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+---
+language: om
+language_name: Oromo
+language_family: cushitic
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-cushitic
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.912
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8881
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Oromo - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Oromo** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.925x | 3.93 | 0.7743% | 539,824 |
+| **16k** | 4.303x | 4.30 | 0.8489% | 492,401 |
+| **32k** | 4.614x | 4.62 | 0.9103% | 459,184 |
+| **64k** | 4.912x 🏆 | 4.91 | 0.9692% | 431,282 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Giinii-Bisaa’uu biyya Afrikaa keessa jirtu. President: Umaró Umbaló Sissoko Prim...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+26 more)` | 36 |
+| 16k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+21 more)` | 31 |
+| 32k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+19 more)` | 29 |
+| 64k | `▁giinii - bisaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu . ... (+11 more)` | 21 |
+
+**Sample 2:** `Godinni Qeellam Wallagaa kan argamu Oromiyaa keessatti. Wabii Oromiyaa Qellami w...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁godinni ▁q eell am ▁wall agaa ▁kan ▁argamu ▁oromiyaa ▁keessatti ... (+15 more)` | 25 |
+| 16k | `▁godinni ▁qeell am ▁wall agaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ... (+13 more)` | 23 |
+| 32k | `▁godinni ▁qeellam ▁wallagaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ▁wabii ▁oromiyaa ... (+8 more)` | 18 |
+| 64k | `▁godinni ▁qeellam ▁wallagaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ▁wabii ▁oromiyaa ... (+6 more)` | 16 |
+
+**Sample 3:** `Indoneeshiyaan biyya Eshiyaa bahaatti argamtu. Indoneeshiyaan odolotaarraa kan u...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ind on eesh iyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁ind ... (+25 more)` | 35 |
+| 16k | `▁indon eesh iyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indon eesh ... (+22 more)` | 32 |
+| 32k | `▁indoneeshiyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indoneeshiyaan ▁odol ot aarraa ... (+18 more)` | 28 |
+| 64k | `▁indoneeshiyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indoneeshiyaan ▁odolotaarraa ▁kan ▁uummamte ... (+16 more)` | 26 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.912x compression
+- **Lowest UNK Rate:** 8k with 0.7743% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 5,143 | 12.33 | 11,123 | 19.4% | 44.4% |
+| **2-gram** | Subword | 213 🏆 | 7.73 | 2,915 | 73.1% | 99.4% |
+| **3-gram** | Word | 6,773 | 12.73 | 11,742 | 15.4% | 34.2% |
+| **3-gram** | Subword | 1,522 | 10.57 | 17,267 | 31.9% | 80.6% |
+| **4-gram** | Word | 27,247 | 14.73 | 32,157 | 4.6% | 12.9% |
+| **4-gram** | Subword | 7,489 | 12.87 | 73,751 | 15.1% | 48.6% |
+| **5-gram** | Word | 24,982 | 14.61 | 27,382 | 3.0% | 11.0% |
+| **5-gram** | Subword | 24,392 | 14.57 | 164,495 | 9.3% | 29.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ta u` | 3,055 |
+| 2 | `yoo ta` | 2,546 |
+| 3 | `ta e` | 1,182 |
+| 4 | `danda a` | 874 |
+| 5 | `kan akka` | 737 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yoo ta u` | 2,255 |
+| 2 | `haa ta u` | 342 |
+| 3 | `ta u malee` | 285 |
+| 4 | `yeroo baay ee` | 252 |
+| 5 | `of keessaa qaba` | 224 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `haa ta u malee` | 282 |
+| 2 | `yoo ta u kunis` | 213 |
+| 3 | `qabu yoo ta u` | 167 |
+| 4 | `ta uu danda a` | 135 |
+| 5 | `tokko yoo ta u` | 128 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kan qabu yoo ta u` | 103 |
+| 2 | `keessaa tokko yoo ta u` | 91 |
+| 3 | `kan qaban yoo ta u` | 61 |
+| 4 | `of keessaa qabu yoo ta` | 41 |
+| 5 | `keessaa qabu yoo ta u` | 41 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a` | 191,675 |
+| 2 | `a _` | 157,464 |
+| 3 | `a n` | 95,295 |
+| 4 | `i _` | 91,344 |
+| 5 | `n _` | 80,089 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a _` | 71,357 |
+| 2 | `a n _` | 40,890 |
+| 3 | `a a n` | 34,146 |
+| 4 | `i i _` | 34,015 |
+| 5 | `t t i` | 25,977 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t t i _` | 21,400 |
+| 2 | `a a n _` | 17,797 |
+| 3 | `_ k a n` | 16,358 |
+| 4 | `e e s s` | 15,993 |
+| 5 | `a t t i` | 15,383 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a t t i _` | 13,078 |
+| 2 | `e e s s a` | 12,757 |
+| 3 | `_ k a n _` | 11,199 |
+| 4 | `k e e s s` | 10,958 |
+| 5 | `_ k e e s` | 10,805 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 213
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.7695 | 1.705 | 4.74 | 79,366 | 23.1% |
+| **1** | Subword | 1.4423 | 2.718 | 10.26 | 768 | 0.0% |
+| **2** | Word | 0.1820 | 1.134 | 1.36 | 375,403 | 81.8% |
+| **2** | Subword | 0.8632 | 1.819 | 4.70 | 7,870 | 13.7% |
+| **3** | Word | 0.0437 | 1.031 | 1.07 | 508,948 | 95.6% |
+| **3** | Subword | 0.7482 | 1.680 | 3.52 | 36,941 | 25.2% |
+| **4** | Word | 0.0138 🏆 | 1.010 | 1.02 | 541,591 | 98.6% |
+| **4** | Subword | 0.5894 | 1.505 | 2.53 | 129,933 | 41.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `fi niwutiroonii kan maatilee gurguddaa macroscopic anatomy fi wantoota qoyyobiyyoo hojjatu ta ee war...`
+2. `kan maddu hedduu kan akka barachuu irra taxon dha innis dheerina ujummoo dhigaa ademma kunis yaada`
+3. `ta uun leenjisaa yoo ta u guuttiiqoota poozatiiviidhaan chaarjii saffisaan hir isa sirrii irratti ka...`
+
+**Context Size 2:**
+
+1. `ta u malee siwaahiliin ummatta hanga million 40 guddatte haa ta u kan oromiyaa dabalatee heera mataa`
+2. `yoo ta u xurbaa alfaa lamaa fi isaa ol walitti makamuun narvii lafee dugda of keessaa qabu`
+3. `ta e garuu kofni isaa sirrii ta een fide namoonni jireenya godaansaa dhiisanii ganda dhaabbataa uumm...`
+
+**Context Size 3:**
+
+1. `yoo ta u hiikni isaanii kofa sirrii jechuudha rektaangiliin rogoonni isaa afran dheerinni isaa walfa...`
