diff --git a/README.md b/README.md
index 25ab99438e5368aabd19fc7a4d6cba143334782a..bd3ffb591ca583ce362b3bb3d988db14cfbfc8a2 100644
--- a/README.md
+++ b/README.md
@@ -23,14 +23,14 @@ dataset_info:
 metrics:
   - name: best_compression_ratio
     type: compression
-    value: 3.456
+    value: 3.497
   - name: best_isotropy
     type: isotropy
-    value: 0.0028
+    value: 0.0023
   - name: vocabulary_size
     type: vocab
-    value: 1659
-generated: 2025-12-28
+    value: 0
+generated: 2026-01-03
 ---
 
 # CHY - Wikilangs Models
@@ -44,12 +44,13 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and
 ### Models & Assets
 
 - Tokenizers (8k, 16k, 32k, 64k)
-- N-gram models (2, 3, 4-gram)
-- Markov chains (context of 1, 2, 3 and 4)
+- 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
+- Embeddings in various sizes and dimensions (aligned and unaligned)
 - Language Vocabulary
 - Language Statistics
+
 ![Performance Dashboard](visualizations/performance_dashboard.png)
 
 ### Analysis and Evaluation
@@ -59,7 +60,8 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and
 - [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)
+- [6. Morphological Analysis (Experimental)](#6-morphological-analysis)
+- [7. Summary & Recommendations](#7-summary--recommendations)
 - [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
 - [Visualizations Index](#visualizations-index)
 
@@ -68,50 +70,45 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and
 
 ![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** | 3.426x | 3.37 | 0.0811% | 33,276 |
-| **16k** | 3.456x 🏆 | 3.40 | 0.0819% | 32,987 |
+| **8k** | 3.497x 🏆 | 3.52 | 0.0960% | 19,785 |
 
 ### Tokenization Examples
 
 Below are sample sentences tokenized with each vocabulary size:
 
-**Sample 1:** `Môxéhéó'o (vé'ho'énêstsestôtse: broom, "sweeping [thing]") Pl: môxéheonôtse.
-
-
-C...`
+**Sample 1:** `Vášêtaëno, Amâho'hestôtse (Pl: amâho'héstotôtse) Ama'éno'hamémôxe'êstoo'o Ama'én...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁môxéhéó ' o ▁( vé ' ho ' énêstsestôtse : ... (+18 more)` | 28 |
-| 16k | `▁môxéhéó ' o ▁( vé ' ho ' énêstsestôtse : ... (+17 more)` | 27 |
+| 8k | `▁vášêtaëno , ▁amâho ' hestôtse ▁( pl : ▁amâho ' ... (+16 more)` | 26 |
 
-**Sample 2:** `Brazil, na'éstse ho'e-éve, Amérika.
-
-Category:Brazil`
+**Sample 2:** `Ma'xeamóvôhtó'hestôtse, Éam-óvôhtó'heo'o. thumb|right thumb|right thumb|Daimler-...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁brazil , ▁na ' éstse ▁ho ' e - éve ... (+6 more)` | 16 |
-| 16k | `▁brazil , ▁na ' éstse ▁ho ' e - éve ... (+6 more)` | 16 |
-
-**Sample 3:** `Boise, na'éstse manâhéno, Idaho.
+| 8k | `▁ma ' xeamóvôhtó ' hestôtse , ▁éam - óvôhtó ' ... (+16 more)` | 26 |
 
-Category:Mâhoestôtse`
+**Sample 3:** `Mâhpémo'éhe (Alces alces) máto héva popóhpoévêsémo'éhe váótséva-éve.`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁boise , ▁na ' éstse ▁manâhéno , ▁idaho . ▁category ... (+2 more)` | 12 |
-| 16k | `▁boise , ▁na ' éstse ▁manâhéno , ▁idaho . ▁category ... (+2 more)` | 12 |
+| 8k | `▁mâhpémo ' éhe ▁( alces ▁alces ) ▁máto ▁héva ▁popóhpoévêsémo ... (+6 more)` | 16 |
 
 
 ### Key Findings
 
-- **Best Compression:** 16k achieves 3.456x compression
-- **Lowest UNK Rate:** 8k with 0.0811% unknown tokens
+- **Best Compression:** 8k achieves 3.497x compression
+- **Lowest UNK Rate:** 8k with 0.0960% unknown tokens
 - **Trade-off:** Larger vocabularies improve compression but increase model size
 - **Recommendation:** 32k vocabulary provides optimal balance for production use
 
@@ -120,57 +117,89 @@ Category:Mâhoestôtse`
 
 ![N-gram Perplexity](visualizations/ngram_perplexity.png)
 
+![N-gram Unique](visualizations/ngram_unique.png)
+
 ![N-gram Coverage](visualizations/ngram_coverage.png)
 
 ### Results
 
-| N-gram | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
-|--------|------------|---------|----------------|------------------|-------------------|
-| **2-gram** | 237 🏆 | 7.89 | 654 | 65.5% | 100.0% |
-| **2-gram** | 360 🏆 | 8.49 | 1,127 | 58.7% | 99.4% |
-| **3-gram** | 533 | 9.06 | 1,211 | 49.6% | 95.0% |
-| **3-gram** | 1,561 | 10.61 | 4,876 | 32.9% | 74.3% |
-| **4-gram** | 1,077 | 10.07 | 2,302 | 37.1% | 77.9% |
-| **4-gram** | 3,419 | 11.74 | 11,151 | 25.8% | 57.5% |
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 102 🏆 | 6.68 | 159 | 86.3% | 100.0% |
+| **2-gram** | Subword | 330 | 8.37 | 871 | 59.3% | 100.0% |
+| **3-gram** | Word | 156 | 7.28 | 245 | 72.6% | 100.0% |
+| **3-gram** | Subword | 1,700 | 10.73 | 3,811 | 27.1% | 73.0% |
+| **4-gram** | Word | 310 | 8.27 | 449 | 52.1% | 100.0% |
+| **4-gram** | Subword | 4,072 | 11.99 | 8,559 | 18.3% | 52.6% |
 
 ### Top 5 N-grams by Size
 
-**2-grams:**
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `na éstse` | 161 |
+| 2 | `vé ho` | 119 |
+| 3 | `ho énêstsestôtse` | 72 |
+| 4 | `republic of` | 67 |
+| 5 | `ho e` | 57 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `vé ho énêstsestôtse` | 72 |
+| 2 | `na éstse manâhéno` | 56 |
+| 3 | `ho e éve` | 49 |
+| 4 | `na éstse ho` | 48 |
+| 5 | `éstse ho e` | 48 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `éstse ho e éve` | 48 |
+| 2 | `na éstse ho e` | 48 |
+| 3 | `ma kaetaévôxe êstoo o` | 25 |
+| 4 | `toháano éve ho etse` | 23 |
+| 5 | `na éstse manâhéno ho` | 22 |
+
+**2-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `category :` | 973 |
-| 2 | `' e` | 663 |
-| 3 | `ho '` | 511 |
-| 4 | `' o` | 391 |
-| 5 | `. category` | 332 |
+| 1 | `e _` | 1,534 |
+| 2 | `s e` | 1,395 |
+| 3 | `s t` | 1,310 |
+| 4 | `t s` | 1,310 |
+| 5 | `h e` | 1,012 |
 
-**3-grams:**
+**3-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `. category :` | 331 |
-| 2 | `na ' éstse` | 288 |
-| 3 | `' ho '` | 225 |
-| 4 | `| thumb |` | 204 |
-| 5 | `| right |` | 201 |
+| 1 | `t s e` | 1,002 |
+| 2 | `s e _` | 580 |
+| 3 | `e s t` | 468 |
+| 4 | `s t s` | 459 |
+| 5 | `h o '` | 443 |
 
-**4-grams:**
+**4-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `, na ' éstse` | 199 |
-| 2 | `| thumb | right` | 167 |
-| 3 | `thumb | right |` | 155 |
-| 4 | `vé ' ho '` | 131 |
-| 5 | `300px | thumb |` | 128 |
+| 1 | `t s e _` | 456 |
+| 2 | `s t s e` | 436 |
+| 3 | `ô t s e` | 287 |
+| 4 | `t ô t s` | 208 |
+| 5 | `e s t ô` | 198 |
 
 
 ### Key Findings
 
-- **Best Perplexity:** 2-gram with 237
+- **Best Perplexity:** 2-gram (word) with 102
 - **Entropy Trend:** Decreases with larger n-grams (more predictable)
-- **Coverage:** Top-1000 patterns cover ~58% of corpus
+- **Coverage:** Top-1000 patterns cover ~53% of corpus
 - **Recommendation:** 4-gram or 5-gram for best predictive performance
 
 ---
@@ -178,55 +207,86 @@ Category:Mâhoestôtse`
 
 ![Markov Entropy](visualizations/markov_entropy.png)
 
+![Markov Contexts](visualizations/markov_contexts.png)
+
 ![Markov Branching](visualizations/markov_branching.png)
 
 ### Results
 
-| Context | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
-|---------|-------------|------------|------------------|-----------------|----------------|
-| **1** | 0.3489 | 1.274 | 2.42 | 4,255 | 65.1% |
-| **1** | 1.3997 | 2.638 | 10.97 | 189 | 0.0% |
-| **2** | 0.1560 | 1.114 | 1.36 | 10,197 | 84.4% |
-| **2** | 1.2345 | 2.353 | 5.27 | 2,073 | 0.0% |
-| **3** | 0.0936 | 1.067 | 1.18 | 13,745 | 90.6% |
-| **3** | 0.6401 | 1.558 | 2.32 | 10,919 | 36.0% |
-| **4** | 0.0555 🏆 | 1.039 | 1.10 | 16,004 | 94.5% |
-| **4** | 0.2796 🏆 | 1.214 | 1.44 | 25,260 | 72.0% |
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.4118 | 1.330 | 2.00 | 3,383 | 58.8% |
+| **1** | Subword | 1.3726 | 2.589 | 9.72 | 175 | 0.0% |
+| **2** | Word | 0.1118 | 1.081 | 1.20 | 6,516 | 88.8% |
+| **2** | Subword | 1.2032 | 2.303 | 5.05 | 1,699 | 0.0% |
+| **3** | Word | 0.0474 | 1.033 | 1.08 | 7,515 | 95.3% |
+| **3** | Subword | 0.6524 | 1.572 | 2.34 | 8,541 | 34.8% |
+| **4** | Word | 0.0269 🏆 | 1.019 | 1.04 | 7,792 | 97.3% |
+| **4** | Subword | 0.2844 | 1.218 | 1.44 | 19,944 | 71.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `e he óonéma enóne éohkê héška ó he hohamháa há continentan naa nêhéóhe násáahéne enomóvóhe tsé`
+2. `ho honáemanėstóoseo o united states manâhestôtse 1 188 lwanda 100px mogadishu somali shilling swahil...`
+3. `o vé keehoohtsêstse vó kaehevôtse vé ho énêstsestôtse billingscheyenne english dictionary chief dull...`
+
+**Context Size 2:**
+
+1. `na éstse ho e éve asia center thumb handelskade in willemstad curaçao`
+2. `vé ho énêstsestôtse black hills ho honáéšé e missouri ó he e pónoeo hé e na éstse`
+3. `ho énêstsestôtse cimarron river bull river forgan heévȧhetanéno`
+
+**Context Size 3:**
+
+1. `vé ho énêstsestôtse bay horse variant tsé vó névóvâtse`
+2. `na éstse manâhéno ho honáéšé e united states óoetaneo o óoetanéno tsé amo eétâhéstove vé ho énêstses...`
+3. `ho e éve hóxovê hooma center frameless upright 1 5`
+
+**Context Size 4:**
+
+1. `éstse ho e éve meško`
+2. `na éstse ho e éve vietnam dong hoi airport`
+3. `ma kaetaévôxe êstoo o sango toháano éve ho etse 622 984 4 216 666 1 198 chad republic of`
 
