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+---
+language: ve
+language_name: Venda
+language_family: bantu_southern
+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-bantu_southern
+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: 4.989
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0347
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Venda - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Venda** 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** | 4.573x | 4.58 | 0.1398% | 90,147 |
+| **16k** | 4.989x 🏆 | 5.00 | 0.1525% | 82,635 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Azwihangwisi Faith Muthambi o bebwa nga la fumitahe la Luhuhi Ndi ndi muthu wa b...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁azwi hangwi si ▁fa ith ▁mutha mbi ▁o ▁bebwa ▁nga ... (+20 more)` | 30 |
+| 16k | `▁azwihangwisi ▁faith ▁muthambi ▁o ▁bebwa ▁nga ▁la ▁fumitahe ▁la ▁luhuhi ... (+15 more)` | 25 |
+
+**Sample 2:** `Maswiakae ndi ḓorobo, ino wanala Makhuduthamaga Local Municipality, Limpopo kha ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁maswi akae ▁ndi ▁ḓorobo , ▁ino ▁wanala ▁makhuduthamaga ▁local ▁municipality ... (+8 more)` | 18 |
+| 16k | `▁maswiakae ▁ndi ▁ḓorobo , ▁ino ▁wanala ▁makhuduthamaga ▁local ▁municipality , ... (+7 more)` | 17 |
+
+**Sample 3:** `Mogorwane ndi ḓorobo, ino wanala Makhuduthamaga Local Municipality, Limpopo kha ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mogo rwa ne ▁ndi ▁ḓorobo , ▁ino ▁wanala ▁makhuduthamaga ▁local ... (+9 more)` | 19 |
+| 16k | `▁mogorwane ▁ndi ▁ḓorobo , ▁ino ▁wanala ▁makhuduthamaga ▁local ▁municipality , ... (+7 more)` | 17 |
+
+
+### Key Findings
+
+- **Best Compression:** 16k achieves 4.989x compression
+- **Lowest UNK Rate:** 8k with 0.1398% 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 | 553 | 9.11 | 1,442 | 48.8% | 91.2% |
+| **2-gram** | Subword | 170 🏆 | 7.41 | 892 | 77.7% | 100.0% |
+| **3-gram** | Word | 370 | 8.53 | 1,366 | 58.6% | 92.9% |
+| **3-gram** | Subword | 929 | 9.86 | 5,199 | 41.7% | 86.8% |
+| **4-gram** | Word | 577 | 9.17 | 2,528 | 55.0% | 78.6% |
+| **4-gram** | Subword | 3,239 | 11.66 | 17,351 | 26.0% | 62.5% |
+| **5-gram** | Word | 445 | 8.80 | 1,799 | 59.6% | 86.1% |
+| **5-gram** | Subword | 6,856 | 12.74 | 28,611 | 19.4% | 48.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `afurika tshipembe` | 744 |
+| 2 | `kha la` | 599 |
+| 3 | `la afurika` | 578 |
+| 4 | `ino wanala` | 553 |
+| 5 | `ndi ḓorobo` | 538 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `la afurika tshipembe` | 575 |
+| 2 | `kha la afurika` | 568 |
+| 3 | `ḓorobo ino wanala` | 530 |
+| 4 | `ndi ḓorobo ino` | 527 |
+| 5 | `limpopo kha la` | 469 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kha la afurika tshipembe` | 568 |
+| 2 | `ndi ḓorobo ino wanala` | 527 |
+| 3 | `limpopo kha la afurika` | 468 |
+| 4 | `local municipality limpopo kha` | 456 |
+| 5 | `municipality limpopo kha la` | 452 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `limpopo kha la afurika tshipembe` | 468 |
+| 2 | `local municipality limpopo kha la` | 452 |
+| 3 | `municipality limpopo kha la afurika` | 452 |
+| 4 | `henefha hu na vhadzulapo vha` | 261 |
+| 5 | `kha la afurika tshipembe dza` | 256 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 28,563 |
+| 2 | `h a` | 11,957 |
+| 3 | `v h` | 9,459 |
+| 4 | `i _` | 9,304 |
+| 5 | `o _` | 8,161 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ v h` | 7,128 |
