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+---
+language: pag
+language_name: Pangasinan
+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: 4.912
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0888
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Pangasinan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Pangasinan** 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** | 4.339x | 4.35 | 0.7127% | 96,109 |
+| **16k** | 4.639x | 4.65 | 0.7621% | 89,884 |
+| **32k** | 4.912x 🏆 | 4.92 | 0.8069% | 84,898 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Category: Listaan na Nakaukulan ya Artikulo ed Pangasinan`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁category : ▁listaan ▁na ▁nakaukulan ▁ya ▁artikulo ▁ed ▁pangasinan` | 9 |
+| 16k | `▁category : ▁listaan ▁na ▁nakaukulan ▁ya ▁artikulo ▁ed ▁pangasinan` | 9 |
+| 32k | `▁category : ▁listaan ▁na ▁nakaukulan ▁ya ▁artikulo ▁ed ▁pangasinan` | 9 |
+
+**Sample 2:** `Say C sakey arapan ya letra diad alpabeto ya Romano. 3`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁say ▁c ▁sakey ▁arapan ▁ya ▁letra ▁diad ▁alpabeto ▁ya ▁romano ... (+3 more)` | 13 |
+| 16k | `▁say ▁c ▁sakey ▁arapan ▁ya ▁letra ▁diad ▁alpabeto ▁ya ▁romano ... (+3 more)` | 13 |
+| 32k | `▁say ▁c ▁sakey ▁arapan ▁ya ▁letra ▁diad ▁alpabeto ▁ya ▁romano ... (+3 more)` | 13 |
+
+**Sample 3:** `Saray Inianak Birthday Niduman Agew Special Day Agew na Letnegan Foundation Day ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁saray ▁inianak ▁birthday ▁niduman ▁agew ▁special ▁day ▁agew ▁na ▁letnegan ... (+5 more)` | 15 |
+| 16k | `▁saray ▁inianak ▁birthday ▁niduman ▁agew ▁special ▁day ▁agew ▁na ▁letnegan ... (+5 more)` | 15 |
+| 32k | `▁saray ▁inianak ▁birthday ▁niduman ▁agew ▁special ▁day ▁agew ▁na ▁letnegan ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.912x compression
+- **Lowest UNK Rate:** 8k with 0.7127% 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 | 205 | 7.68 | 2,255 | 79.7% | 93.6% |
+| **2-gram** | Subword | 213 | 7.74 | 1,452 | 73.2% | 99.8% |
+| **3-gram** | Word | 147 | 7.20 | 2,427 | 86.2% | 95.5% |
+| **3-gram** | Subword | 1,197 | 10.23 | 9,027 | 35.3% | 83.0% |
+| **4-gram** | Word | 152 | 7.25 | 3,955 | 86.3% | 93.7% |
+| **4-gram** | Subword | 3,558 | 11.80 | 37,136 | 23.9% | 64.7% |
+| **5-gram** | Word | 121 🏆 | 6.91 | 2,812 | 89.3% | 96.1% |
+| **5-gram** | Subword | 5,759 | 12.49 | 68,453 | 19.6% | 59.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `to et` | 3,500 |
+| 2 | `na filipinas` | 2,001 |
+| 3 | `saray reperensiya` | 1,826 |
+| 4 | `to ya` | 1,774 |
+| 5 | `luyag na` | 1,769 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `gawing ed labas` | 1,757 |
+| 2 | `saray gawing ed` | 1,753 |
+| 3 | `philippine standard geographic` | 1,738 |
+| 4 | `saray reperensiya saray` | 1,738 |
+| 5 | `standard geographic code` | 1,738 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `saray gawing ed labas` | 1,752 |
+| 2 | `philippine standard geographic code` | 1,738 |
+| 3 | `saray reperensiya saray gawing` | 1,735 |
+| 4 | `reperensiya saray gawing ed` | 1,735 |
+| 5 | `tan sukat to ya` | 1,733 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `reperensiya saray gawing ed labas` | 1,735 |
+| 2 | `saray reperensiya saray gawing ed` | 1,735 |
+| 3 | `kabaleg tan sukat to ya` | 1,733 |
+| 4 | `walay kabaleg tan sukat to` | 1,733 |
+| 5 | `local governance performance management system` | 1,731 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 31,807 |
+| 2 | `_ s` | 28,468 |
+| 3 | `y _` | 25,578 |
+| 4 | `a _` | 23,919 |
+| 5 | `a y` | 21,692 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a y _` | 19,505 |
+| 2 | `_ s a` | 14,577 |
+| 3 | `a n _` | 10,573 |
+| 4 | `a r a` | 10,005 |
+| 5 | `e d _` | 8,979 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ e d _` | 8,348 |
+| 2 | `_ n a _` | 7,570 |
+| 3 | `r a y _` | 7,012 |
+| 4 | `s a r a` | 6,954 |