-### Generated Text Samples
 
-Below are text samples generated from each Markov chain model:
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
 
 **Context Size 1:**
 
-1. `' konénėhesó - éve . manâhestôtse 7 heše ' tavö ' éhoo ' he tsénėxhésemé '`
-2. `, na ' he tsénėxhésemé ' evo ' éno ' e 1904 , na ' otsenáhkohe`
-3. `: turkey , na ' o tsétsêhéstâhese - éve . gus . curitiba - éve .`
+1. `e_a_29150002)_te`
+2. `_rkul_mâxpoeése.`
+3. `a_xema'ėh-évese:`
 
 **Context Size 2:**
 
-1. `category : ó ' he ( vé ' ho ' énestse 71 , 740 6 , 418`
-2. `' e 300px | thumb | amâho ' hestôtse amêške tsémo ' ôhtávoome amâho ' hestôtse category`
-3. `ho ' xó ' mâhoéve ' ho ' énêstsestôtse : purgatoire river , picketwire river ) -`
+1. `e_(vé'še'tó'neadd`
+2. `seotó'o_poestôtse`
+3. `ts_wymnetugual_ju`
 
 **Context Size 3:**
 
-1. `. category : mâhoestôtse category : california`
-2. `na ' éstse ho ' e - éve hóxovê - hooma , asia ) . *`
-3. `' ho ' e - éve , meško . category : mâhoestôtse category : ho ' honáeo '`
+1. `tse._vé'ho'hé'e_bo`
+2. `se_rokese_mâhestȯt`
+3. `estôtse_manâhá'e_(`
 
 **Context Size 4:**
 
-1. `, na ' éstse ho ' e - éve , amérika . *`
-2. `| thumb | right | hóxeeséeto ' hamestôtse 300px | thumb | right | méstaa ' êhéhe category :`
-3. `thumb | right | hotóhkeo ' o tsénésôhtôxese 300px | thumb | right | mámaa ' e mámaa '`
+1. `tse_hotómá'e_12_évȯ`
+2. `stseévenomo_hovanan`
+3. `ôtse:_ten_sage";_ar`
 
 
 ### Key Findings
 
-- **Best Predictability:** Context-4 with 94.5% predictability
+- **Best Predictability:** Context-4 (word) with 97.3% predictability
 - **Branching Factor:** Decreases with context size (more deterministic)
-- **Memory Trade-off:** Larger contexts require more storage (25,260 contexts)
+- **Memory Trade-off:** Larger contexts require more storage (19,944 contexts)
 - **Recommendation:** Context-3 or Context-4 for text generation
 
 ---
@@ -242,64 +302,64 @@ Below are text samples generated from each Markov chain model:
 
 | Metric | Value |
 |--------|-------|
-| Vocabulary Size | 1,659 |
-| Total Tokens | 14,360 |
-| Mean Frequency | 8.66 |
+| Vocabulary Size | 1,237 |
+| Total Tokens | 8,401 |
+| Mean Frequency | 6.79 |
 | Median Frequency | 3 |
-| Frequency Std Dev | 38.60 |
+| Frequency Std Dev | 21.86 |
 
 ### Most Common Words
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | category | 974 |
-| 2 | e | 690 |
-| 3 | ho | 531 |
-| 4 | o | 414 |
-| 5 | na | 293 |
-| 6 | éstse | 288 |
-| 7 | right | 260 |
-| 8 | éve | 259 |
-| 9 | thumb | 226 |
-| 10 | vé | 180 |
+| 1 | e | 431 |
+| 2 | ho | 377 |
+| 3 | o | 237 |
+| 4 | na | 165 |
+| 5 | vé | 161 |
+| 6 | éstse | 161 |
+| 7 | éve | 154 |
+| 8 | of | 118 |
+| 9 | he | 108 |
+| 10 | naa | 108 |
 
 ### Least Common Words (from vocabulary)
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | evenóse | 2 |
-| 2 | mountain | 2 |
-| 3 | cal | 2 |
-| 4 | poly | 2 |
-| 5 | mustangs | 2 |
-| 6 | sevonévo | 2 |
-| 7 | ėstovátamevéotse | 2 |
-| 8 | ėstova | 2 |
-| 9 | nėstse | 2 |
-| 10 | 2025 | 2 |
+| 1 | mustangs | 2 |
+| 2 | sevonévo | 2 |
+| 3 | ėstovátamevéotse | 2 |
+| 4 | ėstova | 2 |
+| 5 | nėstse | 2 |
+| 6 | kūnas | 2 |
+| 7 | epsteins | 2 |
+| 8 | ir | 2 |
+| 9 | felon | 2 |
+| 10 | immigrants | 2 |
 
 ### Zipf's Law Analysis
 
 | Metric | Value |
 |--------|-------|
-| Zipf Coefficient | 0.8829 |
-| R² (Goodness of Fit) | 0.980523 |
+| Zipf Coefficient | 0.8151 |
+| R² (Goodness of Fit) | 0.976018 |
 | Adherence Quality | **excellent** |
 
 ### Coverage Analysis
 
 | Top N Words | Coverage |
 |-------------|----------|
-| Top 100 | 57.7% |
-| Top 1,000 | 90.8% |
+| Top 100 | 54.8% |
+| Top 1,000 | 94.4% |
 | Top 5,000 | 0.0% |
 | Top 10,000 | 0.0% |
 
 ### Key Findings
 
-- **Zipf Compliance:** R²=0.9805 indicates excellent adherence to Zipf's law
-- **High Frequency Dominance:** Top 100 words cover 57.7% of corpus
-- **Long Tail:** -8,341 words needed for remaining 100.0% coverage
+- **Zipf Compliance:** R²=0.9760 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 54.8% of corpus
+- **Long Tail:** -8,763 words needed for remaining 100.0% coverage
 
 ---
 ## 5. Word Embeddings Evaluation
@@ -312,24 +372,109 @@ Below are text samples generated from each Markov chain model:
 
 ![t-SNE Sentences](visualizations/tsne_sentences.png)
 
-### Model Comparison
 
-| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy |
-|-------|------------|-----------|----------|----------|----------|
-| **mono_32d** | 223 | 32 | 1.577 | 0.877 | 0.0028 🏆 |
-| **mono_64d** | 223 | 64 | 1.556 | 0.897 | 0.0009 |
-| **mono_128d** | 223 | 128 | 1.593 | 0.888 | 0.0002 |
-| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |
+### 5.1 Cross-Lingual Alignment
+
+> *Note: Multilingual alignment visualization not available for this language.*
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.0023 🏆 | 0.8533 | N/A | N/A |
+| **mono_64d** | 64 | 0.0008 | 0.9264 | N/A | N/A |
+| **mono_128d** | 128 | 0.0002 | 0.9821 | N/A | N/A |
 
 ### Key Findings
 
-- **Best Isotropy:** mono_32d with 0.0028 (more uniform distribution)
-- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
-- **Vocabulary Coverage:** All models cover 223 words
-- **Recommendation:** 100d for balanced semantic capture and efficiency
+- **Best Isotropy:** mono_32d with 0.0023 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.9206. Lower values indicate better semantic separation.
+- **Alignment Quality:** No aligned models evaluated in this run.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+> ⚠️ **Warning:** This language shows low morphological productivity. The statistical signals used for this analysis may be noisy or less reliable than for morphologically rich languages.
+
+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 | **0.000** | Low morphological productivity | ⚠️ Likely unreliable |
+| Idiomaticity Gap | **-1.000** | 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 |
+|--------|----------|
+| `-ho` | horse, hotoa, hoohëö |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | néstovátamevéotse, kôhtse, where |
+| `-se` | néstovátamevéotse, kôhtse, kaehevotôtse |
+| `-tse` | néstovátamevéotse, kôhtse, kaehevotôtse |
+| `-ne` | mâhoestôtsene, kane, mâheóne |
+| `-ôtse` | kaehevotôtse, oestôtse, xemenôtse |
+| `-ia` | anastacia, abkhazia, shepherdia |
+| `-ve` | êstonêstove, native, hestoháatamaahéstove |
+
+### 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.
+
+*No significant bound stems detected.*
+
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ho` | `-e` | 5 words | horse, hováhne |
+| `-ho` | `-ne` | 2 words | hováhne, hovahne |
+| `-ho` | `-se` | 1 words | horse, hotse |
+| `-ho` | `-tse` | 1 words | hotse, hohpâhtsenámenôtse |
+| `-ho` | `-ôtse` | 1 words | hohpâhtsenámenôtse |
+
+### 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 |
+|------|-----------------|------------|------|
+| mâhoestôtsene | **`mâhoest-ôtse-ne`** | 3.0 | `mâhoest` |
+| sevoneóneve | **`sevoneó-ne-ve`** | 3.0 | `sevoneó` |
+| náhkȯhehetanetse | **`náhkȯheheta-ne-tse`** | 3.0 | `náhkȯheheta` |
+| enóseoneve | **`enóseo-ne-ve`** | 3.0 | `enóseo` |
+| éestsėstóseoneve | **`éestsėstóseo-ne-ve`** | 3.0 | `éestsėstóseo` |
+| kaehevotôtse | **`kaehevot-ôtse`** | 1.5 | `kaehevot` |
+| anastacia | **`anastac-ia`** | 1.5 | `anastac` |
+| névóvâtse | **`névóvâ-tse`** | 1.5 | `névóvâ` |
+| shepherdia | **`shepherd-ia`** | 1.5 | `shepherd` |
+| êstonêstove | **`êstonêsto-ve`** | 1.5 | `êstonêsto` |
+| yellowstone | **`yellowsto-ne`** | 1.5 | `yellowsto` |
+| hoohtseto | **`ho-ohtseto`** | 1.5 | `ohtseto` |
+| xemenôtse | **`xemen-ôtse`** | 1.5 | `xemen` |
+| véhonevoemėstse | **`véhonevoemės-tse`** | 1.5 | `véhonevoemės` |
+| manestôtse | **`manest-ôtse`** | 1.5 | `manest` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language CHY appears to be more isolating or has a highly fixed vocabulary. Word-level models perform nearly as well as subword models, indicating fewer productive morphological processes.
 
