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+---
+language: guw
+language_name: Gun
+language_family: atlantic_kwa
+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_kwa
+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: 4.344
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6893
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Gun - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Gun** 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** | 3.777x | 3.78 | 0.8806% | 316,930 |
+| **16k** | 4.030x | 4.03 | 0.9396% | 297,045 |
+| **32k** | 4.225x | 4.23 | 0.9851% | 283,312 |
+| **64k** | 4.344x 🏆 | 4.35 | 1.0127% | 275,607 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Aliko Dangote GCON (he yin jiji to azán 10tọ Lidosun yin ajọwatọ daho dé wẹ eyin...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ali ko ▁dan go te ▁g con ▁( he ▁yin ... (+26 more)` | 36 |
+| 16k | `▁ali ko ▁dan go te ▁g con ▁( he ▁yin ... (+26 more)` | 36 |
+| 32k | `▁aliko ▁dangote ▁gcon ▁( he ▁yin ▁jiji ▁to ▁azán ▁ ... (+22 more)` | 32 |
+| 64k | `▁aliko ▁dangote ▁gcon ▁( he ▁yin ▁jiji ▁to ▁azán ▁ ... (+22 more)` | 32 |
+
+**Sample 2:** `Fausat Adebola Ibikunle yin Gandutọ na Lizọnyizọn tito na Ayimatẹn Kaduna Tọn (M...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁fa us at ▁ade bola ▁ibi kunle ▁yin ▁gandutọ ▁na ... (+20 more)` | 30 |
+| 16k | `▁fausat ▁ade bola ▁ibikunle ▁yin ▁gandutọ ▁na ▁lizọnyizọn ▁tito ▁na ... (+14 more)` | 24 |
+| 32k | `▁fausat ▁adebola ▁ibikunle ▁yin ▁gandutọ ▁na ▁lizọnyizọn ▁tito ▁na ▁ayimatẹn ... (+13 more)` | 23 |
+| 64k | `▁fausat ▁adebola ▁ibikunle ▁yin ▁gandutọ ▁na ▁lizọnyizọn ▁tito ▁na ▁ayimatẹn ... (+12 more)` | 22 |
+
+**Sample 3:** `Mexico yin otò de to whèzẹtẹnwaji America tọn.he má do ayimatẹn voovo 32 ji`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mexico ▁yin ▁otò ▁de ▁to ▁whèzẹtẹn waji ▁america ▁tọn . ... (+9 more)` | 19 |
+| 16k | `▁mexico ▁yin ▁otò ▁de ▁to ▁whèzẹtẹnwaji ▁america ▁tọn . he ... (+8 more)` | 18 |
+| 32k | `▁mexico ▁yin ▁otò ▁de ▁to ▁whèzẹtẹnwaji ▁america ▁tọn . he ... (+8 more)` | 18 |
+| 64k | `▁mexico ▁yin ▁otò ▁de ▁to ▁whèzẹtẹnwaji ▁america ▁tọn . he ... (+8 more)` | 18 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.344x compression
+- **Lowest UNK Rate:** 8k with 0.8806% 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 | 3,238 | 11.66 | 9,467 | 26.6% | 57.5% |
+| **2-gram** | Subword | 287 🏆 | 8.17 | 2,304 | 65.6% | 98.7% |
+| **3-gram** | Word | 6,817 | 12.73 | 13,761 | 16.3% | 41.1% |
+| **3-gram** | Subword | 2,147 | 11.07 | 16,102 | 29.3% | 71.3% |
+| **4-gram** | Word | 13,775 | 13.75 | 22,441 | 11.1% | 27.8% |
+| **4-gram** | Subword | 9,790 | 13.26 | 67,472 | 15.9% | 44.3% |
+| **5-gram** | Word | 8,850 | 13.11 | 13,471 | 12.7% | 30.6% |
+| **5-gram** | Subword | 25,712 | 14.65 | 135,866 | 10.9% | 31.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tọn mẹ` | 2,333 |
+| 2 | `tọn to` | 1,877 |
+| 3 | `to owhe` | 1,528 |
+| 4 | `tọn lẹ` | 1,460 |
+| 5 | `he yin` | 1,165 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yin jiji to` | 883 |
+| 2 | `lẹ gbẹzan tọn` | 635 |
+| 3 | `tọn mẹ to` | 527 |
+| 4 | `alọdlẹndonu lẹ gbẹzan` | 518 |
+| 5 | `he nọ yin` | 482 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `alọdlẹndonu lẹ gbẹzan tọn` | 518 |
+| 2 | `he ye ji to` | 328 |
+| 3 | `ji to owhe lẹ` | 325 |
+| 4 | `ye ji to owhe` | 325 |
+| 5 | `tọn he ye ji` | 268 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `he ye ji to owhe` | 325 |
+| 2 | `ye ji to owhe lẹ` | 325 |
+| 3 | `gbẹzan tọn he ye ji` | 268 |
+| 4 | `tọn he ye ji to` | 268 |