+2. `haa ta u malee rakkoon haaraan dhufe cunqursaa lakkoofsaa tyranny of numbers naannee walxaxaa ijaaru...`
+3. `ta u malee walii galtee dhabuun isaa fi birgaadeer jeneraal abdulkaariim qaasim gidduutti uumameen m...`
+
+**Context Size 4:**
+
+1. `haa ta u malee addeessi lafa irraa fagoo jira fageenyi inni lafarraa qabus kilomeetira 384 000 ol ta...`
+2. `yoo ta u kunis dhuudhaa haaraa dhaabbatummaa saffisa ifaa fi dhuudhaa duraan beekamaa ture dhuudhaa ...`
+3. `qabu yoo ta u yeroo baay ee meeshaalee bulchiinsaatiin kan qindaa anidha jijjiiramni adeemsa kana ke...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a_awalmbarseeers`
+2. `_ga_isufin_saan_`
+3. `idalee_haaaammi_`
+
+**Context Size 2:**
+
+1. `aan._dhaniilixa_n`
+2. `a_gamu_dhummaaloo`
+3. `anii_dhaa_laawudh`
+
+**Context Size 3:**
+
+1. `aa_biran._see_itti`
+2. `an_dina_maloojiiti`
+3. `aane_galuuf_fakkee`
+
+**Context Size 4:**
+
+1. `tti_buuf_itti_oomis`
+2. `aan_fakkasummataa_q`
+3. `_kan_ariiroo_mancaa`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (129,933 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 | 33,399 |
+| Total Tokens | 552,518 |
+| Mean Frequency | 16.54 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 147.85 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | fi | 12,823 |
+| 2 | kan | 11,436 |
+| 3 | ta | 8,265 |
+| 4 | akka | 6,344 |
+| 5 | keessatti | 5,232 |
+| 6 | u | 4,498 |
+| 7 | yoo | 4,135 |
+| 8 | hin | 3,898 |
+| 9 | yeroo | 3,580 |
+| 10 | tokko | 3,377 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | prezidaantii | 2 |
+| 2 | kasasiyoonaa | 2 |
+| 3 | feltrinelli | 2 |
+| 4 | rizaabii | 2 |
+| 5 | raamyaan | 2 |
+| 6 | rootarii | 2 |
+| 7 | eegsa | 2 |
+| 8 | alta | 2 |
+| 9 | kuusii | 2 |
+| 10 | maalamaa | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0202 |
+| R² (Goodness of Fit) | 0.995340 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 32.2% |
+| Top 1,000 | 59.7% |
+| Top 5,000 | 79.9% |
+| Top 10,000 | 87.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9953 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 32.2% of corpus
+- **Long Tail:** 23,399 words needed for remaining 12.3% 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)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8881 🏆 | 0.3121 | N/A | N/A |
+| **mono_64d** | 64 | 0.7740 | 0.2857 | N/A | N/A |
+| **mono_128d** | 128 | 0.1998 | 0.2448 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8881 | 0.3137 | 0.0140 | 0.1340 |
+| **aligned_64d** | 64 | 0.7740 | 0.2922 | 0.0300 | 0.1800 |
+| **aligned_128d** | 128 | 0.1998 | 0.2360 | 0.0660 | 0.2500 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8881 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2807. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.633** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | alex, amaaradhaan, ambaa |
+| `-s` | silocones, soovieyeet, sexual |
+| `-b` | beekamaafi, baatriin, balaaleffata |
+| `-d` | duruma, document, dheedee |
+| `-m` | milaa, moment, materials |
+| `-ma` | materials, massachusetts, malcolm |
+| `-ba` | baatriin, balaaleffata, baasti |
+| `-g` | garraan, guutuu, guute |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | milaa, qilleensarra, duruma |
+| `-i` | beekamaafi, keetoonoonni, miiti |
+| `-n` | tekinooloojiin, garraan, cimaadhaan |
+| `-ii` | naqanii, giriikii, bismazii |
+| `-aa` | milaa, wiifaa, inaariyaa |
+| `-an` | garraan, cimaadhaan, firoottan |
+| `-e` | caamee, kaaye, dheedee |
+| `-ti` | miiti, baasti, totti |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `araa` | 1.85x | 101 contexts | haraa, karaa, qaraa |
+| `rraa` | 1.90x | 85 contexts | erraa, urraa, orraa |
+| `mmaa` | 2.12x | 47 contexts | ummaa, ammaa, lummaa |
+| `eess` | 1.68x | 124 contexts | eessa, keessa, eessoo |
+| `gudd` | 2.12x | 34 contexts | guddo, gudda, guddae |
+| `aala` | 1.58x | 114 contexts | yaala, jaala, gaala |
+| `arga` | 1.75x | 66 contexts | marga, argan, argaa |
+| `rrat` | 2.24x | 25 contexts | irrati, urratti, arratti |
+| `okko` | 2.26x | 24 contexts | tokko, tokkof, tokkos |
+| `ratt` | 1.90x | 45 contexts | iratti, barattu, biratti |
+| `chuu` | 1.77x | 50 contexts | achuu, kichuu, glchuu |
+| `jedh` | 2.23x | 20 contexts | jedhu, jedhe, jedha |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-b` | `-a` | 163 words | balbala, biyyootaa |
+| `-h` | `-a` | 142 words | hubachaa, harkisuudha |
+| `-a` | `-a` | 141 words | arba, araboota |
+| `-d` | `-a` | 137 words | dhooqa, dandeessa |
+| `-a` | `-i` | 132 words | ashaabi, argamni |
+| `-d` | `-i` | 129 words | dargageessi, dirreetti |
+| `-m` | `-a` | 128 words | milliyoona, molekuloota |
+| `-s` | `-a` | 121 words | shaffaaxa, shayxaana |
+| `-d` | `-n` | 115 words | daawwannaan, dirreewwan |
+| `-g` | `-a` | 110 words | genera, gamaa |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| burtukaanaa | **`burtuka-an-aa`** | 7.5 | `an` |
+| funfannaa | **`funfan-n-aa`** | 7.5 | `n` |
+| dhalattoonni | **`dhalattoon-n-i`** | 7.5 | `n` |
+| aannannoo | **`aannan-n-oo`** | 7.5 | `n` |
+| tambiixni | **`tambiix-n-i`** | 7.5 | `n` |
+| fooyyessanii | **`fooyyess-an-ii`** | 7.5 | `an` |
+| yaadoonni | **`yaadoon-n-i`** | 7.5 | `n` |
+| academies | **`academ-i-es`** | 7.5 | `i` |
+| karibiyaanii | **`karibiya-an-ii`** | 7.5 | `an` |
+| enciclopèdia | **`enciclopèd-i-a`** | 7.5 | `i` |
+| laakkawanii | **`laakkaw-an-ii`** | 7.5 | `an` |
+| jijjiramni | **`jijjiram-n-i`** | 7.5 | `n` |
+| raajeffannoo | **`raajeffan-n-oo`** | 7.5 | `n` |
+| unionoromia | **`unionorom-i-a`** | 7.5 | `i` |
+| raadiyoona | **`raadiyoo-n-a`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Oromo shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.91x) |
+| N-gram | **2-gram** | Lowest perplexity (213) |
+| Markov | **Context-4** | Highest predictability (98.6%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 16:39:48*
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+▁nyaattii	-26305
+▁nyaatuuf	-26306
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+▁ollaasaa	-26311