 ---
-## 6. Summary & Recommendations
+## 7. Summary & Recommendations
 
 ![Performance Dashboard](visualizations/performance_dashboard.png)
 
@@ -337,11 +482,12 @@ Below are text samples generated from each Markov chain model:
 
 | Component | Recommended | Rationale |
 |-----------|-------------|-----------|
-| Tokenizer | **32k BPE** | Best compression (3.46x) with low UNK rate |
-| N-gram | **5-gram** | Lowest perplexity (237) |
-| Markov | **Context-4** | Highest predictability (94.5%) |
+| Tokenizer | **8k BPE** | Best compression (3.50x) |
+| N-gram | **2-gram** | Lowest perplexity (102) |
+| Markov | **Context-4** | Highest predictability (97.3%) |
 | Embeddings | **100d** | Balanced semantic capture and isotropy |
 
+
 ---
 ## Appendix: Metrics Glossary & Interpretation Guide
 
@@ -531,7 +677,8 @@ If you use these models in your research, please cite:
   author = {Kamali, Omar},
   title = {Wikilangs: Open NLP Models for Wikipedia Languages},
   year = {2025},
-  publisher = {HuggingFace},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
   url = {https://huggingface.co/wikilangs}
   institution = {Omneity Labs}
 }
@@ -547,7 +694,8 @@ MIT License - Free for academic and commercial use.
 - 🤗 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: 2025-12-28 22:42:59*
+*Report Date: 2026-01-03 10:13:21*
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--- a/models/embeddings/monolingual/chy_128d.bin
+++ b/models/embeddings/monolingual/chy_128d.bin
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diff --git a/models/embeddings/monolingual/chy_128d_metadata.json b/models/embeddings/monolingual/chy_128d_metadata.json
index e6d7548ad73151b59072f478fccb96cd56bde54f..7cb412f2a05bf300795a1bd9c0deb3cc791e4e3a 100644
--- a/models/embeddings/monolingual/chy_128d_metadata.json
+++ b/models/embeddings/monolingual/chy_128d_metadata.json
@@ -3,11 +3,13 @@
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   "version": "monolingual",
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+    "algorithm": "skipgram",
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+  "vocab_size": 163
 }
\ No newline at end of file
diff --git a/models/embeddings/monolingual/chy_32d.bin b/models/embeddings/monolingual/chy_32d.bin
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@@ -3,11 +3,13 @@
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   "version": "monolingual",
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 }
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 }
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 }
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-res	-1935
-rio	-1936
-sex	-1937
-stâ	-1938
-tri	-1939
-uba	-1940
-uly	-1941
-und	-1942
-ung	-1943
-ure	-1944
-uru	-1945
-uva	-1946
-vec	-1947
-voo	-1948
-véo	-1949
-zer	-1950
-áta	-1951
-çao	-1952
-éoe	-1953
-évâ	-1954
-êhé	-1955
-êna	-1956
-êsé	-1957
-êxo	-1958
-ëhe	-1959
-ëse	-1960
-óma	-1961
-ôsá	-1962
-ėst	-1963
-▁do	-1964
-▁es	-1965
-▁fi	-1966
-▁ht	-1967
-▁ki	-1968
-▁ku	-1969
-▁kö	-1970
-▁oc	-1971
-▁pe	-1972
-▁ru	-1973
-▁sk	-1974
-▁ti	-1975
-▁ut	-1976
-▁wa	-1977
-▁ôh	-1978
-abwe	-1979
-ames	-1980
-anat	-1981
-ants	-1982
-asus	-1983
-axaa	-1984
-axáa	-1985
-cerv	-1986
-char	-1987
-code	-1988
-curi	-1989
-down	-1990
-edir	-1991
-enns	-1992
-eolo	-1993
-eove	-1994
-eral	-1995
-esen	-1996
-está	-1997
-esëö	-1998
-eved	-1999
-fast	-2000
-fire	-2001
-gins	-2002
-hael	-2003
-hahk	-2004
-hama	-2005
-hamȧ	-2006
-hene	-2007
-here	-2008
-hesó	-2009
-hetó	-2010
-heše	-2011
-hová	-2012
-hoxo	-2013
-html	-2014
-htoo	-2015
-héve	-2016
-höva	-2017
-hāme	-2018
-iago	-2019
-ibik	-2020
-ical	-2021
-ific	-2022
-ills	-2023
-inst	-2024
-ires	-2025
-isco	-2026
-ithu	-2027
-khaz	-2028
-komê	-2029
-koné	-2030
-kêsa	-2031
-llow	-2032
-mala	-2033
-mami	-2034
-many	-2035
-mare	-2036
-mark	-2037
-mate	-2038
-mené	-2039
-môse	-2040
-nahe	-2041
-nene	-2042
-nese	-2043
-nâha	-2044
-néta	-2045
-nóse	-2046
-olia	-2047
-otsw	-2048
-page	-2049
-peru	-2050
-pher	-2051
-pper	-2052
-ratt	-2053
-road	-2054
-sane	-2055
-sean	-2056
-seph	-2057
-sian	-2058
-star	-2059
-tavö	-2060
-tiba	-2061
-ties	-2062
-tivi	-2063
-tohe	-2064
-táhe	-2065
-täso	-2066
-tómô	-2067
-tóxe	-2068
-urne	-2069
-utah	-2070
-vada	-2071
-vata	-2072
-voem	-2073
-vove	-2074
-vâho	-2075
-xove	-2076
-áahe	-2077
-âheo	-2078
-âhtö	-2079
-éseo	-2080
-éstó	-2081
-ësta	-2082
-óóhe	-2083
-ôhta	-2084
-ôhéó	-2085
-škee	-2086
-ȯhnó	-2087
-▁air	-2088
-▁ara	-2089
-▁atl	-2090
-▁bay	-2091
-▁ben	-2092
-▁bit	-2093
-▁cal	-2094
-▁day	-2095
-▁del	-2096
-▁din	-2097
-▁gla	-2098
-▁hea	-2099
-▁loc	-2100
-▁mel	-2101
-▁mex	-2102
-▁mos	-2103
-▁nan	-2104
-▁nat	-2105
-▁neg	-2106
-▁pac	-2107
-▁pra	-2108
-▁pre	-2109
-▁rho	-2110
-▁sac	-2111
-▁sco	-2112
-▁sho	-2113
-▁sto	-2114
-▁tra	-2115
-▁tre	-2116
-▁van	-2117
-▁wal	-2118
-▁xaó	-2119
-▁xäö	-2120
-▁šan	-2121
-advec	-2122
-anohe	-2123
-arten	-2124
-atone	-2125
-bania	-2126
-brief	-2127
-chief	-2128
-dagas	-2129
-desia	-2130
-elope	-2131
-guese	-2132
-halus	-2133
-hamâx	-2134
-hketa	-2135
-hkéso	-2136
-hotse	-2137
-house	-2138
-htâhé	-2139
-htóht	-2140
-illet	-2141
-kaeta	-2142
-keaho	-2143
-miten	-2144
-máhta	-2145
-néheo	-2146
-nôtse	-2147
-oemdc	-2148
-póevó	-2149
-river	-2150
-selle	-2151
-sisto	-2152
-ssing	-2153
-stâhe	-2154
-tanév	-2155
-tooxo	-2156
-tšêšk	-2157
-vakia	-2158
-venia	-2159
-vetoo	-2160
-ville	-2161
-vóhpo	-2162
-vôhtó	-2163
-xésta	-2164
-émene	-2165
-énéhe	-2166
-évôse	-2167
-óvéta	-2168
-ôxhoo	-2169
-ȧhéno	-2170
-▁alge	-2171
-▁anna	-2172
-▁anta	-2173
-▁apar	-2174
-▁camp	-2175
-▁cauc	-2176
-▁cura	-2177
-▁dece	-2178
-▁desy	-2179
-▁fire	-2180
-▁hehp	-2181
-▁hese	-2182
-▁john	-2183
-▁kosa	-2184
-▁kôsá	-2185
-▁mene	-2186
-▁mâhö	-2187
-▁mótó	-2188
-▁néta	-2189
-▁nėse	-2190
-▁obvi	-2191
-▁orig	-2192
-▁oéve	-2193
-▁page	-2194
-▁poly	-2195
-▁rock	-2196
-▁sand	-2197
-▁tono	-2198
-▁tséh	-2199
-▁tséx	-2200
-▁tóno	-2201
-▁tôhé	-2202
-▁ural	-2203
-▁vose	-2204
-▁wars	-2205
-▁west	-2206
-▁wiki	-2207
-▁zimb	-2208
-▁éveé	-2209
-▁ôhmo	-2210
-aneonó	-2211
-aénohe	-2212
-chived	-2213
-datama	-2214
-edirne	-2215
-eestse	-2216
-eoestá	-2217
-eétâhé	-2218
-haeolo	-2219
-hanáhe	-2220
-hkoohe	-2221
-hotóao	-2222
-hováve	-2223
-htsemo	-2224
-hótame	-2225
-kemene	-2226
-kôhtse	-2227
-kôhévé	-2228
-marete	-2229
-mêstaa	-2230
-môtove	-2231
-nétâhé	-2232
-onesia	-2233
-person	-2234
-prunus	-2235
-rizona	-2236
-sósone	-2237
-tevfik	-2238
-tivist	-2239
-turkey	-2240
-uation	-2241
-urasia	-2242
