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+---
+language: nup
+language_name: Nupe-Nupe-Tako
+language_family: atlantic_other
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-atlantic_other
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.182
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0436
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Nupe-Nupe-Tako - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Nupe-Nupe-Tako** 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** | 3.745x | 3.75 | 0.1160% | 125,813 |
+| **16k** | 4.044x | 4.05 | 0.1253% | 116,510 |
+| **32k** | 4.182x 🏆 | 4.19 | 0.1296% | 112,656 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Enna bolu zhi nyan Nasarawa wunyi enna na ge na dan ezhi nin Lafiya'o, Nasarawa....`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁enna ▁bolu ▁zhi ▁nyan ▁nasarawa ▁wunyi ▁enna ▁na ▁ge ▁na ... (+21 more)` | 31 |
+| 16k | `▁enna ▁bolu ▁zhi ▁nyan ▁nasarawa ▁wunyi ▁enna ▁na ▁ge ▁na ... (+21 more)` | 31 |
+| 32k | `▁enna ▁bolu ▁zhi ▁nyan ▁nasarawa ▁wunyi ▁enna ▁na ▁ge ▁na ... (+19 more)` | 29 |
+
+**Sample 2:** `Bàbò (Lagenaria siceraria)Blench, Roger. Nupe plants and trees: their names and ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁b à b ò ▁( l agen aria ▁s ic ... (+30 more)` | 40 |
+| 16k | `▁bàbò ▁( lagenaria ▁sicer aria ) blench , ▁roger . ... (+20 more)` | 30 |
+| 32k | `▁bàbò ▁( lagenaria ▁siceraria ) blench , ▁roger . ▁nupe ... (+17 more)` | 27 |
+
+**Sample 3:** `Aisha Muharrar (12 wunga amawuo), wungayi eyankachi yan America Television wunma...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁aisha ▁mu har r ar ▁( 1 2 ▁wunga ▁ama ... (+21 more)` | 31 |
+| 16k | `▁aisha ▁mu harrar ▁( 1 2 ▁wunga ▁amawuo ), ▁wungayi ... (+16 more)` | 26 |
+| 32k | `▁aisha ▁muharrar ▁( 1 2 ▁wunga ▁amawuo ), ▁wungayi ▁eyankachi ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.182x compression
+- **Lowest UNK Rate:** 8k with 0.1160% 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 | 941 | 9.88 | 1,983 | 37.8% | 81.5% |
+| **2-gram** | Subword | 227 🏆 | 7.83 | 1,160 | 69.5% | 99.8% |
+| **3-gram** | Word | 1,254 | 10.29 | 2,206 | 30.4% | 72.8% |
+| **3-gram** | Subword | 1,537 | 10.59 | 7,263 | 32.0% | 77.7% |
+| **4-gram** | Word | 2,126 | 11.05 | 3,106 | 21.3% | 56.3% |
+| **4-gram** | Subword | 6,047 | 12.56 | 26,183 | 19.1% | 50.5% |
+| **5-gram** | Word | 1,529 | 10.58 | 1,902 | 20.6% | 65.7% |
+| **5-gram** | Subword | 12,552 | 13.62 | 42,618 | 14.0% | 38.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wun yi` | 703 |
+| 2 | `o nan` | 596 |
+| 3 | `ah be` | 579 |
+| 4 | `yi o` | 526 |
+| 5 | `nan wun` | 439 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wun yi o` | 454 |
+| 2 | `ah man u` | 238 |
+| 3 | `yi o nan` | 218 |
+| 4 | `nan ah kpeye` | 137 |
+| 5 | `ah kpeye be` | 126 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wun yi o nan` | 187 |
+| 2 | `nan ah kpeye be` | 113 |
+| 3 | `from the original on` | 100 |
+| 4 | `nan wun yi o` | 81 |
+| 5 | `wun yi o wun` | 74 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original on` | 60 |
+| 2 | `kin america wun yi o` | 44 |
+| 3 | `wun yi o nan e` | 42 |
+| 4 | `nyan kin america wun yi` | 39 |
+| 5 | `wun yi o nan de` | 31 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 16,676 |
+| 2 | `n _` | 16,511 |
+| 3 | `a _` | 11,948 |
+| 4 | `e _` | 9,985 |
+| 5 | `_ n` | 9,524 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 8,945 |
