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+---
+language: din
+language_name: DIN
+language_family: african_nilotic
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - monolingual
+  - family-african_nilotic
+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: 4.266
+  - name: best_isotropy
+    type: isotropy
+    value: 0.1273
+  - name: vocabulary_size
+    type: vocab
+    value: 5872
+generated: 2025-12-30
+---
+
+# DIN - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **DIN** 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** | 3.627x | 3.61 | 0.1100% | 146,415 |
+| **16k** | 3.922x | 3.91 | 0.1189% | 135,412 |
+| **32k** | 4.266x 🏆 | 4.25 | 0.1293% | 124,488 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Paankɔc Ciɛl de Libya ee paan cï thiɔ̈ɔ̈k thïn Apirïka ciɛlic. Genamaatnhomde ay...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁paankɔc ▁ciɛl ▁de ▁libya ▁ee ▁paan ▁cï ▁thiɔ̈ɔ̈k ▁thïn ▁apirïka ... (+11 more)` | 21 |
+| 16k | `▁paankɔc ▁ciɛl ▁de ▁libya ▁ee ▁paan ▁cï ▁thiɔ̈ɔ̈k ▁thïn ▁apirïka ... (+11 more)` | 21 |
+| 32k | `▁paankɔc ▁ciɛl ▁de ▁libya ▁ee ▁paan ▁cï ▁thiɔ̈ɔ̈k ▁thïn ▁apirïka ... (+10 more)` | 20 |
+
+**Sample 2:** `Heen acï puööu miet apeidït ne rin  cïï ok rot mat thääi pinynhom yiic.
+
+Piööc k...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁h een ▁acï ▁pu öö u ▁mi et ▁apei dït ... (+21 more)` | 31 |
+| 16k | `▁heen ▁acï ▁pu öö u ▁mi et ▁apeidït ▁ne ▁rin ... (+18 more)` | 28 |
+| 32k | `▁heen ▁acï ▁puööu ▁miet ▁apeidït ▁ne ▁rin ▁cïï ▁ok ▁rot ... (+15 more)` | 25 |
+
+**Sample 3:** `+Japan 125px 135px  300px
+Japan ee pamac tɔ Athiɛ. Genamaatnhomde ayee cɔl Tokyo...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁+ j apan ▁ 1 2 5 px ▁ 1 ... (+23 more)` | 33 |
+| 16k | `▁+ japan ▁ 1 2 5 px ▁ 1 3 ... (+21 more)` | 31 |
+| 32k | `▁+ japan ▁ 1 2 5 px ▁ 1 3 ... (+21 more)` | 31 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.266x compression
+- **Lowest UNK Rate:** 8k with 0.1100% 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** | 969 🏆 | 9.92 | 2,614 | 42.8% | 79.5% |
+| **2-gram** | 353 🏆 | 8.46 | 1,719 | 60.1% | 98.7% |
+| **3-gram** | 958 | 9.90 | 2,667 | 44.4% | 78.1% |
+| **3-gram** | 2,358 | 11.20 | 10,075 | 24.1% | 70.3% |
+| **4-gram** | 1,138 | 10.15 | 3,607 | 44.8% | 70.6% |
+| **4-gram** | 9,037 | 13.14 | 33,136 | 12.8% | 42.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `, ku` | 881 |
+| 2 | `̈ n` | 812 |
+| 3 | `̈ k` | 805 |
+| 4 | `ɔ ̈` | 709 |
+| 5 | `̈ ɔ` | 708 |
+
+**3-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `̈ ɔ ̈` | 704 |
+| 2 | `̈ ɛ ̈` | 526 |
+| 3 | `kɔ ̈ k` | 316 |
+| 4 | `bɛ ̈ n` | 233 |
+| 5 | `. bekätakthook :` | 223 |
+
+**4-grams:**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. 2006 . english` | 167 |
+| 2 | `blench . 2006 .` | 167 |
+| 3 | `2006 . english to` | 167 |
+| 4 | `: derived from sil` | 167 |
+| 5 | `. kay williamson educational` | 167 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram with 353
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~43% 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** | 0.6001 | 1.516 | 4.04 | 17,160 | 40.0% |
+| **1** | 1.6144 | 3.062 | 12.68 | 311 | 0.0% |
+| **2** | 0.2159 | 1.161 | 1.46 | 69,220 | 78.4% |
+| **2** | 1.1540 | 2.225 | 5.73 | 3,939 | 0.0% |
+| **3** | 0.0701 | 1.050 | 1.12 | 101,209 | 93.0% |
+| **3** | 0.7580 | 1.691 | 3.07 | 22,573 | 24.2% |
+| **4** | 0.0275 🏆 | 1.019 | 1.04 | 113,437 | 97.2% |
+| **4** | 0.4989 🏆 | 1.413 | 2.04 | 69,374 | 50.1% |
+
+### Generated Text Samples
+
+Below are text samples generated from each Markov chain model:
+
+**Context Size 1:**
+
+1. `̈ k mɛ ̈ yuganda , tɛ ̈ th aacï röth juiɛr ke lɔ piny de`
+2. `. kɛ ̈ nyë riɛm yɛ ̈ ɔ ̈ në juëkjuëk apɛi ake mɛt thïriɛa ,`
+3. `, në olimpik löŋden cal lɔn yen wën ye lac tïŋ ɣɔn këc guoɔn nyucciëëŋden tueŋ`