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+▁oozoonii	-26318
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+▁paklarii	-26326
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+▁dhuyumxinnoo	-28728
+▁dilbeetummaa	-28729
+▁dinqisiifatu	-28730
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+▁diyaamandiin	-28732
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+▁dokimenterii	-28735
+▁dolfiinottni	-28736
+▁dubartootaaf	-28737
+▁echinococcus	-28738
+▁eebbifameera	-28739
+▁eegalaniidha	-28740
+▁eeramaniidha	-28741
+▁electrolytes	-28742
+▁elektirikiin	-28743
+▁engetelekeke	-28744
+▁epicenteriin	-28745
+▁excentricity	-28746
+▁fageenyarraa	-28747
+▁fakkaataniif	-28748
+▁fakkeenyoota	-28749
+▁fannifamanii	-28750
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+▁fudhatamanii	-28755
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+▁gargaaramuun	-28761
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+▁guddatanidha	-28764
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+▁hemmoorihajj	-28771
+▁heterotrophs	-28772
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+▁ibiddarratti	-28787
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+▁inactivation	-28791
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+▁infotainment	-28794
+▁injinaroonni	-28795
+▁inorgaanikii	-28796
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+▁kilimanjaron	-28827
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+▁kompileeksii	-28829
+▁kondaakterii	-28830
+▁konjugeeshen	-28831
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+▁koovaalentii	-28833
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+▁murteessadha	-28866
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+▁normaalitiin	-28871
+▁nyaachudhaan	-28872
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+▁oomishamudha	-28875
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+▁predatorotta	-28884
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+▁radiationiin	-28901
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+▁testosterone	-28935
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+▁astroonoomara	-28982
+▁awustraaliyaa	-28983
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+▁cucurbitaceae	-29001
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+▁nammoota	-26294
+▁nanawwee	-26295
+▁nansaboo	-26296
+▁narvoota	-26297
+▁neeppaal	-26298
+▁neeymaar	-26299
+▁nereeree	-26300
+▁noorweey	-26301
+▁numerous	-26302
+▁nutrient	-26303
+▁nyaannii	-26304
+▁nyaattii	-26305
+▁nyaatuuf	-26306
+▁observed	-26307
+▁ofkeessa	-26308
+▁okokkeen	-26309
+▁olfuutuu	-26310
+▁ollaasaa	-26311
+▁olofsson	-26312
+▁olympics	-26313
+▁ombossaa	-26314
+▁oolchite	-26315
+▁ooriyoon	-26316
+▁oovaalii	-26317
+▁oozoonii	-26318
+▁original	-26319
+▁ovaariin	-26320
+▁overlaps	-26321
+▁oxygenaa	-26322
+▁paarkiin	-26323
+▁paaskaal	-26324
+▁paayinii	-26325
+▁paklarii	-26326
+▁parsenti	-26327
+▁pavilion	-26328
+▁peedaala	-26329
+▁peektiin	-26330
+▁periodic	-26331
+▁peroxide	-26332
+▁physical	-26333
+▁pigments	-26334
+▁pirootoo	-26335
+▁pirootop	-26336
+▁pisaanoo	-26337
+▁plankton	-26338
+▁plateaus	-26339
+▁platelet	-26340
+▁policies	-26341
+▁ponginae	-26342
+▁poolisaa	-26343
+▁poozitir	-26344
+▁pressure	-26345
+▁princess	-26346
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+▁psyllium	-26348
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+▁qaajisoo	-26350
+▁qaalluun	-26351
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+▁qeeqamuu	-26381
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+▁rakkisoo	-26403
+▁ramaduun	-26404
+▁reaching	-26405
+▁reaction	-26406
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+▁reported	-26408
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+▁rigataan	-26410
+▁rikardis	-26411
+▁rizaabii	-26412
+▁rockwell	-26413
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+▁roobaafi	-26416
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+▁rukkutta	-26421
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+▁rukutamu	-26423
+▁rukutuun	-26424
+▁rumichis	-26425
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+▁saallubb	-26430
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+▁saaxilan	-26435
+▁saaxilee	-26436
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+▁barrefami	-26779
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+▁bilchaate	-26792
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+▁boqqojjii	-26801
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+▁budduqqoo	-26804
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+▁bulchiins	-26806
+▁burgutiin	-26807
+▁burkutess	-26808
+▁burqophee	-26809
+▁burtukaan	-26810
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+▁caqasoota	-26814
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+▁chondrich	-26821
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+▁cimaadhan	-26824
+▁cimaatiin	-26825
+▁cinaaqsan	-26826
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+▁condition	-26828
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+▁daakuudha	-26837
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+▁daawwatan	-26840
+▁daawwatte	-26841
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+▁dabaaqula	-26843
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+▁dabalanne	-26845
+▁dabaleera	-26846
+▁dabarfama	-26847
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+▁dadabarsa	-26849
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+▁dandiitin	-26853
+▁danfinnaa	-26854
+▁daraatuun	-26855
+▁dararamaa	-26856
+▁dargiitin	-26857
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+▁deebistee	-26859
+▁deeggaruu	-26860
+▁deemamuun	-26861
+▁deemtudha	-26862
+▁democracy	-26863
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+▁dhaabataa	-26865
+▁dhaabatoo	-26866
+▁dhaabatte	-26867
+▁dhabamuun	-26868
+▁dhabuudha	-26869
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+▁dhagaggee	-26871
+▁dhagaheef	-26872
+▁dhageetti	-26873
+▁dhagilaal	-26874