+| 2 | `_ n a` | 4,610 |
+| 3 | `n a n` | 4,016 |
+| 4 | `u n _` | 3,299 |
+| 5 | `y a n` | 3,272 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a n` | 3,560 |
+| 2 | `_ w u n` | 3,054 |
+| 3 | `y a n _` | 2,972 |
+| 4 | `n y a n` | 2,846 |
+| 5 | `_ n y a` | 2,812 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n y a n` | 2,652 |
+| 2 | `n y a n _` | 2,610 |
+| 3 | `_ w u n _` | 1,957 |
+| 4 | `_ n a n _` | 1,855 |
+| 5 | `_ k i n _` | 980 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 227
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~38% 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 | 0.7131 | 1.639 | 3.99 | 12,109 | 28.7% |
+| **1** | Subword | 1.1738 | 2.256 | 7.94 | 375 | 0.0% |
+| **2** | Word | 0.2337 | 1.176 | 1.48 | 47,930 | 76.6% |
+| **2** | Subword | 1.0147 | 2.021 | 5.23 | 2,976 | 0.0% |
+| **3** | Word | 0.0783 | 1.056 | 1.12 | 70,052 | 92.2% |
+| **3** | Subword | 0.7842 | 1.722 | 3.28 | 15,575 | 21.6% |
+| **4** | Word | 0.0281 🏆 | 1.020 | 1.04 | 77,857 | 97.2% |
+| **4** | Subword | 0.5165 | 1.430 | 2.10 | 51,106 | 48.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `nan enan wuncin de chikan toh finishing santatun theft auto gta enan siyasa ah de nan`
+2. `be playdata e ce yegboro santatun nyan payin wun yi pentagon etishi chi tun eya fiti`
+3. `nyan tswanyin chi ya toh yizhele be nyana gan nan ewun dan mini yetu wun de`
+
+**Context Size 2:**
+
+1. `wun yi o egi enan bolu wuncin de yesan yizhe kaman wun yi o gap inc ga`
+2. `o nan de egwa du ya be lila keba nyan eni r b afropop pop ah be`
+3. `ah be donald wilson wun wugwa wun man yebo gan nan yi kpako ebo dindan nyan bolu`
+
+**Context Size 3:**
+
+1. `wun yi o chi de kukukeba be eko yilozun e66 eko oud metha be d73 eko 2nd za`
+2. `ah man u august 26 edzo yesan chi stuntman ah be cowboy nan ah la dan prorodeo hall`
+3. `yi o nan e che bolu ta zuma o na ya kin retrieved 9 april santatun`
+
+**Context Size 4:**
+
+1. `wun yi o nan de tswitswa gwata kampany motorola mobility zuk mobile ah be medio gwala lenovo ela apr...`
+2. `nan ah kpeye be doka madureira koma doka nan egi kin brazil nan yi coach toh bolu chechi nyan`
+3. `from the original on 29 august retrieved 3 september 2baba ga yi eza chaba nan gi riatwa mtv ema`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dorn_(a_eand_n_`
+2. `a_e_nyann_nsa_e_`
+3. `n_wspr_betunatst`
+
+**Context Size 2:**
+
+1. `angeraticoundan_1`
+2. `n_ellemi_eko_ment`
+3. `a_shot_nangi_larf`
+
+**Context Size 3:**
+
+1. `an_de_li_gan_janu'`
+2. `_nan_zhe_fool_on_n`
+3. `nan._millege_u.s_k`
+
+**Context Size 4:**
+
+1. `_nan_tswafo_gwegi_v`
+2. `_wun_marchived_18_a`
+3. `yan_payin_wun_yilaz`
+
+
+### 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 (51,106 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 | 4,787 |
+| Total Tokens | 80,735 |
+| Mean Frequency | 16.87 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 107.35 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | nan | 3,508 |
+| 2 | be | 2,579 |
+| 3 | nyan | 2,500 |
+| 4 | o | 2,417 |
+| 5 | wun | 2,108 |
+| 6 | yi | 1,722 |
+| 7 | ah | 1,483 |
+| 8 | de | 1,371 |
+| 9 | chi | 1,047 |
+| 10 | kin | 995 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | alderny | 2 |
+| 2 | jersey | 2 |
+| 3 | halmstad | 2 |
+| 4 | basshunter | 2 |
+| 5 | gunini | 2 |
+| 6 | cox | 2 |
+| 7 | wikitorial | 2 |
+| 8 | rangaunu | 2 |
+| 9 | kaiwaka | 2 |
+| 10 | application | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0809 |
