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README.md

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+---
+language: got
+language_name: Gothic
+language_family: germanic_historical
+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_historical
+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: 2.884
+  - name: best_isotropy
+    type: isotropy
+    value: 0.1831
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Gothic - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Gothic** 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** | 2.525x | 2.53 | 0.0669% | 260,190 |
+| **16k** | 2.674x | 2.68 | 0.0708% | 245,725 |
+| **32k** | 2.884x 🏆 | 2.89 | 0.0764% | 227,819 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `𐌺𐌰𐌽𐌰𐌳𐌰 𐌹𐍃𐍄 𐌻𐌰𐌽𐌳 𐌰𐌽𐌰 𐌰𐌹𐍂𐌸𐌰𐌳𐌰𐌹𐌻𐌰𐌹 𐌽𐌰𐌿𐍂𐌸𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰 𐌾𐌰𐌷 𐌲𐌰𐌼𐌰𐍂𐌺𐍉𐌸 𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌹𐌳𐌰 𐍂𐌴𐌹𐌺𐌾𐌰𐌹. ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁𐌺𐌰𐌽𐌰𐌳𐌰 ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ▁𐌰𐌽𐌰 ▁𐌰𐌹𐍂𐌸𐌰𐌳𐌰𐌹𐌻 𐌰𐌹 ▁𐌽𐌰𐌿𐍂𐌸 𐌰𐌼𐌰𐌹𐍂𐌹𐌺 𐌰 ▁𐌾𐌰𐌷 ... (+20 more)` | 30 |
+| 16k | `▁𐌺𐌰𐌽𐌰𐌳𐌰 ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ▁𐌰𐌽𐌰 ▁𐌰𐌹𐍂𐌸𐌰𐌳𐌰𐌹𐌻𐌰𐌹 ▁𐌽𐌰𐌿𐍂𐌸 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰 ▁𐌾𐌰𐌷 ▁𐌲𐌰𐌼𐌰𐍂𐌺𐍉𐌸 ▁𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌹𐌳𐌰 ... (+16 more)` | 26 |
+| 32k | `▁𐌺𐌰𐌽𐌰𐌳𐌰 ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ▁𐌰𐌽𐌰 ▁𐌰𐌹𐍂𐌸𐌰𐌳𐌰𐌹𐌻𐌰𐌹 ▁𐌽𐌰𐌿𐍂𐌸𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰 ▁𐌾𐌰𐌷 ▁𐌲𐌰𐌼𐌰𐍂𐌺𐍉𐌸 ▁𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌹𐌳𐌰 ▁𐍂𐌴𐌹𐌺𐌾𐌰𐌹 ... (+12 more)` | 22 |
+
+**Sample 2:** `𐌰𐍀𐌻𐍃 — 𐌰𐌺𐍂𐌰𐌽 𐌰𐍀𐌻𐌰𐌱𐌰𐌲𐌼𐌴 𐌾𐌰𐌷 𐍅𐌰𐌹𐌻𐌰𐌺𐌿𐌽𐌸𐌰 𐍆𐍉𐌳𐌴𐌹𐌽𐍃 𐌹𐍃𐍄·`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁𐌰𐍀𐌻𐍃 ▁— ▁𐌰𐌺𐍂𐌰𐌽 ▁𐌰𐍀 𐌻 𐌰𐌱𐌰𐌲𐌼𐌴 ▁𐌾𐌰𐌷 ▁𐍅𐌰𐌹𐌻 𐌰𐌺𐌿𐌽𐌸𐌰 ▁𐍆𐍉𐌳𐌴𐌹𐌽𐍃 ... (+2 more)` | 12 |
+| 16k | `▁𐌰𐍀𐌻𐍃 ▁— ▁𐌰𐌺𐍂𐌰𐌽 ▁𐌰𐍀 𐌻 𐌰𐌱𐌰𐌲𐌼𐌴 ▁𐌾𐌰𐌷 ▁𐍅𐌰𐌹𐌻 𐌰𐌺𐌿𐌽𐌸𐌰 ▁𐍆𐍉𐌳𐌴𐌹𐌽𐍃 ... (+2 more)` | 12 |