-vation	-2243
-veotse	-2244
-xemenó	-2245
-xemâho	-2246
-xepóno	-2247
-xêhest	-2248
-éestse	-2249
-éseohé	-2250
-êstóne	-2251
-óhtáhe	-2252
-ėstane	-2253
-šeméhe	-2254
-ȯhkėha	-2255
-▁aruba	-2256
-▁botsw	-2257
-▁coast	-2258
-▁dutch	-2259
-▁hestá	-2260
-▁horse	-2261
-▁hotóa	-2262
-▁hésta	-2263
-▁idaho	-2264
-▁korea	-2265
-▁lasio	-2266
-▁lithu	-2267
-▁meave	-2268
-▁pages	-2269
-▁penns	-2270
-▁press	-2271
-▁retri	-2272
-▁sibik	-2273
-▁there	-2274
-▁tsehe	-2275
-▁ubykh	-2276
-▁xamae	-2277
-antiago	-2278
-article	-2279
-atonebi	-2280
-estséat	-2281
-hestôxe	-2282
-heóvemá	-2283
-hkêsono	-2284
-hohtóva	-2285
-honáeka	-2286
-háhnoma	-2287
-hótsêha	-2288
-kêsaéve	-2289
-mamione	-2290
-matôtse	-2291
-montana	-2292
-noonáhe	-2293
-ohtôtse	-2294
-panâhke	-2295
-populus	-2296
-sistots	-2297
-sonants	-2298
-tsévovó	-2299
-tsêheta	-2300
-tómôhéó	-2301
-vetanév	-2302
-vêstséa	-2303
-vóneške	-2304
-xeméhne	-2305
-xâhonoo	-2306
-xémâhéó	-2307
-átamáno	-2308
-âhtötse	-2309
-éhestat	-2310
-éotóaho	-2311
-éstónéó	-2312
-óhéhéve	-2313
-▁ahkôhe	-2314
-▁donald	-2315
-▁europe	-2316
-▁hoésto	-2317
-▁hóvôse	-2318
-▁kóhkon	-2319
-▁kȧhamȧ	-2320
-▁manaan	-2321
-▁mariti	-2322
-▁matana	-2323
-▁ménôhe	-2324
-▁mónahe	-2325
-▁nanóse	-2326
-▁native	-2327
-▁nevada	-2328
-▁néstse	-2329
-▁pirahã	-2330
-▁reôhke	-2331
-▁tóhtoo	-2332
-▁univer	-2333
-▁éestse	-2334
-▁êstove	-2335
-aseohtsé	-2336
-cephalus	-2337
-etanetse	-2338
-external	-2339
-hahtsená	-2340
-hamâxéve	-2341
-háestôhe	-2342
-hóxovôho	-2343
-keahonoo	-2344
-kâsénoma	-2345
-kėhanáhe	-2346
-language	-2347
-manâhéno	-2348
-móxêšéne	-2349
-peruvian	-2350
-tšêškévâ	-2351
-vovetäso	-2352
-weoworld	-2353
-éoeškëso	-2354
-êškóneho	-2355
-ôhketóxe	-2356
-šestôtse	-2357
-▁aéstome	-2358
-▁chicago	-2359
-▁curaçao	-2360
-▁denmark	-2361
-▁douglas	-2362
-▁hahpenó	-2363
-▁heévȧhe	-2364
-▁heóvâhá	-2365
-▁hohamma	-2366
-▁islands	-2367
-▁maarten	-2368
-▁madagas	-2369
-▁menôtse	-2370
-▁million	-2371
-▁mâhpémo	-2372
-▁sanders	-2373
-▁skillet	-2374
-▁tseohke	-2375
-▁xamaevo	-2376
-anėsóvóne	-2377
-enestôtse	-2378
-eotsestsé	-2379
-hamémâhéó	-2380
-netsénóne	-2381
-nêškovávo	-2382
-ohtsévôse	-2383
-xemâhoévé	-2384
-ȧhestȯtse	-2385
-▁activist	-2386
-▁archived	-2387
-▁botswana	-2388
-▁caucasus	-2389
-▁crossing	-2390
-▁december	-2391
-▁hohtsemo	-2392
-▁hotóhkeo	-2393
-▁héstooma	-2394
-▁manȧhéno	-2395
-▁oklahoma	-2396
-▁original	-2397
-▁pennsylv	-2398
-▁rhodesia	-2399
-▁tsisinst	-2400
-▁tsêhésto	-2401
-▁zimbabwe	-2402
-california	-2403
-datamapper	-2404
-hkoestséat	-2405
-hánėsóvóne	-2406
-hóxeeséeto	-2407
-kaetaévôxe	-2408
-kévénėstse	-2409
-kôhévénéhe	-2410
-nêstsevôse	-2411
-sêhestôtse	-2412
-tsêhéstahe	-2413
-xėseotsean	-2414
-▁apartheid	-2415
-▁argentina	-2416
-▁caribbean	-2417
-▁geography	-2418
-▁hoéstónéó	-2419
-▁lithuania	-2420
-▁monêškeho	-2421
-▁obviative	-2422
-▁religions	-2423
-▁retrieved	-2424
-▁sibikeove	-2425
-▁tasemiten	-2426
-enenestôtse	-2427
-eétâhéstove	-2428
-héstánóvaan	-2429
-komêšéstótó	-2430
-mȧhoestȯtse	-2431
-êstonêstove	-2432
-ôhkêheóvemá	-2433
-ôhtávaestse	-2434
-▁consonants	-2435
-▁heóvonêheo	-2436
-▁hóvôsenâha	-2437
-▁hóxâhtsená	-2438
-▁koronestse	-2439
-▁kȧhamȧxévo	-2440
-▁lasiocarpa	-2441
-▁mâhóomoehá	-2442
-▁môxemarete	-2443
-▁nėsesėstse	-2444
-▁portuguese	-2445
-▁tootómôhéó	-2446
-▁tsêhestôxe	-2447
-▁university	-2448
-▁véhestôtse	-2449
-▁véhestȯtse	-2450
-▁washington	-2451
-eestsestȯtse	-2452
-staahtsémeno	-2453
-vátameveotse	-2454
-▁ahkôheöhtse	-2455
-▁desyatonebi	-2456
-▁háeohémahpe	-2457
-▁nemâmamione	-2458
-▁vétapâhaeto	-2459
-▁éškôseeséma	-2460
-mâhoestôtsene	-2461
-vetanévȯhkėha	-2462
-xêhestâhtötse	-2463
-êstonemâheone	-2464
-šenonetsénóne	-2465
-▁heóvêháhnoma	-2466
-▁reôhkemôtove	-2467
-aehesanestôtse	-2468
-disambiguation	-2469
-hamâxéveóhtáhe	-2470
-héstoeotsestsé	-2471
-kemâhaemenôtse	-2472
-▁otaesémenôtse	-2473
-▁héstoomaestôtse	-2474
-▁héstánóvaannéta	-2475
-▁tsisinstsistots	-2476
-▁tsêhéstoestôtse	-2477
-▁vóhkoohémâhoéve	-2478
-af	-2479
-ag	-2480
-cf	-2481
-cr	-2482
-ec	-2483
-fe	-2484
-fo	-2485
-fr	-2486
-ip	-2487
-jp	-2488
-pė	-2489
-sz	-2490
-yo	-2491
-ão	-2492
-aga	-2493
-anl	-2494
-aná	-2495
-ban	-2496
-bat	-2497
-big	-2498
-blo	-2499
-);	-2500
-ai	-2501
-bn	-2502
-eg	-2503
-eó	-2504
-gc	-2505
-ix	-2506
-kȧ	-2507
-lm	-2508
-my	-2509
-nd	-2510
-oq	-2511
-rh	-2512
-up	-2513
-wh	-2514
-ép	-2515
-anz	-2516
-cfm	-2517
-cot	-2518
-cre	-2519
-day	-2520
-dif	-2521
-dil	-2522
-din	-2523
-don	-2524
-dor	-2525
-ean	-2526
-eat	-2527
-esó	-2528
-ext	-2529
-ffa	-2530
-for	-2531
-gne	-2532
-gta	-2533
-hin	-2534
-hké	-2535
-hna	-2536
-hou	-2537
-hoé	-2538
-htä	-2539
-htȧ	-2540
-hum	-2541
-hôh	-2542
-ibe	-2543
-ibi	-2544
-ics	-2545
-ide	-2546
-ied	-2547
-ils	-2548
-inh	-2549
-iov	-2550
-irc	-2551
-iry	-2552
-ita	-2553
-iti	-2554
-jap	-2555
-jpg	-2556
-kes	-2557
-kyo	-2558
-lag	-2559
-lay	-2560
-lsx	-2561
-mau	-2562
-meo	-2563
-mib	-2564
-mot	-2565
-nee	-2566
-odo	-2567
-ork	-2568
-orn	-2569
-out	-2570
-ped	-2571
-poe	-2572
-ppi	-2573
-pua	-2574
-ron	-2575
-sci	-2576
-sha	-2577
-slo	-2578
-sse	-2579
-ste	-2580
-tah	-2581
-táo	-2582
-uco	-2583
-ump	-2584
-une	-2585
-usp	-2586
-vel	-2587
-wad	-2588
-yan	-2589
-ype	-2590
-zco	-2591
-äse	-2592
-épó	-2593
-éšé	-2594
-êhá	-2595
-êma	-2596
-êšé	-2597
-óvâ	-2598
-ôhö	-2599
-..	-2600
-</	-2601
-gg	-2602
-xȧ	-2603
-▁;	-2604
-▁ȯ	-2605
-▁а	-2606
-hkâ	-2607
-ito	-2608
-ooi	-2609
-ose	-2610
-rea	-2611
-sev	-2612
-sey	-2613
-tic	-2614
-vey	-2615
-zus	-2616
-ápe	-2617
-ëha	-2618
-ôsa	-2619
-ôšé	-2620
-öna	-2621
-öva	-2622
-āma	-2623
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-ėse	-2625
-ęst	-2626
-šev	-2627
-▁ay	-2628
-▁ba	-2629
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-▁gl	-2631
-▁nǐ	-2632
-▁ph	-2633
-▁pr	-2634
-▁pâ	-2635
-▁pó	-2636
-▁yo	-2637
-▁za	-2638
-aata	-2639
-abic	-2640
-abpo	-2641
-ache	-2642
-alls	-2643
-ally	-2644
-ameo	-2645
-amoo	-2646
-andi	-2647
-anin	-2648
-anni	-2649
-antu	-2650
-arro	-2651
-ashi	-2652
-asta	-2653
-atch	-2654
-ator	-2655
-aust	-2656
-axen	-2657
-bama	-2658
-burg	-2659
-ción	-2660
-ckho	-2661
-cook	-2662
-crat	-2663
-cree	-2664
-ctaw	-2665
-data	-2666
-demo	-2667
-deta	-2668
-ebec	-2669
-ehné	-2670
-eneo	-2671
-enos	-2672
-ercy	-2673
-etos	-2674
-etse	-2675
-eváh	-2676
-fael	-2677
-gary	-2678
-gene	-2679
-ghan	-2680
-gola	-2681
-hahá	-2682
-hant	-2683
-heno	-2684
-hirt	-2685
-hiya	-2686
-hkon	-2687
-hmot	-2688
-home	-2689
-homo	-2690
-hoxá	-2691
-huat	-2692
-hámá	-2693
-hōva	-2694
-idea	-2695