+
+**Context Size 2:**
+
+1. `, ku tënɔŋ adhande ke keek . kuat yic bɛ ̈ nywut . keekë kɔckɛ ̈ ,`
+2. `̈ n ya bɛ ̈ nykoormacbaai tueŋ zubair mohamed salih , bɛ ̈ n . kɔc ke`
+3. `̈ k alëu bïkï nhïïm nɔŋ tuŋ bär ye cɔl mayoŋ . pïu rac cï cöp abuk`
+
+**Context Size 3:**
+
+1. `̈ ɔ ̈ k ɣaa këc pööc lac dööt , ciɛŋden thɛɛr ku pïïr ɣene töŋ maɣëmë ke`
+2. `̈ ɛ ̈ r cïke baar në rɛɛnken tɔŋbaai de rou kɔc ke thudän ( ylkt ) ku`
+3. `kɔ ̈ k ke 15 , 000 cï dɔm . burjuŋ aka rilic bï ye thok poc kek`
+
+**Context Size 4:**
+
+1. `from sil international ' s 2005 draft dinka - english dictionary . kay williamson educational founda...`
+2. `' s 2005 draft dinka - english dictionary . kay williamson educational foundation / sil internationa...`
+3. `draft dinka - english dictionary . kay williamson educational foundation / sil international . dikco...`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 with 97.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (69,374 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 | 5,872 |
+| Total Tokens | 91,802 |
+| Mean Frequency | 15.63 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 91.07 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ku | 3,547 |
+| 2 | në | 2,806 |
+| 3 | de | 2,159 |
+| 4 | ë | 1,891 |
+| 5 | ke | 1,779 |
+| 6 | ye | 1,488 |
+| 7 | kɔc | 1,187 |
+| 8 | ee | 1,187 |
+| 9 | cï | 883 |
+| 10 | k | 882 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mayall | 2 |
+| 2 | cream | 2 |
+| 3 | layla | 2 |
+| 4 | adëgëk | 2 |
+| 5 | 1988 | 2 |
+| 6 | skobarkä | 2 |
+| 7 | pïlïbït | 2 |
+| 8 | tïgër | 2 |
+| 9 | rësärwë | 2 |
+| 10 | terai | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0740 |
+| R² (Goodness of Fit) | 0.989252 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.2% |
+| Top 1,000 | 80.3% |
+| Top 5,000 | 98.1% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9893 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.2% of corpus
+- **Long Tail:** -4,128 words needed for remaining 100.0% 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)
+
+### Model Comparison
+
+| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy |
+|-------|------------|-----------|----------|----------|----------|
+| **mono_32d** | 2,175 | 32 | 2.250 | 0.776 | 0.1273 🏆 |
+| **mono_64d** | 2,175 | 64 | 2.220 | 0.783 | 0.0336 |
+| **mono_128d** | 2,175 | 128 | 2.208 | 0.762 | 0.0072 |
+| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.1273 (more uniform distribution)
+- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
+- **Vocabulary Coverage:** All models cover 2,175 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 (4.27x) with low UNK rate |
+| N-gram | **5-gram** | Lowest perplexity (353) |
+| Markov | **Context-4** | Highest predictability (97.2%) |
+| 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: 2025-12-30 08:24:47*
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+▁cuɔ̈ɔ̈kdh	-18298
+▁cärëriɛɛl	-18299
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+▁kïtetany	-18380
+▁lɔ̈ɔ̈kɛ̈	-18381
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+▁cärëtuany	-18387
+▁cïpiŋapɛi	-18388
+▁cïthɔ̈ɔ̈ŋ	-18389
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+▁deyictueŋ	-18396
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+tuguɾi	-18445
+uɔ̈ɔ̈ŋ	-18446
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+▁dhiëndeen	-18489
+▁dhiënhden	-18490
+▁dhiɛlcɔɔl	-18491
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+▁dhïildeic	-18495
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+esh	-18501
+iɓi	-18502
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+tuek	-18509
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+▁köŋ	-18520
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+ɔ̈ŋwei	-18543
+▁alɛ̈u	-18544
