4.758x 🏆 | 4.76 | 0.0201% | 277,944 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `"The Poet" ensáyu nang Ralph Waldo Emerson "The Poet" kawatásan nang María Teres...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁" the ▁poet " ▁ens áyu ▁nang ▁r al ph ... (+18 more)` | 28 |
+| 16k | `▁" the ▁poet " ▁ens áyu ▁nang ▁ralph ▁w aldo ... (+13 more)` | 23 |
+| 32k | `▁" the ▁poet " ▁ens áyu ▁nang ▁ralph ▁w aldo ... (+12 more)` | 22 |
+| 64k | `▁" the ▁poet " ▁ens áyu ▁nang ▁ralph ▁waldo ▁emerson ... (+10 more)` | 20 |
+
+**Sample 2:** `Ing Antheny metung yang balen at commune king Ardennes département king mauling ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ing ▁ant hen y ▁metung ▁yang ▁balen ▁at ▁commune ▁king ... (+9 more)` | 19 |
+| 16k | `▁ing ▁ant hen y ▁metung ▁yang ▁balen ▁at ▁commune ▁king ... (+9 more)` | 19 |
+| 32k | `▁ing ▁ant heny ▁metung ▁yang ▁balen ▁at ▁commune ▁king ▁ardennes ... (+8 more)` | 18 |
+| 64k | `▁ing ▁ant heny ▁metung ▁yang ▁balen ▁at ▁commune ▁king ▁ardennes ... (+8 more)` | 18 |
+
+**Sample 3:** `I Gonzalo Sta. Maria metung yang Kapampangan watas. Talambie Ding Kayang Kinudta...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁i ▁gonz alo ▁sta . ▁maria ▁metung ▁yang ▁kapampangan ▁watas ... (+9 more)` | 19 |
+| 16k | `▁i ▁gonz alo ▁sta . ▁maria ▁metung ▁yang ▁kapampangan ▁watas ... (+9 more)` | 19 |
+| 32k | `▁i ▁gonzalo ▁sta . ▁maria ▁metung ▁yang ▁kapampangan ▁watas . ... (+8 more)` | 18 |
+| 64k | `▁i ▁gonzalo ▁sta . ▁maria ▁metung ▁yang ▁kapampangan ▁watas . ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.758x compression
+- **Lowest UNK Rate:** 8k with 0.0164% 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 | 7,456 | 12.86 | 27,430 | 22.7% | 45.5% |
+| **2-gram** | Subword | 264 🏆 | 8.04 | 3,441 | 67.9% | 99.1% |
+| **3-gram** | Word | 7,873 | 12.94 | 31,773 | 24.5% | 44.9% |
+| **3-gram** | Subword | 2,323 | 11.18 | 27,102 | 27.4% | 69.0% |
+| **4-gram** | Word | 11,866 | 13.53 | 55,046 | 24.5% | 41.0% |
+| **4-gram** | Subword | 13,207 | 13.69 | 142,685 | 15.3% | 39.7% |
+| **5-gram** | Word | 7,287 | 12.83 | 39,747 | 29.4% | 47.0% |
+| **5-gram** | Subword | 43,230 | 15.40 | 366,395 | 10.0% | 28.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `metung yang` | 5,374 |
+| 2 | `atin yang` | 4,476 |
+| 3 | `ya ing` | 4,463 |
+| 4 | `of the` | 4,330 |
+| 5 | `suglung palwal` | 3,524 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yang populasyun a` | 1,862 |
+| 2 | `atin yang populasyun` | 1,856 |
+| 3 | `king lalawigan ning` | 1,836 |
+| 4 | `standard geographic code` | 1,739 |
+| 5 | `philippine standard geographic` | 1,739 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `atin yang populasyun a` | 1,855 |
+| 2 | `philippine standard geographic code` | 1,739 |
+| 3 | `governance performance management system` | 1,736 |
+| 4 | `local governance performance management` | 1,736 |
+| 5 | `standard geographic code local` | 1,731 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `local governance performance management system` | 1,736 |
+| 2 | `philippine standard geographic code local` | 1,731 |
+| 3 | `philatlas com philippine standard geographic` | 1,731 |
+| 4 | `standard geographic code local governance` | 1,731 |
+| 5 | `geographic code local governance performance` | 1,731 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g` | 304,553 |
+| 2 | `g _` | 266,268 |
+| 3 | `a n` | 253,944 |
+| 4 | `i n` | 209,259 |
+| 5 | `_ a` | 140,744 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 258,684 |
+| 2 | `i n g` | 127,387 |
+| 3 | `a n g` | 89,009 |
+| 4 | `a n _` | 61,397 |
+| 5 | `_ i n` | 46,327 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i n g _` | 120,149 |
+| 2 | `a n g _` | 62,550 |
+| 3 | `n g _ p` | 34,131 |
+| 4 | `n i n g` | 33,893 |
+| 5 | `k i n g` | 33,067 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n i n g _` | 33,478 |
+| 2 | `k i n g _` | 32,583 |