-idop	-2696
-igra	-2697
-ilak	-2698
-imal	-2699
---	-2700
-eh	-2701
-hë	-2702
-.),	-2703
-ani	-2704
-aqu	-2705
-asp	-2706
-bet	-2707
-ble	-2708
-cia	-2709
-dxe	-2710
-ede	-2711
-eét	-2712
-fra	-2713
-fry	-2714
-fus	-2715
-ico	-2716
-ier	-2717
-ira	-2718
-isi	-2719
-lop	-2720
-los	-2721
-mek	-2722
-mic	-2723
-méá	-2724
-mȧx	-2725
-omo	-2726
-owa	-2727
-pus	-2728
-rev	-2729
-rof	-2730
-rus	-2731
-ted	-2732
-tle	-2733
-ult	-2734
-via	-2735
-vis	-2736
-vot	-2737
-war	-2738
-ávó	-2739
-éné	-2740
-ías	-2741
-ónâ	-2742
-ška	-2743
-ȯhe	-2744
-▁be	-2745
-▁še	-2746
-alca	-2747
-alia	-2748
-anda	-2749
-angs	-2750
-atra	-2751
-boud	-2752
-comp	-2753
-desh	-2754
-eheo	-2755
-eone	-2756
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-egypt	-2991
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-esevo	-2998
-estre	-2999
-eséhe	-3000
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-icine	-3023
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-iligo	-3027
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-nesia	-3043
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-onévo	-3050
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-tober	-3069
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-évenó	-3088
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-êsema	-3090
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-ónoeo	-3092
-óéséó	-3093
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-ôhéve	-3096
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-arabic	-3191
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-ituary	-3221
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-olskie	-3234
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-ugenio	-3244
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-éstoto	-3251
-éškôsa	-3252
-éškôse	-3253
-êsevot	-3254
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-óhkono	-3257
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-óvôhtó	-3260
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-▁jimmy	-3285
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-▁papua	-3304
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-eralism	-3339
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-lepidop	-3351
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-mámáhta	-3353
-mâhoeve	-3354
-mâhóomo	-3355
-mâhöhko	-3356
-netâhev	-3357
-onáhevo	-3358
-otseonó	-3359
-pherdia	-3360
-pulorum	-3361
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-ristian	-3364
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-vonesto	-3374
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-âhevëno	-3379
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-éhoéstá	-3381
-épeeneo	-3382
-êstaneo	-3383
-êstóoné	-3384
-ôhtséve	-3385
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-ęstaneo	-3387
-ȧhéotse	-3388
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-▁amazon	-3391
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-▁animal	-3394
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-▁lesser	-3421
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-▁player	-3434
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-▁voaxaa	-3445
-▁voaxáa	-3446
-▁vóhpev	-3447
-▁woqini	-3448
-▁zambia	-3449
-▁êstone	-3450
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-amóneane	-3456
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-category	-3458
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-curitiba	-3461
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-japanese	-3478
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-tonôhené	-3498
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-ôheonané	-3504
-šenovoto	-3505
-ȯhnóvahe	-3506
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-▁héesevo	-3540
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-▁montana	-3550
-▁mâhöhko	-3551
-▁méstaeo	-3552
-▁namibia	-3553
-▁natalia	-3554
-▁negundo	-3555
-▁notämaé	-3556
-▁náhkȯhe	-3557
-▁oblique	-3558
-▁oceania	-3559
-▁october	-3560
-▁ontario	-3561
-▁oómêsta	-3562
-▁oóxésta	-3563
-▁pacific	-3564
-▁panâhke	-3565
-▁pradesh	-3566
-▁péékáne	-3567
-▁quercus	-3568
-▁springs	-3569
-▁stories	-3570
-▁switzer	-3571
-▁tsétóno	-3572
-▁turkish	-3573
-▁vonesto	-3574
-▁vétaneo	-3575
-▁vóhpoma	-3576
-▁website	-3577
-▁xamaesé	-3578
-▁šéstótó	-3579
-abpopprof	-3580
-australia	-3581
-axenôhöne	-3582
-continent	-3583
-etanâháve	-3584
-evátsėséó	-3585
-hanéstove	-3586
-hetanetse	-3587
-hetóeotse	-3588
-htóhtôhéé	-3589
-ircassian	-3590
-keekóhkon	-3591
-kemenôtse	-3592
-konásésto	-3593
-kêstséahe	-3594
-kêstséašé	-3595
-maruspial	-3596
-nevóneške	-3597
-néhevotoo	-3598
-ométanéno	-3599
-ponéheono	-3600
-sevestâhe	-3601
-taneanéve	-3602
-tsémêstaa	-3603
-táxâhonoo	-3604
-venezuela	-3605
-vêstséavo	-3606
-xemenôtse	-3607
-xhaahketa	-3608
-xêheóvema	-3609
-xȧhtáhpeo	-3610
-éestsėstó	-3611
-éhemenéko	-3612
-énêhótame	-3613
-évetováto	-3614
-éškôseëse	-3615
-êhávêsévê	-3616
-ôheonôtse	-3617
-ôhtsévóse	-3618
-šenovotsé	-3619
-▁abkhazia	-3620
-▁americas	-3621
-▁anonévee	-3622
-▁antelope	-3623
-▁colorado	-3624
-▁concolor	-3625
-▁facebook	-3626
-▁giovanni	-3627
-▁guerrero	-3628
-▁hetanevö	-3629
-▁hoeseonó	-3630
-▁hoxomôho	-3631
-▁hóhkeehe	-3632
-▁hóxovôho	-3633
-▁internet	-3634
-▁kuyavian	-3635
-▁locative	-3636
-▁minomôxe	-3637
-▁mokesdxe	-3638
-▁morrison	-3639
-▁mustangs	-3640
-▁méstahke	-3641
-▁móxêšéne	-3642
-▁môxemate	-3643
-▁november	-3644
-▁nêševâhá	-3645
-▁obituary	-3646
-▁observer	-3647
-▁portugal	-3648
-▁pónôhéne	-3649
-▁rockstar	-3650
-▁santiago	-3651
-▁scotland	-3652
-▁slovakia	-3653
-▁slovenia	-3654
-▁southern	-3655
-▁territor	-3656
-▁trillion	-3657
-▁variants	-3658
-▁vekepete	-3659
-▁vohkoohe	-3660
-▁véenôtse	-3661
-▁vóhkóóhe	-3662
-▁xaóemata	-3663
-▁óoetaneo	-3664
-▁ôhkemôxe	-3665
-centration	-3666
-emonêškeho	-3667
-evêšéstótó	-3668
-hahtsékósa	-3669
-haliaeetus	-3670
-hesenėstse	-3671
-héstanėheo	-3672
-hóvahtôtse	-3673
-hóxâhtsená	-3674
-kaehevôtse	-3675
-kóhkonôheo	-3676
-mâhóomoëha	-3677
-odocoileus	-3678
-pomeranian	-3679
-póevónaneo	-3680
-searchtext	-3681
-searchtype	-3682
-véhonevoem	-3683
-xaénéhetse	-3684
-éhestatseo	-3685
-êséestôtse	-3686
-ôhkêhenove	-3687
-ôhtséheone	-3688
-ėhoéseotse	-3689
-▁adventure	-3690
-▁alnifolia	-3691
-▁americana	-3692
-▁anastacia	-3693
-▁antartica	-3694
-▁artemisia	-3695
-▁arunachal	-3696
-▁azusagawa	-3697
-▁aéstomevé	-3698
-▁blackfire	-3699
-▁cheyennes	-3700
-▁christian	-3701
-▁continent	-3702
-▁deltoides	-3703