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+▁piuɔ̈	-18551
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+▁gɛ̈ɛ̈k	-18560
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+▁thudun	-18567
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+▁kamthin	-18577
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+▁tënëdöm	-18579
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+▁yäpëwël	-18581
+këdɔ̈ŋwei	-18582
+▁abapdiɛt	-18583
+▁akuënkɔc	-18584
+▁akölëkol	-18585
+▁amethuny	-18586
+▁kuandɛ̈t	-18587
+▁luɔ̈prou	-18588
+▁perthiɛɛ	-18589
+▁riëthkɔu	-18590
+▁tuektuek	-18591
+akibatkeun	-18592
+▁akoolkɔ̈k	-18593
+▁ciëkëmïth	-18594
+▁diɛtnhial	-18595
+▁doctrines	-18596
+▁dokthiɔ̈l	-18597
+▁duciɛ̈ɛ̈k	-18598
+▁dugeelken	-18599
+eɲ	-18600
+pǝ	-18601
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+olu	-18603
+pǝr	-18604
+wɛn	-18605
+ɣɛr	-18606
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+emeɲ	-18610
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+ɣaai	-18615
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+ɛlɣɛr	-18625
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+ɛ̈nde	-18627
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+mɛ̈nde	-18635
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+▁liokë	-18643
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+▁aŋuɛ̈ɛ̈n	-18669
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+▁honolulu	-18673
+▁kepɛ̈ɛ̈c	-18674
+▁kuaatken	-18675
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+▁security	-18679
+▁thoŋɣaai	-18680
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+▁alɔ̈lbrisco	-25792
+▁amackuattök	-25793
+▁amatjuëcwël	-25794
+▁amatnhomden	-25795
+▁amatnhomkɔc	-25796
+▁amikëëtdiët	-25797
+▁amuktuŋthok	-25798
+▁amëluuiakim	-25799
+▁amëluuiëwëu	-25800
+▁amëmöcaguan	-25801
+▁amëtukcïloc	-25802
+▁anyalbrisco	-25803
+▁anyichɔ̈ɔ̈m	-25804
+▁anyorbrisco	-25805
+▁anɛ̈kmayiel	-25806
+▁apoukwɛ̈ɛ̈r	-25807
+▁apurukakuma	-25808
+▁apurukëdöör	-25809
+▁apuruukmeth	-25810
+▁apuökwɛ̈ɛ̈r	-25811
+▁apɛnybrisco	-25812
+▁aristocracy	-25813
+▁aromcökdiäk	-25814
+▁arɔmcökdiak	-25815
+▁asperjiluth	-25816
+▁atitguɔpden	-25817
+▁atuɛnycuäny	-25818
+▁atuɛnytuɛny	-25819
+▁atïŋkɛ̈thii	-25820
+▁awatkɔ̈ɔ̈th	-25821
+▁awattɔŋnhom	-25822
+▁aweecbrisco	-25823
+▁awicthumwei	-25824
+▁aŋicwëlbany	-25825
+▁aŋierbrisco	-25826
+▁aɣuutbrisco	-25827
+▁bakpinynhom	-25828
+▁banymacbaai	-25829
+▁benymukbaai	-25830
+▁benynomriɛl	-25831
+▁benyŋaknhom	-25832
+▁bänyapuruuk	-25833
+▁bänywatwïïn	-25834
+▁bëëlëthukar	-25835
+▁bɛnymacbääi	-25836
+▁bɛ̈icilyiic	-25837
+▁bɛ̈nthïnyök	-25838
+▁bɛ̈nymacbai	-25839
+▁bɛ̈nypawunë	-25840
+▁bɛ̈nypawuut	-25841
+▁bɛ̈nywälrac	-25842
+▁bɛ̈ɛ̈ipiööc	-25843
+▁christopher	-25844
+▁ciinakɔ̈ɔ̈n	-25845
+▁ciërayɔ̈ɔ̈l	-25846
+▁ciɛɛŋdendic	-25847
+▁ciɛ̈nhdenic	-25848
+▁communities	-25849
+▁competition	-25850
+▁confessions	-25851
+▁contrastive	-25852
+▁cäkbëkäŋbei	-25853
+▁cïkökbëtɛɛm	-25854
+▁cïpiŋbëtɛɛm	-25855
+▁cïïthöŋnhom	-25856
+▁daithithdɛn	-25857
+▁desperately	-25858
+▁dhelenthkii	-25859
+▁dhienhdenic	-25860
+▁dhiënhdenic	-25861
+▁dhiɔpbetɛɛm	-25862
+▁dhiɔpbëtɛɛm	-25863
+▁dhiɛlnyuɔth	-25864
+▁dihasilkeun	-25865
+▁dipikaresep	-25866
+▁dishonoured	-25867
+▁ditugaskeun	-25868
+▁dituturkeun	-25869
+▁diɛ̈rbrisco	-25870
+▁documentary	-25871
+▁duwëëëtluɔi	-25872
+▁duŋɔ̈ɔ̈rkɛ̈	-25873
+▁duŋɛ̈ɛ̈rken	-25874
+▁duɔ̈triŋcäl	-25875
+▁dëtiitthööc	-25876
+▁döömëdhɛŋic	-25877
+▁dɛmatophait	-25878
+▁dɛnɣɔ̈ɔ̈ric	-25879
+▁gelawdewoth	-25880
+▁genamatnhöm	-25881
+▁genpinynhom	-25882
+▁grenadineth	-25883
+▁guethbrisco	-25884
+▁guiërlatueŋ	-25885
+▁guiërëcieeŋ	-25886
+▁guɔ̈licëpïu	-25887
+▁gämthɛɛrceŋ	-25888
+▁gäämdïtapɛi	-25889
+▁gäärthɛɛric	-25890
+▁gɛkyeethany	-25891
+▁gɛɛthditiic	-25892
+▁hailemariam	-25893
+▁hellenistic	-25894
+▁heomophiluɛ	-25895
+▁hieroglyphs	-25896
+▁hɔ̈rbɛ̈kläi	-25897
+▁immediately	-25898