+| 5 | `lẹ gbẹzan tọn he ye` | 193 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 74,173 |
+| 2 | `_ t` | 57,983 |
+| 3 | `o _` | 53,938 |
+| 4 | `e _` | 47,550 |
+| 5 | `ọ n` | 34,910 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ọ n _` | 24,580 |
+| 2 | `_ t o` | 24,003 |
+| 3 | `t ọ n` | 22,974 |
+| 4 | `t o _` | 22,793 |
+| 5 | `_ t ọ` | 18,136 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t o _` | 21,322 |
+| 2 | `t ọ n _` | 19,056 |
+| 3 | `_ t ọ n` | 17,899 |
+| 4 | `_ y i n` | 10,442 |
+| 5 | `y i n _` | 10,150 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t ọ n _` | 14,591 |
+| 2 | `_ y i n _` | 9,182 |
+| 3 | `n _ t o _` | 5,040 |
+| 4 | `_ t o _ a` | 4,718 |
+| 5 | `e t ọ n _` | 4,023 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 287
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~32% 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 | 0.8470 | 1.799 | 5.09 | 31,940 | 15.3% |
+| **1** | Subword | 1.3188 | 2.495 | 11.91 | 349 | 0.0% |
+| **2** | Word | 0.2988 | 1.230 | 1.72 | 162,357 | 70.1% |
+| **2** | Subword | 1.1476 | 2.215 | 6.83 | 4,157 | 0.0% |
+| **3** | Word | 0.1238 | 1.090 | 1.22 | 279,288 | 87.6% |
+| **3** | Subword | 0.8350 | 1.784 | 3.86 | 28,401 | 16.5% |
+| **4** | Word | 0.0494 🏆 | 1.035 | 1.07 | 339,321 | 95.1% |
+| **4** | Subword | 0.5898 | 1.505 | 2.45 | 109,522 | 41.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `to otogbo naijilia tọn ga ògán ná leke po akwashiki to abẹokuta to otannugbo kano e`
+2. `tọn mẹ zọ̀nlinzinzin lọ bọ e hẹn azọn whẹdida tọn mẹ wa jogbe ɖɔ silent cal`
+3. `yin didọ gándego e sọ pọn todohukanji kọnugbe hogbe po diọ yinkọ he e kú to`
+
+**Context Size 2:**
+
+1. `tọn mẹ to owhe kandewiatọ̀n yinyin mẹ alọdlẹndonu lẹ gbẹzan tọn he ko sọawuhia to aihundida cantata`
+2. `tọn to abeokuta dopolọ finẹ wẹ zẹ́ndótọzọ́nwatẹn ladi kwali tọn ladi kwali tọn ladi kwali mẹhe sin`
+3. `to owhe lẹ kú to whenẹnu freedom park lopo awọnlin tọn sọta dahomey first franco dahomean war`
+
+**Context Size 3:**
+
+1. `yin jiji to oto kutaisi nọvisunnu etọn we revaz gamkrelidze ewọ lọsu yin kanlinmọ de he yin hinhẹn`
+2. `tọn mẹ to ayimatẹn kano tọn podọ to nu taidi owhe enẹlẹ e zindonukọn nado wazọn taidi ayinamẹtọ`
+3. `alọdlẹndonu lẹ gbẹzan tọn lẹ he ye ji to owhe lẹ kú to owhe lẹ lẹ lẹ to`
+
+**Context Size 4:**
+
+1. `alọdlẹndonu lẹ gbẹzan tọn he ye ji to owhe lẹ kú to owhe lẹ lẹ lẹ to naijilia lẹ`
+2. `he ye ji to owhe lẹ benẹnu gbẹzan tọn`
+3. `ye ji to owhe lẹ lẹ lẹ to naijilia he ye ji to owhe lẹ kú to owhe lẹ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_e_ay_topl_e_aba`
+2. `ntọntohe_maovi_a`
+3. `an_whunkalazunto`
+
+**Context Size 2:**
+
+1. `n_awe_yionu,_gbọn`
+2. `_to_ogbàn_lẹzane_`
+3. `o_wharcy_sia_yinu`
+
+**Context Size 3:**
+
+1. `ọn_mussive_sọn_alọ`
+2. `_to_gbankan_e_nọ_m`
+3. `to_arau_zogbe_kuku`
+
+**Context Size 4:**
+
+1. `_to_ogbe_de_avọ̀ta_l`
+2. `tọn_azan_kpóɖɔ_to_n`
+3. `_tọn_mẹ_e_jẹ_yọnnu_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (109,522 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 | 15,734 |
+| Total Tokens | 380,906 |
+| Mean Frequency | 24.21 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 294.06 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | to | 21,455 |
+| 2 | tọn | 17,851 |
+| 3 | lẹ | 9,999 |
+| 4 | yin | 9,460 |
+| 5 | e | 7,419 |
+| 6 | he | 7,045 |
+| 7 | po | 6,884 |
+| 8 | mẹ | 6,420 |
+| 9 | na | 4,037 |
+| 10 | nọ | 3,975 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | zimbabwe | 2 |
+| 2 | zambezi | 2 |
+| 3 | okavango | 2 |
+| 4 | nyagbé | 2 |
+| 5 | malgache | 2 |
+| 6 | enseignement | 2 |
+| 7 | supérieur | 2 |