-▁hestoxeho	-3704
-▁honóvóhko	-3705
-▁honóxeäse	-3706
-▁hotóhkéso	-3707
-▁indonesia	-3708
-▁maritimus	-3709
-▁melbourne	-3710
-▁mountains	-3711
-▁netsêhese	-3712
-▁nêškovávo	-3713
-▁saccharum	-3714
-▁stockholm	-3715
-▁sóhkomevó	-3716
-▁tséhestoo	-3717
-▁venezuela	-3718
-▁vóhpâhtse	-3719
-▁vóóhéhéve	-3720
-amelanchier	-3721
-demographic	-3722
-honáevosean	-3723
-hotóhkêsono	-3724
-hótôxámâhéó	-3725
-konemâhoeve	-3726
-kėhanáhetse	-3727
-menépénôhéó	-3728
-netherlands	-3729
-otsenáhkohe	-3730
-recensement	-3731
-weoworldgdp	-3732
-êstámenôtse	-3733
-êxovahtôtse	-3734
-ôhkemôxeove	-3735
-ôhtávêhohpe	-3736
-▁americanus	-3737
-▁chrysaetos	-3738
-▁democratic	-3739
-▁geographic	-3740
-▁hestáhpánó	-3741
-▁hohkeeheso	-3742
-▁horribilis	-3743
-▁liberalism	-3744
-▁macrocarpa	-3745
-▁manȧhénoan	-3746
-▁matšėškome	-3747
-▁navéhoname	-3748
-▁otaesétane	-3749
-▁póevónaëno	-3750
-▁scopulorum	-3751
-▁sylvaticus	-3752
-▁tsesaatonê	-3753
-▁virginiana	-3754
-▁vétšêškévâ	-3755
-▁éškôsénéhe	-3756
-▁êstonemâne	-3757
-eétâhestôtse	-3758
-heóvêstséavo	-3759
-hmȧhoestȯtse	-3760
-hohtóvamâhëö	-3761
-héstánóvaéve	-3762
-kaehevotôtse	-3763
-lepidopterae	-3764
-manâhestôtse	-3765
-mâhomóhtâheo	-3766
-xâhtóhonôtse	-3767
-xêhevovetäso	-3768
-éhnėseehóhtá	-3769
-êstónestôtse	-3770
-êstóonémâhéó	-3771
-ôhtáenáhkohe	-3772
-▁afghanistan	-3773
-▁brazzaville	-3774
-▁heséeotséve	-3775
-▁hevavâhkema	-3776
-▁hohpâhtsená	-3777
-▁hoóneanatóe	-3778
-▁háhnomápano	-3779
-▁héstánóvamá	-3780
-▁hótôxámâhéó	-3781
-▁kémeonáhevo	-3782
-▁mississippi	-3783
-▁otaesétaneo	-3784
-▁reservation	-3785
-▁sanápevóoma	-3786
-▁switzerland	-3787
-▁sásêškóvéta	-3788
-▁tremuloides	-3789
-▁virginianus	-3790
-▁vétapâhaetó	-3791
-hkoestséatane	-3792
-keekóhkonôhëö	-3793
-néhevotoomáhe	-3794
-▁colonisation	-3795
-▁demographics	-3796
-▁hestoháatama	-3797
-▁hollowbreast	-3798
-▁madagasikara	-3799
-▁manȧhestȯtse	-3800
-▁môséškanetsé	-3801
-▁nemâhmoteone	-3802
-▁oévemanâhéno	-3803
-▁vóhpenáhkohe	-3804
-▁ónonevonêške	-3805
-hémâhaemenôtse	-3806
-hóxâhtsenáesëö	-3807
-nétâhévatamáno	-3808
-senêstsestôtse	-3809
-vâhonónestôtse	-3810
-xemenékȧhámáxe	-3811
-ónonevonêškeho	-3812
-▁hévoetanâháve	-3813
-▁leucocephalus	-3814
-▁pennsylvanica	-3815
-▁pâhoešestôtse	-3816
-▁véhpotséhohpe	-3817
-kohomoxhaahketa	-3818
-méšenonetsénóne	-3819
-sevestâhemôsane	-3820
-staahtsémenôtse	-3821
-vemóhtâhestôtse	-3822
-véhonevoemėstse	-3823
-êhávêsévêheséeo	-3824
-▁kóhkonôheonané	-3825
-▁náhkôhematôtse	-3826
-▁vóhpevonêškeho	-3827
-▁vóhpoométanéno	-3828
-tséohketoetanóto	-3829
-éestsėstóseoneve	-3830
-ėstovátamevéotse	-3831
-▁dictionarychief	-3832
-▁kibiôhkemôxeove	-3833
-▁vétšêškévâhonoo	-3834
-''	-3835
-+-	-3836
-dd	-3837
-ez	-3838
-ox	-3839
-pb	-3840
-rl	-3841
-së	-3842
-uń	-3843
-ês	-3844
-øt	-3845
-рл	-3846
-ын	-3847
-ાર	-3848
-▁u	-3849
-ada	-3850
-adi	-3851
-ahk	-3852
-aku	-3853
-ali	-3854
-amê	-3855
-anu	-3856
-ark	-3857
-arp	-3858
-ask	-3859
-bar	-3860
-bon	-3861
-cas	-3862
-cle	-3863
-cor	-3864
-cow	-3865
-ddh	-3866
-der	-3867
-dra	-3868
-emp	-3869
-ere	-3870
-ery	-3871
-esú	-3872
-etó	-3873
-evó	-3874
-exe	-3875
-gic	-3876
-gri	-3877
-gus	-3878
-haf	-3879
-heh	-3880
-hit	-3881
-huá	-3882
-ici	-3883
-ige	-3884
-ilc	-3885
-ins	-3886
-inu	-3887
-iso	-3888
-itê	-3889
-juk	-3890
-kaa	-3891
-kay	-3892
-kro	-3893
-kye	-3894
-las	-3895
-leu	-3896
-lev	-3897
-lez	-3898
-mel	-3899
-▁ā	-3900
-▁ė	-3901
-een	-3902
-end	-3903
-enm	-3904
-enė	-3905
-ikl	-3906
-met	-3907
-moa	-3908
-moe	-3909
-mxe	-3910
-mée	-3911
-mén	-3912
-nań	-3913
-ned	-3914
-nic	-3915
-nne	-3916
-nte	-3917
-nym	-3918
-nés	-3919
-old	-3920
-ori	-3921
-ous	-3922
-pha	-3923
-php	-3924
-pia	-3925
-raq	-3926
-reb	-3927
-ref	-3928
-rit	-3929
-roj	-3930
-sal	-3931
-sar	-3932
-sed	-3933
-stȧ	-3934
-suo	-3935
-sáa	-3936
-tie	-3937
-tin	-3938
-tóo	-3939
-ual	-3940
-uic	-3941
-upi	-3942
-urc	-3943
-urn	-3944
-urr	-3945
-ury	-3946
-ush	-3947
-vañ	-3948
-vul	-3949
-vát	-3950
-váá	-3951
-zxo	-3952
-zán	-3953
-éan	-3954
-éta	-3955
-êka	-3956
-íso	-3957
-óna	-3958
-ónė	-3959
-ôhá	-3960
-úra	-3961
-āhp	-3962
-čhé	-3963
-ėsé	-3964
-łop	-3965
-šeo	-3966
-škê	-3967
-ȯho	-3968
-рле	-3969
-әын	-3970
-▁ah	-3971
-▁ai	-3972
-▁by	-3973
-▁fu	-3974
-▁ge	-3975
-▁gò	-3976
-▁kp	-3977
-▁ká	-3978
-▁ld	-3979
-▁pm	-3980
-▁sh	-3981
-▁sy	-3982
-▁só	-3983
-▁we	-3984
-▁wh	-3985
-▁wp	-3986
-▁wu	-3987
-▁zu	-3988
-▁én	-3989
-▁óo	-3990
-▁óx	-3991
-▁óé	-3992
-▁āb	-3993
-''),	-3994
-acad	-3995
-acas	-3996
-adus	-3997
-ahua	-3998
-anel	-3999
-.,	-4000
-?)	-4001
-ms	-4002
-sm	-4003
-vë	-4004
-~:	-4005
-ûn	-4006
-ść	-4007
-▁&	-4008
-▁|	-4009
-(?)	-4010
-:~:	-4011
-ams	-4012
-any	-4013
-dûn	-4014
-hho	-4015
-pan	-4016
-ten	-4017
-▁pg	-4018
-#:~:	-4019
-acty	-4020
-amri	-4021
-aner	-4022
-ango	-4023
-anis	-4024
-anka	-4025
-arta	-4026
-arus	-4027
-ashe	-4028
-atic	-4029
-atsê	-4030
-atun	-4031
-atus	-4032
-axac	-4033
-blik	-4034
-bloo	-4035
-bosa	-4036
-cali	-4037
-cepc	-4038
-chlo	-4039
-deho	-4040
-dera	-4041
-each	-4042
-edar	-4043
-eehe	-4044
-enao	-4045
-ense	-4046
-enus	-4047
-eome	-4048
-erto	-4049
-erty	-4050
-esús	-4051
-evee	-4052
-eéxo	-4053
-eóko	-4054
-fact	-4055
-fare	-4056
-furn	-4057
-gari	-4058
-gosz	-4059
-hamo	-4060
-hehó	-4061
-hesé	-4062
-hevá	-4063
-hkom	-4064
-hoac	-4065
-hona	-4066
-hosa	-4067
-hpar	-4068
-htol	-4069
-htsê	-4070
-htše	-4071
-huca	-4072
-ianj	-4073
-ilab	-4074
-ilva	-4075
-inac	-4076
-ingf	-4077
-iris	-4078
-juku	-4079
-kese	-4080
-kówi	-4081
-kôhe	-4082
-lest	-4083
-lezi	-4084
-lter	-4085
-lyri	-4086
-macl	-4087
-mbri	-4088
-momé	-4089
-ménó	-4090
-mévo	-4091
-móxê	-4092
-mȧhé	-4093
-nané	-4094
-need	-4095
-nenó	-4096
-nerl	-4097
-nnen	-4098
-nocl	-4099
--%	-4100
-],	-4101
-np	-4102
-sq	-4103
-yg	-4104
-ûd	-4105
-би	-4106
-▁<	-4107
-jud	-4108
-lat	-4109
-mnp	-4110
-pts	-4111
-ros	-4112
-vci	-4113
-öst	-4114
-ėšé	-4115
-рби	-4116
-▁nu	-4117
-amil	-4118
-anth	-4119
-flat	-4120
-ituo	-4121
-noha	-4122
-nonó	-4123
-nous	-4124
-nové	-4125
-ność	-4126
-náno	-4127
-nóin	-4128
-nėšé	-4129
-oero	-4130
-olan	-4131
-oliy	-4132
-omet	-4133
-omul	-4134
-onia	-4135
-oono	-4136
-oriz	-4137
-orra	-4138
-oruń	-4139
-oseo	-4140
-otsë	-4141
-ovéo	-4142
-pane	-4143
-pend	-4144
-ping	-4145
-prus	-4146
-qurc	-4147
-rage	-4148
-rald	-4149
-ranc	-4150
-ries	-4151
-rige	-4152
-roes	-4153
-saba	-4154
-saka	-4155
-sehó	-4156
-sevó	-4157
-shal	-4158
-silc	-4159
-sing	-4160
-snia	-4161
-sova	-4162
-spot	-4163
-stas	-4164
-stnó	-4165
-stro	-4166
-séno	-4167
-sóve	-4168
-sėha	-4169