+▁impairement	-25899
+▁imperialism	-25900
+▁jamthuokken	-25901
+▁juɛ̈cbetɛɛm	-25902
+▁jɔɔrɔbrisco	-25903
+▁jɔ̈thbrisco	-25904
+▁kaamdeakööl	-25905
+▁kaartuɔ̈ɔ̈m	-25906
+▁kadhakthtan	-25907
+▁kadharouiic	-25908
+▁kakthiɔ̈ɔ̈ŋ	-25909
+▁kamerdikaan	-25910
+▁kardashians	-25911
+▁kededhaŋuan	-25912
+▁keluimiiric	-25913
+▁kethinnyɔɔt	-25914
+▁ketokonazol	-25915
+▁keyewelpiny	-25916
+▁kinyaruanda	-25917
+▁kiɛ̈cbrisco	-25918
+▁kiɛ̈tathööŋ	-25919
+▁kocpinynhom	-25920
+▁kondoleedha	-25921
+▁krithianity	-25922
+▁kuandethiäŋ	-25923
+▁kuatdïtapɛi	-25924
+▁kuatëkuatic	-25925
+▁kuɔnyëmuɔ̈k	-25926
+▁kuɛrkɔ̈ɔ̈th	-25927
+▁kuɛtkeagɔɔk	-25928
+▁kuɛ̈nkäjuëc	-25929
+▁käkalɔŋthïn	-25930
+▁käkthiɔ̈ɔ̈ŋ	-25931
+▁käkëcïïthöŋ	-25932
+▁kämalurtiɔp	-25933
+▁känyeyökken	-25934
+▁käyewecpiny	-25935
+▁kääcëruɔtde	-25936
+▁käŋbeiëbɛ̈n	-25937
+▁këaarotlooi	-25938
+▁kënaköluööt	-25939
+▁kërëubrisco	-25940
+▁kërïthanooi	-25941
+▁kɔccuëënyde	-25942
+▁kɔccïcariic	-25943
+▁kɔclooipiny	-25944
+▁kɔcpinynhom	-25945
+▁kɔcwëlluɛɛl	-25946
+▁kɔcɣanthany	-25947
+▁kɔ̈ckiirkɔu	-25948
+▁laicatariɛk	-25949
+▁lancuɛtlääi	-25950
+▁latueŋëraan	-25951
+▁londeendïït	-25952
+▁lonpïrdeyic	-25953
+▁loperamaite	-25954
+▁luaŋcäärdït	-25955
+▁luëknhialic	-25956
+▁luɔiëciɛŋde	-25957
+▁luɔŋɣɔ̈riic	-25958
+▁läicïtariɛk	-25959
+▁läiëmïththu	-25960
+▁läncuɛtlääi	-25961
+▁läncïcamken	-25962
+▁läncïtariɛk	-25963
+▁ländhïtmïth	-25964
+▁lëëthëmiith	-25965
+▁lööŋmääckɔc	-25966
+▁lɔŋökyepuou	-25967
+▁lɔ̈ɔ̈kɛ̈yic	-25968
+▁maguirwëëth	-25969
+▁maguirëdiër	-25970
+▁mahabharata	-25971
+▁maliththoor	-25972
+▁malɛ̈ŋthith	-25973
+▁manëkɔckɔ̈k	-25974
+▁matajiirdït	-25975
+▁mathematics	-25976
+▁mathölpiööc	-25977
+▁mayielëwäth	-25978
+▁mediterania	-25979
+▁megotwëlcok	-25980
+▁megotwëlcök	-25981
+▁miirdïtmɛ̈c	-25982
+▁miirkoorden	-25983
+▁miirmacbaai	-25984
+▁mikuäknhiim	-25985
+▁mirïclɔckɔc	-25986
+▁miɛtdepiɔ̈u	-25987
+▁monteaguila	-25988
+▁mäcŋaknhïim	-25989
+▁määcëkäpïïr	-25990
+▁määthkedïït	-25991
+▁mëdhiëthken	-25992
+▁mëlälängtöc	-25993
+▁mïjɛ̈mëguöp	-25994
+▁mïthmïthken	-25995
+▁mɛ̈ɛ̈cthook	-25996
+▁natychmiast	-25997
+▁naŋnhombaai	-25998
+▁ngabantosan	-25999
+▁ngaronjatna	-26000
+▁ngumpulkeun	-26001
+▁nhialbrisco	-26002
+▁nhialicdeen	-26003
+▁nhiɛɛrëbaai	-26004
+▁nhiɛ̈ɛ̈rden	-26005
+▁nhomlääucök	-26006
+▁nhomlääuken	-26007
+▁nouackchott	-26008
+▁nyanëmɛthde	-26009
+▁nyanŋaknhom	-26010
+▁nyueththook	-26011
+▁nyɔ̈kbrisco	-26012
+▁nääkkɔcjuëc	-26013
+▁nɔŋakeunhom	-26014
+▁orthography	-26015
+▁paandïïtden	-26016
+▁paanëwärken	-26017
+▁paleolithic	-26018
+▁pamanggihan	-26019
+▁pangbadagna	-26020
+▁panpiocëbën	-26021
+▁panpiöcëbɛn	-26022
+▁panpiööcden	-26023
+▁pantɔŋpiööc	-26024
+▁panŋaknhïïm	-26025
+▁paromaithin	-26026
+▁pencaharian	-26027
+▁penëdhaŋuan	-26028
+▁penëdhoguan	-26029
+▁penëdhoŋuan	-26030
+▁perspective	-26031
+▁pertumbuhan	-26032
+▁piathlɔtueŋ	-26033
+▁pinylautɔ̈ŋ	-26034
+▁pinythɔ̈ŋic	-26035
+▁pinytonydït	-26036
+▁piöcluɔikër	-26037
+▁piööcdethok	-26038
+▁piööclɔtueŋ	-26039
+▁piööcëkäpïr	-26040
+▁piööcŋickäŋ	-26041
+▁pneumococus	-26042
+▁prumentioth	-26043
+▁puɔthbëtɛɛm	-26044
+▁puɔ̈rbrisco	-26045
+▁päthapɛidït	-26046
+▁pëkweipuɔ̈u	-26047
+▁pïrdenthɛɛr	-26048
+▁pɛ̈lweibaai	-26049
+▁raandiɛ̈tic	-26050
+▁raanɣönlööŋ	-26051
+▁rehydration	-26052
+▁rekkituaany	-26053
+▁republicans	-26054
+▁rianëgëkluŋ	-26055
+▁riëɛthpiiny	-26056
+▁riŋcedhiath	-26057
+▁riŋëguɔploi	-26058
+▁riɔ̈ɔ̈kkääŋ	-26059
+▁riɛldïtapɛi	-26060
+▁riɛlkäŋluɔi	-26061
+▁riɛ̈cbrisco	-26062
+▁rudolfensis	-26063
+▁ruɔ̈nkedhiɛ	-26064
+▁ruɔ̈ɔ̈mpiny	-26065
+▁ruɔ̈ɔ̈ntöŋë	-26066
+▁ruɛ̈iruɛ̈ië	-26067
+▁rïŋcëdhiäth	-26068
+▁röörcëthiëk	-26069
+▁springfield	-26070
+▁switzerland	-26071
+▁thantïmitïr	-26072
+▁tharnhomlau	-26073
+▁thawadhilan	-26074
+▁thiaarkurou	-26075
+▁thiakthinic	-26076
+▁thiarcïkuen	-26077
+▁thiarkudiak	-26078
+▁thieerkutök	-26079
+▁thiepbrisco	-26080
+▁thierdhetem	-26081