+| R² (Goodness of Fit) | 0.989658 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 55.6% |
+| Top 1,000 | 84.5% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9897 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 55.6% of corpus
+- **Long Tail:** -5,213 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 | 0.0436 🏆 | 0.6527 | N/A | N/A |
+| **mono_64d** | 64 | 0.0084 | 0.6738 | N/A | N/A |
+| **mono_128d** | 128 | 0.0017 | 0.6732 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0436 | 0.6316 | 0.0040 | 0.0520 |
+| **aligned_64d** | 64 | 0.0084 | 0.6533 | 0.0100 | 0.0480 |
+| **aligned_128d** | 128 | 0.0017 | 0.6773 | 0.0040 | 0.0460 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.0436 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.6603. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.0% 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 | **0.719** | 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` | sati, southern, stage |
+| `-a` | australian, alaska, adara |
+| `-b` | bodo, bididi, behind |
+| `-m` | my, minority, miss |
+| `-e` | ezagbakozhi, etin, egwagan |
+| `-g` | gwala, gap, ganwagi |
+| `-k` | kpeuye, kamina, kala |
+| `-c` | continent, climate, cambridge |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | australian, etin, dukun |
+| `-a` | gwala, alaska, tarawa |
+| `-i` | ezagbakozhi, ganwagi, dasuki |
+| `-e` | kpeuye, climate, kpeye |
+| `-s` | this, miss, macleans |
+| `-r` | register, factor, myanmar |
+| `-an` | australian, urban, egwagan |
+| `-o` | ronaldinho, bodo, kano |
+
+### 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 |
+|------|----------|------------------|----------|
+| `angi` | 1.30x | 15 contexts | dangi, nangi, sangi |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-e` | `-i` | 29 words | ezagbakozhi, emi |
+| `-e` | `-n` | 29 words | etin, egwagan |
+| `-a` | `-a` | 22 words | alaska, adara |
+| `-c` | `-n` | 21 words | canadian, children |
+| `-a` | `-s` | 21 words | assets, athletes |
+| `-k` | `-a` | 20 words | kamina, kala |
+| `-m` | `-i` | 19 words | mardini, makarini |
+| `-c` | `-s` | 19 words | chillies, christmas |
+| `-s` | `-s` | 19 words | ships, s |
+| `-m` | `-a` | 18 words | mehsana, mokwa |
+
+### 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 |
+|------|-----------------|------------|------|
+| kabalagala | **`kabalag-al-a`** | 7.5 | `al` |
+| gbagbangi | **`g-ba-gbangi`** | 7.5 | `gbangi` |
+| augustine | **`august-in-e`** | 7.5 | `in` |
+| chinwanchi | **`ch-in-wanchi`** | 7.5 | `wanchi` |
+| musulunci | **`musulu-n-ci`** | 7.5 | `n` |
+| universiade | **`universia-d-e`** | 7.5 | `d` |
+| kamindondo | **`ka-mi-ndondo`** | 6.0 | `ndondo` |
+| enyanichi | **`enyan-ic-hi`** | 6.0 | `enyan` |
+| brazilian | **`brazil-i-an`** | 6.0 | `brazil` |
+| ezhiminsun | **`ezhimi-ns-un`** | 6.0 | `ezhimi` |
+| journalist | **`journal-i-st`** | 6.0 | `journal` |
+| engineering | **`engineer-i-ng`** | 6.0 | `engineer` |
+| nationale | **`national-e`** | 4.5 | `national` |
+| amalouchio | **`a-ma-louchio`** | 4.5 | `louchio` |
+| commissioner | **`commission-er`** | 4.5 | `commission` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Nupe-Nupe-Tako shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **32k BPE** | Best compression (4.18x) |
+| N-gram | **2-gram** | Lowest perplexity (227) |
+| 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 16:17:39*
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