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+---
+language: sg
+language_name: Sango
+language_family: atlantic_gur
+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-atlantic_gur
+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: 3.952
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0186
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sango - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sango** 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** | 3.952x 🏆 | 3.96 | 0.9228% | 51,148 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Sêse tî kömändâ-kötä tî Bamïngï-Bangoran yeke sêse tî kömändâ-kötä nî ayeke tî K...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sêse ▁tî ▁kömändâ - kötä ▁tî ▁bamïngï - bangoran ▁yeke ... (+27 more)` | 37 |
+
+**Sample 2:** `Laâ mbênî sêse. Wuhngo tî âzo nî ayeke Tî lo likodoro Kuala Lumpur.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁laâ ▁mbênî ▁sêse . ▁wuhngo ▁tî ▁âzo ▁nî ▁ayeke ▁tî ... (+5 more)` | 15 |
+
+**Sample 3:** `Gbêko tî Ngunuhalëzo tî Brésil yeke sêse nî ayeke tî Amerîka. Tî lo likodoro Bré...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gbêko ▁tî ▁ngunuhalëzo ▁tî ▁brésil ▁yeke ▁sêse ▁nî ▁ayeke ▁tî ... (+15 more)` | 25 |
+
+
+### Key Findings
+
+- **Best Compression:** 8k achieves 3.952x compression
+- **Lowest UNK Rate:** 8k with 0.9228% 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 | 182 | 7.51 | 544 | 75.4% | 100.0% |
+| **2-gram** | Subword | 323 | 8.34 | 1,244 | 61.6% | 99.3% |
+| **3-gram** | Word | 134 | 7.07 | 617 | 80.7% | 100.0% |
+| **3-gram** | Subword | 1,512 | 10.56 | 5,472 | 29.1% | 79.0% |
+| **4-gram** | Word | 148 | 7.21 | 1,001 | 78.5% | 100.0% |
+| **4-gram** | Subword | 3,422 | 11.74 | 14,491 | 20.6% | 62.7% |
+| **5-gram** | Word | 93 🏆 | 6.54 | 629 | 85.5% | 100.0% |
+| **5-gram** | Subword | 4,119 | 12.01 | 17,226 | 18.0% | 59.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nî ayeke` | 342 |
+| 2 | `ayeke tî` | 274 |
+| 3 | `diki kidiri` | 244 |
+| 4 | `sango français` | 241 |
+| 5 | `jean marie` | 241 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dictionnaire sango français` | 241 |
+| 2 | `vallet jacqueline behaghel` | 240 |
+| 3 | `kidiri marcel vallet` | 240 |
+| 4 | `diki kidiri marcel` | 240 |
+| 5 | `marie diki kidiri` | 240 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kidiri marcel vallet jacqueline` | 240 |
+| 2 | `vallet jacqueline behaghel anne` | 240 |
+| 3 | `jacqueline behaghel anne dictionnaire` | 240 |
+| 4 | `et lexique français sango` | 240 |
+| 5 | `lexique français sango paris` | 240 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `lexique français sango paris société` | 240 |
+| 2 | `français sango paris société des` | 240 |
+| 3 | `sango paris société des etudes` | 240 |
+| 4 | `jean marie diki kidiri marcel` | 240 |
+| 5 | `paris société des etudes linguistiques` | 240 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 4,452 |
+| 2 | `a _` | 3,921 |
+| 3 | `_ t` | 3,234 |
+| 4 | `a n` | 3,077 |
+| 5 | `_ n` | 2,785 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t î` | 1,557 |
+| 2 | `t î _` | 1,534 |
+| 3 | `n a _` | 1,488 |
+| 4 | `_ n a` | 1,435 |
+| 5 | `e s _` | 1,233 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t î _` | 1,528 |
+| 2 | `_ n a _` | 1,338 |
+| 3 | `y e k e` | 1,023 |
+| 4 | `e k e _` | 999 |
+| 5 | `_ t i _` | 949 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `y e k e _` | 973 |
+| 2 | `a y e k e` | 700 |