+| 32k | `▁𐌰𐍀𐌻𐍃 ▁— ▁𐌰𐌺𐍂𐌰𐌽 ▁𐌰𐍀𐌻𐌰𐌱𐌰𐌲𐌼𐌴 ▁𐌾𐌰𐌷 ▁𐍅𐌰𐌹𐌻𐌰𐌺𐌿𐌽𐌸𐌰 ▁𐍆𐍉𐌳𐌴𐌹𐌽𐍃 ▁𐌹𐍃𐍄 ·` | 9 |
+
+**Sample 3:** `𐌺𐌰𐌿𐌻𐌿𐌼𐌱𐌾𐌰 (Colombia) 𐌹𐍃𐍄 𐌻𐌰𐌽𐌳 𐌹𐌽 𐍃𐌿𐌽𐌸𐍂𐌰𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹. 𐌰𐌼𐌴𐍂𐌹𐌺𐌰 This page is brought t...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁𐌺𐌰𐌿𐌻𐌿𐌼𐌱 𐌾𐌰 ▁( col om b ia ) ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ... (+19 more)` | 29 |
+| 16k | `▁𐌺𐌰𐌿𐌻𐌿𐌼𐌱𐌾𐌰 ▁( colombia ) ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ▁𐌹𐌽 ▁𐍃𐌿𐌽𐌸𐍂𐌰𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 . ▁𐌰𐌼𐌴𐍂𐌹𐌺𐌰 ... (+12 more)` | 22 |
+| 32k | `▁𐌺𐌰𐌿𐌻𐌿𐌼𐌱𐌾𐌰 ▁( colombia ) ▁𐌹𐍃𐍄 ▁𐌻𐌰𐌽𐌳 ▁𐌹𐌽 ▁𐍃𐌿𐌽𐌸𐍂𐌰𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 . ▁𐌰𐌼𐌴𐍂𐌹𐌺𐌰 ... (+10 more)` | 20 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 2.884x compression
+- **Lowest UNK Rate:** 8k with 0.0669% 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 | 773 | 9.60 | 1,213 | 36.4% | 92.9% |
+| **2-gram** | Subword | 546 🏆 | 9.09 | 2,316 | 47.1% | 96.7% |
+| **3-gram** | Word | 630 | 9.30 | 1,041 | 40.1% | 98.0% |
+| **3-gram** | Subword | 4,140 | 12.02 | 14,315 | 17.0% | 56.1% |
+| **4-gram** | Word | 3,152 | 11.62 | 3,669 | 12.9% | 38.4% |
+| **4-gram** | Subword | 17,609 | 14.10 | 51,785 | 8.9% | 30.1% |
+| **5-gram** | Word | 2,230 | 11.12 | 2,508 | 13.1% | 46.3% |
+| **5-gram** | Subword | 36,495 | 15.16 | 84,401 | 6.7% | 21.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i to` | 325 |
+| 2 | `wv i` | 315 |
+| 3 | `akin to` | 129 |
+| 4 | `iii to` | 106 |
+| 5 | `𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹` | 102 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wv i to` | 276 |
+| 2 | `akin to eng` | 78 |
+| 3 | `sv vii to` | 64 |
+| 4 | `sv iii to` | 61 |
+| 5 | `𐌹𐍃𐍄 𐌻𐌰𐌽𐌳 𐌹𐌽` | 54 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽` | 48 |
+| 2 | `𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽` | 48 |
+| 3 | `𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃` | 48 |
