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README.md

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+---
+language: co
+language_name: CO
+language_family: romance_galloitalic
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - monolingual
+  - family-romance_galloitalic
+license: mit
+library_name: wikilangs
+pipeline_tag: feature-extraction
+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: 4.112
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8279
+  - name: vocabulary_size
+    type: vocab
+    value: 60410
+generated: 2025-12-28
+---
+
+# CO - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **CO** 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-gram)
+- Markov chains (context of 1, 2, 3 and 4)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions
+- 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. Summary & Recommendations](#6-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.411x | 3.34 | 0.0362% | 381,463 |
+| **16k** | 3.668x | 3.59 | 0.0389% | 354,761 |
+| **32k** | 3.899x | 3.81 | 0.0414% | 333,726 |
+| **64k** | 4.112x 🏆 | 4.02 | 0.0436% | 316,458 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `hè una cumuna spagnola di a pruvincia di Soria, in a cumunità autonoma di Castig...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁hè ▁una ▁cumuna ▁spagnola ▁di ▁a ▁pruvincia ▁di ▁soria , ... (+14 more)` | 24 |
+| 16k | `▁hè ▁una ▁cumuna ▁spagnola ▁di ▁a ▁pruvincia ▁di ▁soria , ... (+14 more)` | 24 |
+| 32k | `▁hè ▁una ▁cumuna ▁spagnola ▁di ▁a ▁pruvincia ▁di ▁soria , ... (+14 more)` | 24 |
+| 64k | `▁hè ▁una ▁cumuna ▁spagnola ▁di ▁a ▁pruvincia ▁di ▁soria , ... (+14 more)` | 24 |
+
+**Sample 2:** `Aries Spears hè un attore americanu.
+
+ Biugrafia 
+
+ Da vede dinò 
+ Listinu di l'...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ari es ▁spe ars ▁hè ▁un ▁attore ▁americanu . ▁biugrafia ... (+22 more)` | 32 |
+| 16k | `▁ari es ▁spe ars ▁hè ▁un ▁attore ▁americanu . ▁biugrafia ... (+22 more)` | 32 |
+| 32k | `▁ari es ▁spe ars ▁hè ▁un ▁attore ▁americanu . ▁biugrafia ... (+22 more)` | 32 |
+| 64k | `▁aries ▁spears ▁hè ▁un ▁attore ▁americanu . ▁biugrafia ▁da ▁vede ... (+16 more)` | 26 |
+
+**Sample 3:** `Arliss Howard (18 ottobre 1954) hè un attore americanu.
+
+Da vede dinò
+Listinu di...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ar li ss ▁ho ward ▁( 1 8 ▁ottobre ▁ ... (+23 more)` | 33 |
+| 16k | `▁ar li ss ▁ho ward ▁( 1 8 ▁ottobre ▁ ... (+23 more)` | 33 |
+| 32k | `▁ar li ss ▁howard ▁( 1 8 ▁ottobre ▁ 1 ... (+20 more)` | 30 |
+| 64k | `▁arli ss ▁howard ▁( 1 8 ▁ottobre ▁ 1 9 ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.112x compression
+- **Lowest UNK Rate:** 8k with 0.0362% 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 Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | 8,586 🏆 | 13.07 | 59,847 | 24.1% | 47.7% |
+| **2-gram** | 252 🏆 | 7.98 | 3,720 | 68.2% | 99.4% |
+| **3-gram** | 25,561 | 14.64 | 108,167 | 12.3% | 31.9% |
+| **3-gram** | 1,976 | 10.95 | 27,040 | 26.7% | 74.8% |
+| **4-gram** | 50,965 | 15.64 | 187,731 | 9.1% | 24.8% |
+| **4-gram** | 10,378 | 13.34 | 132,259 | 13.2% | 40.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l '` | 45,334 |
+| 2 | `di u` | 18,690 |
+| 3 | `di a` | 18,510 |
+| 4 | `d '` | 14,383 |
+| 5 | `di l` | 13,245 |
+
+**3-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `di l '` | 12,757 |
+| 2 | `à l '` | 5,757 |
+| 3 | `a famiglia di` | 4,354 |
+| 4 | `hè una spezia` | 3,364 |
+| 5 | `com ' è` | 3,225 |
+
+**4-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `di a famiglia di` | 2,632 |
+| 2 | `a famiglia di i` | 2,171 |
+| 3 | `hè una spezia di` | 2,067 |
+| 4 | `à wikimedia commons .` | 1,947 |
+| 5 | `annantu à wikimedia commons` | 1,947 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram with 252
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~41% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | 0.8152 | 1.760 | 5.44 | 128,497 | 18.5% |
+| **1** | 1.0047 | 2.007 | 7.61 | 1,338 | 0.0% |