+▁dhagnoota	-26875
+▁dhagoogaa	-26876
+▁dhalaatti	-26877
+▁dhamdhama	-26878
+▁dhaqqaban	-26879
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+▁dhedheess	-26881
+▁dheeratan	-26882
+▁dherrinan	-26883
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+▁dhiifamni	-26886
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+▁dhiqannaa	-26893
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+▁digdaamii	-26905
+▁digridhan	-26906
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+▁faalamuun	-26948
+▁faallamee	-26949
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+dabbasseeyyii	-28633
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+hayidireetiin	-28636
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+lawrenceville	-28638
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+uureffateedha	-28645
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+▁addunyaarraa	-28650
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+▁benishaangul	-28692
+▁bililleedhaa	-28693
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+▁bineeldotaan	-28695
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+▁biosynthesis	-28697
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+▁inactivation	-28791
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+amsoorraa	-31685
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+assiiwwan	-31691
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+▁hdl	-31704
+▁ldl	-31705
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+▁بوسح	-31718
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+haftee	-31726
+iffamu	-31727
+juunii	-31728
+▁asgor	-31729
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+▁maayils	-31750
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+▁tilahun	-31753
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+chycephal	-31777
+ciniinnaa	-31778
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+daazoolii	-31787
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+dhiibbame	-31798
+dhiifamee	-31799
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+▁germano	-31806
+▁kyhtiin	-31807
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+dhoqonnee	-31810
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+enceladus	-31826
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+eqabeyyii	-31828
+essuummaa	-31829
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+meetrikii	-31892
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+miliyoona	-31894
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+jective	-31902
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+ishiiniin	-31918
+naannawaa	-31919
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+processor	-31950
+proenzyme	-31951
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+qulqulluu	-31955
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+seeruumam	-31963
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+simbirroo	-31966
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+waasummaa	-31986
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+والسَّبْت	-31996
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+▁aansunis	-31998
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+▁arrad	-32000
+▁falax	-32001
+▁joons	-32002
+▁machb	-32003
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+▁saayir	-32012
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+▁falmuun	-32019
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+▁jumoo	-32108
+▁nuuff	-32109
+▁pflan	-32110
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+▁qimaan	-32129
+▁qottoo	-32130
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+▁yaliin	-32132
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+▁qodamuu	-32146
+▁qotatuu	-32147
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+▁boqoteef	-32175
+▁botaanii	-32176
+▁bronchop	-32177
+▁bulchiti	-32178
+▁bultumaa	-32179
+▁bussadee	-32180
+▁buufteef	-32181
+▁buusudha	-32182
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+▁canaadaa	-32186
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+▁caroline	-32188
+▁casamane	-32189
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+▁chochone	-32195
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+▁chuukkoo	-32197
+▁ciciiruu	-32198
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+qurxum	-32209
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+▁biqul	-32215
+▁bolii	-32216
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+▁ceekaa	-32226
+▁ceetee	-32227
+▁jaguar	-32228
+▁ofiser	-32229
+▁revolu	-32230
+▁whiser	-32231
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+hundumaa	-32235
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+whelming	-32237
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+▁abdomen	-32239
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+▁bryozoa	-32241
+▁danqama	-32242
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+▁liibana	-32244
+▁lubrica	-32245
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+▁nafxaan	-32247
+▁sulxaan	-32248
+▁السّلام	-32249
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+▁ampeerii	-32257
+▁biqultuu	-32258
+▁cimtuufi	-32259
+▁cirullus	-32260
+▁citagita	-32261
+▁clifford	-32262
+▁clippers	-32263
+▁clouseau	-32264
+▁cocorree	-32265
+▁collects	-32266
+▁comedian	-32267
+▁congrega	-32268
+▁contents	-32269
+▁contribu	-32270
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+▁cufamess	-32272
+▁cultural	-32273
+▁cuqullis	-32274
+▁cuuphame	-32275
+▁cuuphuuf	-32276
+▁cysteine	-32277
+▁daacciin	-32278
+▁daannnoo	-32279
+▁daawudii	-32280
+▁dabbusii	-32281
+▁dagamaat	-32282
+▁daggalaa	-32283
+▁dakeetti	-32284
+▁dallawaa	-32285
+▁dambituu	-32286
+▁dandanda	-32287
+▁dandanee	-32288
+▁danunkee	-32289
+▁deceased	-32290
+▁deenmaar	-32291
+▁deesseef	-32292
+▁demneefi	-32293
+▁deoxyrib	-32294
+▁dhabuhar	-32295
+▁dhalteef	-32296
+▁dhaqabne	-32297
+▁dhaqabuf	-32298
+▁dhaqabuu	-32299
+film	-32300
+jitr	-32301
+lete	-32302
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+qarn	-32305