+| 4 | `𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 𐌲𐌰𐍅𐌹𐍃𐍃𐌴𐌹𐍃 www` | 48 |
+| 5 | `𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 𐌷𐌰𐌿𐌱𐌹𐌳𐌰𐌱𐌰𐌿𐍂𐌲𐍃 𐌹𐍃𐍄` | 40 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽` | 48 |
+| 2 | `𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽` | 48 |
+| 3 | `𐌹𐍃𐍄 𐌲𐌰𐍅𐌹 𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 𐌷𐌰𐌿𐌱𐌹𐌳𐌰𐌱𐌰𐌿𐍂𐌲𐍃` | 36 |
+| 4 | `𐌲𐌰𐍅𐌹 𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 𐌷𐌰𐌿𐌱𐌹𐌳𐌰𐌱𐌰𐌿𐍂𐌲𐍃 𐌹𐍃𐍄` | 36 |
+| 5 | `𐌷𐌰𐌿𐌱𐌹𐌳𐌰𐌱𐌰𐌿𐍂𐌲𐍃 𐌾𐌰𐌷 𐍃𐍉 𐌼𐌰𐌹𐍃𐍄𐍉 𐌱𐌰𐌿𐍂𐌲𐍃` | 21 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `, _` | 17,634 |
+| 2 | `. _` | 14,540 |
+| 3 | `𐌰 𐌹` | 7,870 |
+| 4 | `𐍃 _` | 7,637 |
+| 5 | `𐌹 𐍃` | 6,470 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ - _` | 2,452 |
+| 2 | `n , _` | 2,251 |
+| 3 | `s , _` | 2,187 |
+| 4 | `𐌹 𐌽 _` | 2,125 |
+| 5 | `, _ s` | 2,064 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ 𐌹 𐌽 _` | 1,670 |
+| 2 | `_ t o _` | 1,483 |
+| 3 | `_ 𐌾 𐌰 𐌷` | 1,475 |
+| 4 | `𐌾 𐌰 𐌷 _` | 1,472 |
+| 5 | `a n , _` | 1,390 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ 𐌾 𐌰 𐌷 _` | 1,469 |
+| 2 | `_ 𐌹 𐍃 𐍄 _` | 1,060 |
+| 3 | `_ t h e _` | 885 |
+| 4 | `, _ t o _` | 881 |
+| 5 | `_ o e . _` | 839 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 546
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~22% 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 | 0.5463 | 1.460 | 2.78 | 26,779 | 45.4% |
+| **1** | Subword | 1.3185 | 2.494 | 9.24 | 600 | 0.0% |
+| **2** | Word | 0.1349 | 1.098 | 1.22 | 73,655 | 86.5% |
+| **2** | Subword | 0.9989 | 1.999 | 5.20 | 5,543 | 0.1% |
+| **3** | Word | 0.0401 | 1.028 | 1.06 | 89,056 | 96.0% |
+| **3** | Subword | 0.7885 | 1.727 | 3.23 | 28,771 | 21.2% |
+| **4** | Word | 0.0157 🏆 | 1.011 | 1.02 | 93,235 | 98.4% |
+| **4** | Subword | 0.5184 | 1.432 | 2.05 | 92,872 | 48.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `𐌹𐌽 𐍅𐌹𐍃𐍄𐍂𐌰𐌹 𐌰𐍃𐌹𐌰𐌹 𐌽𐌴𐌷𐍅𐌿𐌽𐌳𐍉𐍃 𐌿𐍆𐌰𐍂 500 𐍆𐌰𐌿𐍂𐌰 𐍇𐍂𐌹𐍃𐍄𐌰𐌿 𐍃𐌰 𐌼𐌰𐌹𐍃𐍄𐌰 𐌰𐌻𐌻𐌰𐌹𐌶𐌴 𐌰𐌹𐍅𐌴 𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌵𐌹𐌼𐌰𐌽𐌳 𐍆𐍂𐌰𐌼`