+▁thithbrisco	-26082
+▁thiärdhorou	-26083
+▁thiärkubɛ̈t	-26084
+▁thiärkudiäk	-26085
+▁thiärthorou	-26086
+▁thiäärkutök	-26087
+▁thiëërkutök	-26088
+▁thiökbëtɛɛm	-26089
+▁thiɔ̈rëɣɔ̈c	-26090
+▁thiɛkbrisco	-26091
+▁thokpiuguop	-26092
+▁thoŋdïŋlïth	-26093
+▁thuarbrisco	-26094
+▁thuɔ̈ɔ̈thic	-26095
+▁thönakɔ̈ɔ̈n	-26096
+▁thɔ̈nybääny	-26097
+▁thɔ̈ɔ̈ŋnhom	-26098
+▁thɛɛrbëtɛɛm	-26099
+▁tiitbɛ̈ɛ̈ny	-26100
+▁tiŋluɔ̈iwëu	-26101
+▁turnyinpiny	-26102
+▁tuɔlkecilöi	-26103
+▁tuɔ̈tbrisco	-26104
+▁tuɔ̈ɔ̈mthok	-26105
+▁tuɛnytëktëk	-26106
+▁tëcïtroorli	-26107
+▁tɔ̈ukabëcil	-26108
+▁univerthity	-26109
+▁wewengkonna	-26110
+▁woldemikael	-26111
+▁wänmääthdït	-26112
+▁wäärthɛɛric	-26113
+▁wëlabäänyic	-26114
+▁wëlalɔŋaɣer	-26115
+▁wëlrotamäth	-26116
+▁wëlthɛɛriic	-26117
+▁wënathunden	-26118
+▁wëëthkɔ̈ɔ̈u	-26119
+▁wɛ̈ɛ̈rëkuur	-26120
+▁yemënthiɔɔk	-26121
+▁yunïbërthtï	-26122
+▁yunïbɛ̈rthï	-26123
+▁yöömracpuou	-26124
+▁yɔɔmapuruuk	-26125
+▁yɔɔmtɔŋkoor	-26126
+▁ŋicwëlbääny	-26127
+▁ŋuönakɔ̈ɔ̈n	-26128
+▁ŋïcëmukadir	-26129
+▁ŋɛnëroŋpïïr	-26130
+▁ɣonëgɛtnyin	-26131
+▁ɣorëpiujaac	-26132
+▁ɣänthonykɛm	-26133
+▁ɣöngɛtënyin	-26134
+▁ɣönkoorlööŋ	-26135
+▁ɣönëdhiëric	-26136
+▁ɣönënhialic	-26137
+▁ɣönïnkamaan	-26138
+▁ɣöötlooikäŋ	-26139
+▁ɣöötnhïïmkë	-26140
+▁ɣɔndeceŋken	-26141
+▁ɣɔ̈napuruuk	-26142
+agɛmkɔckedhiɛ	-26143
+ajuiɛrmɛ̈cbai	-26144
+alpabeticwɛ̈t	-26145
+ardropogoneae	-26146
+athpuɔ̈upatha	-26147
+balkaninsight	-26148
+cëthëtkɔ̈ɔ̈th	-26149
+diperkirakeun	-26150
+dïtaäbcddheëɛ	-26151
+granddaughter	-26152
+hermaproditic	-26153
+heterogametes	-26154
+käleeclonpath	-26155
+käpïrthoinyïn	-26156
+nyiɛrëthändït	-26157
+regurgitation	-26158
+revolutionary	-26159
+runëkkurnhial	-26160
+▁abaŋpinynhom	-26161
+▁abdelrahmane	-26162
+▁abuɔɔkbrisco	-26163
+▁adhuoŋbrisco	-26164
+▁agumutbrisco	-26165
+▁agöcwɛ̈recɛŋ	-26166
+▁agɛkeŋɛkdïtë	-26167
+▁ajuerwelbäny	-26168
+▁ajuiɛɛrbaric	-26169
+▁akarapbrisco	-26170
+▁akuatmatluoi	-26171
+▁akutadiircök	-26172
+▁akutajuɛ̈ɛ̈r	-26173
+▁akutluɔiëbai	-26174
+▁akutmacnyiic	-26175
+▁akutnhomdɛ̈t	-26176
+▁akutnhomluɔi	-26177
+▁akutwëlbääny	-26178
+▁akutëdiɛtden	-26179
+▁akutëtɔŋriil	-26180
+▁akutɣönëlööŋ	-26181
+▁akuutëdiɛtde	-26182
+▁akuutëdiɛtke	-26183
+▁akölënhialic	-26184
+▁akɛɛthnhïï̈m	-26185
+▁akɛ̈ɛ̈nythïn	-26186
+▁amatdɔmlöŋde	-26187
+▁amatnhombɛɛi	-26188
+▁amatpiööctök	-26189
+▁amerikacilic	-26190
+▁amëcuɛɛtpath	-26191
+▁andropogonïï	-26192
+▁antananarivo	-26193
+▁antropologis	-26194
+▁antïmoniɔlth	-26195
+▁anyikölëpïïr	-26196
+▁anyothökpiöc	-26197
+▁apirïkaëbɛ̈n	-26198
+▁apurukyampoc	-26199
+▁arilicbëtɛɛm	-26200
+▁associations	-26201
+▁athiɔŋëbääny	-26202
+▁athuɔkbrisco	-26203
+▁athörëtɛ̈tri	-26204
+▁atuɔckïtcath	-26205
+▁atuɔɔrbɛ̈ɛ̈i	-26206
+▁aŋiecwëlbaai	-26207
+▁aŋiɛwëlbääny	-26208
+▁aɣukarbrisco	-26209
+▁aɣuwaubrisco	-26210
+▁baforabrisco	-26211
+▁banyakutbaai	-26212
+▁berthailleth	-26213
+▁birkecbrisco	-26214
+▁buliɔkbrisco	-26215
+▁buɔthyithcök	-26216
+▁bänyŋaknhïïm	-26217
+▁bɛikɛthnhiim	-26218
+▁bɛnyluɔitueŋ	-26219
+▁bɛnyluɔituëŋ	-26220
+▁bɛ̈nybänydït	-26221
+▁bɛ̈nymacpaan	-26222
+▁bɛ̈ɛ̈ikeniic	-26223
+▁ciɛlicbrisco	-26224
+▁ciɛɛŋthɛɛric	-26225
+▁colpiubrisco	-26226
+▁cuɔ̈ɔ̈kdhäär	-26227
+▁cäthrïäkpuöu	-26228
+▁cëtiambëtɛɛm	-26229
+▁cïnkëdɔ̈ŋwei	-26230
+▁cïnraanëbɛ̈n	-26231
+▁cɔ̈tmecëcäth	-26232
+▁dhaŋmucbëric	-26233
+▁dupööcderiäŋ	-26234
+▁duŋɔɔrgɔkröl	-26235
+▁genamatnhomë	-26236
+▁grigoryevich	-26237
+▁gäärthɛɛrden	-26238
+▁gɛrgɛrbrisco	-26239
+▁gɛ̈ɛ̈mgɛ̈ɛ̈m	-26240
+▁immunoglobin	-26241
+▁independence	-26242
+▁inflammation	-26243
+▁jëëmguɔ̈pdït	-26244
+▁kaadhetemiic	-26245
+▁kacithoŋlaic	-26246
+▁kadhoŋuaniic	-26247
+▁keebiänabuur	-26248
+▁kelëlonpuoth	-26249
+▁kenakölluööt	-26250
+▁kerethanuɔɔi	-26251
+▁khatulistiwa	-26252
+▁kiritokokäth	-26253
+▁kiɛɛthbrisco	-26254
+▁klotruimazol	-26255
+▁koothrappali	-26256
+▁kuenyluɔicök	-26257