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+iqaxx	-32307
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+pighi	-32311
+▁hylo	-32312
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+filatu	-32319
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+filuuk	-32321
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+qqadee	-32328
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+▁yaaya	-32336
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+▁bonkon	-32344
+▁faagos	-32345
+▁greens	-32346
+▁hyloba	-32347
+▁hypoga	-32348
+▁images	-32349
+▁korony	-32350
+▁miliqa	-32351
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+▁semein	-32354
+▁shiifa	-32355
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+▁waanjoo	-32369
+agesingle	-32370
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+▁bonkonto	-32372
+▁dhatokko	-32373
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+kaarboonik	-33791
+karrooyyee	-33792
+keemikaala	-33793
+kkaballoon	-33794
+konveeksii	-33795
+kutaagiddu	-33796
+kuufannoon	-33797
+kuusdeetaa	-33798
+lakkiffamu	-33799
+ijaar	-33800
+jaars	-33801
+ziyon	-33802
+▁easy	-33803
+▁hisq	-33804
+▁outb	-33805
+▁poto	-33806
+graama	-33807
+housii	-33808
+mdheer	-33809
+shydro	-33810
+strait	-33811
+échain	-33812
+▁bubuu	-33813
+▁circu	-33814
+▁fakto	-33815
+▁hutub	-33816
+▁moman	-33817
+▁notch	-33818
+▁timor	-33819
+▁velum	-33820
+alaasuu	-33821
+ancitii	-33822
+makkoon	-33823
+reunion	-33824
+utubduu	-33825
+▁aleeks	-33826
+▁anteat	-33827
+▁boonee	-33828
+▁ciroon	-33829
+▁divine	-33830
+▁lalani	-33831
+▁length	-33832
+▁notary	-33833
+▁pacers	-33834
+▁quuqaa	-33835
+▁thapar	-33836
+▁thauma	-33837
+▁uttaal	-33838
+▁viibir	-33839
+ffachisu	-33840
+ijaarame	-33841
+jaarraan	-33842
+raacitii	-33843
+▁ajajama	-33844
+▁ajajamu	-33845
+▁anatina	-33846
+▁bikkoon	-33847
+▁birolle	-33848
+▁fakenya	-33849
+▁gorsiti	-33850
+▁lalisee	-33851
+▁méchain	-33852
+▁outbrea	-33853
+▁quuqqoo	-33854
+antshydro	-33855
+dheerinna	-33856
+kutaawwan	-33857
+ollection	-33858
+phaaziyon	-33859
+vertebral	-33860
+yaadrimee	-33861
+▁anatinae	-33862
+▁bubuutuu	-33863
+▁circuits	-33864
+▁fakaatuu	-33865
+▁hutubamu	-33866
+▁intalaas	-33867
+▁lalistuu	-33868
+▁miiliyon	-33869
+▁momentii	-33870
+▁tanarras	-33871
+▁tasifama	-33872
+▁thaumati	-33873
+▁viibiram	-33874
+ancitiitti	-33875
+eessefaana	-33876
+jaarsummaa	-33877
+ligamentii	-33878
+locomotion	-33879
+mansuriyat	-33880
+mdheerinni	-33881
+mesophiles	-33882
+metabolism	-33883
+millsaddee	-33884
+misaayelii	-33885
+monohybrid	-33886
+moosoorraa	-33887
+murteessuu	-33888
+niyuroogil	-33889
+normalitti	-33890
+oglobulins	-33891
+okordeetii	-33892
+okraatasii	-33893
+olysisvery	-33894
+omnipotent	-33895
+omniscient	-33896
+oobaakteer	-33897
+oogiraafii	-33898
+oominidiin	-33899
+dner	-33900
+dung	-33901
+krit	-33902
+sors	-33903
+▁cdc	-33904
+amnni	-33905
+irrif	-33906
+orsed	-33907
+pella	-33908
+tamsa	-33909
+urrif	-33910
+▁almg	-33911
+▁dros	-33912
+altung	-33913
+chagar	-33914
+consin	-33915
+crysta	-33916
+oonnni	-33917
+rittaa	-33918
+rosome	-33919
+sineef	-33920
+▁arsen	-33921
+▁arsin	-33922
+▁bring	-33923
+▁eerit	-33924
+▁tinni	-33925
+agargoo	-33926
+agarree	-33927
+agartii	-33928
+animate	-33929
+bildung	-33930
+cursors	-33931
+graamii	-33932
+herford	-33933
+irrifam	-33934
+lipopro	-33935
+qqamnni	-33936
+▁aldaar	-33937
+▁anabol	-33938
+▁brings	-33939
+▁dinner	-33940
+▁furrif	-33941
+▁gospel	-33942
+▁larsen	-33943
+▁loozaa	-33944
+▁moloka	-33945
+▁namnni	-33946
+▁sagner	-33947
+▁warner	-33948
+aamurrif	-33949
+agarumma	-33950
+amenamni	-33951
+dhufaala	-33952
+graafiin	-33953
+tbildung	-33954
+tiyuunii	-33955
+▁capella	-33956
+▁cddhaan	-33957
+▁drosera	-33958
+▁gardner	-33959
+▁ijjesse	-33960
+▁liidaar	-33961
+▁molokai	-33962
+▁nitamsa	-33963
+▁prakrit	-33964
+▁sarcoma	-33965
+▁siiktoo	-33966
+▁tinniqa	-33967
+chagargar	-33968
+hololoofi	-33969
+imbisaani	-33970
+lipoprote	-33971
+ragarumma	-33972
+sspaltung	-33973
+utherford	-33974
+▁acrosome	-33975
+▁aldaarri	-33976
+▁almganaa	-33977
+▁dandesse	-33978
+▁endorsed	-33979
+▁fibrosis	-33980
+▁habuttaa	-33981
+▁taheedhe	-33982
+irrifamoof	-33983
+ooskopawaa	-33984
+ootooppiin	-33985
+ooyidoonni	-33986
+opotamuses	-33987
+orgomsiisa	-33988
+oromooyyaa	-33989
+osinophils	-33990
+osphereiin	-33991
+ospheretti	-33992
+ostaatiksi	-33993
+otaamiyaan	-33994
+otheriidae	-33995
+oyiisterii	-33996
+parasympat	-33997
+pedosphere	-33998
+periodical	-33999
+gnn	-34000
+▁].	-34001
+▁}}	-34002
+cuit	-34003
+epic	-34004
+gnat	-34005
+gnum	-34006
+gnus	-34007
+like	-34008
+limf	-34009
+oade	-34010
+▁msn	-34011
+crusa	-34012
+gasus	-34013
+iviya	-34014
+rical	-34015
+spike	-34016
+suits	-34017
+▁hits	-34018
+▁mucu	-34019
+▁wear	-34020
+▁wife	-34021
+cygnus	-34022
+gnatho	-34023
+ologue	-34024
+pascal	-34025
+procal	-34026
+vergue	-34027
+▁bayou	-34028
+▁deriv	-34029
+▁duits	-34030
+▁foliv	-34031
+▁orgas	-34032
+▁rogue	-34033
+▁shear	-34034
+▁tonga	-34035
+crusade	-34036
+ivoorii	-34037
+nologue	-34038
+▁ayoade	-34039
+▁biliqa	-34040
+▁facade	-34041
+▁gaadul	-34042
+▁orgasm	-34043
+▁regnum	-34044
+endrical	-34045
+epicotyl	-34046
+jedhamun	-34047
+lavergue	-34048
+▁adventi	-34049
+▁biscuit	-34050
+▁jesuits	-34051
+▁mucucaa	-34052
+▁nebulae	-34053
+▁nikooma	-34054
+▁pegasus	-34055
+aanzibaar	-34056
+gnathozoa	-34057
+resprocal	-34058
+▁beksisee	-34059
+▁firrikoo	-34060
+▁folivora	-34061
+▁gaadulla	-34062
+▁gindhiir	-34063
+▁wearable	-34064
+absorption	-34065
+iisorbitol	-34066
+ootapascal	-34067
+phosphorus	-34068
+pirootinii	-34069
+ploblastic	-34070
+ppabilikii	-34071
+printeryaa	-34072
+processing	-34073
+properties	-34074
+proteinase	-34075
+ptenodytes	-34076
+qaanqorsaa	-34077
+qabaachuun	-34078
+qabangaruu	-34079
+qajeelchaa	-34080
+qulqullina	-34081
+raashiyaan	-34082
+reeyiiakka	-34083
+rhinovirus	-34084
+ripaablika	-34085
+roglobulin	-34086
+rooskoopii	-34087
+rovascular	-34088
+ruubummaan	-34089
+saaniitiin	-34090
+salamander	-34091
+salāmassal	-34092