+| 5 | `a r a y` | 6,936 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s a r a y` | 6,895 |
+| 2 | `a r a y _` | 6,892 |
+| 3 | `_ s a r a` | 6,692 |
+| 4 | `_ t a n _` | 4,954 |
+| 5 | `_ s a y _` | 4,849 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 5-gram (word) with 121
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~60% 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 | 0.6201 | 1.537 | 3.30 | 21,681 | 38.0% |
+| **1** | Subword | 0.9010 | 1.867 | 5.17 | 994 | 9.9% |
+| **2** | Word | 0.1693 | 1.124 | 1.30 | 71,109 | 83.1% |
+| **2** | Subword | 0.6653 | 1.586 | 3.95 | 5,133 | 33.5% |
+| **3** | Word | 0.0511 | 1.036 | 1.08 | 91,940 | 94.9% |
+| **3** | Subword | 0.7224 | 1.650 | 3.38 | 20,253 | 27.8% |
+| **4** | Word | 0.0195 🏆 | 1.014 | 1.03 | 98,208 | 98.1% |
+| **4** | Subword | 0.5566 | 1.471 | 2.28 | 68,355 | 44.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ed labas philatlas com philippine standard geographic code to ya barangay demograpiko saray siyudad ...`
+2. `na filipinas unong ed saray alahas pati angga ed europa tan abong walay kabaleg tan abong`
+3. `say zip code local governance performance by transporting warm gun kayari na oriental mindoro garcia...`
+
+**Context Size 2:**
+
+1. `to et totoo tan abong walay kabaleg tan sukat to ya sq km say zip code to`
+2. `saray reperensiya saray gawing ed labas philatlas com philippine standard geographic code local gove...`
+3. `to ya sq km say zip code to et saray barangay demograpiko saray reperensiya saray gawing ed`
+
+**Context Size 3:**
+
+1. `gawing ed labas philatlas com philippine standard geographic code local governance performance manag...`
+2. `saray gawing ed labas philatlas com philippine standard geographic code local governance performance...`
+3. `saray reperensiya saray gawing ed labas philatlas com philippine standard geographic code local gove...`
+
+**Context Size 4:**
+
+1. `saray gawing ed labas philatlas com philippine standard geographic code local governance performance...`
+2. `philippine standard geographic code local governance performance management system baley na quezon`
+3. `reperensiya saray gawing ed labas philatlas com philippine standard geographic code local governance...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_kanderan_n_anay`
+2. `a_vay_ssta_sakis`
+3. `ninilayara_y_sq.`
+
+**Context Size 2:**
+
+1. `angemol_gew_so_ph`
+2. `_siya_barchrivers`
+3. `y_geograp-le_to_e`
+
+**Context Size 3:**
+
+1. `ay_et_ed_labangay_`
+2. `_say_gawing_ed_met`
+3. `an_to_et_kids'_pan`
+
+**Context Size 4:**
+
+1. `_ed_et_totoo_a_dapi`
+2. `_na_,_filipinas._un`
+3. `ray_repúblic_oceano`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (68,355 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 | 8,492 |
+| Total Tokens | 189,672 |
+| Mean Frequency | 22.34 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 233.09 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ed | 8,364 |
+| 2 | na | 7,609 |
+| 3 | say | 6,874 |
+| 4 | saray | 6,859 |
+| 5 | et | 6,059 |
+| 6 | to | 5,969 |
+| 7 | ya | 5,232 |
+| 8 | tan | 4,961 |
+| 9 | code | 3,372 |
+| 10 | filipinas | 2,140 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | kabisera | 2 |
+| 2 | wiesbaden | 2 |
+| 3 | lento | 2 |
+| 4 | lacrimoso | 2 |
+| 5 | ceremonial | 2 |
+| 6 | seremonyal | 2 |
+| 7 | chikvaidze | 2 |
+| 8 | kanlurang | 2 |
+| 9 | soan | 2 |
+| 10 | makiabay | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0335 |
+| R² (Goodness of Fit) | 0.985808 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 67.4% |
+| Top 1,000 | 84.3% |
+| Top 5,000 | 96.2% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9858 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 67.4% of corpus
+- **Long Tail:** -1,508 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 | 0.0888 | 0.4796 | N/A | N/A |
+| **mono_64d** | 64 | 0.0147 | 0.4928 | N/A | N/A |
