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+---
+language: srn
+language_name: Sranan Tongo
+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: 4.163
+  - name: best_isotropy
+    type: isotropy
+    value: 0.1199
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sranan Tongo - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sranan Tongo** 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** | 3.920x | 3.93 | 0.1075% | 123,705 |
+| **16k** | 4.163x 🏆 | 4.17 | 0.1142% | 116,482 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Wan fisi e ben wan guru fu a Sabi fu libi biologisi Meti metiriki. Den fisi e li...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁wan ▁fisi ▁e ▁ben ▁wan ▁guru ▁fu ▁a ▁sabi ▁fu ... (+23 more)` | 33 |
+| 16k | `▁wan ▁fisi ▁e ▁ben ▁wan ▁guru ▁fu ▁a ▁sabi ▁fu ... (+20 more)` | 30 |
+
+**Sample 2:** `Atlanta ben wan presi ini Kondre Makandrameki. Flaku: 343 km² Man: 420 003`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁atlan ta ▁ben ▁wan ▁presi ▁ini ▁kondre ▁makandrameki . ▁flaku ... (+17 more)` | 27 |
+| 16k | `▁atlanta ▁ben ▁wan ▁presi ▁ini ▁kondre ▁makandrameki . ▁flaku : ... (+16 more)` | 26 |
+
+**Sample 3:** `George Washington (Fostu 22 dey, – Fostwarfu 14 dey, ben wan presidenti A Kondre...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁george ▁washington ▁( fos tu ▁ 2 2 ▁dey , ... (+14 more)` | 24 |
+| 16k | `▁george ▁washington ▁( fostu ▁ 2 2 ▁dey , ▁– ... (+13 more)` | 23 |
+
+
+### Key Findings
+
+- **Best Compression:** 16k achieves 4.163x compression
+- **Lowest UNK Rate:** 8k with 0.1075% 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 | 342 | 8.42 | 1,014 | 51.4% | 99.9% |
+| **2-gram** | Subword | 182 🏆 | 7.51 | 1,106 | 77.6% | 99.9% |
+| **3-gram** | Word | 482 | 8.91 | 1,278 | 40.5% | 98.7% |
+| **3-gram** | Subword | 963 | 9.91 | 6,594 | 42.8% | 86.2% |
+| **4-gram** | Word | 550 | 9.10 | 1,733 | 36.1% | 97.6% |
+| **4-gram** | Subword | 2,662 | 11.38 | 22,081 | 30.3% | 70.1% |
+| **5-gram** | Word | 506 | 8.98 | 1,385 | 37.3% | 98.7% |
+| **5-gram** | Subword | 3,796 | 11.89 | 29,372 | 24.6% | 64.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e ben` | 4,382 |
+| 2 | `ben wan` | 2,155 |
+| 3 | `ini a` | 804 |
+| 4 | `fu a` | 779 |
+| 5 | `e taki` | 743 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e ben wan` | 1,586 |
+| 2 | `yari e ben` | 695 |
+| 3 | `disi e ben` | 693 |
+| 4 | `a e ben` | 498 |
+| 5 | `man e taki` | 489 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a e ben wan` | 266 |
+| 2 | `yari e ben wan` | 239 |
+| 3 | `e ben taki a` | 230 |
+| 4 | `e ben disi e` | 229 |
+| 5 | `ben disi e ben` | 229 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e ben disi e ben` | 229 |
+| 2 | `ben leki ala yari e` | 228 |
+| 3 | `e ben leki ala yari` | 228 |
+| 4 | `tu no frugeti ma man` | 228 |
+| 5 | `no frugeti ma man e` | 228 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _` | 21,858 |
+| 2 | `n _` | 16,553 |
+| 3 | `a n` | 13,548 |
+| 4 | `a _` | 13,330 |
+| 5 | `e n` | 12,307 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n _` | 8,392 |
+| 2 | `_ e _` | 6,647 |
+| 3 | `a n _` | 6,634 |
+| 4 | `_ b e` | 6,317 |
+| 5 | `b e n` | 6,136 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b e n` | 6,064 |
+| 2 | `b e n _` | 4,777 |
+| 3 | `e _ b e` | 4,448 |
