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+---
+language: tpi
+language_name: Tok Pisin
+language_family: germanic_west_anglofrisian
+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-germanic_west_anglofrisian
+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: 4.037
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0778
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tok Pisin - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tok Pisin** 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** | 3.783x | 3.79 | 0.8512% | 89,876 |
+| **16k** | 4.037x 🏆 | 4.05 | 0.9083% | 84,227 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `emi wanpela taun long Soria provins, Castile na León, Spen. provins`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁emi ▁wanpela ▁taun ▁long ▁soria ▁provins , ▁castile ▁na ▁león ... (+4 more)` | 14 |
+| 16k | `▁emi ▁wanpela ▁taun ▁long ▁soria ▁provins , ▁castile ▁na ▁león ... (+4 more)` | 14 |
+
+**Sample 2:** `Kerema em i kapitol na taun bikpela tumas bilong Gulf provins long Papua Niugini...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁kerema ▁em ▁i ▁kapitol ▁na ▁taun ▁bikpela ▁tumas ▁bilong ▁gulf ... (+5 more)` | 15 |
+| 16k | `▁kerema ▁em ▁i ▁kapitol ▁na ▁taun ▁bikpela ▁tumas ▁bilong ▁gulf ... (+5 more)` | 15 |
+
+**Sample 3:** `Palermo em i wanpela taun long Sisili long kantri Itali. Em igat 678.492 manmeri...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁palermo ▁em ▁i ▁wanpela ▁taun ▁long ▁sisili ▁long ▁kantri ▁itali ... (+14 more)` | 24 |
+| 16k | `▁palermo ▁em ▁i ▁wanpela ▁taun ▁long ▁sisili ▁long ▁kantri ▁itali ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 16k achieves 4.037x compression
+- **Lowest UNK Rate:** 8k with 0.8512% 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 | 765 | 9.58 | 1,782 | 41.8% | 85.7% |
+| **2-gram** | Subword | 220 🏆 | 7.78 | 1,423 | 75.2% | 99.3% |
+| **3-gram** | Word | 1,436 | 10.49 | 2,504 | 30.0% | 71.1% |
+| **3-gram** | Subword | 1,252 | 10.29 | 7,330 | 36.8% | 80.5% |
+| **4-gram** | Word | 3,719 | 11.86 | 5,474 | 17.7% | 43.3% |
+| **4-gram** | Subword | 4,262 | 12.06 | 25,004 | 24.7% | 57.2% |
+| **5-gram** | Word | 3,008 | 11.55 | 4,258 | 18.3% | 44.4% |
+| **5-gram** | Subword | 7,473 | 12.87 | 36,235 | 20.2% | 48.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `em i` | 1,565 |
+| 2 | `ol i` | 502 |
+| 3 | `i gat` | 454 |
+| 4 | `i bin` | 429 |
+| 5 | `i wanpela` | 353 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `em i wanpela` | 277 |
+| 2 | `em i intanet` | 170 |
+| 3 | `i intanet kod` | 169 |
+| 4 | `intanet kod bilong` | 168 |
+| 5 | `i stap long` | 152 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `em i intanet kod` | 169 |
+| 2 | `i intanet kod bilong` | 168 |
+| 3 | `intanet kod bilong kantri` | 150 |
+| 4 | `emi wanpela taun long` | 77 |
+| 5 | `na león spen provins` | 73 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `em i intanet kod bilong` | 168 |
+| 2 | `i intanet kod bilong kantri` | 150 |
+| 3 | `provins castile na león spen` | 73 |
+| 4 | `castile na león spen provins` | 73 |
+| 5 | `wanpela taun long soria provins` | 70 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g` | 9,784 |
+| 2 | `o n` | 9,572 |
+| 3 | `i _` | 8,914 |
+| 4 | `l o` | 8,912 |
+| 5 | `a _` | 8,788 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 8,594 |
+| 2 | `o n g` | 8,176 |
+| 3 | `l o n` | 8,105 |
+| 4 | `_ i _` | 4,901 |
+| 5 | `_ b i` | 4,777 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l o n g` | 8,042 |
