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+---
+language: dag
+language_name: DAG
+language_family: atlantic_gur
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - monolingual
+  - family-atlantic_gur
+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: 3.798
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7945
+  - name: vocabulary_size
+    type: vocab
+    value: 144073
+generated: 2025-12-29
+---
+
+# DAG - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **DAG** 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** | 3.288x | 3.27 | 0.0331% | 869,625 |
+| **16k** | 3.508x | 3.49 | 0.0353% | 815,031 |
+| **32k** | 3.685x | 3.66 | 0.0371% | 775,924 |
+| **64k** | 3.798x 🏆 | 3.78 | 0.0383% | 752,740 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Soyauxia velutina nyɛla Soyauxia zuliya la puuni zaɣa yini.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁soya u xia ▁v el ut ina ▁nyɛla ▁soya u ... (+7 more)` | 17 |
+| 16k | `▁soya u xia ▁vel ut ina ▁nyɛla ▁soya u xia ... (+6 more)` | 16 |
+| 32k | `▁soya u xia ▁vel ut ina ▁nyɛla ▁soya u xia ... (+6 more)` | 16 |
+| 64k | `▁soya u xia ▁vel utina ▁nyɛla ▁soya u xia ▁zuliya ... (+5 more)` | 15 |
+
+**Sample 2:** `Dagbanli Wikipedia nyɛla Wikipedia yaɣishɛli din sabbu nyɛla Dagbanli ka di tiri...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁dagbanli ▁wikipedia ▁nyɛla ▁wikipedia ▁yaɣi shɛli ▁din ▁sabbu ▁nyɛla ▁dagbanli ... (+23 more)` | 33 |
+| 16k | `▁dagbanli ▁wikipedia ▁nyɛla ▁wikipedia ▁yaɣishɛli ▁din ▁sabbu ▁nyɛla ▁dagbanli ▁ka ... (+19 more)` | 29 |
+| 32k | `▁dagbanli ▁wikipedia ▁nyɛla ▁wikipedia ▁yaɣishɛli ▁din ▁sabbu ▁nyɛla ▁dagbanli ▁ka ... (+18 more)` | 28 |
+| 64k | `▁dagbanli ▁wikipedia ▁nyɛla ▁wikipedia ▁yaɣishɛli ▁din ▁sabbu ▁nyɛla ▁dagbanli ▁ka ... (+18 more)` | 28 |
+
+**Sample 3:** `Kaʒiya nyɛla Yaa Naa paɣa ŋun pahiri ayi yuli.
+Taarihi
+Kundivihira`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ka ʒi ya ▁nyɛla ▁yaa ▁naa ▁paɣa ▁ŋun ▁pahiri ▁ayi ... (+4 more)` | 14 |
+| 16k | `▁ka ʒi ya ▁nyɛla ▁yaa ▁naa ▁paɣa ▁ŋun ▁pahiri ▁ayi ... (+4 more)` | 14 |
+| 32k | `▁ka ʒiya ▁nyɛla ▁yaa ▁naa ▁paɣa ▁ŋun ▁pahiri ▁ayi ▁yuli ... (+3 more)` | 13 |
+| 64k | `▁ka ʒiya ▁nyɛla ▁yaa ▁naa ▁paɣa ▁ŋun ▁pahiri ▁ayi ▁yuli ... (+3 more)` | 13 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.798x compression
+- **Lowest UNK Rate:** 8k with 0.0331% 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** | 24,966 🏆 | 14.61 | 188,164 | 17.0% | 36.0% |
+| **2-gram** | 423 🏆 | 8.72 | 7,859 | 55.8% | 97.7% |
+| **3-gram** | 60,310 | 15.88 | 362,741 | 14.0% | 28.1% |
+| **3-gram** | 4,210 | 12.04 | 70,197 | 17.9% | 59.2% |
+| **4-gram** | 110,926 | 16.76 | 665,471 | 13.6% | 24.8% |
+| **4-gram** | 26,280 | 14.68 | 408,931 | 8.8% | 29.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `| |` | 134,746 |
+| 2 | `align =` | 45,935 |
+| 3 | `| align` | 45,817 |
+| 4 | `= "` | 42,517 |
+| 5 | `" |` | 41,324 |
+
+**3-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `| align =` | 45,817 |
+| 2 | `align = "` | 38,202 |
+| 3 | `| | align` | 38,026 |
+| 4 | `= " center` | 23,000 |
+| 5 | `" center "` | 23,000 |
+
+**4-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `| align = "` | 38,119 |
+| 2 | `| | align =` | 38,026 |
+| 3 | `align = " center` | 23,000 |
+| 4 | `= " center "` | 22,999 |
+| 5 | `" center " |` | 22,948 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram with 423
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~29% 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** | 0.5033 | 1.417 | 4.95 | 440,521 | 49.7% |