+scentriole	-34093
+seentigree	-34094
+seerumamaa	-34095
+shammaanee	-34096
+siiftootaa	-34097
+siiliikaan	-34098
+solubility	-34099
+xaaree	-34100
+climate	-34101
+georgia	-34102
+ppermin	-34103
+welling	-34104
+▁amster	-34105
+ppermint	-34106
+▁progres	-34107
+▁superma	-34108
+▁dwelling	-34109
+▁galiiloo	-34110
+▁hepatiti	-34111
+▁hinjiruu	-34112
+▁kenneera	-34113
+▁kitaabne	-34114
+▁mannaati	-34115
+▁sinbiraa	-34116
+▁walqixxa	-34117
+▁waltarre	-34118
+aafameetti	-34119
+sufficient	-34120
+superorder	-34121
+suuddowwan	-34122
+suudoowwan	-34123
+taateewwan	-34124
+teeksiitti	-34125
+tegireetid	-34126
+thonectida	-34127
+tikilotnii	-34128
+tiodactyla	-34129
+tistiiksii	-34130
+titiyuudii	-34131
+transverse	-34132
+triangulum	-34133
+trikiitiin	-34134
+tursprache	-34135
+xaaliyaani	-34136
+yaaliisiin	-34137
+yoophiyaan	-34138
+yukaariyoo	-34139
+zaarvichaa	-34140
+ésentation	-34141
+والسَّبْتُ	-34142
+▁aanaankun	-34143
+▁aangessoo	-34144
+▁aarriiman	-34145
+▁abdulaahi	-34146
+▁abdullaah	-34147
+▁abijaadii	-34148
+▁abiyuudin	-34149
+▁abnormali	-34150
+▁abraashaa	-34151
+▁abukaadoo	-34152
+▁actinides	-34153
+▁addaattuu	-34154
+▁adeemsisu	-34155
+▁adenosine	-34156
+▁advantage	-34157
+▁adventiti	-34158
+▁affecting	-34159
+▁affeeruun	-34160
+▁afrikaafi	-34161
+▁afrikains	-34162
+▁afuriitti	-34163
+▁afuuffaan	-34164
+▁afuurenni	-34165
+▁agaarfaan	-34166
+▁aguuganii	-34167
+▁aguuggame	-34168
+▁aircraftt	-34169
+▁ajajamtuu	-34170
+▁ajajichaa	-34171
+▁ajeeseera	-34172
+▁ajeesuufi	-34173
+▁ajjeeseef	-34174
+▁ajjeesuuf	-34175
+▁ajjefaman	-34176
+▁akasumusa	-34177
+▁akkammiin	-34178
+▁akkammiti	-34179
+▁alalaasaa	-34180
+▁alalaasuu	-34181
+▁albamicha	-34182
+▁alephlink	-34183
+▁alexander	-34184
+▁aljebraan	-34185
+▁alkaayiin	-34186
+▁allantois	-34187
+▁allotropy	-34188
+▁altokkoon	-34189
+▁amaaratin	-34190
+▁amanamtus	-34191
+▁ambootiin	-34192
+▁ammanttaa	-34193
+▁ammayyaan	-34194
+▁amoositii	-34195
+▁amsterdam	-34196
+▁analogous	-34197
+▁analoogii	-34198
+▁angafoota	-34199
+eplf	-34200
+yiso	-34201
+finnan	-34202
+silxee	-34203
+slides	-34204
+▁atomi	-34205
+▁dasee	-34206
+▁beelli	-34207
+▁filmib	-34208
+▁gaddan	-34209
+▁gaddib	-34210
+▁kuffus	-34211
+▁miccee	-34212
+▁niiwut	-34213
+▁qayiso	-34214
+fachiisu	-34215
+▁abyssal	-34216
+▁beellam	-34217
+▁classif	-34218
+▁dadarba	-34219
+▁diiguuf	-34220
+▁ebantuu	-34221
+▁filmibe	-34222
+▁goobana	-34223
+▁gubachu	-34224
+▁kaayoon	-34225
+▁kuffama	-34226
+▁muldhif	-34227
+▁niiwtan	-34228
+▁qabammu	-34229
+▁siinaar	-34230
+▁warrani	-34231
+batolchee	-34232
+feraansii	-34233
+▁atomoota	-34234
+▁chaayina	-34235
+▁danaakil	-34236
+▁diiganii	-34237
+▁diigessa	-34238
+▁furdatta	-34239
+▁gochuffi	-34240
+▁hesronin	-34241
+▁kaayanii	-34242
+▁laalamuu	-34243
+▁niiwuton	-34244
+▁olbaasaa	-34245
+▁ulfinnan	-34246
+▁voolkaan	-34247
+aansiichii	-34248
+▁angiology	-34249
+▁anisaatin	-34250
+▁antarctic	-34251
+▁antiquark	-34252
+▁apartment	-34253
+▁appendage	-34254
+▁apteribis	-34255
+▁arabiffaa	-34256
+▁areerootu	-34257
+▁argamnior	-34258
+▁arginudha	-34259
+▁argisisuu	-34260
+▁argitudha	-34261
+▁aristotle	-34262
+▁arkiitiin	-34263
+▁asfaaltii	-34264
+▁ashaaraan	-34265
+▁astroonom	-34266
+▁atamoonni	-34267
+▁atimosfer	-34268
+▁atomlamee	-34269
+▁aurooppaa	-34270
+▁ausnahmen	-34271
+▁auxiliari	-34272
+▁avirejjii	-34273
+▁avreejiii	-34274
+▁awrooppaa	-34275
+▁ayiiranii	-34276
+▁ayivoorii	-34277
+▁ayyanoota	-34278
+▁baahireen	-34279
+▁baakteeri	-34280
+▁baaseefis	-34281
+▁baballatu	-34282
+▁baballise	-34283
+▁bakakkaan	-34284
+▁balaliitu	-34285
+▁balbiitti	-34286
+▁balfummaa	-34287
+▁baqaannii	-34288
+▁barannota	-34289
+▁bareedaas	-34290
+▁bareentuu	-34291
+▁barkumeee	-34292
+▁barresaan	-34293
+▁battaluma	-34294
+▁bayyettin	-34295
+▁beeksisuu	-34296
+▁beektotas	-34297
+▁beekuufuu	-34298
+▁beeljiing	-34299
+novo	-34300
+▁zoa	-34301
+npoon	-34302
+▁adiv	-34303
+▁qomj	-34304
+eefits	-34305
+enness	-34306
+ffness	-34307
+maalif	-34308
+npoons	-34309
+richiy	-34310
+router	-34311
+sector	-34312
+thumbq	-34313
+ylomic	-34314
+▁chihi	-34315
+▁chipo	-34316
+▁honna	-34317
+▁paaki	-34318
+agining	-34319
+aseedaa	-34320
+madonna	-34321
+pansion	-34322
+reading	-34323
+richtii	-34324
+spector	-34325
+taceans	-34326
+tension	-34327
+uusaati	-34328
+▁armana	-34329
+▁luuwii	-34330
+▁maxill	-34331
+▁maxima	-34332
+▁nasion	-34333
+▁oceans	-34334
+▁purism	-34335
+▁reppit	-34336
+aggeeffa	-34337
+chylomic	-34338
+ennessee	-34339
+nyaachuu	-34340
+oomudhii	-34341
+orderiin	-34342
+ppitarii	-34343
+tainties	-34344
+wwanmana	-34345
+▁allawii	-34346
+▁hinnyaa	-34347
+▁illness	-34348
+▁kijibuu	-34349
+▁mahmood	-34350
+▁natrium	-34351
+▁oceanii	-34352
+▁tarnovo	-34353
+▁teefits	-34354
+chrichtii	-34355
+istrichiy	-34356
+poonentii	-34357
+stiffness	-34358
+▁akkoofsa	-34359
+▁angoorra	-34360
+▁cabsuuar	-34361
+▁chidhaan	-34362
+▁counties	-34363
+▁crustace	-34364
+▁dulloomu	-34365
+▁isnpoons	-34366
+▁jabbicha	-34367
+▁keepiler	-34368
+▁nasional	-34369
+▁qubateen	-34370
+▁ringmann	-34371
+aaraatorii	-34372
+dhaggeeffa	-34373
+▁affeelame	-34374
+▁alarjinii	-34375
+▁beellamaa	-34376
+▁beenzilaa	-34377
+▁beeyladoo	-34378
+▁bekkamoni	-34379
+▁bellatrix	-34380
+▁beneshang	-34381
+▁bernhardt	-34382
+▁biffiisni	-34383
+▁bifgurmee	-34384
+▁bikkoomni	-34385
+▁bilisumma	-34386
+▁binoodhon	-34387
+▁binooqila	-34388
+▁binooqili	-34389
+▁biqiilota	-34390
+▁biraatiis	-34391
+▁biraaylee	-34392
+▁biriitish	-34393
+▁birmadiin	-34394
+▁biroollee	-34395
+▁birrattii	-34396
+▁bishalbuu	-34397
+▁bobbaatti	-34398
+▁bokkooloo	-34399
+denu	-34400
+jets	-34401
+▁zwe	-34402
+aazma	-34403
+dsrna	-34404
+emrna	-34405
+homoz	-34406
+ilets	-34407
+ssrna	-34408
+toxin	-34409
+wayra	-34410
+weier	-34411
+xoosa	-34412
+▁pets	-34413
+▁rrna	-34414
+araway	-34415
+burman	-34416
+burqaa	-34417
+dhammu	-34418
+dubbii	-34419
+finden	-34420
+golden	-34421
+inaudi	-34422
+ischer	-34423
+ndamii	-34424
+opinae	-34425
+oretta	-34426
+periph	-34427
+spinal	-34428
+xaxima	-34429
+▁align	-34430
+▁suden	-34431
+▁zimaa	-34432
+▁zweig	-34433