-sėst	-4170
-taan	-4171
-tama	-4172
-tdûn	-4173
-thro	-4174
-tiev	-4175
-treb	-4176
-tual	-4177
-uits	-4178
-ulti	-4179
-umna	-4180
-unes	-4181
-upia	-4182
-urre	-4183
-vañe	-4184
-veol	-4185
-vida	-4186
-vlev	-4187
-vomo	-4188
-vêšé	-4189
-vóhe	-4190
-vȯhó	-4191
-wing	-4192
-xaóe	-4193
-xema	-4194
-xevé	-4195
-xêhë	-4196
-ycle	-4197
-yghe	-4198
-ytur	-4199
-:;	-4200
-nā	-4201
-xc	-4202
-yā	-4203
-ны	-4204
-bbc	-4205
-rey	-4206
-thy	-4207
-win	-4208
-yāl	-4209
-épe	-4210
-сны	-4211
-▁ce	-4212
-laxc	-4213
-leva	-4214
-urdo	-4215
-znań	-4216
-áyāl	-4217
-èmxe	-4218
-éheo	-4219
-éhéó	-4220
-éméá	-4221
-énéó	-4222
-éotá	-4223
-épea	-4224
-êste	-4225
-óneo	-4226
-ôhoo	-4227
-ôtsé	-4228
-ēwin	-4229
-šeho	-4230
-šenó	-4231
-ševe	-4232
-ȯheo	-4233
-срби	-4234
-▁(),	-4235
-▁agu	-4236
-▁ati	-4237
-▁bei	-4238
-▁bes	-4239
-▁bio	-4240
-▁cor	-4241
-▁die	-4242
-▁dre	-4243
-▁dur	-4244
-▁dva	-4245
-▁ele	-4246
-▁ery	-4247
-▁evê	-4248
-▁fac	-4249
-▁fit	-4250
-▁foo	-4251
-▁fro	-4252
-▁gal	-4253
-▁gil	-4254
-▁gòn	-4255
-▁hen	-4256
-▁her	-4257
-▁hte	-4258
-▁inf	-4259
-▁ipi	-4260
-▁iyy	-4261
-▁kin	-4262
-▁kno	-4263
-▁kor	-4264
-▁lak	-4265
-▁las	-4266
-▁lat	-4267
-▁lha	-4268
-▁lim	-4269
-▁lin	-4270
-▁lun	-4271
-▁lûd	-4272
-▁maa	-4273
-▁mad	-4274
-▁mai	-4275
-▁med	-4276
-▁meo	-4277
-▁mid	-4278
-▁mit	-4279
-▁moc	-4280
-▁mod	-4281
-▁mus	-4282
-▁nit	-4283
-▁nøt	-4284
-▁occ	-4285
-▁off	-4286
-▁ohe	-4287
-▁pab	-4288
-▁pal	-4289
-▁pil	-4290
-▁pin	-4291
-▁pug	-4292
-▁rat	-4293
-▁rok	-4294
-▁sep	-4295
-▁smo	-4296
-▁soc	-4297
-▁sol	-4298
-▁sve	-4299
-?]	-4300
-hd	-4301
-je	-4302
-sn	-4303
-vs	-4304
-xô	-4305
-yk	-4306
-óó	-4307
-ʃx	-4308
-си	-4309
-әа	-4310
-▁{	-4311
-?]"	-4312
-atl	-4313
-bin	-4314
-gaz	-4315
-glo	-4316
-por	-4317
-uar	-4318
-vaa	-4319
-vas	-4320
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-óseono	-5058
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-ȯhohtȧ	-5067
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-iâ	-5100
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-▁hesta	-5152
-▁heóvô	-5153
-▁heško	-5154
-▁hohtó	-5155
-▁hostu	-5156
-▁hoéve	-5157
-▁háahk	-5158
-▁hétsê	-5159
-▁influ	-5160
-▁inupi	-5161
-▁jonat	-5162
-▁kaede	-5163
-▁kayah	-5164
-▁kayin	-5165
-▁koala	-5166
-▁kyoto	-5167
-▁label	-5168
-▁lassa	-5169
-▁layal	-5170
-▁lhasa	-5171
-▁limpo	-5172
-▁litef	-5173
-▁maaie	-5174
-▁maici	-5175
-▁mande	-5176
-▁marco	-5177
-▁matse	-5178
-▁małop	-5179
-▁mochk	-5180
-▁model	-5181
-▁montt	-5182
-▁mouse	-5183
-▁moéšk	-5184
-▁musco	-5185
-▁máéve	-5186
-▁mâhtä	-5187
-▁mâhöö	-5188
-▁méóne	-5189
-▁mȧhve	-5190
-▁naura	-5191
-▁negle	-5192
-▁nehno	-5193
-▁nexhe	-5194
-▁nuevo	-5195
-▁occid	-5196
-▁omale	-5197
-▁ooiso	-5198
-▁osaka	-5199
-sà	-5200
-êt	-5201
-ín	-5202
-%).	-5203
-aan	-5204
-aos	-5205
-bul	-5206
-gee	-5207
-lor	-5208
-oho	-5209
-sài	-5210
-šan	-5211
-▁('	-5212
-▁my	-5213
-▁ni	-5214
-alis	-5215
-bell	-5216
-chua	-5217
-file	-5218
-heää	-5219
-hóma	-5220
-loua	-5221
-naco	-5222
-nate	-5223
-pike	-5224
-ramo	-5225
-ssed	-5226
-stor	-5227
-êtet	-5228
-íngu	-5229
-alpan	-5230
-brush	-5231
-categ	-5232
-edish	-5233
-hestá	-5234
-hkoné	-5235
-lorid	-5236
-pping	-5237
-ramos	-5238
-soonê	-5239
-taner	-5240
-those	-5241
-ulmus	-5242
-énove	-5243
-ínguó	-5244
-ėhesó	-5245
-▁guam	-5246
-▁joli	-5247
-▁laos	-5248
-▁mysp	-5249
-▁niue	-5250
-▁phar	-5251
-▁pick	-5252
-▁reag	-5253
-▁road	-5254
-▁yoko	-5255
-castor	-5256
-emenoo	-5257
-ioicus	-5258
-lalpan	-5259
-soonêó	-5260
-teloua	-5261
-tôxáha	-5262
-êtetet	-5263
-▁motsé	-5264
-▁outle	-5265
-▁pablo	-5266
-▁pharō	-5267
-▁phono	-5268
-▁picas	-5269
-▁pilak	-5270
-▁ponte	-5271
-▁prefi	-5272
-▁ratib	-5273
-▁salva	-5274
-▁samoa	-5275
-▁septe	-5276
-▁shenz	-5277
-▁silva	-5278
-▁sites	-5279
-▁sleep	-5280
-▁solov	-5281
-▁sotxi	-5282
-▁space	-5283
-▁spell	-5284
-▁spice	-5285
-▁stone	-5286
-▁story	-5287
-▁susan	-5288
-▁sáhea	-5289
-▁tahpó	-5290
-▁taxus	-5291
-▁terry	-5292
-▁those	-5293
-▁timor	-5294
-▁tlaxc	-5295
-▁tongu	-5296
-▁tooxo	-5297
-▁toruń	-5298
-▁track	-5299
-▁ê	-5300
-ema	-5301
-imi	-5302
-nei	-5303
-oht	-5304
-sty	-5305
-vee	-5306
-ves	-5307
-éhá	-5308
-îso	-5309
-ારા	-5310
-▁ju	-5311
-▁tu	-5312
-▁xi	-5313
-acia	-5314
-chor	-5315
-clar	-5316
-hter	-5317
-iles	-5318
-ooma	-5319
-oroc	-5320
-tann	-5321
-vôhk	-5322
-xová	-5323
-ėsée	-5324
-▁...	-5325
-▁iki	-5326
-▁juu	-5327
-▁kra	-5328
-▁pol	-5329
-dalgo	-5330
-héaxa	-5331
-ilili	-5332
-liîso	-5333
-netse	-5334
-tovaa	-5335
-waves	-5336
-▁simi	-5337
-▁tube	-5338
-ampede	-5339
-chorde	-5340
-ililiw	-5341
-kemenó	-5342
-orocco	-5343
-▁broom	-5344
-▁cairo	-5345
-▁hóema	-5346
-▁krakó	-5347
-▁polit	-5348
-▁tonga	-5349
-▁tosty	-5350
-▁tribe	-5351
-▁trpin	-5352
-▁tsémo	-5353
-▁tséné	-5354
-▁tsévó	-5355
-▁turqu	-5356
-▁union	-5357
-▁vulga	-5358
-▁véhpe	-5359
-▁vétan	-5360
-▁vóhko	-5361
-▁water	-5362
-▁where	-5363
-▁which	-5364
-▁woman	-5365
-▁xaóen	-5366
-▁xhosa	-5367
-▁ésevá	-5368
-▁évevé	-5369
-▁éȯtse	-5370
-▁óesto	-5371
-achille	-5372
-algonqu	-5373
-allisut	-5374
-ametane	-5375
-amêškév	-5376
-aneeske	-5377
-anerpes	-5378
-angzhou	-5379
-anzania	-5380
-apongas	-5381
-appaloo	-5382
-artheid	-5383
-ashiise	-5384
-atemala	-5385
-athroom	-5386
-ationun	-5387
-britann	-5388
-cepcion	-5389
-cobatus	-5390
-creased	-5391
-crossed	-5392
-derahne	-5393
-enstein	-5394
-entalis	-5395
-enėševé	-5396
-erreich	-5397
-esencin	-5398
-eséheho	-5399
-ona	-5400
-▁as	-5401
-▁pp	-5402
-cope	-5403
-corn	-5404
-ding	-5405
-gasy	-5406
-hpat	-5407
-héhe	-5408
-nono	-5409
-teor	-5410
-tovo	-5411
-wana	-5412
-êsta	-5413
-▁age	-5414
-▁bnt	-5415
-▁nau	-5416
-▁reg	-5417
-▁way	-5418
-hpató	-5419
-yanen	-5420
-êškóo	-5421
-ôxese	-5422
-▁asso	-5423
-▁bona	-5424
-▁dard	-5425
-▁hoho	-5426
-▁kere	-5427
-▁saud	-5428
-▁sehe	-5429
-▁unie	-5430
-cornus	-5431
-dorata	-5432
-haesto	-5433
-mátane	-5434
-nonomö	-5435
-strong	-5436
-tsétsė	-5437
-▁hohpe	-5438
-▁lance	-5439
-▁saudi	-5440
-▁tȯtse	-5441
-colding	-5442
-enminbi	-5443
-eugenio	-5444
-evêhene	-5445
-evėhene	-5446
-eéhoham	-5447
-felidae	-5448
-finland	-5449
-fontein	-5450
-furnitu	-5451
-gallina	-5452
-gazeted	-5453
-grindel	-5454
-gymnocl	-5455
-hafrica	-5456
-hahátóo	-5457
-hauling	-5458
-hevéesé	-5459
-heónese	-5460
-heóneve	-5461
-hnéméne	-5462
-hpôtsea	-5463
-htšeehe	-5464
-humibol	-5465
-igenous	-5466
-imarães	-5467
-inburgh	-5468
-ingston	-5469
-inutive	-5470
-iñupiaq	-5471
-koemenó	-5472
-koierue	-5473
-kóhtâxé	-5474
-kôheaxa	-5475
-kôhtávo	-5476
-lephant	-5477
-lescope	-5478
-lestine	-5479