+▁kuɛɛtkeagɔɔk	-26258
+▁kuɛ̈nykuɛ̈ny	-26259
+▁käbiöthkacök	-26260
+▁käkjämalïric	-26261
+▁käkyëwëcpiny	-26262
+▁käkëpinynhom	-26263
+▁kämpïrcietïŋ	-26264
+▁kätɔ̈tiɔɔpic	-26265
+▁käyeebɛ̈nbei	-26266
+▁këcrotkaŋloi	-26267
+▁këroordollec	-26268
+▁kërëthanuɔɔi	-26269
+▁kɔcrɔ̈ɔ̈mken	-26270
+▁kɔcëɣonëlööŋ	-26271
+▁kɔryɔmbrisco	-26272
+▁kɔ̈bïkɔnkony	-26273
+▁kɔ̈ɔ̈cëpuɔ̈u	-26274
+▁landhïetmïth	-26275
+▁leeileeiëkaŋ	-26276
+▁lithabairath	-26277
+▁lonycuetlɛ̈i	-26278
+▁lonycïtariik	-26279
+▁luaŋënhialic	-26280
+▁luklukbrisco	-26281
+▁luɔiëciɛŋden	-26282
+▁luɔnycuetlai	-26283
+▁luɔ̈ŋɣɔ̈riic	-26284
+▁länmuotmarou	-26285
+▁lööŋmackɔcic	-26286
+▁maguarbrisco	-26287
+▁maguïrkacieŋ	-26288
+▁maguɛibrisco	-26289
+▁malmalbrisco	-26290
+▁manhthiinkor	-26291
+▁mapinycielic	-26292
+▁mapïnyliɛlic	-26293
+▁marialbrisco	-26294
+▁maŋgaabrisco	-26295
+▁mecëriɛlnhom	-26296
+▁mediterraniɛ	-26297
+▁meenëdhiemic	-26298
+▁meenëdhïemic	-26299
+▁meningitidis	-26300
+▁miiramatnhom	-26301
+▁miircïlɔckɔc	-26302
+▁miiriwatwuɔt	-26303
+▁miirëmetwuɔt	-26304
+▁miökëluŋthii	-26305
+▁miökëluɔŋdït	-26306
+▁miɛ̈thdendït	-26307
+▁muökkɛ̈ŋthïn	-26308
+▁muɔnyjiɛ̈ɛ̈ŋ	-26309
+▁muɔ̈ɔ̈këthol	-26310
+▁mïnhiërëbaai	-26311
+▁mɛ̈cmɛ̈ckɛ̈ŋ	-26312
+▁mɛ̈cthokthin	-26313
+▁mɛ̈ɛ̈thpɛɛth	-26314
+▁neanderthals	-26315
+▁ngabagéakeun	-26316
+▁ngahasilkeun	-26317
+▁ngakibatkeun	-26318
+▁nhiɛ̈liicken	-26319
+▁nkurundhidha	-26320
+▁nyoothkɛ̈ɛ̈c	-26321
+▁nyucamatnhom	-26322
+▁nyucciëëŋden	-26323
+▁nyunyamanhom	-26324
+▁nyuucmɛtëjäm	-26325
+▁nyuɔ̈mbrisco	-26326
+▁näkëkäkëruth	-26327
+▁pachuɔleruɛl	-26328
+▁paguotcuɔlic	-26329
+▁paguotthudän	-26330
+▁paguötbëtɛɛm	-26331
+▁pamacnaŋriɛl	-26332
+▁pangwangunan	-26333
+▁panpiöcbuɔth	-26334
+▁pennsylvania	-26335
+▁penëdhetemic	-26336
+▁philadelphia	-26337
+▁philippineth	-26338
+▁phylogenetic	-26339
+▁piirakutnhom	-26340
+▁pinykuäknhom	-26341
+▁pinythokaräm	-26342
+▁pinythöŋnhom	-26343
+▁piocmanhraan	-26344
+▁piöcëkapïrïc	-26345
+▁piöcëkäcïcak	-26346
+▁piöcëkäthɛɛr	-26347
+▁piöcëwëlmiir	-26348
+▁piöcŋickëŋic	-26349
+▁piööckatiɔɔp	-26350
+▁piööcëkatɔɔr	-26351
+▁piööcëŋïckäŋ	-26352
+▁piɛnyliɛɛtic	-26353
+▁praziquantil	-26354
+▁prumentiɛɛth	-26355
+▁puɔ̈rkumacic	-26356
+▁pänyëmakuɛ̈n	-26357
+▁pënëdhonguan	-26358
+▁pɛnëdhiiŋuan	-26359
+▁raanajɛ̈mkɔc	-26360
+▁raanbuɔthcök	-26361
+▁raanguirjiɛk	-26362
+▁raanluɔiɣɔɔc	-26363
+▁registration	-26364
+▁reproduction	-26365
+▁republicanic	-26366
+▁rianyabrisco	-26367
+▁riɔ̈ɔ̈cthith	-26368
+▁rostenkowski	-26369
+▁runtueŋthumb	-26370
+▁rutënhiäkden	-26371
+▁ruëënymakuen	-26372
+▁ruɔ̈ɔ̈nëbɛ̈n	-26373
+▁rɛnriɛldekɔc	-26374
+▁rɛ̈ɛ̈ŋëɣɛɛcë	-26375
+▁shahjahanpur	-26376
+▁sudamericana	-26377
+▁takbëtɛ̈tkäk	-26378
+▁takdebɛ̈imac	-26379
+▁thiaarbëtɛɛm	-26380
+▁thilmonnyiin	-26381
+▁thiärcïïkuen	-26382
+▁thiäärkuŋuan	-26383
+▁thiëërdhetem	-26384
+▁thiɛ̈ipinyde	-26385
+▁thiɛ̈rdhetem	-26386
+▁thiɛ̈rnyɛɛny	-26387
+▁thiɛ̈ɛ̈kthïn	-26388
+▁thookthiëkic	-26389
+▁thouththudän	-26390
+▁thudänpaguöt	-26391
+▁thɔntueŋkoor	-26392
+▁thɔrɔlbrisco	-26393
+▁tiimkudhorou	-26394
+▁tiɔpdhöŋnhom	-26395
+▁tuaanyëcuäny	-26396
+▁tuɔ̈ɔ̈mnhïïm	-26397
+▁tëlɔliäŋpiny	-26398
+▁tïmëtïmdätök	-26399
+▁walnɛ̈kmayoŋ	-26400
+▁wenhthieiwël	-26401
+▁wuɔ̈tapuruuk	-26402
+▁wëlrötamääth	-26403
+▁wëlërotamäth	-26404
+▁wënhwëltuɔɔc	-26405
+▁wëëupanpiööc	-26406
+▁wɛ̈nhatuöcic	-26407
+▁yaryarbrisco	-26408
+▁yunïbɛ̈rthtï	-26409
+▁ŋicëpuɔ̈rkäŋ	-26410
+▁ŋiëcwëlbääny	-26411
+▁ɣonepiööcŋar	-26412
+▁ɣöndïïtpiööc	-26413
+▁ɣönpamatnhom	-26414
+▁ɣɔnmɛ̈ɛ̈twel	-26415
+anthropologist	-26416
+astrophysicist	-26417
+constitutional	-26418
+cäthatïïpëyith	-26419
+decolonization	-26420
+dhiethëmïthdït	-26421
+duotebaaiebɛ̈n	-26422
+epidermophyton	-26423
+fricativeɣcäät	-26424
+kiirpiɛnyliɛɛt	-26425
+këleecëlonpath	-26426
+penëthiarkutok	-26427
+piööcriɛmthɛɛr	-26428
+ɛ̈ɛ̈kpbt̪d̪tdc	-26429
+▁aaköljuëëciic	-26430
+▁abaŋrunëkuric	-26431