+| **1** | 1.2280 | 2.342 | 7.57 | 3,894 | 0.0% |
+| **2** | 0.3109 | 1.241 | 1.99 | 2,180,092 | 68.9% |
+| **2** | 0.7059 | 1.631 | 5.02 | 29,465 | 29.4% |
+| **3** | 0.1506 | 1.110 | 1.33 | 4,328,117 | 84.9% |
+| **3** | 0.8636 | 1.820 | 4.71 | 148,003 | 13.6% |
+| **4** | 0.0788 🏆 | 1.056 | 1.15 | 5,753,766 | 92.1% |
+| **4** | 0.7332 🏆 | 1.662 | 3.29 | 697,702 | 26.7% |
+
+### Generated Text Samples
+
+Below are text samples generated from each Markov chain model:
+
+**Context Size 1:**
+
+1. `, trevor jones and circulation . " | 2019 . taylor sheridan , bruxelles ’ shεli`
+2. `| g | jim sturgess , emma thompson , kabɛ zani mi binyahiri maalibu mini queens`
+3. `. > ŋɔ ka tuunvɛla tumbu bee yahibu zuɣu o ŋmai jia , domin bɛ yεli`
+
+**Context Size 2:**
+
+1. `| | 2 . 2the price is rightvirtuosthe price is right : the voyage of sinbad ted`
+2. `align = center | | align = " top " | north carolina arts coalition 1979 :`
+3. `| align = " center " | | - | align = " center " | -`
+
+**Context Size 3:**
+
+1. `| align = " left " | kentucky | | align = center | | - | align`
+2. `align = " left " | real madrid | align = " left " | grambling state |`
+3. `| | align = " center " | | | align = center | | - | align`
+
+**Context Size 4:**
+
+1. `| align = " left " | tulsa | | align = " left " | liu brooklyn |`
+2. `| | align = " center " | 1 | | align = " center " | f |`
+3. `align = " center " | 1 | | align = " left " | utah | | align`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 with 92.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (697,702 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 | 144,073 |
+| Total Tokens | 6,561,803 |
+| Mean Frequency | 45.54 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 780.34 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ni | 105,049 |
+| 2 | the | 93,758 |
+| 3 | of | 89,633 |
+| 4 | daa | 75,893 |
+| 5 | o | 71,379 |
+| 6 | ka | 70,354 |
+| 7 | n | 52,468 |
+| 8 | nyɛla | 50,022 |
+| 9 | din | 48,371 |
+| 10 | align | 46,328 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | hadja | 2 |
+| 2 | labcitoyen | 2 |
+| 3 | yikonim | 2 |
+| 4 | fiqhi | 2 |
+| 5 | sapuhi | 2 |
+| 6 | hoti | 2 |
+| 7 | xai | 2 |
+| 8 | coloboma | 2 |
+| 9 | ziɛ | 2 |
+| 10 | bɔɔlɔ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0748 |
+| R² (Goodness of Fit) | 0.994305 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 31.7% |
+| Top 1,000 | 59.4% |
+| Top 5,000 | 78.0% |
+| Top 10,000 | 84.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9943 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 31.7% of corpus
+- **Long Tail:** 134,073 words needed for remaining 15.3% 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** | 82,599 | 32 | 3.850 | 1.140 | 0.7771 |
+| **mono_64d** | 82,599 | 64 | 4.436 | 1.061 | 0.7860 |
+| **mono_128d** | 82,599 | 128 | 5.151 | 0.960 | 0.7945 🏆 |
+| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_128d with 0.7945 (more uniform distribution)
+- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
+- **Vocabulary Coverage:** All models cover 82,599 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 (3.80x) with low UNK rate |
+| N-gram | **5-gram** | Lowest perplexity (423) |
+| Markov | **Context-4** | Highest predictability (92.1%) |
+| 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},
+  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: 2025-12-29 09:14:13*
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