+| **2** | 0.3514 | 1.276 | 1.95 | 698,773 | 64.9% |
+| **2** | 0.9540 | 1.937 | 5.63 | 10,183 | 4.6% |
+| **3** | 0.1551 | 1.114 | 1.31 | 1,359,938 | 84.5% |
+| **3** | 0.8234 | 1.770 | 4.05 | 57,282 | 17.7% |
+| **4** | 0.0762 🏆 | 1.054 | 1.13 | 1,780,901 | 92.4% |
+| **4** | 0.6487 🏆 | 1.568 | 2.82 | 232,155 | 35.1% |
+
+### Generated Text Samples
+
+Below are text samples generated from each Markov chain model:
+
+**Context Size 1:**
+
+1. `di l ' edda l ' uparatori devi u rè unificadori , u rodanu è à`
+2. `, i bordi di a so pusizioni giugrafica . mentri chì và , ti pristaraghju u`
+3. `. a so nomu di a femina face di a prima u primu piannu fundata u`
+
+**Context Size 2:**
+
+1. `l ' esemplare più grossu mai truvatu fubbe tumbatu in angola in u mediterraniu . faci parti`
+2. `di u cubu di u 2011 , airbnb hà raccoltu $ 119 , 8 % in isuzu`
+3. `di a nutazioni pusiziunali micca assà diffarenti rispettu à l ' animali chì si pò mintuvà :`
+
+**Context Size 3:**
+
+1. `di l ' arte è di a fine di listessu annu deposita à l ' iranu . porphyrio`
+2. `à l ' università di versailles , hè cunnisciutu sottu u nome di locu vicinu di u vechju`
+3. `a famiglia di e liliaceae . descrizzione lucalisazione referenze ligami categoria : cumuna di corsic...`
+
+**Context Size 4:**
+
+1. `di a famiglia di i ranunculaceae . discrizzioni ranunculus kuepferi hè una spezia di acellu chì face...`
+2. `a famiglia di i laniidae , chì cumprende dinù altre spezie d ' aythya . si ciba per u`
+3. `hè una spezia di acellu chì face parte di a famiglia di i polypodiaceae . ' ssa pianta priferisce`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 with 92.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (232,155 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 | 60,410 |
+| Total Tokens | 2,262,071 |
+| Mean Frequency | 37.45 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 967.51 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | di | 143,631 |
+| 2 | u | 84,326 |
+| 3 | a | 76,229 |
+| 4 | è | 67,228 |
+| 5 | in | 58,950 |
+| 6 | à | 58,500 |
+| 7 | l | 48,443 |
+| 8 | hè | 46,128 |
+| 9 | i | 45,172 |
+| 10 | da | 24,646 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | corsehttps | 2 |
+| 2 | secchia | 2 |
+| 3 | bergeries | 2 |
+| 4 | douleur | 2 |
+| 5 | bouvier | 2 |
+| 6 | spezialità | 2 |
+| 7 | alerta | 2 |
+| 8 | francebleu | 2 |
+| 9 | emissions | 2 |
+| 10 | ꦈꦠꦩ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0521 |
+| R² (Goodness of Fit) | 0.997001 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.2% |
+| Top 1,000 | 68.9% |
+| Top 5,000 | 83.6% |
+| Top 10,000 | 89.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9970 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.2% of corpus
+- **Long Tail:** 50,410 words needed for remaining 10.9% 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)
+
+### Model Comparison
+
+| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy |
+|-------|------------|-----------|----------|----------|----------|
+| **mono_32d** | 32,860 | 32 | 4.128 | 1.155 | 0.8279 🏆 |
+| **mono_64d** | 32,860 | 64 | 4.723 | 1.023 | 0.8160 |
+| **mono_128d** | 32,860 | 128 | 5.374 | 0.857 | 0.7544 |
+| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8279 (more uniform distribution)
+- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
+- **Vocabulary Coverage:** All models cover 32,860 words
+- **Recommendation:** 100d for balanced semantic capture and efficiency
+
+---
+## 6. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **32k BPE** | Best compression (4.11x) with low UNK rate |
+| N-gram | **5-gram** | Lowest perplexity (252) |
+| Markov | **Context-4** | Highest predictability (92.4%) |
+| 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},
+  publisher = {HuggingFace},
+  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)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2025-12-28 23:12:11*

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