+| 2 | `o n g _` | 7,994 |
+| 3 | `_ l o n` | 4,532 |
+| 4 | `_ b i l` | 3,254 |
+| 5 | `i l o n` | 3,199 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l o n g _` | 7,945 |
+| 2 | `_ l o n g` | 4,521 |
+| 3 | `_ b i l o` | 3,195 |
+| 4 | `b i l o n` | 3,195 |
+| 5 | `i l o n g` | 3,194 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 220
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~48% 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 | 0.6340 | 1.552 | 3.43 | 10,055 | 36.6% |
+| **1** | Subword | 0.6982 | 1.622 | 4.77 | 907 | 30.2% |
+| **2** | Word | 0.2413 | 1.182 | 1.52 | 34,078 | 75.9% |
+| **2** | Subword | 0.7924 | 1.732 | 4.03 | 4,305 | 20.8% |
+| **3** | Word | 0.0987 | 1.071 | 1.16 | 51,273 | 90.1% |
+| **3** | Subword | 0.6704 | 1.591 | 2.82 | 17,280 | 33.0% |
+| **4** | Word | 0.0388 🏆 | 1.027 | 1.05 | 58,656 | 96.1% |
+| **4** | Subword | 0.4282 | 1.346 | 1.86 | 48,609 | 57.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `i mas save pairap inglis molecule o latvijas republika latvija letonia lv sv toppdomän f bihain`
+2. `long em ol kaikai long giraun papua niugini i save luksave olsem wanpela teritori bilong kantri`
+3. `bilong zeus`
+
+**Context Size 2:**
+
+1. `em i wanpela distrik long is samar provins nau long taim ol i makim bill skate i`
+2. `ol i yusim diatomit bilong wokim giaman stori bilong aeneas i gat mo rot tu tasol long`
+3. `i gat biknem long lotu na bagarap na yumi igat rait long senisim asples o kantri inap`
+
+**Context Size 3:**
+
+1. `em i wanpela pasin bilong raitim ol tok olsem wan wan leta i makim wanpela krai dispela i`
+2. `em i intanet kod bilong kantri siapan long esia 36 milion manmeri i stap abrus o waitpela manmeri`
+3. `i intanet kod bilong kantri kiribas ki sv toppdomän k`
+
+**Context Size 4:**
+
+1. `em i intanet kod bilong kantri siamani de sv toppdomän d`
+2. `i intanet kod bilong ascension insait kantri sen helena ascension na tristan da kuna ac`
+3. `intanet kod bilong kantri solomon ailans slb`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_тступоваув_5976`
+2. `alelutaina_binge`
+3. `i_блав_le_lon_vi`
+
+**Context Size 2:**
+
+1. `ng_kong_van_wan_t`
+2. `ong_kripenis:_лек`
+3. `i_lusianwanpeleón`
+
+**Context Size 3:**
+
+1. `ng_holimigur_20_49`
+2. `ong_mp3_familipim_`
+3. `long_manmeri_inter`
+
+**Context Size 4:**
+
+1. `long_graun_bikpela_`
+2. `ong_diksen_bilong_s`
+3. `_long_haus_wanpela_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (48,609 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 | 4,414 |
+| Total Tokens | 68,197 |
+| Mean Frequency | 15.45 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 129.66 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | i | 4,945 |
+| 2 | long | 4,543 |
+| 3 | bilong | 3,174 |
+| 4 | na | 2,044 |
+| 5 | em | 2,006 |
+| 6 | ol | 2,005 |
+| 7 | wanpela | 937 |
+| 8 | kantri | 793 |
+| 9 | tok | 737 |
+| 10 | olsem | 581 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | iucn | 2 |
+| 2 | tudakpela | 2 |
+| 3 | haitim | 2 |
+| 4 | transformer | 2 |
+| 5 | pletfom | 2 |
+| 6 | nintendo | 2 |
+| 7 | return | 2 |
+| 8 | deluxe | 2 |
+| 9 | allies | 2 |
+| 10 | forgotten | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0374 |
+| R² (Goodness of Fit) | 0.984176 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 58.9% |
+| Top 1,000 | 85.6% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9842 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 58.9% of corpus
+- **Long Tail:** -5,586 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 | 0.0778 🏆 | 0.6368 | N/A | N/A |