+alixaan	-34434
+charles	-34435
+ireekti	-34436
+laminae	-34437
+muramee	-34438
+suwayra	-34439
+toilets	-34440
+▁amboos	-34441
+▁baabur	-34442
+▁carden	-34443
+▁filiph	-34444
+▁ilboos	-34445
+▁normes	-34446
+▁saboka	-34447
+▁zimaam	-34448
+crustiin	-34449
+oplastic	-34450
+therinae	-34451
+unndamii	-34452
+▁afurrif	-34453
+▁caraway	-34454
+▁dubaart	-34455
+▁einaudi	-34456
+▁hexoosa	-34457
+▁loretta	-34458
+ameriikaa	-34459
+tilopinae	-34460
+▁baaburri	-34461
+▁cardenas	-34462
+▁erfinden	-34463
+▁pilaazma	-34464
+▁schweier	-34465
+denudation	-34466
+ländischer	-34467
+oonmuramee	-34468
+peripheral	-34469
+▁antitoxin	-34470
+▁bekkamana	-34471
+▁bokkootti	-34472
+▁boltzmann	-34473
+▁booranaan	-34474
+▁boqonnaan	-34475
+▁boqotutti	-34476
+▁boraalloo	-34477
+▁boraatiin	-34478
+▁botanical	-34479
+▁boyyeewan	-34480
+▁bulmaataa	-34481
+▁buqqiftuu	-34482
+▁buufadhaa	-34483
+▁buusuufis	-34484
+▁carnivore	-34485
+▁cavendish	-34486
+▁cedhedhaa	-34487
+▁cetaceans	-34488
+▁chayootee	-34489
+▁chochonne	-34490
+▁chorionic	-34491
+▁christmas	-34492
+▁ciccimmoo	-34493
+▁cifgaanfa	-34494
+▁cimdeesuu	-34495
+▁citagitaa	-34496
+▁citrullus	-34497
+▁classical	-34498
+▁cokamanii	-34499
+ünd	-34500
+bakr	-34501
+egbe	-34502
+egem	-34503
+egre	-34504
+▁hnd	-34505
+▁ukr	-34506
+▁weg	-34507
+gründ	-34508
+krone	-34509
+oliths	-34510
+rivers	-34511
+spitze	-34512
+▁nasta	-34513
+▁truck	-34514
+kuufama	-34515
+▁alegre	-34516
+▁cateau	-34517
+▁princi	-34518
+▁tombol	-34519
+gründung	-34520
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+▁haaromfame	-38793
+▁habaqaalli	-38794
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+▁hafaanitti	-38796
+▁haguugamee	-38797
+▁hamilummaa	-38798
+▁hammasitti	-38799
+▁dtp	-38800
+hicle	-38801
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+aaluul	-38804
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+laaluu	-38807
+maaltu	-38808
+nayyoo	-38809
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+▁keysa	-38812
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+▁angaaf	-38826
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+▁bodybu	-38828
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+▁jeemaa	-38830
+▁laaluu	-38831
+▁marble	-38832
+▁qotiyy	-38833
+▁tumsuu	-38834
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+qacurraa	-38838
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+▁duurraa	-38841
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+▁libidoo	-38843
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+▁sagliin	-38845
+▁tumsaan	-38846
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+▁daayinam	-38849
+▁inaajiin	-38850
+▁jeechuun	-38851
+▁jirurraa	-38852
+▁kenticha	-38853
+▁libartaa	-38854
+▁mufachuu	-38855
+▁tumameen	-38856
+▁yoomessa	-38857
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+saqacurraa	-38859
+▁afferanis	-38860
+▁angaafaaf	-38861
+▁dabarseen	-38862
+▁dhaburraa	-38863
+▁furdaadag	-38864
+▁gosaarroo	-38865
+▁hojiirraa	-38866
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+▁qeeqaniif	-38868
+▁xinnayyoo	-38869
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+▁afferanisi	-38872
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+▁geneticsit	-38874
+▁giiliitiin	-38875
+▁hamoommata	-38876
+▁handaaqqoo	-38877
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+▁hanqaaqxee	-38879
+▁hantuuttoo	-38880
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+▁harkifamee	-38882
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+▁haromaayaa	-38884
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+▁hayyuummaa	-38888
+▁heduunnama	-38889
+▁hemostatic	-38890
+▁herregotaa	-38891
+▁hibbakkaan	-38892
+▁hidhachiis	-38893
+▁hiikansaas	-38894
+▁hiikkannaa	-38895
+▁hiiliyemii	-38896
+▁hilleettii	-38897
+▁himaalaaya	-38898
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+table	-38914
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+▁duwwa	-38935
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+▁tieba	-38938
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+zbaahuu	-38946
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+▁miliqe	-38949
+▁nuffii	-38950
+▁ribose	-38951
+▁termin	-38952
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+▁crimson	-38960
+▁dhoohuu	-38961
+▁dibadhu	-38962
+▁diddiqa	-38963
+▁iyeesuu	-38964
+▁nuyamti	-38965
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+▁dhitahuu	-38973
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+▁iyeesuus	-38975
+▁lammafaa	-38976
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+▁dhagenyee	-38987
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+▁irralafaa	-38990
+▁maandisaa	-38991
+▁medaaliya	-38992
+▁mizbaahuu	-38993
+▁sidaminya	-38994
+▁southeast	-38995
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+▁abdachiise	-38998
+▁bekkamooti	-38999
+="'	-39000
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+umps	-39004
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+▁anaksaa	-39023
+▁antigen	-39024
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+▁caccabe	-39026
+▁cisetti	-39027
+▁ejensii	-39028
+▁epsilon	-39029
+▁mandiid	-39030
+▁midhasu	-39031
+▁tayyiin	-39032
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+kootiilii	-39034
+nyaatanii	-39035
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+▁caccabsa	-39037
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+▁jidduuti	-39040
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+▁takkadha	-39043
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+▁waltajii	-39046
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+▁burunaayi	-39048
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+▁meetaawaa	-39055
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+pokootiilii	-39059
+▁anaksaagoo	-39060
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+▁caamsaatti	-39062
+▁caccabanii	-39063
+▁darbuuttii	-39064
+▁elektirook	-39065
+▁executable	-39066
+▁hawaxaatti	-39067
+▁hinbeektuu	-39068
+▁hindabarsu	-39069
+▁hinnyaadhu	-39070