+2. `to tame 170 182 354 fulla ga náitjan wv i am trying to call cry aloud`
+3. `𐌾𐌰𐌷 𐌰𐌽𐌸𐌰𐍂𐌰𐌹𐌼 𐌱𐌰𐍂𐌱𐌰𐍂𐌹𐍅𐌴 𐌸𐌰𐌹𐌴𐌹 𐌺𐌿𐌽𐌽𐌰𐌽 𐍈𐌰 𐌹𐌽 𐌾𐌴𐍂𐌰 𐌿𐍃𐍅𐌰𐌹𐍂𐍀𐌰𐌽 𐌼𐌰𐌷𐍄𐌴𐌹𐌲 𐍅𐌰𐍃 𐌸𐌰𐍄𐌴𐌹 𐌰𐍂𐌰𐌱𐌹𐍃𐌺𐌰 𐍂𐌰𐌶𐌳𐌰 𐍂𐌰𐌶𐌳𐌰 𐌿𐌺𐍂𐌰...`
+
+**Context Size 2:**
+
+1. `i to lighten 424 ohg lohazzen láun sn pay reward 22 141 175 211 oe ht a`
+2. `wv i see ga eitjan eits aj white 140 165 oe hwt ohg hw 329a an av`
+3. `akin to eng ask treat shamefully oe ntan ohg neien ga nasjan wv i to permit allow`
+
+**Context Size 3:**
+
+1. `wv i to give light 63 85 105 320 oe lehtan liuhten liusan sv ii see af skiuban`
+2. `akin to eng arrow arrow arjan distantly akin to lat anima spirit pant comp uzanan exhale and anda`
+3. `sv vii to call to one profess confess acknowledge give thanks to and háusjan wv i to sin`
+
+**Context Size 4:**
+
+1. `𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽 𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽 𐌱𐌰𐌽𐌳𐌰𐍂𐌴𐌹𐌺𐌾𐌹𐍃`
+2. `𐌹𐌽 𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌰𐌹 𐌲𐌰𐍅𐌹𐍃𐍃𐌴𐌹𐍃 www stpaul gov`
+3. `𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽 𐍃𐌴𐌹𐌳𐍉 𐌸𐍉𐌶𐌴𐌹 𐌰𐌻𐌻𐍉𐍃 𐍅𐌹𐌺𐌹𐍀𐌰𐌹𐌳𐌾𐍉𐍃 𐍃𐌺𐌿𐌻𐌿𐌽 𐌷𐌰𐌱𐌰𐌽 𐌱𐌰𐌽𐌳𐌰𐍂𐌴𐌹𐌺𐌾𐌹𐍃`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_sl_1_scoperutce`
+2. `𐌰𐌹𐌺𐌿𐌸_mago_𐌸𐌰_k,`
+3. `𐌹𐍈𐌰𐌷𐌹_(*wve._bal`
+
+**Context Size 2:**
+
+1. `,_𐍃𐌴𐌹𐌽𐍃_𐌾𐌰𐌳𐌰,_ble`
+2. `._oe._arkjan_ram,`
+3. `𐌰𐌹._infornarusess`
+
+**Context Size 3:**
+
+1. `_-_chimess,_munia)`
+2. `n,_with_kaúlustriv`
+3. `s,_mallmers_but_at`
+
+**Context Size 4:**
+
+1. `_𐌹𐌽_𐌰𐌼𐌰𐌹𐍂𐌹𐌺𐌹𐍃_𐌿𐌽𐌳_𐌳`
+2. `_to_restone_...hadu`
+3. `_𐌾𐌰𐌷_𐌻𐌹𐌿𐌲𐍉𐍃𐌻𐌰𐌱𐌹𐍃𐌺𐌹𐍃`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (92,872 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 | 10,445 |
+| Total Tokens | 85,682 |
+| Mean Frequency | 8.20 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 41.75 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | 𐌹𐌽 | 1,691 |