+▁aberŋicbrisco	-26432
+▁abërarekwëlic	-26433
+▁adɛ̈kdiɛtnhom	-26434
+▁akuanyluɔicök	-26435
+▁akutaɣaarnhom	-26436
+▁akutjuiermiir	-26437
+▁akutluoilɔjik	-26438
+▁akutmɛtloiweu	-26439
+▁akutmɛtloiwëu	-26440
+▁akutëthɔ̈rril	-26441
+▁akuutguirmiir	-26442
+▁akuänyluoicök	-26443
+▁akuënciɛlicde	-26444
+▁aköölkadhorou	-26445
+▁akɔ̈ɔ̈rkɔ̈ɔ̈r	-26446
+▁alekdhandaria	-26447
+▁alɔ̈ɔ̈ubrisco	-26448
+▁amatnhomjuääc	-26449
+▁anyidolbrisco	-26450
+▁anyidɔlbrisco	-26451
+▁apabuoŋbrisco	-26452
+▁apaluocbrisco	-26453
+▁apiriitbrisco	-26454
+▁arumjokbrisco	-26455
+▁aruɔɔmpuɔ̈ɔ̈u	-26456
+▁athörcïgɔ̈ɔ̈r	-26457
+▁awɛ̈ɛ̈rbrisco	-26458
+▁ayaŋkɔcbëtɛɛm	-26459
+▁azïthromaicin	-26460
+▁aŋaŋlecbrisco	-26461
+▁aŋiɛcwelbäany	-26462
+▁aŋiɛcwelbääny	-26463
+▁aŋiɛcwëlbaany	-26464
+▁baaibïkrötmac	-26465
+▁banypanthuooc	-26466
+▁bluesbreakers	-26467
+▁buɔtkadhoŋuan	-26468
+▁bɛ̈nykoorluɔi	-26469
+▁chloromycetin	-26470
+▁cikothlobakia	-26471
+▁ciëkëpinykɔ̈u	-26472
+▁confederación	-26473
+▁confédération	-26474
+▁dhëëŋgɛ̈tgɛ̈t	-26475
+▁dhɛ̈ɛ̈rdenhom	-26476
+▁dibudidayakan	-26477
+▁dikelompokeun	-26478
+▁dimakokbrisco	-26479
+▁dupïockathɛɛr	-26480
+▁duɔ̈trïŋcälic	-26481
+▁electroenceph	-26482
+▁erythromaicin	-26483
+▁gaŋriɛlbrisco	-26484
+▁guɔ̈ppiɔ̈lden	-26485
+▁gɛnamatnhɔmde	-26486
+▁hyperglycemia	-26487
+▁immédiatement	-26488
+▁internatianal	-26489
+▁kaamkɔcakutic	-26490
+▁kenthaäthetët	-26491
+▁kenyanthropus	-26492
+▁koronabairath	-26493
+▁koronabairäth	-26494
+▁kurnyukbrisco	-26495
+▁kuurcïgɔtthïn	-26496
+▁kuënykuënydït	-26497
+▁kuörbɛ̈rnhïïm	-26498
+▁kuɛnëgukëkuel	-26499
+▁käbëtuɔ̈lthïn	-26500
+▁käkpialëguɔ̈p	-26501
+▁käyeluɔilakut	-26502
+▁kɛ̈denyintiit	-26503
+▁ladhiethemïth	-26504
+▁latueŋgäkthin	-26505
+▁leerëɣäänguir	-26506
+▁lonëcäthatiip	-26507
+▁luonycïtariik	-26508
+▁luëkëciëkëkäŋ	-26509
+▁luŋbaarhooric	-26510
+▁luŋbaarɣɔɔric	-26511
+▁maguiirkäcieŋ	-26512
+▁mapiöcmääckäŋ	-26513
+▁mathiaŋbrisco	-26514
+▁mediterranean	-26515
+▁meningococcus	-26516
+▁metallurgical	-26517
+▁miirmatnhomic	-26518
+▁muokkäthɛɛric	-26519
+▁neanderthalis	-26520
+▁neumonokokkal	-26521
+▁nyaginobrisco	-26522
+▁nyankumethken	-26523
+▁nyiɛrëthandït	-26524
+▁nïnkadhorouic	-26525
+▁paguötëpinyli	-26526
+▁pamaacriɛldït	-26527
+▁panpiööcnhial	-26528
+▁panpïööcnhïal	-26529
+▁panëpiöcëbɛ̈n	-26530
+▁panëthökpiööc	-26531
+▁pinynhomageer	-26532
+▁piuɔ̈nëpathde	-26533
+▁piöcëciɛŋakut	-26534
+▁piöcëkäyethëm	-26535
+▁piöcëkäŋnhial	-26536
+▁piöcëmääcëkäŋ	-26537
+▁piööcëkatiɔɔp	-26538
+▁pradhikuantil	-26539
+▁puɔ̈rëduumthi	-26540
+▁riɛlkammïriic	-26541
+▁roŋpïrcälthöŋ	-26542
+▁rutherfordium	-26543
+▁rälënyithnyol	-26544
+▁rötguiɛɛrthïn	-26545
+▁streptococcus	-26546
+▁tareerkekthïn	-26547
+▁tempuöthtëtök	-26548
+▁thempïïraɣeer	-26549
+▁thiäipinythïn	-26550
+▁thiärkudhetem	-26551
+▁thiɛ̈iwëlpiny	-26552
+▁thkandinabian	-26553
+▁thongporanthu	-26554
+▁thumbdjibouti	-26555
+▁thïmcäthatïïp	-26556
+▁thïncathatïip	-26557
+▁transcription	-26558
+▁tuanykämluöök	-26559
+▁tïmëtïmëtïmdä	-26560
+▁töŋkithbëtɛɛm	-26561
+▁tɛ̈kbëkaŋtɛ̈t	-26562
+▁ugandajamhuri	-26563
+▁understanding	-26564
+▁wanthadɛ̈kiɛt	-26565
+▁werpinybëtɛɛm	-26566
+▁wolverhampton	-26567
+▁yorbɛɛrbrisco	-26568
+▁yɔ̈kyɔ̈ɔ̈kiic	-26569
+▁yɔ̈ɔ̈mtɔŋkoor	-26570
+▁ŋiɛ̈ckäŋthɛɛr	-26571
+▁ŋuɔ̈tkathɔnde	-26572
+▁ɣuruptɛ̈udeic	-26573
+▁ɣäänjuɛ̈cpïïr	-26574
+▁ɣöndepiöcëkäŋ	-26575
+▁ɣɔndïïtëthäth	-26576
+alalbeularpalat	-26577
+multilateralism	-26578
+pathophysiology	-26579
+yuïntempinykɔ̈u	-26580
+▁abëkruöönicden	-26581
+▁agaŋrialbrisco	-26582
+▁ajuëërcïnadhuk	-26583
+▁ajɛ̈mkɔcbëtɛɛm	-26584
+▁akonydugɛɛrdït	-26585
+▁akutguirmacbai	-26586
+▁akutjuɛ̈ɛ̈rden	-26587
+▁akutmɛ̈tloiweu	-26588
+▁akutnoŋluoidït	-26589
+▁akutëbänyëluɔi	-26590
+▁amaibaaibrisco	-26591
+▁amëtukpïrëriaŋ	-26592
+▁amɛ̈tnhomdetɔŋ	-26593
+▁anyiɛcwelbääny	-26594
+▁anyiɛcwëlbääny	-26595
+▁apirïkaködörös	-26596