+| **mono_128d** | 128 | 0.0021 | 0.5047 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0888 🏆 | 0.4864 | 0.0120 | 0.1220 |
+| **aligned_64d** | 64 | 0.0147 | 0.4907 | 0.0180 | 0.1620 |
+| **aligned_128d** | 128 | 0.0021 | 0.5245 | 0.0220 | 0.1900 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.0888 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4964. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.2% 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 | **3.984** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.605** | 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 |
+|--------|----------|
+| `-a` | argel, achievement, administrasyon |
+| `-s` | shounen, streisands, sebastian |
+| `-ma` | marijuana, magasin, malaysia |
+| `-b` | basel, bonifacio, buendia |
+| `-p` | pati, paraan, partner |
+| `-m` | marijuana, magasin, malaysia |
+| `-d` | diverse, diskograpiya, derby |
+| `-t` | teritorya, tanom, tango |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | jefferson, shounen, paraan |
+| `-a` | teritorya, halina, republika |
+| `-an` | paraan, sebastian, sankamaimpluensyan |
+| `-s` | wikimedians, streisands, basbas |
+| `-o` | bonifacio, wario, tango |
+| `-e` | diverse, bustamante, save |
+| `-on` | jefferson, generation, terminon |
+| `-g` | nyog, trung, gandang |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `anga` | 1.40x | 35 contexts | banga, angat, sanga |
+| `pana` | 1.68x | 13 contexts | panag, espana, panaon |
+| `ngga` | 1.51x | 16 contexts | angga, anggan, anggad |
+| `angg` | 1.51x | 15 contexts | angga, anggan, anggad |
+| `angi` | 1.62x | 12 contexts | sangi, angie, mangi |
+| `anla` | 1.67x | 10 contexts | kanlaon, nanlapu, nanlapo |
+| `kaba` | 1.51x | 12 contexts | kabay, kabat, akabat |
+| `tion` | 1.33x | 14 contexts | action, nation, motion |
+| `laba` | 1.50x | 10 contexts | labay, labat, labas |
+| `nter` | 1.37x | 12 contexts | inter, hunter, center |
+| `inte` | 1.45x | 10 contexts | inter, intero, winter |
+| `bale` | 1.43x | 10 contexts | baley, baler, baleg |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ka` | `-n` | 94 words | kayon, kareenan |
+| `-p` | `-n` | 91 words | paraan, panguman |
+| `-ka` | `-an` | 83 words | kareenan, kayamanan |
+| `-s` | `-n` | 81 words | shounen, sebastian |
+| `-a` | `-n` | 60 words | administrasyon, aviation |
+| `-p` | `-an` | 57 words | paraan, panguman |
+| `-p` | `-a` | 56 words | probinsiya, pampanga |
+| `-s` | `-an` | 54 words | sebastian, sankamaimpluensyan |
+| `-a` | `-o` | 51 words | apo, apolinario |
+| `-p` | `-s` | 48 words | productions, posadas |
+
+### 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 |
+|------|-----------------|------------|------|
+| wonderland | **`wonderl-an-d`** | 7.5 | `an` |
+| ipakitana | **`ipakit-an-a`** | 7.5 | `an` |
+| kayamanan | **`kayam-an-an`** | 7.5 | `an` |
+| relihyoson | **`relihyo-s-on`** | 7.5 | `s` |
+| josephine | **`joseph-in-e`** | 7.5 | `in` |
+| angadanan | **`angad-an-an`** | 7.5 | `an` |
+| metropolitano | **`metropolit-an-o`** | 7.5 | `an` |
+| masaganan | **`masag-an-an`** | 7.5 | `an` |
+| awstralyano | **`awstraly-an-o`** | 7.5 | `an` |
+| michigans | **`michig-an-s`** | 7.5 | `an` |
+| lithuania | **`lithu-an-ia`** | 7.5 | `an` |
+| baranggay | **`barang-g-ay`** | 7.5 | `g` |
+| ginampanan | **`ginamp-an-an`** | 7.5 | `an` |
+| manngaran | **`ma-n-ngaran`** | 7.5 | `ngaran` |
+| agtrabaho | **`a-g-trabaho`** | 7.5 | `trabaho` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Pangasinan 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 | **32k BPE** | Best compression (4.91x) |
+| N-gram | **5-gram** | Lowest perplexity (121) |
+| Markov | **Context-4** | Highest predictability (98.1%) |
+| 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 17:16:06*
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+}
\ No newline at end of file
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