+| 2 | `h a _` | 6,686 |
+| 3 | `v h a` | 5,533 |
+| 4 | `t s h` | 4,409 |
+| 5 | `_ t s` | 3,991 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ v h a` | 4,614 |
+| 2 | `_ t s h` | 3,640 |
+| 3 | `a _ v h` | 3,320 |
+| 4 | `t s h i` | 3,188 |
+| 5 | `v h a _` | 2,961 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t s h i` | 2,846 |
+| 2 | `_ v h a _` | 2,451 |
+| 3 | `a _ t s h` | 2,354 |
+| 4 | `a _ v h a` | 2,049 |
+| 5 | `_ n d i _` | 1,599 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 170
+- **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 | 0.6638 | 1.584 | 3.61 | 9,716 | 33.6% |
+| **1** | Subword | 1.3289 | 2.512 | 10.31 | 162 | 0.0% |
+| **2** | Word | 0.2215 | 1.166 | 1.43 | 34,846 | 77.9% |
+| **2** | Subword | 1.2039 | 2.304 | 6.12 | 1,665 | 0.0% |
+| **3** | Word | 0.0671 | 1.048 | 1.10 | 49,273 | 93.3% |
+| **3** | Subword | 0.7757 | 1.712 | 3.17 | 10,158 | 22.4% |
+| **4** | Word | 0.0208 🏆 | 1.015 | 1.03 | 53,626 | 97.9% |
+| **4** | Subword | 0.4655 | 1.381 | 2.00 | 32,136 | 53.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `vha no mametja vha vha vho kavhiwa nga itshi vhathu vha tshisimani fet college publishers zwi`
+2. `u bvisa tshilonda tshi kha vhutshilo ha zwiila zwa pfunzo ya nga vhahulwane na kunwalele kwa`
+3. `na vhashumeli vha mbo ḓi tambela tshanda ha ngo tea u kona u anzela u dzhenelela`
+
+**Context Size 2:**
+
+1. `afurika tshipembe vhathu vhunzhi ha vhathu vha u bva asia dzinwe thoro dzine dza vha uri zwo`
+2. `kha la afurika tshipembe dza limpopo dza limpopo dza dze dza vh dzi thamumbuloni ya muvhuso wa`
+3. `la afurika tshipembe dorobo dza tsini ndi thohoyandou na tzaneen i tsini na muserenga tondo dzingi d...`
+
+**Context Size 3:**
+
+1. `la afurika tshipembe henefha hu na vhadzulapo vha 1 265 dza limpopo`
+2. `kha la afurika tshipembe henefha hu na vhadzulapo vha 4 452 ka xikundu references dza limpopo`
+3. `ḓorobo ino wanala limpopo kha la afurika tshipembe dza limpopo`
+
+**Context Size 4:**
+
+1. `kha la afurika tshipembe dza limpopo`
+2. `ndi ḓorobo ino wanala greater tzaneen local municipality limpopo kha la afurika tshipembe dza limpop...`
+3. `limpopo kha la afurika tshipembe dza limpopo`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ḓi._ali_ts_l_e_`
+2. `alaipophiavho_na`
+3. `hi_hwafhamufso_v`
+
+**Context Size 2:**
+
+1. `a_zwina_me_kwa_kh`
+2. `ha_ha_jerendi_no_`
+3. `vhou_vha_vha_ya_v`
+
+**Context Size 3:**
+
+1. `_vho_90px_27.934_d`
+2. `ha_la_a_i_wana._mu`
+3. `vha_lipida_vha_kha`
+
+**Context Size 4:**
+
+1. `_vha_mitshedzo_nga_`
+2. `_tsha_sovengo_la_af`
+3. `a_vhaisimane_na_kal`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (32,136 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 | 4,057 |
+| Total Tokens | 63,019 |
+| Mean Frequency | 15.53 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 95.58 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | vha | 2,496 |
+| 2 | u | 2,148 |
+| 3 | na | 2,117 |
+| 4 | ndi | 1,632 |
+| 5 | kha | 1,576 |
+| 6 | nga | 1,426 |
+| 7 | ya | 1,347 |
+| 8 | a | 1,211 |
+| 9 | dza | 1,085 |
+| 10 | limpopo | 1,062 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | out | 2 |
+| 2 | ḽihoro | 2 |
+| 3 | stanley | 2 |
+| 4 | announces | 2 |
+| 5 | tells | 2 |
+| 6 | open | 2 |
+| 7 | books | 2 |
+| 8 | close | 2 |
+| 9 | your | 2 |
+| 10 | hourlyhits | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0307 |
