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README.md

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+---
+language: nia
+language_name: Nias
+language_family: austronesian_other
+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_other
+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: 4.014
+  - name: best_isotropy
+    type: isotropy
+    value: 0.5991
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Nias - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Nias** 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** | 3.685x | 3.69 | 0.1179% | 377,555 |
+| **16k** | 3.874x | 3.88 | 0.1239% | 359,160 |
+| **32k** | 4.014x 🏆 | 4.02 | 0.1284% | 346,649 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Mo'awö no töi mbanua ba Danö Niha: Mo'awö, Kecamatan Gunungsitoli, Kota Gunungsi...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mo ' awö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ... (+19 more)` | 29 |
+| 16k | `▁mo ' awö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ... (+19 more)` | 29 |
+| 32k | `▁mo ' awö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ... (+19 more)` | 29 |
+
+**Sample 2:** `Do ya'ia da'ö si hulö nidanö sola'a-la'a oyo ba sofanö-fanö bakha ba mboto sanör...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁do ▁ya ' ia ▁da ' ö ▁si ▁hulö ▁nidanö ... (+20 more)` | 30 |
+| 16k | `▁do ▁ya ' ia ▁da ' ö ▁si ▁hulö ▁nidanö ... (+20 more)` | 30 |
+| 32k | `▁do ▁ya ' ia ▁da ' ö ▁si ▁hulö ▁nidanö ... (+20 more)` | 30 |
+
+**Sample 3:** `Baruzö no töi mbanua ba Danö Niha: Baruzö, Kecamatan Idanögawo, Kabupaten Nias B...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁baruzö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ▁baruzö , ... (+14 more)` | 24 |
+| 16k | `▁baruzö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ▁baruzö , ... (+14 more)` | 24 |
+| 32k | `▁baruzö ▁no ▁töi ▁mbanua ▁ba ▁danö ▁niha : ▁baruzö , ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.014x compression
+- **Lowest UNK Rate:** 8k with 0.1179% 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 | 2,758 | 11.43 | 10,121 | 32.1% | 59.9% |
+| **2-gram** | Subword | 217 🏆 | 7.76 | 1,534 | 71.6% | 99.7% |
+| **3-gram** | Word | 4,344 | 12.08 | 14,140 | 28.0% | 49.1% |
+| **3-gram** | Subword | 1,588 | 10.63 | 12,306 | 31.9% | 78.5% |
+| **4-gram** | Word | 6,308 | 12.62 | 20,991 | 25.9% | 43.0% |
+| **4-gram** | Subword | 7,212 | 12.82 | 54,669 | 16.4% | 49.8% |
+| **5-gram** | Word | 3,712 | 11.86 | 13,231 | 30.4% | 51.6% |
+| **5-gram** | Subword | 19,478 | 14.25 | 118,364 | 11.4% | 35.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `da ö` | 3,555 |
+| 2 | `moroi ba` | 2,755 |
+| 3 | `ba danö` | 2,721 |
+| 4 | `ya ia` | 2,653 |
+| 5 | `danö niha` | 2,149 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ba danö niha` | 2,084 |
+| 2 | `ya ia da` | 1,571 |
+| 3 | `ia da ö` | 1,561 |
+| 4 | `menteri dalam negeri` | 1,033 |
+| 5 | `dalam negeri no` | 1,028 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ya ia da ö` | 1,558 |
+| 2 | `menteri dalam negeri no` | 1,028 |
+| 3 | `dalam negeri no 72` | 1,027 |
+| 4 | `negeri no 72 tahun` | 1,027 |
+| 5 | `kodenia ba wamatörö indonesia` | 1,025 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dalam negeri no 72 tahun` | 1,027 |
+| 2 | `menteri dalam negeri no 72` | 1,027 |
+| 3 | `negeri no 72 tahun pdf` | 992 |
+| 4 | `no sambua desa ba kecamatan` | 924 |
+| 5 | `peraturan menteri dalam negeri no` | 890 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 125,464 |
+| 2 | `b a` | 51,774 |
+| 3 | `i _` | 51,062 |
+| 4 | `a n` | 49,901 |
+| 5 | `_ b` | 48,112 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b a` | 40,895 |
+| 2 | `b a _` | 35,312 |
+| 3 | `i a _` | 16,399 |
+| 4 | `_ n i` | 15,214 |