+| 3 | `_ a y e k` | 699 |
+| 4 | `s a n g o` | 508 |
+| 5 | `_ f r a n` | 499 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 5-gram (word) with 93
+- **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 | 0.5456 | 1.460 | 2.61 | 5,855 | 45.4% |
+| **1** | Subword | 1.8459 | 3.595 | 13.77 | 187 | 0.0% |
+| **2** | Word | 0.1805 | 1.133 | 1.32 | 15,119 | 81.9% |
+| **2** | Subword | 1.0753 | 2.107 | 4.96 | 2,575 | 0.0% |
+| **3** | Word | 0.0672 | 1.048 | 1.10 | 19,680 | 93.3% |
+| **3** | Subword | 0.6217 | 1.539 | 2.52 | 12,742 | 37.8% |
+| **4** | Word | 0.0283 🏆 | 1.020 | 1.04 | 21,418 | 97.2% |
+| **4** | Subword | 0.3412 | 1.267 | 1.61 | 32,005 | 65.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `tî web na text video tî ködörö nî âyeke na institut ed šiprage list karte 1`
+2. `na bomoi ya spécialisés pona environnement fabrication asengaka esika ya mvula économie ya boîtier m...`
+3. `ti kodoro ti lo yeke tohgbata nî dïngö ïrï tî kömändâ kötä tî attaque trois front`
+
+**Context Size 2:**
+
+1. `nî ayeke 45 421 tî bêafrîka wuhngo tî âzo nî ayeke wuhngo tî âzo nî ayeke tî`
+2. `ayeke tî utiliser pou tî écrire document ex word envoyer message ex whatsapp jouer vidéo ex youtube`
+3. `diki kidiri marcel vallet jacqueline behaghel anne dictionnaire sango français et lexique français s...`
+
+**Context Size 3:**
+
+1. `dictionnaire sango français et lexique français sango paris société des etudes linguistiques et anth...`
+2. `des etudes linguistiques et anthropologiques de france selaf isbn lïndïpa fîtasü ngbônga`
+3. `jean marie diki kidiri marcel vallet jacqueline behaghel anne dictionnaire sango français et lexique...`
+
+**Context Size 4:**
+
+1. `français et lexique français sango paris société des etudes linguistiques et anthropologiques de fra...`
+2. `paris société des etudes linguistiques et anthropologiques de france selaf isbn lïndïpa fîtasü ngbôn...`
+3. `kobozo jean marie diki kidiri marcel vallet jacqueline behaghel anne dictionnaire sango français et ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ya_del;_aprfr_n`
+2. `andetidre_zes_ti`
+3. `e_goi_rcet,_eoye`
+
+**Context Size 2:**
+
+1. `e_tî_fonnazo,_let`
+2. `a_victi_irie;_köd`
+3. `_tî_bênî_bur_tî_a`
+
+**Context Size 3:**
+
+1. `_tî_piècle_tî_lexi`
+2. `tî_bê_na_mbit_envi`
+3. `na_portablet,_jean`
+
+**Context Size 4:**
+
+1. `_tî_19_june_♆_sêse_`
+2. `_na_ngoi_ni_matéris`
+3. `eke_na_nde,_safety_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (32,005 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 | 2,202 |
+| Total Tokens | 28,828 |
+| Mean Frequency | 13.09 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 63.07 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tî | 1,555 |
+| 2 | na | 1,394 |
+| 3 | ti | 954 |
+| 4 | ayeke | 700 |
+| 5 | sango | 501 |
+| 6 | français | 491 |
+| 7 | et | 490 |
+| 8 | nî | 429 |
+| 9 | ya | 353 |
+| 10 | de | 348 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | cryptocurrency | 2 |
+| 2 | revenue | 2 |
+| 3 | annually | 2 |
+| 4 | networking | 2 |
+| 5 | kômbûtêrê | 2 |
+| 6 | ebimisaki | 2 |
+| 7 | makambo | 2 |
+| 8 | versions | 2 |
+| 9 | linyama | 2 |
+| 10 | pasëpë | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0050 |
+| R² (Goodness of Fit) | 0.981661 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 64.3% |
+| Top 1,000 | 90.5% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9817 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 64.3% of corpus