+| 2 | to | 1,570 |
+| 3 | 𐌾𐌰𐌷 | 1,478 |
+| 4 | 𐌹𐍃𐍄 | 1,269 |
+| 5 | the | 906 |
+| 6 | i | 903 |
+| 7 | oe | 851 |
+| 8 | ohg | 841 |
+| 9 | a | 719 |
+| 10 | 𐍅𐌰𐍃 | 616 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | 𐌳𐌿𐍄𐍄𐌴 | 2 |
+| 2 | 𐍆𐌹𐌲𐌲𐍂𐌰𐌽𐍃 | 2 |
+| 3 | 𐍃𐌹𐌿𐌺𐌰𐌹𐌶𐌴 | 2 |
+| 4 | 𐌺𐌿𐌺𐌾𐌰𐌽𐌳 | 2 |
+| 5 | 𐌷𐌰𐌹𐍄𐌹𐍃 | 2 |
+| 6 | 𐍃𐌿𐌽𐌸𐍂𐌹𐍃 | 2 |
+| 7 | 𐌷𐌹𐌱𐌰𐌹𐍂𐌾𐍉𐍃 | 2 |
+| 8 | citerior | 2 |
+| 9 | ulterior | 2 |
+| 10 | 𐌸𐌿𐍂𐌺𐌴𐌹𐍃 | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8663 |
+| R² (Goodness of Fit) | 0.982156 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 33.8% |
+| Top 1,000 | 63.2% |
+| Top 5,000 | 86.7% |
+| Top 10,000 | 99.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9822 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 33.8% of corpus
+- **Long Tail:** 445 words needed for remaining 1.0% 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 | 0.1831 🏆 | 0.4505 | N/A | N/A |
+| **mono_64d** | 64 | 0.0766 | 0.4301 | N/A | N/A |
+| **mono_128d** | 128 | 0.0136 | 0.4355 | N/A | N/A |
+| **aligned_32d** | 32 | 0.1831 | 0.4429 | 0.0080 | 0.0680 |
+| **aligned_64d** | 64 | 0.0766 | 0.4301 | 0.0080 | 0.0740 |
+| **aligned_128d** | 128 | 0.0136 | 0.4348 | 0.0160 | 0.0900 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.1831 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4373. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.6% 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 | **1.146** | 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 |
+|--------|----------|
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-an` | ocean, wan, hauhjan |
+| `-𐌽𐍃` | 𐌵𐌴𐌽𐍃, 𐌺𐌰𐌷𐍅𐌴𐌹𐌽𐍃, 𐌱𐍂𐌿𐌺𐌴𐌹𐌽𐍃 |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ther` | 2.06x | 24 contexts | there, other, others |
+| `𐌰𐌿𐍂𐌳` | 1.98x | 18 contexts | 𐍅𐌰𐌿𐍂𐌳, 𐍅𐌰𐌿𐍂𐌳𐌴, 𐍅𐌰𐌿𐍂𐌳𐌰 |
+| `tion` | 2.11x | 14 contexts | option, motion, nation |
