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+---
+language: sm
+language_name: Samoan
+language_family: austronesian_polynesian
+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_polynesian
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.699
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2278
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Samoan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Samoan** 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** | 3.479x | 3.48 | 0.3471% | 262,440 |
+| **16k** | 3.631x | 3.63 | 0.3622% | 251,487 |
+| **32k** | 3.699x 🏆 | 3.70 | 0.3691% | 246,822 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Faleu o le motu i Samoa e tu i le va o Upolu ma Savai'i. E 354 tagata e nonofo i...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁faleu ▁o ▁le ▁motu ▁i ▁samoa ▁e ▁tu ▁i ▁le ... (+18 more)` | 28 |
+| 16k | `▁faleu ▁o ▁le ▁motu ▁i ▁samoa ▁e ▁tu ▁i ▁le ... (+18 more)` | 28 |
+| 32k | `▁faleu ▁o ▁le ▁motu ▁i ▁samoa ▁e ▁tu ▁i ▁le ... (+18 more)` | 28 |
+
+**Sample 2:** `'O Porirua, 'o se pitonu'u o Ueligitone, e tū i le itū i mātū o Ueligitone. 'O l...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁' o ▁po rirua , ▁' o ▁se ▁pitonu ' ... (+35 more)` | 45 |
+| 16k | `▁' o ▁porirua , ▁' o ▁se ▁pitonu ' u ... (+33 more)` | 43 |
+| 32k | `▁' o ▁porirua , ▁' o ▁se ▁pitonu ' u ... (+33 more)` | 43 |
+
+**Sample 3:** `Gagana Urdu o le igoa o se tasi o gagana sili e tautalagia i Asia i Saute. o se ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gagana ▁u rd u ▁o ▁le ▁igoa ▁o ▁se ▁tasi ... (+19 more)` | 29 |
+| 16k | `▁gagana ▁urdu ▁o ▁le ▁igoa ▁o ▁se ▁tasi ▁o ▁gagana ... (+17 more)` | 27 |
+| 32k | `▁gagana ▁urdu ▁o ▁le ▁igoa ▁o ▁se ▁tasi ▁o ▁gagana ... (+17 more)` | 27 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 3.699x compression
+- **Lowest UNK Rate:** 8k with 0.3471% 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 | 1,420 | 10.47 | 5,447 | 34.7% | 69.5% |
+| **2-gram** | Subword | 148 🏆 | 7.21 | 1,516 | 82.0% | 99.6% |
+| **3-gram** | Word | 4,688 | 12.19 | 9,293 | 16.7% | 49.5% |
+| **3-gram** | Subword | 941 | 9.88 | 9,076 | 43.7% | 85.0% |
+| **4-gram** | Word | 8,012 | 12.97 | 14,168 | 15.4% | 36.7% |
+| **4-gram** | Subword | 3,888 | 11.92 | 32,524 | 25.1% | 60.3% |
+| **5-gram** | Word | 5,147 | 12.33 | 8,822 | 19.7% | 40.7% |
+| **5-gram** | Subword | 9,558 | 13.22 | 54,942 | 16.3% | 44.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `o le` | 9,077 |
+| 2 | `i le` | 5,656 |
+| 3 | `ma le` | 1,981 |
+| 4 | `o se` | 1,645 |
+| 5 | `ai le` | 934 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `le itu i` | 323 |
+| 2 | `i totonu o` | 318 |
+| 3 | `le tele o` | 314 |
+| 4 | `i le itu` | 292 |
+| 5 | `i le taimi` | 261 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i le itu i` | 270 |
+| 2 | `i totonu o le` | 162 |
+| 3 | `i luga o le` | 161 |
+| 4 | `i le taimi o` | 148 |
+| 5 | `ina ua mavae le` | 144 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i le taimi o le` | 117 |
+| 2 | `le fuainumera o roma e` | 109 |
+| 3 | `ma le numera i luma` | 109 |
+| 4 | `i le fuainumera o roma` | 109 |
+| 5 | `numera ina ua mavae le` | 109 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 53,637 |
+| 2 | `e _` | 43,766 |
+| 3 | `_ l` | 34,339 |
+| 4 | `l e` | 32,460 |
+| 5 | `i _` | 31,222 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l e` | 26,576 |
+| 2 | `l e _` | 26,204 |
+| 3 | `_ o _` | 19,315 |
+| 4 | `_ m a` | 14,321 |
+| 5 | `o _ l` | 11,791 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l e _` | 23,778 |
+| 2 | `o _ l e` | 10,327 |
+| 3 | `_ o _ l` | 10,137 |
+| 4 | `i _ l e` | 8,107 |