+| 3 | `_ n i n g` | 32,343 |
+| 4 | `_ k i n g` | 32,097 |
+| 5 | `_ i n g _` | 26,434 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 264
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~29% 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 | 0.7130 | 1.639 | 4.54 | 146,008 | 28.7% |
+| **1** | Subword | 0.8484 | 1.800 | 4.90 | 2,745 | 15.2% |
+| **2** | Word | 0.2159 | 1.161 | 1.48 | 660,193 | 78.4% |
+| **2** | Subword | 0.6588 | 1.579 | 4.28 | 13,435 | 34.1% |
+| **3** | Word | 0.0716 | 1.051 | 1.12 | 975,530 | 92.8% |
+| **3** | Subword | 0.8075 | 1.750 | 4.22 | 57,475 | 19.3% |
+| **4** | Word | 0.0277 🏆 | 1.019 | 1.04 | 1,090,857 | 97.2% |
+| **4** | Subword | 0.7126 | 1.639 | 3.02 | 242,181 | 28.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a lossy lossy lossy data a bangsa king pilatan ning banwang manimunang taluk lon la linto`
+2. `ing visa loca biningan callaguip cayubog dolores farm tanaman a new york philharmonic kéng wanan kai...`
+3. `ning tsina atyu king maulingalbugan ning bayung variant form the yellow pages isbn vietnam bắc ninhb...`
+
+**Context Size 2:**
+
+1. `metung yang lakanbalen king hokkaidō prefecture towns king japan bukud pa kareti kayabe no reng luga...`
+2. `atin yang 24 a barangay bacnor east bacnor west caliguian catabban cullalabo del norte zamboanga del...`
+3. `ya ing septiembre métung yang compositor pianista ampóng compositor a i julian ning norwich c kayaba...`
+
+**Context Size 3:**
+
+1. `yang populasyun a a katau kareng a pamimalemale deng barangay ing tubigon atin yang 34 a barangay ab...`
+2. `atin yang populasyun a a katau kareng a pamimalemale ing pasay lakanbalen metung ya kareng pekamagal...`
+3. `king lalawigan ning masbate filipinas agpang keng ning sensus atin yang populasyun a a katau kareng ...`
+
+**Context Size 4:**
+
+1. `atin yang populasyun a a katau kareng a pamimalemale deng barangay ing silay lakanbalen atin yang 16...`
+2. `philippine standard geographic code local governance performance management system municipality of o...`
+3. `local governance performance management system ning negros oriental`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_in,_droras_danc`
+2. `ancalguro_pa,_pr`
+3. `ndit_nininem_tyi`
+
+**Context Size 2:**
+
+1. `ng_susing_dakover`
+2. `g_kaux-pamakang_a`
+3. `ang_hictu_ventain`
+
+**Context Size 3:**
+
+1. `ng_kol._ing_pang_s`
+2. `ing_ampóng_palwali`
+3. `ang_twerte_escus_i`
+
+**Context Size 4:**
+
+1. `ing_ning_banua._míb`
+2. `ang_artistandavid_r`
+3. `ng_pátaka_ning_anti`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (242,181 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 | 56,109 |
+| Total Tokens | 1,253,954 |
+| Mean Frequency | 22.35 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 348.02 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 35,962 |
+| 2 | ing | 33,120 |
+| 3 | ning | 32,392 |
+| 4 | king | 31,916 |
+| 5 | of | 18,248 |
+| 6 | yang | 17,493 |
+| 7 | the | 15,199 |
+| 8 | ya | 12,902 |
+| 9 | at | 10,686 |
+| 10 | metung | 8,722 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | handog | 2 |
+| 2 | telatawag | 2 |
+| 3 | rason | 2 |
+| 4 | halaman | 2 |
+| 5 | tatambal | 2 |
+| 6 | punso | 2 |
+| 7 | bisayang | 2 |
+| 8 | itinuturing | 2 |
+| 9 | dáyâ | 2 |
+| 10 | thoughtco | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0309 |
+| R² (Goodness of Fit) | 0.996988 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 37.3% |
+| Top 1,000 | 61.5% |
+| Top 5,000 | 79.2% |
+| Top 10,000 | 86.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9970 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 37.3% of corpus
+- **Long Tail:** 46,109 words needed for remaining 14.0% 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 | 0.8287 🏆 | 0.3226 | N/A | N/A |
+| **mono_64d** | 64 | 0.6810 | 0.2653 | N/A | N/A |
+| **mono_128d** | 128 | 0.3086 | 0.2583 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8287 | 0.3246 | 0.0980 | 0.4360 |