+▁hinnyaanne	-39071
+▁hinnyaatin	-39072
+▁hirmaachuf	-39073
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+▁hirmaattan	-39075
+▁hirrifamte	-39076
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+▁hojiiwwaan	-39078
+▁hojjatamuf	-39079
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+▁hoogganaan	-39081
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+▁hoospitaal	-39084
+▁horaadhaan	-39085
+▁hordofnuuf	-39086
+▁hormaachuu	-39087
+▁horokokkaa	-39088
+▁hulluuxxoo	-39089
+▁hundeessun	-39090
+▁hundooftuf	-39091
+▁huxuchanii	-39092
+▁hypocentre	-39093
+▁ibiddaatti	-39094
+▁iddoodhuma	-39095
+▁iinveermee	-39096
+▁ijaarameef	-39097
+▁ijaarratte	-39098
+▁ijaarsaafi	-39099
+jennu	-39100
+stern	-39101
+uyyee	-39102
+▁redu	-39103
+iyyyee	-39104
+kophee	-39105
+uuyyee	-39106
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+▁diqoo	-39109
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+▁iyyee	-39111
+▁muzea	-39112
+▁samia	-39113
+▁samro	-39114
+▁tesso	-39115
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+ttaarra	-39122
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+▁moyyee	-39128
+▁naachi	-39129
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+▁potash	-39131
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+▁tirrii	-39134
+▁uffizi	-39135
+▁uturaa	-39136
+▁waaben	-39137
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+saaniiti	-39139
+▁arkamaa	-39140
+▁arktiik	-39141
+▁demetri	-39142
+▁dhawaat	-39143
+▁diqicha	-39144
+▁eganoon	-39145
+▁gaafifi	-39146
+▁haburaa	-39147
+▁sammmuu	-39148
+▁tiruufi	-39149
+▁tooyyee	-39150
+▁uffisii	-39151
+▁xururaa	-39152
+▁aanatiin	-39153
+▁addaarra	-39154
+▁baauyyee	-39155
+▁dagatiin	-39156
+▁diqachuu	-39157
+▁gabaarra	-39158
+▁hinjennu	-39159
+▁ibiddaan	-39160
+▁jedhuraa	-39161
+▁lafeelee	-39162
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+▁samrobia	-39164
+▁taattoon	-39165
+▁uffannaa	-39166
+▁uffattee	-39167
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+oominniifi	-39169
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+▁alternate	-39171
+▁ballaarra	-39172
+▁demetrius	-39173
+▁dhagaarra	-39174
+▁guyyatiin	-39175
+▁hundaarra	-39176
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+▁qabiyyyee	-39178
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+▁faallatiin	-39180
+▁fosfarasii	-39181
+▁guuttiyyee	-39182
+▁ijaarsaati	-39183
+▁ijaarsisaa	-39184
+▁ijoolluumm	-39185
+▁ikooniyaan	-39186
+▁ilaalchise	-39187
+▁ilaludhaan	-39188
+▁illaastiin	-39189
+▁imapaayera	-39190
+▁imimmaanii	-39191
+▁imimmaantu	-39192
+▁impression	-39193
+▁indooplaaz	-39194
+▁industrial	-39195
+▁industurii	-39196
+▁inhibitors	-39197
+▁insectotta	-39198
+▁inseladuus	-39199
+auc	-39200
+cauc	-39201
+iven	-39202
+kehr	-39203
+milk	-39204
+odhe	-39205
+aucus	-39206
+baale	-39207
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+faale	-39210
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+shaye	-39214
+tazar	-39215
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+▁flam	-39217
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+▁swiz	-39219
+amelus	-39220
+coding	-39221
+enance	-39222
+garves	-39223
+itidae	-39224
+league	-39225
+onidae	-39226
+piroon	-39227
+umaale	-39228
+xirroo	-39229
+▁chili	-39230
+▁deven	-39231
+▁kaven	-39232
+▁mamel	-39233
+▁naama	-39234
+▁quino	-39235
+▁seven	-39236
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+essiven	-39238
+finance	-39239
+illance	-39240
+oysters	-39241
+pirical	-39242
+tiraali	-39243
+yooniif	-39244
+▁adiven	-39245
+▁bohaar	-39246
+▁conway	-39247
+▁cubbuu	-39248
+▁diazep	-39249
+▁dilbii	-39250
+▁goolaa	-39251
+▁mamelo	-39252
+▁phylum	-39253
+▁quinoa	-39254
+▁swizer	-39255
+▁torino	-39256
+caucasus	-39257
+donation	-39258
+lammonni	-39259
+oleucine	-39260
+phitidae	-39261
+▁alkaali	-39262
+▁alumini	-39263
+▁bassoos	-39264
+▁camelus	-39265
+▁flaming	-39266
+▁nagodhe	-39267
+▁salaale	-39268
+▁talaali	-39269
+▁verkehr	-39270
+empirical	-39271
+intenance	-39272
+lammoonni	-39273
+lammootaa	-39274
+ocentesis	-39275
+ssessiven	-39276
+tiraaliaa	-39277
+▁adductor	-39278
+▁algarves	-39279
+▁baltazar	-39280
+▁dhaladhe	-39281
+▁dhifamaa	-39282
+▁diazepam	-39283
+▁dirshaye	-39284
+▁flamingo	-39285
+▁guddadhe	-39286
+▁haagayya	-39287
+▁jedhaama	-39288
+▁kavendii	-39289
+▁mamelodi	-39290
+▁reserves	-39291
+▁secaucus	-39292
+dubbachiis	-39293
+mephitidae	-39294
+▁bohaarsuu	-39295
+▁dhungadhe	-39296
+▁goolaaduu	-39297
+▁gursumiin	-39298
+▁kunuunsus	-39299
+scal	-39300
+scop	-39301
+scut	-39302
+ρόμβ	-39303
+scaly	-39304
+sform	-39305
+scopic	-39306
+scutum	-39307
+tibeet	-39308
+ρόμβοσ	-39309
+▁jajan	-39310
+gooriin	-39311
+sformal	-39312
+▁tinker	-39313
+fameedha	-39314
+gaaddidd	-39315
+▁arkaana	-39316
+▁hoongee	-39317
+▁warraah	-39318
+aafamuufi	-39319
+▁centurii	-39320
+▁ilmeenii	-39321
+▁ispoorti	-39322
+▁takkazee	-39323
+▁tinkerer	-39324
+ionsformal	-39325
+wwfameedha	-39326
+▁clockwise	-39327
+▁lammesoon	-39328
+▁murtiitti	-39329
+▁nutrition	-39330
+▁omishamuu	-39331
+▁soogiddaa	-39332
+▁stachyose	-39333
+▁waldaakoo	-39334
+▁wallaalan	-39335
+▁baayinnaan	-39336
+▁faayibirii	-39337
+▁gurmeessaa	-39338
+▁hirameedha	-39339
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+▁interfacec	-39342
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+▁irraagadee	-39344
+▁irraanfatu	-39345
+▁isheetiini	-39346
+▁iskiriiwii	-39347
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+▁istaadiyem	-39350
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+▁iyuukaariy	-39355
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+▁kaachisuun	-39377
+▁kaafamutti	-39378
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diff --git a/models/tokenizer/om_tokenizer_8k.model b/models/tokenizer/om_tokenizer_8k.model
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--- /dev/null
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diff --git a/models/tokenizer/om_tokenizer_8k.vocab b/models/tokenizer/om_tokenizer_8k.vocab
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