+| **mono_64d** | 64 | 0.0142 | 0.6826 | N/A | N/A |
+| **mono_128d** | 128 | 0.0027 | 0.6822 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0778 | 0.6434 | 0.0080 | 0.0900 |
+| **aligned_64d** | 64 | 0.0142 | 0.6713 | 0.0120 | 0.0680 |
+| **aligned_128d** | 128 | 0.0027 | 0.6897 | 0.0060 | 0.0560 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.0778 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.6677. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.2% 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 | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.076** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | sutim, stude, science |
+| `-p` | ponoloji, papa, puławy |
+| `-b` | bikpla, by, bringim |
+| `-m` | montreal, mick, mindanao |
+| `-a` | andersen, amamas, anderson |
+| `-k` | katim, konversen, kainantu |
+| `-t` | toledo, tuesday, territories |
+| `-ma` | maui, mathew, masta |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | despla, bikpla, papa |
+| `-n` | circumcision, andersen, yunien |
+| `-s` | opis, ogastas, territories |
+| `-e` | stude, hangre, science |
+| `-m` | sutim, lukautim, katim |
+| `-en` | andersen, yunien, konversen |
+| `-an` | giaman, independan, aislan |
+| `-l` | montreal, medal, kaunsil |
+
+### 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 |
+|------|----------|------------------|----------|
+| `tpel` | 1.44x | 7 contexts | etpela, retpela, sotpela |
+| `inim` | 1.38x | 6 contexts | winim, minim, painim |
+| `arap` | 1.37x | 6 contexts | narapla, bagarap, arapela |
+| `amba` | 1.35x | 6 contexts | namba, nambafo, nambaut |
+| `namb` | 1.36x | 5 contexts | namba, nambis, nambafo |
+
+### 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` | 27 words | palawan, plen |
+| `-m` | `-a` | 25 words | mipela, masta |
+| `-s` | `-a` | 23 words | sevilla, sta |
+| `-a` | `-n` | 22 words | andersen, anderson |
+| `-s` | `-n` | 21 words | sandaun, suwisalan |
+| `-s` | `-s` | 20 words | saiens, songs |
+| `-a` | `-a` | 18 words | aljiria, angila |
+| `-p` | `-a` | 17 words | papa, palencia |
+| `-b` | `-a` | 17 words | bikpla, brata |
+| `-k` | `-a` | 16 words | kaledonia, kompyuta |
+
+### 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 |
+|------|-----------------|------------|------|
+| independans | **`independ-an-s`** | 7.5 | `an` |
+| vientiane | **`vienti-an-e`** | 7.5 | `an` |
+| pensilvania | **`pensilv-an-ia`** | 7.5 | `an` |
+| filipinas | **`filipin-a-s`** | 7.5 | `a` |
+| eksaminim | **`eksam-in-im`** | 7.5 | `in` |
+| konstitusen | **`konstitu-s-en`** | 7.5 | `s` |
+| deutschland | **`deutsch-la-nd`** | 7.5 | `la` |
+| plantikain | **`planti-ka-in`** | 7.5 | `ka` |
+| manmanmeri | **`m-an-manmeri`** | 7.5 | `manmeri` |
+| toktokman | **`toktok-m-an`** | 7.5 | `m` |
+| representim | **`re-present-im`** | 6.0 | `present` |
+| periodical | **`periodic-al`** | 4.5 | `periodic` |
+| champions | **`champion-s`** | 4.5 | `champion` |
+| provinsel | **`provins-el`** | 4.5 | `provins` |
+| internationale | **`international-e`** | 4.5 | `international` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tok Pisin 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 | **16k BPE** | Best compression (4.04x) |
+| N-gram | **2-gram** | Lowest perplexity (220) |
+| Markov | **Context-4** | Highest predictability (96.1%) |
+| 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: 2026-01-11 01:31:19*
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