+- **Long Tail:** -7,798 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 | 0.0186 | 0.6671 | N/A | N/A |
+| **mono_64d** | 64 | 0.0028 | 0.7207 | N/A | N/A |
+| **mono_128d** | 128 | 0.0006 | 0.7186 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0186 🏆 | 0.6706 | 0.0181 | 0.1088 |
+| **aligned_64d** | 64 | 0.0028 | 0.7005 | 0.0181 | 0.0967 |
+| **aligned_128d** | 128 | 0.0006 | 0.7069 | 0.0181 | 0.0967 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.0186 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.6974. 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 | **2.386** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.669** | 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` | ahébreu, aider, arm |
+| `-m` | mitindá, mîlyon, microsoft |
+| `-co` | contenus, consecutivos, company |
+| `-ma` | market, marie, matthieu |
+| `-ba` | bakarî, bakurê, basalelaka |
+| `-mo` | modèle, mobile, moke |
+| `-mb` | mbala, mbâgë, mbilimbili |
+| `-pr` | produit, projets, produits |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | chronique, renaissance, wande |
+| `-s` | temps, cross, platforms |
+| `-a` | kopeta, kamâra, mbala |
+| `-on` | mîlyon, billion, distraction |
+| `-er` | aider, créer, afficher |
+| `-es` | patrocinadores, externes, tendances |
+| `-re` | décembre, transmettre, vêre |
+| `-le` | modèle, gbele, symbole |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ango` | 1.40x | 15 contexts | angoi, sango, fango |
+| `anga` | 1.35x | 10 contexts | yanga, kanga, banga |
+| `ique` | 1.32x | 6 contexts | logique, lexique, cliquer |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-e` | 20 words | attaque, akomanse |
+| `-m` | `-e` | 17 words | modèle, marie |
+| `-co` | `-s` | 12 words | contenus, consecutivos |
+| `-in` | `-e` | 11 words | industrielle, informatique |
+| `-a` | `-a` | 10 words | akpa, asara |
+| `-a` | `-s` | 9 words | anglais, accès |
+| `-pr` | `-s` | 8 words | projets, produits |
+| `-in` | `-on` | 5 words | integration, information |
+| `-m` | `-s` | 5 words | mariages, melhores |
+| `-a` | `-re` | 5 words | agriculture, arrière |
+
+### 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 |
+|------|-----------------|------------|------|
+| platforms | **`platform-s`** | 4.5 | `platform` |
+| développeurs | **`développeur-s`** | 4.5 | `développeur` |
+| environnemental | **`environnement-al`** | 4.5 | `environnement` |
+| applications | **`application-s`** | 4.5 | `application` |
+| standards | **`standard-s`** | 4.5 | `standard` |
+| institute | **`institut-e`** | 4.5 | `institut` |
+| utilisateurs | **`utilisateur-s`** | 4.5 | `utilisateur` |
+| informations | **`information-s`** | 4.5 | `information` |
+| importante | **`important-e`** | 4.5 | `important` |
+| processeurs | **`processeur-s`** | 4.5 | `processeur` |
+| fonctions | **`fonction-s`** | 4.5 | `fonction` |
+| pratiques | **`pr-a-tiques`** | 4.5 | `tiques` |
+| documenter | **`document-er`** | 4.5 | `document` |
+| computers | **`computer-s`** | 4.5 | `computer` |
+| logiciels | **`logiciel-s`** | 4.5 | `logiciel` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sango 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 | **8k BPE** | Best compression (3.95x) |
+| N-gram | **5-gram** | Lowest perplexity (93) |
+| Markov | **Context-4** | Highest predictability (97.2%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 19:55:01*
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