+| R² (Goodness of Fit) | 0.989758 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 59.5% |
+| Top 1,000 | 85.1% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9898 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 59.5% of corpus
+- **Long Tail:** -5,943 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 | 0.0347 🏆 | 0.7804 | N/A | N/A |
+| **mono_64d** | 64 | 0.0065 | 0.7768 | N/A | N/A |
+| **mono_128d** | 128 | 0.0015 | 0.7951 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0347 | 0.7762 | 0.0096 | 0.0927 |
+| **aligned_64d** | 64 | 0.0065 | 0.8086 | 0.0096 | 0.0831 |
+| **aligned_128d** | 128 | 0.0015 | 0.7992 | 0.0128 | 0.0767 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.0347 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.7894. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.3% 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 | **0.784** | 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 |
+|--------|----------|
+| `-m` | marema, minisța, maṱo |
+| `-ma` | marema, maṱo, mahosi |
+| `-vh` | vhudifari, vhudzekani, vhengiwa |
+| `-mu` | muṅwe, muvhilini, mueni |
+| `-t` | tshenetshi, tea, teya |
+| `-n` | nkhumbela, ngavha, north |
+| `-s` | springer, stellenbosch, shandukani |
+| `-k` | kongomisa, khirikhete, kamakosha |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | kongomisa, marema, wina |
+| `-i` | vhudifari, vhudzekani, zwavhuḓi |
+| `-o` | dzinyambo, petro, onoyo |
+| `-e` | khirikhete, jane, gude |
+| `-wa` | vhengiwa, vuswa, livhuwa |
+| `-ni` | vhudzekani, lifhasini, vhukonani |
+| `-la` | nkhumbela, ambelela, dalela |
+| `-ho` | ḓivheaho, henefho, fanaho |
+
+### 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.
+
+*No significant bound stems detected.*
+
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-t` | `-a` | 131 words | tea, teya |
+| `-m` | `-a` | 125 words | marema, minisța |
+| `-m` | `-o` | 88 words | maṱo, mbuno |
+| `-m` | `-i` | 86 words | mahosi, mathomoni |
+| `-vh` | `-i` | 82 words | vhudifari, vhudzekani |
+| `-vh` | `-a` | 68 words | vhengiwa, vhovha |
+| `-t` | `-o` | 58 words | tshumisano, thendelano |
+| `-t` | `-i` | 51 words | tshenetshi, takalani |
+| `-m` | `-e` | 50 words | muṅwe, marriage |
+| `-k` | `-a` | 48 words | kongomisa, kamakosha |
+
+### 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 |
+|------|-----------------|------------|------|
+| tshinnani | **`tshin-na-ni`** | 7.5 | `na` |
+| bvelesisa | **`bvele-si-sa`** | 7.5 | `si` |
+| ramabindu | **`ra-ma-bindu`** | 7.5 | `bindu` |
+| tshikhala | **`tshik-ha-la`** | 7.5 | `ha` |
+| swikelela | **`swike-le-la`** | 7.5 | `le` |
+| tshiphani | **`tship-ha-ni`** | 7.5 | `ha` |
+| humbulela | **`humbu-le-la`** | 7.5 | `le` |
+| vhonalaho | **`vhona-la-ho`** | 6.0 | `vhona` |
+| vhatshini | **`vh-atshi-ni`** | 6.0 | `atshi` |
+| maḓuvhani | **`ma-ḓuvha-ni`** | 6.0 | `ḓuvha` |
+| mashangoni | **`ma-shango-ni`** | 6.0 | `shango` |
+| mavhulani | **`ma-vhula-ni`** | 6.0 | `vhula` |
+| tshikoloni | **`tshikolo-ni`** | 4.5 | `tshikolo` |
+| muhulwane | **`mu-hulwane`** | 4.5 | `hulwane` |
+| mashuvhuru | **`ma-shuvhuru`** | 4.5 | `shuvhuru` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Venda 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 (4.99x) |
+| N-gram | **2-gram** | Lowest perplexity (170) |
+| Markov | **Context-4** | Highest predictability (97.9%) |
+| 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 02:39:50*
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