+| **aligned_64d** | 64 | 0.6810 | 0.2716 | 0.1760 | 0.5740 |
+| **aligned_128d** | 128 | 0.3086 | 0.2627 | 0.2700 | 0.6220 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8287 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2842. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 27.0% 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 | **0.634** | 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` | makatyatsat, malútû, maned |
+| `-a` | aliste, anc, ayaring |
+| `-s` | salmbach, sorcy, sang |
+| `-m` | mormon, murphy, makatyatsat |
+| `-b` | brée, belfort, basilisa |
+| `-p` | phú, pekamaluat, pamiugne |
+| `-pa` | pamiugne, pareung, pasantingan |
+| `-c` | crest, chesnois, circuit |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | mormon, pirinan, gaillon |
+| `-s` | chesnois, magellans, runners |
+| `-ng` | lilung, pareung, tanikalang |
+| `-g` | lilung, pareung, tanikalang |
+| `-e` | desire, laye, aliste |
+| `-an` | pirinan, disnan, kapupusan |
+| `-a` | villalonga, ruspolia, basilisa |
+| `-t` | crest, feat, circuit |
+
+### 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 |
+|------|----------|------------------|----------|
+| `aman` | 2.30x | 108 contexts | amanu, daman, raman |
+| `ling` | 1.84x | 113 contexts | úling, aling, lingo |
+| `tion` | 1.94x | 41 contexts | potion, motion, action |
+| `atio` | 2.04x | 30 contexts | ratio, nation, babatio |
+| `aren` | 1.80x | 45 contexts | yaren, arena, areni |
+| `alaw` | 2.02x | 25 contexts | kalaw, lalawe, malawi |
+| `ment` | 1.61x | 44 contexts | mental, cement, moment |
+| `laka` | 1.80x | 25 contexts | lakan, plaka, lakay |
+| `akan` | 1.61x | 37 contexts | lakan, yakan, bakan |
+| `niba` | 1.84x | 23 contexts | aniban, mánibat, nibaliw |
+| `kare` | 2.12x | 14 contexts | karen, karel, kareti |
+| `alen` | 1.63x | 32 contexts | balen, halen, aalen |
+
+### 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` | 137 words | pigagamitan, pangadapun |
+| `-p` | `-an` | 104 words | pigagamitan, panlalawigan |
+| `-c` | `-s` | 90 words | camarines, cultures |
+| `-c` | `-n` | 85 words | caingin, chairwoman |
+| `-p` | `-g` | 82 words | paútang, pangmaluatang |
+| `-p` | `-s` | 82 words | patents, paparazzis |
+| `-b` | `-n` | 78 words | bléquin, binawian |
+| `-p` | `-ng` | 74 words | paútang, pangmaluatang |
+| `-ma` | `-g` | 74 words | macalang, mag |
+| `-p` | `-a` | 74 words | panga, pamagparla |
+
+### 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 |
+|------|-----------------|------------|------|
+| communism | **`communi-s-m`** | 7.5 | `s` |
+| inglesang | **`ingles-an-g`** | 7.5 | `an` |
+| cabiasnan | **`cabias-n-an`** | 7.5 | `n` |
+| kilalanan | **`kilal-an-an`** | 7.5 | `an` |
+| kapamiltan | **`kapamil-t-an`** | 7.5 | `t` |
+| makatukang | **`makatuk-an-g`** | 7.5 | `an` |
+| dramaturga | **`dramatur-g-a`** | 7.5 | `g` |
+| thüringen | **`thüring-e-n`** | 7.5 | `e` |
+| gravenhage | **`gravenha-g-e`** | 7.5 | `g` |
+| pampangans | **`pampang-an-s`** | 7.5 | `an` |
+| paliwasan | **`paliwa-s-an`** | 7.5 | `s` |
+| migsamantala | **`mi-g-samantala`** | 7.5 | `samantala` |
+| intertwined | **`intertwi-n-ed`** | 7.5 | `n` |
+| fouesnant | **`fouesn-an-t`** | 7.5 | `an` |
+| pamanalto | **`pamanal-t-o`** | 7.5 | `t` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Pampanga 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 (4.76x) |
+| N-gram | **2-gram** | Lowest perplexity (264) |
+| Markov | **Context-4** | Highest predictability (97.2%) |
+| 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 17:28:27*
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diff --git a/models/tokenizer/pam_tokenizer_64k.model b/models/tokenizer/pam_tokenizer_64k.model
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diff --git a/models/tokenizer/pam_tokenizer_64k.vocab b/models/tokenizer/pam_tokenizer_64k.vocab
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