+| 5 | `a _ o _` | 6,868 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `o _ l e _` | 9,763 |
+| 2 | `_ o _ l e` | 8,925 |
+| 3 | `i _ l e _` | 7,577 |
+| 4 | `_ i _ l e` | 5,715 |
+| 5 | `a _ l e _` | 4,253 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 148
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~44% 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 | 0.7561 | 1.689 | 4.50 | 15,898 | 24.4% |
+| **1** | Subword | 0.7846 | 1.723 | 5.33 | 833 | 21.5% |
+| **2** | Word | 0.3231 | 1.251 | 1.84 | 71,107 | 67.7% |
+| **2** | Subword | 0.8391 | 1.789 | 4.50 | 4,437 | 16.1% |
+| **3** | Word | 0.1599 | 1.117 | 1.31 | 130,468 | 84.0% |
+| **3** | Subword | 0.7319 | 1.661 | 3.20 | 19,925 | 26.8% |
+| **4** | Word | 0.0696 🏆 | 1.049 | 1.11 | 170,247 | 93.0% |
+| **4** | Subword | 0.4868 | 1.401 | 2.10 | 63,588 | 51.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `le fasi vaega aai tagata saina iunite setete o le taimi lona tino o se tamaoaiga`
+2. `o le ʻulu taumamao pick the pacific ma talitonuga i le tausaga e mafai ona tagata`
+3. `i comoros ma aganu u ma o se tasi pe nautele e sumpini ma agafesootai faasalalauga`
+
+**Context Size 2:**
+
+1. `o le numera i luma 13 i saint léonard de noblat mau faasino o isi taaloga lauiloa`
+2. `i le i umi a ua o le atunuu i matu ma i ni tausaga o le`
+3. `ma le pulega a siamani sa ina ua maeʻa ona faʻaleaogaina le tulafono lea na faʻatulagaina e`
+
+**Context Size 3:**
+
+1. `le itu i sasae ma vao mago i le ogatotonu ma le taufaaiuiuga o le na faatoilaloina malo`
+2. `i totonu o fale gaosi mea manogi ma le fuala au e a ai iai tagata`
+3. `le tele o malaga militeli i amazonia ma na latou manumalo i au peretania ma holani na faʻatutuina`
+
+**Context Size 4:**
+
+1. `i le itu i matu i le ina ua manumalo ia mehmet ali o le na toe faafoi mai`
+2. `i totonu o le taimi μ 2σ ma le mea e le ai μ o le galuega taua ona`
+3. `i luga o le koluse e pei o le us ma fa atau atu i lapopo a masani po`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_chale_mailaʻa,j`
+2. `aau_me_akma_ta_l`
+3. `ino._ma_ve_o'ita`
+
+**Context Size 2:**
+
+1. `a_se_181_mafa'i_f`
+2. `e_kalosi_e_faʻalo`
+3. `_le_pala,_e_mesei`
+
+**Context Size 3:**
+
+1. `_le_o_featrodriver`
+2. `le_tusitu_o_luga_f`
+3. `_o_le_upu_i_le_lal`
+
+**Context Size 4:**
+
+1. `_le_masani_ma_pi'i_`
+2. `o_le_fa'atatau_e_om`
+3. `_o_le_vaomalo_o_le_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (63,588 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 | 6,946 |
+| Total Tokens | 205,396 |
+| Mean Frequency | 29.57 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 439.18 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | le | 23,989 |
+| 2 | o | 21,093 |
+| 3 | i | 12,188 |
+| 4 | e | 7,623 |
+| 5 | ma | 6,494 |
+| 6 | ai | 3,240 |
+| 7 | se | 2,986 |
+| 8 | fa | 2,814 |
+| 9 | a | 2,774 |
+| 10 | na | 2,325 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | eisleben | 2 |
+| 2 | magdeburg | 2 |
+| 3 | halle | 2 |
+| 4 | saale | 2 |
+| 5 | 451 | 2 |
+| 6 | komiunisi | 2 |
+| 7 | stasi | 2 |
+| 8 | henryk | 2 |
+| 9 | dominiak | 2 |
+| 10 | tychy | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1786 |
+| R² (Goodness of Fit) | 0.991320 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 63.2% |
+| Top 1,000 | 86.7% |
+| Top 5,000 | 98.1% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9913 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 63.2% of corpus
+- **Long Tail:** -3,054 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 | 0.2278 🏆 | 0.4650 | N/A | N/A |
+| **mono_64d** | 64 | 0.0423 | 0.4640 | N/A | N/A |
+| **mono_128d** | 128 | 0.0056 | 0.4667 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2278 | 0.4475 | 0.0180 | 0.1140 |