+| 8 | labo | 2 |
+| 9 | gadomè | 2 |
+| 10 | linguistique | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1244 |
+| R² (Goodness of Fit) | 0.995927 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 51.3% |
+| Top 1,000 | 76.5% |
+| Top 5,000 | 91.6% |
+| Top 10,000 | 96.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9959 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 51.3% of corpus
+- **Long Tail:** 5,734 words needed for remaining 3.1% 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 | 0.6893 | 0.3774 | N/A | N/A |
+| **mono_64d** | 64 | 0.2689 | 0.3713 | N/A | N/A |
+| **mono_128d** | 128 | 0.0512 | 0.3704 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6893 🏆 | 0.3922 | 0.0440 | 0.1880 |
+| **aligned_64d** | 64 | 0.2689 | 0.3680 | 0.0480 | 0.2180 |
+| **aligned_128d** | 128 | 0.0512 | 0.3656 | 0.0560 | 0.2900 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6893 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3742. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 5.6% 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 | **-0.030** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-tọ` | wehiatọ, wàtọ, banọhotọ |
+| `-an` | gban, avɔsinsan, pan |
+
+### 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 |
+|------|----------|------------------|----------|
+| `tion` | 1.71x | 16 contexts | action, nation, auction |
+| `ọnnu` | 1.63x | 15 contexts | fọnnu, yọnnu, dọnnu |
+| `nukọ` | 1.62x | 15 contexts | nukọn, nukọ̀n, jẹnukọn |
+| `ukun` | 1.59x | 14 contexts | wukun, nukun, kukuna |
+| `nuku` | 1.63x | 13 contexts | anuku, nukun, jinukun |
+| `ukọn` | 1.60x | 13 contexts | nukọn, jẹnukọn, nukọnna |
+| `yọnẹ` | 1.69x | 11 contexts | yọnẹn, oyọnẹn, nuyọnẹn |
+| `hund` | 1.52x | 14 contexts | aihunda, hundote, ahundopo |
+| `ọnẹn` | 1.78x | 9 contexts | yọnẹn, oyọnẹn, nuyọnẹn |
+| `nlin` | 1.47x | 15 contexts | online, kanlin, linlin |
+| `henu` | 1.79x | 8 contexts | whenu, whenue, vuwhenu |
+| `gand` | 1.47x | 12 contexts | gando, gandó, gandọ |
+
+### 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.
+
+*No significant affix co-occurrences detected.*
+
+
+### 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 |
+|------|-----------------|------------|------|
+| lẹdogbedevomẹtọ | **`lẹdogbedevomẹ-tọ`** | 4.5 | `lẹdogbedevomẹ` |
+| dahomeyan | **`dahomey-an`** | 4.5 | `dahomey` |
+| ṣiantọ̀ntọ | **`ṣiantọ̀n-tọ`** | 4.5 | `ṣiantọ̀n` |
+| nuplọnmẹtọ | **`nuplọnmẹ-tọ`** | 4.5 | `nuplọnmẹ` |
+| gbanewheawetọ | **`gbanewheawe-tọ`** | 4.5 | `gbanewheawe` |
+| azọ́nwatọ | **`azọ́nwa-tọ`** | 4.5 | `azọ́nwa` |
+| nukunpedonugotọ | **`nukunpedonugo-tọ`** | 4.5 | `nukunpedonugo` |
+| weplọnmẹtọ | **`weplọnmẹ-tọ`** | 4.5 | `weplọnmẹ` |
+| alọgọnamẹtọ | **`alọgọnamẹ-tọ`** | 4.5 | `alọgọnamẹ` |
+| togbogantọ | **`togbog-an-tọ`** | 3.0 | `togbog` |
+| linlinwekantọ | **`linlinwek-an-tọ`** | 3.0 | `linlinwek` |
+| whenuhokàntọ | **`whenuhokàn-tọ`** | 1.5 | `whenuhokàn` |
+| avọ́sinsan | **`avọ́sins-an`** | 1.5 | `avọ́sins` |
+| koewhèdopotọ | **`koewhèdopo-tọ`** | 1.5 | `koewhèdopo` |
+| walɔyizan | **`walɔyiz-an`** | 1.5 | `walɔyiz` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Gun shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.34x) |
+| N-gram | **2-gram** | Lowest perplexity (287) |
+| Markov | **Context-4** | Highest predictability (95.1%) |
+| 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 00:40:48*
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diff --git a/models/tokenizer/guw_tokenizer_64k.vocab b/models/tokenizer/guw_tokenizer_64k.vocab
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