+| 5 | `a _ b` | 15,172 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b a _` | 34,789 |
+| 2 | `a _ b a` | 13,160 |
+| 3 | `n i h a` | 7,319 |
+| 4 | `_ n i h` | 7,204 |
+| 5 | `a _ d a` | 7,113 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ b a _` | 11,657 |
+| 2 | `_ n i h a` | 7,150 |
+| 3 | `i _ b a _` | 6,034 |
+| 4 | `_ b a _ w` | 5,169 |
+| 5 | `n i h a _` | 4,502 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 217
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~36% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.8816 | 1.842 | 5.55 | 31,686 | 11.8% |
+| **1** | Subword | 1.1187 | 2.172 | 7.98 | 442 | 0.0% |
+| **2** | Word | 0.2829 | 1.217 | 1.70 | 175,623 | 71.7% |
+| **2** | Subword | 1.0534 | 2.075 | 6.37 | 3,528 | 0.0% |
+| **3** | Word | 0.1113 | 1.080 | 1.20 | 297,898 | 88.9% |
+| **3** | Subword | 0.8852 | 1.847 | 4.09 | 22,462 | 11.5% |
+| **4** | Word | 0.0413 🏆 | 1.029 | 1.06 | 356,990 | 95.9% |
+| **4** | Subword | 0.6276 | 1.545 | 2.58 | 91,854 | 37.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ba da ö papias moroi ba wa anumana la a ira ma suku logika teologi ba`
+2. `ö fa auri mazauwu nitötöi faoma lakue fa ebua aruzö ruzö bua wangera ngerania zi lalö`
+3. `no 72 tahun pdf nga örö 491 umbu ba tradisi nifotöi keynesian ya ia tsunami tehalö`
+
+**Context Size 2:**
+
+1. `da ö zikala na la aturai wama ema hadia kabarata nösi gosali sifakhai ba dalö cina fananö`
+2. `moroi ba provinsi bengkulu indonesia kota bandung göi no faola sibai ia ba iklim pegunungan sokafu k...`
+3. `ba danö niha ma abölö sökhi ba si lö sökhi kreeft nga örö 361 umbu ba danö`
+
+**Context Size 3:**
+
+1. `ya ia da ö labe e khönia dandra ya ia da ö kecoak lipas mazui coro sambua moroi`
+2. `ia da ö cabai ma lombok hiza lada no fao ia ba wamasindro mbolo gu ö facebook awö`
+3. `ba danö niha ma ono niha sangarato ba danö misiyefo asala niha si onarai danö niha böi sofu`
+
+**Context Size 4:**
+
+1. `ya ia da ö öröba si öli ma baru wasuwöta nihaogö moroi ba zi öli so göi wondraru moroi`
+2. `menteri dalam negeri no 72 tahun pdf kementerian dalam negeri nga örö 492 umbu ba danö niha mufareso`
+3. `dalam negeri no 72 tahun pdf nga örö 581 umbu ba danö niha ba hiza sukhoi nisura andre ya`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_falewabu-_ma_8.`
+2. `a_kseca_pani_bö_`
+3. `in_fa-3172,_ikha`
+
+**Context Size 2:**
+
+1. `a_mei_ina_perifak`
+2. `bai_da'a_dalö'örö`
+3. `i_pate'ogu_i,_no_`
+
+**Context Size 3:**
+
+1. `_ba_pencos,_pdf),_`
+2. `ba_goia_ba_world_s`
+3. `ia_bukoroi_bau,_in`
+
+**Context Size 4:**
+
+1. `_ba_zi_töi-töra_wa'`
+2. `a_ba_lalau_bible_in`
+3. `_niha,_niha_ya'ia_d`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (91,854 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 | 15,228 |
+| Total Tokens | 420,205 |
+| Mean Frequency | 27.59 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 348.05 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ba | 34,969 |
+| 2 | ö | 11,724 |
+| 3 | no | 7,652 |
+| 4 | niha | 6,922 |
+| 5 | ia | 6,234 |
+| 6 | si | 6,090 |
+| 7 | da | 5,122 |
+| 8 | so | 4,824 |
+| 9 | ma | 4,273 |
+| 10 | a | 3,856 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | faustina | 2 |
+| 2 | kowalska | 2 |
+| 3 | margaret | 2 |
+| 4 | keynote | 2 |
+| 5 | sondröi | 2 |
+| 6 | penanggulangan | 2 |
+| 7 | bnpb | 2 |
+| 8 | risks | 2 |
+| 9 | maenamölö | 2 |
+| 10 | sadaŵa | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1818 |
+| R² (Goodness of Fit) | 0.992823 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 49.9% |
+| Top 1,000 | 77.0% |
+| Top 5,000 | 92.6% |
+| Top 10,000 | 97.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9928 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 49.9% of corpus
+- **Long Tail:** 5,228 words needed for remaining 2.5% 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 | 0.5991 🏆 | 0.3687 | N/A | N/A |