+| **aligned_64d** | 64 | 0.0423 | 0.4740 | 0.0100 | 0.1280 |
+| **aligned_128d** | 128 | 0.0056 | 0.4559 | 0.0100 | 0.1320 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.2278 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4622. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.8% 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 | **-0.073** | 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 |
+|--------|----------|
+| `-a` | aofaiga, antoine, amaloloina |
+| `-t` | taunuu, tamaloloa, tioata |
+| `-s` | sofia, saita, siaki |
+| `-fa` | faautauta, faatumauina, faamatalaina |
+| `-ma` | macon, mataʻafa, maui |
+| `-m` | macon, mataʻafa, maui |
+| `-f` | faautauta, fetolofi, fuga |
+| `-p` | perth, pa, portuguese |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | faautauta, aofaiga, tamaloloa |
+| `-na` | faatumauina, amaloloina, faamatalaina |
+| `-i` | fetolofi, igilisi, siaki |
+| `-ga` | aofaiga, fuga, aleaga |
+| `-e` | antoine, portuguese, die |
+| `-ia` | sofia, alapenia, omia |
+| `-o` | faalagolago, fono, lafo |
+| `-n` | macon, region, australien |
+
+### 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 |
+|------|----------|------------------|----------|
+| `faat` | 1.79x | 10 contexts | faatoa, faatau, faatonu |
+| `usia` | 1.48x | 15 contexts | lusia, fusia, tusia |
+| `aata` | 1.78x | 9 contexts | alaata, faatau, faatasi |
+| `alol` | 1.56x | 11 contexts | malolo, malole, palolo |
+| `atas` | 1.46x | 13 contexts | atasi, atasia, atassi |
+| `amat` | 1.36x | 14 contexts | amata, tamato, mamate |
+| `loga` | 1.51x | 10 contexts | iloga, aloga, pologa |
+| `aʻat` | 1.86x | 6 contexts | faʻatau, faʻatasi, faʻatusa |
+| `atal` | 1.30x | 15 contexts | atali, matala, atalii |
+| `faas` | 1.65x | 7 contexts | faasee, faasao, faasoa |
+| `tion` | 1.54x | 8 contexts | action, station, section |
+| `mafa` | 1.56x | 7 contexts | mafai, mafaia, mamafa |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-fa` | `-a` | 323 words | faautauta, faatumauina |
+| `-a` | `-a` | 202 words | aofaiga, amaloloina |
+| `-t` | `-a` | 140 words | tamaloloa, tioata |
+| `-fa` | `-na` | 128 words | faatumauina, faamatalaina |
+| `-fa` | `-ga` | 104 words | faʻasinomaga, faʻauʻuga |
+| `-s` | `-a` | 70 words | sofia, saita |
+| `-a` | `-na` | 67 words | amaloloina, aolaolaina |
+| `-fa` | `-i` | 61 words | faafetaui, faamaoti |
+| `-f` | `-a` | 61 words | faautauta, fuga |
+| `-ma` | `-a` | 60 words | mataʻafa, manatuaina |
+
+### 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 |
+|------|-----------------|------------|------|
+| faatapulaa | **`faatapul-a-a`** | 7.5 | `a` |
+| mulimulitai | **`mulimuli-ta-i`** | 7.5 | `ta` |
+| television | **`televis-i-on`** | 7.5 | `i` |
+| atinaeina | **`atinae-i-na`** | 7.5 | `i` |
+| faatulaga | **`fa-a-tulaga`** | 7.5 | `tulaga` |
+| faʻamoemoeina | **`faʻamoemoe-i-na`** | 7.5 | `i` |
+| faataunuuina | **`faataunuu-i-na`** | 7.5 | `i` |
+| felagolagomai | **`felagolagom-a-i`** | 7.5 | `a` |
+| mataituina | **`mataitu-i-na`** | 7.5 | `i` |
+| faatosina | **`faato-si-na`** | 7.5 | `si` |
+| faaitulagi | **`fa-a-itulagi`** | 7.5 | `itulagi` |
+| limasefulu | **`li-ma-sefulu`** | 7.5 | `sefulu` |
+| faʻatulaga | **`faʻatul-a-ga`** | 7.5 | `a` |
+| fonotatalo | **`fonotat-a-lo`** | 7.5 | `a` |
+| vaʻavaʻaia | **`vaʻavaʻ-a-ia`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Samoan 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 (3.70x) |
+| N-gram | **2-gram** | Lowest perplexity (148) |
+| Markov | **Context-4** | Highest predictability (93.0%) |
+| 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 21:21:35*
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