+▁assassinations	-26597
+▁athokpurbrisco	-26598
+▁atuɔckïtyithic	-26599
+▁aŋicwëëlëbääny	-26600
+▁aŋiɛcwëlëbääny	-26601
+▁baknɔŋnhomtiim	-26602
+▁banymuknhomŋɛk	-26603
+▁bɛ̈nydïtapuruk	-26604
+▁bɛ̈ɛ̈ipiööciic	-26605
+▁cathatiïpëyith	-26606
+▁centrafricaine	-26607
+▁ciɛnkïtetuaany	-26608
+▁cäthatïïpeyith	-26609
+▁cäthatïïpëyith	-26610
+▁dekuëthmatïtït	-26611
+▁federalization	-26612
+▁gualgualbrisco	-26613
+▁guokkaŋuanthöŋ	-26614
+▁hippopotamidae	-26615
+▁immediatamente	-26616
+▁internationale	-26617
+▁juääcdaaiëbɛ̈n	-26618
+▁jɔ̈ɔ̈kdemɔ̈ɔ̈c	-26619
+▁kedethiärkutök	-26620
+▁keleecëlonpath	-26621
+▁keleecëlonpäth	-26622
+▁kenbɛnymacbaai	-26623
+▁kiriptokokkuth	-26624
+▁konyëkiimëlɛ̈i	-26625
+▁kuthiärkudhiëc	-26626
+▁kuthiɛrdhoŋuan	-26627
+▁kuënykuënydeen	-26628
+▁kɔcnhiaardiɛtë	-26629
+▁kɔcpiööckätɔɔr	-26630
+▁kɔcwëërkɔ̈ɔ̈th	-26631
+▁luɔiëciɛŋdeyic	-26632
+▁luɔ̈iëmanhbaai	-26633
+▁läiroorënyuëth	-26634
+▁läirroornɔŋtuŋ	-26635
+▁länɔŋmuotmarou	-26636
+▁lɛ̈idhiëthmith	-26637
+▁maalithkuɛlrou	-26638
+▁magorgorbrisco	-26639
+▁magɔ̈rɔ̈ɔ̈kiic	-26640
+▁maikobaktiriöm	-26641
+▁maikrosporiɔ̈m	-26642
+▁maleŋdïtbrisco	-26643
+▁meninyjokokkal	-26644
+▁nuëtcïkeknuɛ̈t	-26645
+▁nyaŋyielbrisco	-26646
+▁nyiɛ̈cwëlbääny	-26647
+▁panmuorŋaknhom	-26648
+▁panëpiööcdɛtic	-26649
+▁panëthökëpiööc	-26650
+▁penthiɛɛrkutök	-26651
+▁penëthiärkurou	-26652
+▁penëthiärkutok	-26653
+▁pinyopabakciɛl	-26654
+▁piocmääcëkagic	-26655
+▁piöcmääcëkäŋic	-26656
+▁piöcëriɛmthɛɛr	-26657
+▁piööcëkatɔ̈ɔ̈r	-26658
+▁prethbaaitïrɛn	-26659
+▁pënëthiarkutök	-26660
+▁raancëlöckudöc	-26661
+▁takdekäkëluɔɔi	-26662
+▁tandhaniɛ̈nyin	-26663
+▁themëyicdiäkic	-26664
+▁thiärdiäkubɛ̈t	-26665
+▁thiɔ̈ɔ̈kwëniic	-26666
+▁thiɛ̈idepinyde	-26667
+▁thiɛ̈ɛ̈rkudiäk	-26668
+▁thudänrepublic	-26669
+▁thïëkbëtɛɛmiic	-26670
+▁tokaamiccielic	-26671
+▁truaikophaiton	-26672
+▁truaimethoprim	-26673
+▁tuektuekbrisco	-26674
+▁tɔɔknyinbrisco	-26675
+▁ugandarepublic	-26676
+▁walnäkkämluöök	-26677
+▁wëlthɛɛrcïgɔ̈t	-26678
+▁wëlëmänybaiyic	-26679
+▁wɛricɛ̈manlooŋ	-26680
+▁yiicaäbcddheëɛ	-26681
+▁yithkekëbkëben	-26682
+▁yɔ̈ɔ̈mlääunhom	-26683
+▁yɔ̈ɔ̈mnhomlääu	-26684
+▁ŋicëguɛ̈rëguɔp	-26685
+▁ŋiëcwëlbäänyic	-26686
+▁ŋuälayicdiäkic	-26687
+▁ŋuötakɔ̈ɔ̈ndït	-26688
+▁ɔlekthandrowic	-26689
+▁ɣɔnethiëkthiëk	-26690
+▁ɣɔnlakbëliplip	-26691
+industralization	-26692
+sulfamethoxazole	-26693
+▁aabɛ̈kmɛ̈cthook	-26694
+▁akutamatnhombai	-26695
+▁akutamëtuuktueŋ	-26696
+▁akutguirmiirmat	-26697
+▁akutnhomapuruuk	-26698
+▁akuttueŋkadhiëc	-26699
+▁akutwelemiirtok	-26700
+▁akutëdiɛttueŋde	-26701
+▁akuutmɛ̈tloiweu	-26702
+▁akɔlmagarbrisco	-26703
+▁aluotweerbrisco	-26704
+▁alɔ̈ɔ̈lëmakuɛ̈n	-26705
+▁amatcialëriɛmic	-26706
+▁aruɔ̈ɔ̈mpinykum	-26707
+▁athɛ̈ɛ̈kthiekic	-26708
+▁atɛmkɔ̈uëgölbär	-26709
+▁aŋuotpuoubrisco	-26710
+▁banykoormacbaai	-26711
+▁charlottesville	-26712
+▁colombiajamhuri	-26713
+▁cäthatïïpëyitac	-26714
+▁dhunyduolbrisco	-26715
+▁dhëlëŋiëcwëlden	-26716
+▁dnipropetrovthk	-26717
+▁dunyuuthŋaknhom	-26718
+▁dupiööcmanhraan	-26719
+▁dɛmatophaitosis	-26720
+▁epïdɛrmophaiton	-26721
+▁frontbackpuɔ̈up	-26722
+▁juɛrciɛŋkulöŋic	-26723
+▁kadhiärkudhetem	-26724
+▁klarithromaicin	-26725
+▁kuornyielbrisco	-26726
+▁kuthiaardhoŋuan	-26727
+▁këleecëluɔipath	-26728
+▁kɔccërötmatthin	-26729
+▁kɔcŋictɔcapiɛth	-26730
+▁lukashenkohttps	-26731
+▁luŋbaarɣɔ̈ɔ̈ric	-26732
+▁luɔiëteetdhëŋic	-26733
+▁lɛ̈ndhiethëmïth	-26734
+▁makuɛ̈nëraantök	-26735
+▁maŋicgäätcïthök	-26736
+▁maŋicpiööcithök	-26737
+▁meninyjaitidith	-26738
+▁mɛtnhomakutluɔi	-26739
+▁nicëguɛ̈rëguɔ̈p	-26740
+▁nɔrweithumbthɔn	-26741
+▁penethiäärkutök	-26742
+▁penëthiaarkurou	-26743
+▁piinylïɛɛtëbɛ̈n	-26744
+▁piöcmääcëkäŋiic	-26745
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diff --git a/models/tokenizer/din_tokenizer_8k.vocab b/models/tokenizer/din_tokenizer_8k.vocab
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