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README.md

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+---
+language: bbc
+language_name: BBC
+language_family: austronesian_batak
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - monolingual
+  - family-austronesian_batak
+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: 4.433
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8253
+  - name: vocabulary_size
+    type: vocab
+    value: 24711
+generated: 2025-12-28
+---
+
+# BBC - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **BBC** 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** | 3.867x | 3.83 | 0.1235% | 1,466,873 |
+| **16k** | 4.118x | 4.08 | 0.1315% | 1,377,727 |
+| **32k** | 4.304x | 4.27 | 0.1375% | 1,318,061 |
+| **64k** | 4.433x 🏆 | 4.39 | 0.1416% | 1,279,829 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Panjunan i ma sada huta  na adong di Kecamatan Petarukan, Kabupaten Pemalang, Pr...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁panj unan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 16k | `▁panj unan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 32k | `▁panj unan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 64k | `▁panjunan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁petarukan ... (+10 more)` | 20 |
+
+**Sample 2:** `Ampapaga (Surat Batak:ᯀᯔ᯲ᯇᯇᯎ) i ma sada suansuanan na tubu di gadu ni hauma.
+
+ P...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁amp ap aga ▁( surat ▁batak : ᯀ ᯔ᯲ ᯇ ... (+20 more)` | 30 |
+| 16k | `▁amp ap aga ▁( surat ▁batak : ᯀ ᯔ᯲ᯇ ᯇ ... (+18 more)` | 28 |
+| 32k | `▁amp apaga ▁( surat ▁batak : ᯀ ᯔ᯲ᯇᯇᯎ ) ▁i ... (+15 more)` | 25 |
+| 64k | `▁ampapaga ▁( surat ▁batak : ᯀᯔ᯲ᯇᯇᯎ ) ▁i ▁ma ▁sada ... (+13 more)` | 23 |
+
+**Sample 3:** `Sungapan i ma sada huta  na adong di Kecamatan Pemalang, Kabupaten Pemalang, Pro...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sung apan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 16k | `▁sung apan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 32k | `▁sung apan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 64k | `▁sungapan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁pemalang ... (+10 more)` | 20 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.433x compression
+- **Lowest UNK Rate:** 8k with 0.1235% 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** | 7,191 🏆 | 12.81 | 30,496 | 17.8% | 49.6% |
+| **2-gram** | 209 🏆 | 7.70 | 2,868 | 75.2% | 99.1% |
+| **3-gram** | 25,989 | 14.67 | 62,993 | 8.7% | 26.3% |
+| **3-gram** | 1,399 | 10.45 | 19,851 | 36.8% | 80.6% |
+| **4-gram** | 63,619 | 15.96 | 114,847 | 4.7% | 15.6% |
+| **4-gram** | 6,375 | 12.64 | 81,359 | 18.9% | 52.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `, jala` | 8,780 |
+| 2 | `i ,` | 6,997 |
+| 3 | `ᯬ ᯲` | 4,573 |
+| 4 | `angka na` | 4,409 |
+| 5 | `dung i` | 4,328 |
+
+**3-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `anak ni si` | 1,611 |
+| 2 | `, angka na` | 1,528 |
+| 3 | `. 2 :` | 1,079 |
+| 4 | `, anak ni` | 1,069 |
+| 5 | `ᯰ ᯄ ᯦` | 1,063 |
+
+**4-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `, anak ni si` | 906 |
+| 2 | `ᯀ ᯰ ᯄ ᯦` | 686 |
+| 3 | `ᯘ ᯪ ᯀᯉ ᯲` | 457 |
+| 4 | `ᯬ ᯂᯖ ᯬ ᯲` | 432 |
+| 5 | `on do hata ni` | 421 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram with 209
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~53% 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** | 0.8281 | 1.775 | 6.09 | 50,531 | 17.2% |
+| **1** | 1.0960 | 2.138 | 7.35 | 1,187 | 0.0% |