+| **mono_64d** | 64 | 0.2083 | 0.3585 | N/A | N/A |
+| **mono_128d** | 128 | 0.0338 | 0.3754 | N/A | N/A |
+| **aligned_32d** | 32 | 0.5991 | 0.3706 | 0.0080 | 0.1460 |
+| **aligned_64d** | 64 | 0.2083 | 0.3741 | 0.0360 | 0.2220 |
+| **aligned_128d** | 128 | 0.0338 | 0.3809 | 0.0660 | 0.2520 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.5991 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3714. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.6% 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 | **0.441** | 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 |
+|--------|----------|
+| `-s` | samaria, sifaudu, sanadrösa |
+| `-a` | annual, april, asimola |
+| `-m` | mealu, musa, maharaja |
+| `-fa` | fakhögusa, famakiko, fanemali |
+| `-la` | lagundri, law, labuan |
+| `-ma` | maharaja, mangebua, mangolombu |
+| `-t` | tafo, tebözi, terletak |
+| `-b` | bawaslu, boyo, bela |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | fakhögusa, gahulua, kelera |
+| `-i` | orahuahili, nifaböbözi, tebözi |
+| `-n` | cablin, pembibitan, linn |
+| `-an` | pembibitan, labuan, saluran |
+| `-ia` | samaria, furinia, norwegia |
+| `-ö` | tö, wurugö, awögö |
+| `-s` | teoretis, cardinals, habis |
+| `-e` | dete, carmelite, importance |
+
+### 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 |
+|------|----------|------------------|----------|
+| `andr` | 1.62x | 63 contexts | andrö, andre, andré |
+| `anga` | 1.46x | 86 contexts | zanga, fanga, tanga |
+| `wang` | 1.76x | 29 contexts | wango, wanga, wangi |
+| `amat` | 1.56x | 40 contexts | amate, camat, zamati |
+| `akha` | 1.53x | 43 contexts | lakha, bakha, fakha |
+| `atör` | 1.93x | 18 contexts | atöra, atörö, tatörö |
+| `ndrö` | 1.54x | 36 contexts | andrö, indrö, ndröni |
+| `ambu` | 1.57x | 30 contexts | sambu, tambu, hambu |
+| `anua` | 1.77x | 20 contexts | wanua, banua, manual |
+| `ndre` | 1.63x | 18 contexts | andre, undre, ndrela |
+| `nötö` | 1.60x | 19 contexts | inötö, ginötö, sanötö |
+| `ogun` | 1.94x | 10 contexts | oguna, ogunaö, moguna |
+
+### 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` | `-a` | 115 words | samuza, siraha |
+| `-a` | `-a` | 99 words | abölönia, alawenia |
+| `-m` | `-a` | 87 words | morena, manila |
+| `-s` | `-i` | 77 words | samaehusi, solakhömi |
+| `-k` | `-n` | 75 words | kelaparan, keputusan |
+| `-b` | `-a` | 73 words | bersabda, bawanguma |
+| `-fa` | `-a` | 70 words | fangöhöna, fadekha |
+| `-m` | `-i` | 66 words | manöi, molakhömi |
+| `-k` | `-an` | 64 words | kelaparan, keputusan |
+| `-t` | `-a` | 60 words | terpena, timna |
+
+### 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 |
+|------|-----------------|------------|------|
+| whitehead | **`whitehe-a-d`** | 7.5 | `a` |
+| mangomasi | **`mangom-a-si`** | 7.5 | `a` |
+| tandraigö | **`tandra-i-gö`** | 7.5 | `i` |
+| mengenang | **`mengen-a-ng`** | 7.5 | `a` |
+| hikayania | **`hikay-an-ia`** | 7.5 | `an` |
+| hilibanua | **`hilib-an-ua`** | 7.5 | `an` |
+| nifahaönia | **`nifahaö-n-ia`** | 7.5 | `n` |
+| ahuluania | **`ahulu-an-ia`** | 7.5 | `an` |
+| mangalani | **`mangal-an-i`** | 7.5 | `an` |
+| nituriaigö | **`nituria-i-gö`** | 7.5 | `i` |
+| proposisi | **`propo-si-si`** | 7.5 | `si` |
+| galadanga | **`ga-la-danga`** | 7.5 | `danga` |
+| sangosili | **`sango-si-li`** | 7.5 | `si` |
+| hilimböwöma | **`hilimböwö-m-a`** | 7.5 | `m` |
+| nifaduhusira | **`nifaduhu-si-ra`** | 7.5 | `si` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Nias 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 (4.01x) |
+| N-gram | **2-gram** | Lowest perplexity (217) |
+| Markov | **Context-4** | Highest predictability (95.9%) |
+| 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 14:55:14*

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