+| 4 | `_ e _ b` | 4,390 |
+| 5 | `w a n _` | 4,035 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b e n _` | 4,747 |
+| 2 | `e _ b e n` | 4,433 |
+| 3 | `_ e _ b e` | 4,384 |
+| 4 | `_ w a n _` | 3,791 |
+| 5 | `_ d i s i` | 3,412 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 182
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~64% 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 | 0.4288 | 1.346 | 2.41 | 11,359 | 57.1% |
+| **1** | Subword | 0.9691 | 1.958 | 6.55 | 475 | 3.1% |
+| **2** | Word | 0.1256 | 1.091 | 1.25 | 27,059 | 87.4% |
+| **2** | Subword | 0.8794 | 1.840 | 4.73 | 3,103 | 12.1% |
+| **3** | Word | 0.0454 | 1.032 | 1.08 | 33,532 | 95.5% |
+| **3** | Subword | 0.7551 | 1.688 | 3.16 | 14,656 | 24.5% |
+| **4** | Word | 0.0194 🏆 | 1.014 | 1.03 | 35,649 | 98.1% |
+| **4** | Subword | 0.4506 | 1.367 | 1.89 | 46,131 | 54.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `e taki dati disi e ben wili wan presi fu teri numro leki dekstiri`
+2. `ben wan tipisi frugetism yari ini go ten middeleeuwen ini disi e ben nc41 burco ben`
+3. `a minti arienzo e du a numro fu feti ini a mamafoto na 10 fostu instansi`
+
+**Context Size 2:**
+
+1. `e ben wan presi ini sranankondre stori geografi demografi legi si oktu tafra 1`
+2. `ben wan man ska abra taki disi ten ini go ten abra tengi di man pramisi tu`
+3. `ini a sranantongo tongo efru yu pasa no abra disi ondrowerpi yu e ben disi e ben`
+
+**Context Size 3:**
+
+1. `e ben wan kuri fu den owrur ten fu den medium ten middeleeuwen ini a bakratongo jesus dy`
+2. `yari e ben wili wan man meki no u wi somtengi frugeti e ben disi e ben taki`
+3. `disi e ben taki a salekism si oktu trawan meni fu ten tafra`
+
+**Context Size 4:**
+
+1. `a e ben wan ondrodeli fu a arrondissementi briey geografi a opoflaku fu aboncourt meurthe et moselle...`
+2. `yari e ben wan yari nanga pasa peyna nanga ledi ma oktu nanga gu tengi disi e ben no`
+3. `e ben taki a salekism si oktu tafra 82`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_n_(ma_a_ikateng`
+2. `anoramalinascing`
+3. `i_facoku_ten_ngu`
+
+**Context Size 2:**
+
+1. `i_wangi_masi_disi`
+2. `n_a_otecium_re_re`
+3. `an_num_res,_wariu`
+
+**Context Size 3:**
+
+1. `en_saleki_disi_yu_`
+2. `_e_ben_on_ini_alat`
+3. `an_komili_wan_the_`
+
+**Context Size 4:**
+
+1. `_ben_nowtu_(arabi_s`
+2. `ben_wan_merkirasil_`
+3. `e_ben_wan_fubenin_f`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (46,131 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 | 3,476 |
+| Total Tokens | 93,169 |
+| Mean Frequency | 26.80 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 228.66 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | e | 6,667 |
+| 2 | ben | 6,040 |
+| 3 | a | 5,047 |
+| 4 | wan | 3,840 |
+| 5 | fu | 3,602 |
+| 6 | disi | 3,403 |
+| 7 | ini | 2,307 |
+| 8 | nanga | 2,251 |
+| 9 | man | 1,857 |
+| 10 | no | 1,719 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | helsinki | 2 |
+| 2 | voro | 2 |
+| 3 | nationale | 2 |
+| 4 | bedrijf | 2 |
+| 5 | jari | 2 |
+| 6 | winod | 2 |
+| 7 | bba | 2 |
+| 8 | whanau | 2 |
+| 9 | pirimia | 2 |
+| 10 | wahine | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1374 |
+| R² (Goodness of Fit) | 0.962381 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 75.5% |
+| Top 1,000 | 93.3% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9624 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 75.5% of corpus