+| `𐌴𐌹𐌽𐌰` | 1.83x | 16 contexts | 𐌺𐌴𐌹𐌽𐌰, 𐌼𐌴𐌹𐌽𐌰, 𐍅𐌴𐌹𐌽𐌰 |
+| `𐍅𐌰𐌿𐍂` | 1.80x | 14 contexts | 𐍅𐌰𐌿𐍂𐌳, 𐍅𐌰𐌿𐍂𐌳𐌴, 𐍅𐌰𐌿𐍂𐌳𐌰 |
+| `𐌿𐌳𐌰𐌽` | 2.08x | 9 contexts | 𐌲𐌿𐌳𐌰𐌽𐍃, 𐌸𐌹𐌿𐌳𐌰𐌽, 𐌸𐌹𐌿𐌳𐌰𐌽𐍃 |
+| `𐌹𐌿𐌳𐌰` | 1.71x | 14 contexts | 𐌻𐌹𐌿𐌳𐌰, 𐌸𐌹𐌿𐌳𐌰, 𐌸𐌹𐌿𐌳𐌰𐌹 |
+| `𐌾𐌰𐌽𐌳` | 1.62x | 16 contexts | 𐍃𐍉𐌺𐌾𐌰𐌽𐌳, 𐍅𐌰𐌲𐌾𐌰𐌽𐌳, 𐌼𐌰𐍄𐌾𐌰𐌽𐌳 |
+| `𐍂𐌰𐌶𐌳` | 1.98x | 9 contexts | 𐍂𐌰𐌶𐌳𐍉, 𐍂𐌰𐌶𐌳𐌰, 𐍂𐌰𐌶𐌳𐍉𐌼 |
+| `𐌹𐌽𐌰𐌹` | 1.88x | 10 contexts | 𐌰𐌹𐌽𐌰𐌹, 𐍃𐌹𐌽𐌰𐌹, 𐍃𐌴𐌹𐌽𐌰𐌹 |
+| `𐌷𐌰𐌱𐌰` | 1.91x | 9 contexts | ���𐌰𐌱𐌰𐌽, 𐌷𐌰𐌱𐌰𐌼, 𐌷𐌰𐌱𐌰𐌹𐌸 |
+| `𐍂𐌴𐌹𐌺` | 1.82x | 10 contexts | 𐍂𐌴𐌹𐌺𐍃, 𐍂𐌴𐌹𐌺𐌹, 𐍂𐌴𐌹𐌺𐌹𐍃 |
+
+### 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 |
+|------|-----------------|------------|------|
+| 𐍃𐌺𐌰𐌿𐌽𐌴𐌹𐌽𐍃 | **`𐍃𐌺𐌰𐌿𐌽𐌴𐌹-𐌽𐍃`** | 4.5 | `𐍃𐌺𐌰𐌿𐌽𐌴𐌹` |
+| 𐍆𐍂𐌿𐌼𐌹𐍃𐍄𐍉𐌽𐍃 | **`𐍆𐍂𐌿𐌼𐌹𐍃𐍄𐍉-𐌽𐍃`** | 4.5 | `𐍆𐍂𐌿𐌼𐌹𐍃𐍄𐍉` |
+| 𐌼𐌿𐌽𐌳𐍂𐌴𐌹𐌽𐍃 | **`𐌼𐌿𐌽𐌳𐍂𐌴𐌹-𐌽𐍃`** | 4.5 | `𐌼𐌿𐌽𐌳𐍂𐌴𐌹` |
+| 𐌰𐌿𐍃𐍄𐍂𐌰𐌲𐌿𐍄𐌰𐌽𐍃 | **`𐌰𐌿𐍃𐍄𐍂𐌰𐌲𐌿𐍄𐌰-𐌽𐍃`** | 4.5 | `𐌰𐌿𐍃𐍄𐍂𐌰𐌲𐌿𐍄𐌰` |
+| 𐌰𐌽𐌳𐌽𐌿𐌼𐌰𐌽𐍃 | **`𐌰𐌽𐌳𐌽𐌿𐌼𐌰-𐌽𐍃`** | 1.5 | `𐌰𐌽𐌳𐌽𐌿𐌼𐌰` |
+| 𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌾𐌰𐌽𐌳𐌰𐌽𐍃 | **`𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌾𐌰𐌽𐌳𐌰-𐌽𐍃`** | 1.5 | `𐌲𐌰𐌲𐌰𐌷𐌰𐍆𐍄𐌾𐌰𐌽𐌳𐌰` |
+| porthpean | **`porthpe-an`** | 1.5 | `porthpe` |
+| barbarian | **`barbari-an`** | 1.5 | `barbari` |
+| scandinavian | **`scandinavi-an`** | 1.5 | `scandinavi` |
+| 𐍆𐍂𐌹𐌾𐌰𐍄𐌹𐌼𐍂𐌴𐌹𐌽𐍃 | **`𐍆𐍂𐌹𐌾𐌰𐍄𐌹𐌼𐍂𐌴𐌹-𐌽𐍃`** | 1.5 | `𐍆𐍂𐌹𐌾𐌰𐍄𐌹𐌼𐍂𐌴𐌹` |
+| 𐌷𐍂𐌿𐌲𐌾𐌰𐌱𐌰𐌹𐌽𐌰𐌽𐍃 | **`𐌷𐍂𐌿𐌲𐌾𐌰𐌱𐌰𐌹𐌽𐌰-𐌽𐍃`** | 1.5 | `𐌷𐍂𐌿𐌲𐌾𐌰𐌱𐌰𐌹𐌽𐌰` |
+| 𐌼𐌰𐌾𐌰𐌹𐌽𐌾𐍉𐌽𐍃 | **`𐌼𐌰𐌾𐌰𐌹𐌽𐌾𐍉-𐌽𐍃`** | 1.5 | `𐌼𐌰𐌾𐌰𐌹𐌽𐌾𐍉` |
+| macmillan | **`macmill-an`** | 1.5 | `macmill` |
+| 𐌼𐌹𐌻𐌿𐌺𐍃𐍆𐍉𐌳𐌾𐌰𐌽𐍃 | **`𐌼𐌹𐌻𐌿𐌺𐍃𐍆𐍉𐌳𐌾𐌰-𐌽𐍃`** | 1.5 | `𐌼𐌹𐌻𐌿𐌺𐍃𐍆𐍉𐌳𐌾𐌰` |
+| 𐌽𐌹𐍂𐌱𐌰𐌽𐌹𐌽𐍃 | **`𐌽𐌹𐍂𐌱𐌰𐌽𐌹-𐌽𐍃`** | 1.5 | `𐌽𐌹𐍂𐌱𐌰𐌽𐌹` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Gothic 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 (2.88x) |
+| N-gram | **2-gram** | Lowest perplexity (546) |
+| Markov | **Context-4** | Highest predictability (98.4%) |
+| 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: 2026-01-04 15:24:37*

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