+| **2** | 0.3983 | 1.318 | 2.17 | 307,642 | 60.2% |
+| **2** | 0.8091 | 1.752 | 4.70 | 8,725 | 19.1% |
+| **3** | 0.1982 | 1.147 | 1.41 | 667,901 | 80.2% |
+| **3** | 0.7878 | 1.726 | 3.57 | 41,004 | 21.2% |
+| **4** | 0.0988 🏆 | 1.071 | 1.16 | 943,940 | 90.1% |
+| **4** | 0.5526 🏆 | 1.467 | 2.40 | 146,535 | 44.7% |
+
+### Generated Text Samples
+
+Below are text samples generated from each Markov chain model:
+
+**Context Size 1:**
+
+1. `, jala tu si wasti , jala dipadeakdeak hamu sian i jala peakkononna tu palaspalas pamurunan`
+2. `. 12 : songon i hatangku tu bagasan saluhut angka ari puasa bintang di jolo i`
+3. `: ida ma angka na margoar milo dohot paredangedangan , 2 : 35 : 25 pelean`
+
+**Context Size 2:**
+
+1. `, jala tudoshon gumora angka anak ni si joab soara ni sarune i laho ma ho ,`
+2. `i , naung pinauli ni tangan ni halak pangarupa umpogo upa ni pambahenannasida . 99 : 7`
+3. `ᯬ ᯲ ᯘᯞ ᯮ ᯂᯖ ᯮ ᯲ ᯑ ᯩ ᯇᯔ ᯲ ᯅᯂ ᯩ ᯉᯉ ᯲ ᯔ ᯉ`
+
+**Context Size 3:**
+
+1. `anak ni si ahilud do panuturi . 18 : 13 tung sura ahu parohon begu masa tu luat`
+2. `, angka na so bangso hian ; marhite sian bangso na asing , jala marbarita goarmu di betlehem`
+3. `. 2 : 15 alai tarrimas situtu ma si abner dohot di halak na marroha pangansion , ndang`
+
+**Context Size 4:**
+
+1. `, anak ni si ammiel . 3 : 6 ndada tu torop bangso , angka parhata bobang , manang`
+2. `ᯀ ᯰ ᯄ ᯦ ᯂᯖ ᯉ ᯪ ᯘ ᯪ ᯅ ᯩ ᯀ ᯩ ᯒ ᯪ ᯑ ᯪ ᯉᯘ ᯪ`
+3. `ᯘ ᯪ ᯀᯉ ᯲ ᯂᯔ ᯪ ᯐ ᯮ ᯔ ᯬ ᯞ ᯬ ᯉ ᯪ ᯑ ᯩ ᯅᯖ 2 :`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 with 90.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (146,535 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 | 24,711 |
+| Total Tokens | 1,019,541 |
+| Mean Frequency | 41.26 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 565.94 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ni | 35,101 |
+| 2 | na | 34,088 |
+| 3 | i | 33,056 |
+| 4 | ma | 26,769 |
+| 5 | di | 26,053 |
+| 6 | tu | 20,450 |
+| 7 | do | 19,163 |
+| 8 | angka | 17,428 |
+| 9 | jala | 14,598 |
+| 10 | dohot | 13,609 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ᯝᯇᯞ | 2 |
+| 2 | kayo | 2 |
+| 3 | uttar | 2 |
+| 4 | ltr | 2 |
+| 5 | ebrima | 2 |
+| 6 | 290px | 2 |
+| 7 | td | 2 |
+| 8 | height | 2 |
+| 9 | 260px | 2 |
+| 10 | 22251 | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1956 |
+| R² (Goodness of Fit) | 0.996705 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 52.8% |
+| Top 1,000 | 78.6% |
+| Top 5,000 | 91.7% |
+| Top 10,000 | 95.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9967 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 52.8% of corpus
+- **Long Tail:** 14,711 words needed for remaining 4.1% 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)
+
+### Model Comparison
+
+| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy |
+|-------|------------|-----------|----------|----------|----------|
+| **mono_32d** | 15,079 | 32 | 3.458 | 0.818 | 0.8253 🏆 |
+| **mono_64d** | 15,079 | 64 | 3.886 | 0.738 | 0.7641 |
+| **mono_128d** | 15,079 | 128 | 4.143 | 0.691 | 0.4668 |
+| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8253 (more uniform distribution)
+- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
+- **Vocabulary Coverage:** All models cover 15,079 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 (4.43x) with low UNK rate |
+| N-gram | **5-gram** | Lowest perplexity (209) |
+| Markov | **Context-4** | Highest predictability (90.1%) |
+| 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: 2025-12-28 00:12:38*

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