+- **Long Tail:** -6,524 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 | 0.1199 | 0.5444 | N/A | N/A |
+| **mono_64d** | 64 | 0.0180 | 0.5699 | N/A | N/A |
+| **mono_128d** | 128 | 0.0021 | 0.5677 | N/A | N/A |
+| **aligned_32d** | 32 | 0.1199 🏆 | 0.5345 | 0.0113 | 0.0998 |
+| **aligned_64d** | 64 | 0.0180 | 0.5387 | 0.0091 | 0.1293 |
+| **aligned_128d** | 128 | 0.0021 | 0.5606 | 0.0249 | 0.1406 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.1199 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.5526. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.5% 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 | **0.537** | 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` | surud, smadoti, siki |
+| `-a` | aban, animalia, area |
+| `-b` | bisi, boosaaso, botticelli |
+| `-m` | meijer, me, major |
+| `-k` | kankan, kanguru, kirkedomo |
+| `-p` | puspusi, peyna, part |
+| `-d` | dattie, doro, damme |
+| `-ma` | major, mar, mapana |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-i` | interaksi, feti, puspusi |
+| `-e` | trowe, me, camille |
+| `-n` | tjon, aban, granman |
+| `-a` | ndyuka, tarra, animalia |
+| `-s` | goolis, spijkers, ons |
+| `-o` | boosaaso, kirkedomo, trio |
+| `-ti` | feti, smadoti, santi |
+| `-re` | bere, italiyanikondre, condre |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `anga` | 1.31x | 12 contexts | langa, nanga, ganga |
+| `enti` | 1.47x | 7 contexts | efenti, sentir, peenti |
+
+### 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` | `-i` | 36 words | smadoti, siki |
+| `-p` | `-i` | 31 words | puspusi, prosenti |
+| `-k` | `-i` | 30 words | kripi, kongri |
+| `-m` | `-i` | 22 words | mindri, malsi |
+| `-b` | `-i` | 20 words | bisi, botticelli |
+| `-s` | `-e` | 20 words | stallone, singie |
+| `-a` | `-e` | 17 words | associazione, australiyankondre |
+| `-a` | `-i` | 17 words | akutimi, aktivisti |
+| `-d` | `-i` | 16 words | darmi, doysri |
+| `-m` | `-e` | 15 words | me, mike |
+
+### 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 |
+|------|-----------------|------------|------|
+| ukrainalanti | **`ukrainal-an-ti`** | 7.5 | `an` |
+| demokrasia | **`demokra-si-a`** | 7.5 | `si` |
+| politongo | **`po-li-tongo`** | 7.5 | `tongo` |
+| sentralanti | **`sentral-an-ti`** | 7.5 | `an` |
+| plandasie | **`planda-si-e`** | 7.5 | `si` |
+| sranantaki | **`sranant-a-ki`** | 7.5 | `a` |
+| importanti | **`import-an-ti`** | 7.5 | `an` |
+| ondrobenin | **`ondroben-i-n`** | 6.0 | `ondroben` |
+| victorien | **`victor-i-en`** | 6.0 | `victor` |
+| koptisches | **`koptische-s`** | 4.5 | `koptische` |
+| massimiliano | **`ma-s-similiano`** | 4.5 | `similiano` |
+| nederlands | **`nederland-s`** | 4.5 | `nederland` |
+| institute | **`institut-e`** | 4.5 | `institut` |
+| koptische | **`koptisch-e`** | 4.5 | `koptisch` |
+| verenigde | **`verenigd-e`** | 4.5 | `verenigd` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sranan Tongo 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 | **16k BPE** | Best compression (4.16x) |
+| N-gram | **2-gram** | Lowest perplexity (182) |
+| Markov | **Context-4** | Highest predictability (98.1%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 22:33:22*
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