-livesto	-5480
-lloween	-5481
-loridum	-5482
-maclura	-5483
-manėstó	-5484
-matrica	-5485
-mbridge	-5486
-michael	-5487
-mnplosi	-5488
-molinos	-5489
-morning	-5490
-mougins	-5491
-mâhaahe	-5492
-mâhpeve	-5493
-móhtôxe	-5494
-mónêške	-5495
-nciples	-5496
-nenééve	-5497
-nóincor	-5498
-oestose	-5499
-isl	-5500
-kok	-5501
-pas	-5502
-éot	-5503
-▁ša	-5504
-ciet	-5505
-gisl	-5506
-hche	-5507
-info	-5508
-mona	-5509
-orus	-5510
-vâhé	-5511
-▁nén	-5512
-▁pén	-5513
-▁vén	-5514
-atory	-5515
-ciety	-5516
-honee	-5517
-iapas	-5518
-omene	-5519
-thành	-5520
-tóhne	-5521
-ussel	-5522
-váhtó	-5523
-ónôxe	-5524
-▁futa	-5525
-▁hest	-5526
-▁lebr	-5527
-▁násá	-5528
-▁team	-5529
-atoire	-5530
-franca	-5531
-hahénȯ	-5532
-hchena	-5533
-legisl	-5534
-otátse	-5535
-taeváh	-5536
-ussell	-5537
-verage	-5538
-▁brein	-5539
-▁enomo	-5540
-▁geary	-5541
-▁genus	-5542
-▁néhmé	-5543
-anelles	-5544
-astaneo	-5545
-enovâhé	-5546
-hessian	-5547
-heónôxe	-5548
-krguide	-5549
-lesnake	-5550
-ohtȯtse	-5551
-olition	-5552
-omenehe	-5553
-ometanó	-5554
-omifera	-5555
-ondatra	-5556
-onifera	-5557
-onávêhá	-5558
-oéstoto	-5559
-pbworks	-5560
-pishapa	-5561
-pituoph	-5562
-porated	-5563
-potátse	-5564
-procyon	-5565
-póhpoév	-5566
-quechua	-5567
-ralasia	-5568
-ronesia	-5569
-russian	-5570
-science	-5571
-seovana	-5572
-sertdûn	-5573
-setsëhe	-5574
-siangic	-5575
-siberia	-5576
-silcisi	-5577
-sjonger	-5578
-solomon	-5579
-spotted	-5580
-stanbul	-5581
-stanomo	-5582
-statist	-5583
-storywi	-5584
-strodon	-5585
-sturnel	-5586
-stánové	-5587
-swedish	-5588
-sėstano	-5589
-tahhest	-5590
-tavemée	-5591
-thelezi	-5592
-ulyadej	-5593
-umnaris	-5594
-ungbloo	-5595
-uritiba	-5596
-various	-5597
-veptkʔf	-5598
-versial	-5599
-flo	-5600
-hai	-5601
-hká	-5602
-mbi	-5603
-ren	-5604
-try	-5605
-ôhó	-5606
-anne	-5607
-anëö	-5608
-ghai	-5609
-haso	-5610
-ilus	-5611
-kohe	-5612
-kose	-5613
-logs	-5614
-lost	-5615
-momó	-5616
-nôxe	-5617
-olum	-5618
-tané	-5619
-text	-5620
-▁rot	-5621
-ckles	-5622
-colub	-5623
-esity	-5624
-floyd	-5625
-hestó	-5626
-háhne	-5627
-mêste	-5628
-oetan	-5629
-talis	-5630
-vonan	-5631
-xôhéó	-5632
-zambi	-5633
-▁inuk	-5634
-▁race	-5635
-▁repu	-5636
-ationc	-5637
-haahpo	-5638
-hestoo	-5639
-hetané	-5640
-hkáhne	-5641
-ubilus	-5642
-▁inukt	-5643
-▁lunch	-5644
-▁mâheo	-5645
-▁mâhtó	-5646
-▁nėsto	-5647
-▁pilai	-5648
-▁évôhó	-5649
-coluber	-5650
-erétaro	-5651
-hanghai	-5652
-heévâhe	-5653
-momóhto	-5654
-nuckles	-5655
-olumune	-5656
-omeanne	-5657
-postela	-5658
-váhtóva	-5659
-vâhonoo	-5660
-vónáévó	-5661
-xemenês	-5662
-xėstahe	-5663
-yanenko	-5664
-ytorial	-5665
-éhemóxê	-5666
-éhenôxe	-5667
-énâhené	-5668
-énâhéno	-5669
-énėhesó	-5670
-évenome	-5671
-êsêhevá	-5672
-êšenévé	-5673
-êškóovo	-5674
-óehóhtá	-5675
-óneohëö	-5676
-ósaného	-5677
-ósévóse	-5678
-óvevone	-5679
-óvâhtse	-5680
-óvâhtsê	-5681
-ôhtameö	-5682
-ôhtsévé	-5683
-ševeame	-5684
-škêsono	-5685
-▁abboud	-5686
-▁acacia	-5687
-▁adyghe	-5688
-▁amóová	-5689
-▁angela	-5690
-▁anthox	-5691
-▁arabia	-5692
-▁associ	-5693
-▁baache	-5694
-▁banlir	-5695
-▁barack	-5696
-▁basket	-5697
-▁belfry	-5698
-▁bessey	-5699
-)–	-5700
-méo	-5701
-oot	-5702
-uca	-5703
-urs	-5704
-apps	-5705
-aroo	-5706
-atán	-5707
-inaw	-5708
-ioma	-5709
-otor	-5710
-pipe	-5711
-pxco	-5712
-sina	-5713
-▁doi	-5714
-▁gdp	-5715
-▁jan	-5716
-▁óko	-5717
-hanëö	-5718
-hicle	-5719
-nauru	-5720
-seono	-5721
-▁chin	-5722
-▁hovë	-5723
-▁háne	-5724
-▁kokê	-5725
-▁luis	-5726
-▁shin	-5727
-▁tuva	-5728
-athumb	-5729
-hanéhe	-5730
-onéstâ	-5731
-pxcoat	-5732
-rities	-5733
-ucatán	-5734
-viated	-5735
-▁alpha	-5736
-▁amemo	-5737
-▁ameév	-5738
-▁delic	-5739
-▁lotor	-5740
-▁pinto	-5741
-▁thanh	-5742
-angaroo	-5743
-haanehe	-5744
-monôtse	-5745
-nauruan	-5746
-oeseono	-5747
-rikaans	-5748
-ursidae	-5749
-▁atsina	-5750
-▁bosnia	-5751
-▁bridle	-5752
-▁buenos	-5753
-▁canvas	-5754
-▁carbon	-5755
-▁castle	-5756
-▁cheese	-5757
-▁chiefs	-5758
-▁coastl	-5759
-▁cocoon	-5760
-▁colima	-5761
-▁commit	-5762
-▁corbin	-5763
-▁cowboy	-5764
-▁crazzz	-5765
-▁curric	-5766
-▁damned	-5767
-▁declar	-5768
-▁delawa	-5769
-▁doings	-5770
-▁duarte	-5771
-▁ehehse	-5772
-▁facing	-5773
-▁family	-5774
-▁ferdin	-5775
-▁fimbri	-5776
-▁firing	-5777
-▁forest	-5778
-▁fruits	-5779
-▁fujita	-5780
-▁futaba	-5781
-▁gakuen	-5782
-▁glauca	-5783
-▁halter	-5784
-▁hankye	-5785
-▁hardin	-5786
-▁hehpao	-5787
-▁henenó	-5788
-▁henson	-5789
-▁herald	-5790
-▁heroes	-5791
-▁highto	-5792
-▁hoitan	-5793
-▁hotama	-5794
-▁hotóva	-5795
-▁htoóxe	-5796
-▁hubble	-5797
-▁héséne	-5798
-▁hévȯhó	-5799
-ôn	-5800
-ben	-5801
-bit	-5802
-nba	-5803
-obl	-5804
-ôxa	-5805
-boer	-5806
-bran	-5807
-bull	-5808
-case	-5809
-duct	-5810
-eric	-5811
-innu	-5812
-ketw	-5813
-laan	-5814
-mbus	-5815
-néxa	-5816
-pare	-5817
-xama	-5818
-ônio	-5819
-▁apa	-5820
-▁chí	-5821
-▁cla	-5822
-▁did	-5823
-▁gay	-5824
-▁guy	-5825
-▁jay	-5826
-haéhe	-5827
-ledon	-5828
-mento	-5829
-mâhtä	-5830
-peace	-5831
-piapó	-5832
-▁apax	-5833
-▁biza	-5834
-▁http	-5835
-▁tony	-5836
-bitter	-5837
-bombus	-5838
-branta	-5839
-fossil	-5840
-gether	-5841
-hhesto	-5842
-htsévó	-5843
-héhese	-5844
-manëhe	-5845
-shapes	-5846
-êstome	-5847
-▁claim	-5848
-▁lines	-5849
-anarivo	-5850
-ketwire	-5851
-ledonia	-5852
-mâhóoné	-5853
-méstaeo	-5854
-ramento	-5855
-xamaevo	-5856
-ȯhtsévó	-5857
-▁apaxco	-5858
-▁bizaad	-5859
-▁guyana	-5860
-▁ililiw	-5861
-▁ingoma	-5862
-▁ingrid	-5863
-▁jalten	-5864
-▁jersey	-5865
-▁joliet	-5866
-▁juuhin	-5867
-▁kereto	-5868
-▁kokôhé	-5869
-▁kraków	-5870
-▁kwanza	-5871
-▁kánome	-5872
-▁kâsova	-5873
-▁kösaan	-5874
-▁lakers	-5875
-▁latvia	-5876
-▁lebron	-5877
-▁liecht	-5878
-▁luanda	-5879
-▁madrid	-5880
-▁malawi	-5881
-▁marcos	-5882
-▁melano	-5883
-▁meteor	-5884
-▁middle	-5885
-▁mákohe	-5886
-▁mâhtao	-5887
-▁mínguó	-5888
-▁môxéhe	-5889
-▁nanose	-5890
-▁naoero	-5891
-▁nasono	-5892
-▁nation	-5893
-▁nature	-5894
-▁nauseo	-5895
-▁nitens	-5896
-▁northw	-5897
-▁notame	-5898
-▁notseo	-5899
-dem	-5900
-pos	-5901
-atam	-5902
-atro	-5903
-beki	-5904
-hnéé	-5905
-mosa	-5906
-ntan	-5907
-néso	-5908
-pole	-5909
-pono	-5910
-quia	-5911
-rock	-5912
-root	-5913
-stif	-5914
-tȧhé	-5915
-ória	-5916
-▁bet	-5917
-▁hva	-5918
-amena	-5919
-atown	-5920
-blogs	-5921
-demon	-5922
-htséo	-5923
-ianen	-5924
-latam	-5925
-onéxa	-5926
-quiac	-5927
-quick	-5928
-there	-5929
-tovka	-5930
-types	-5931
-▁armi	-5932
-▁guer	-5933
-▁hoem	-5934
-▁july	-5935
-▁leso	-5936
-▁mili	-5937
-▁nago	-5938
-▁nooi	-5939
-▁néso	-5940
-▁poun	-5941
-gustif	-5942
-hamaxe	-5943
-hkoeto	-5944
-lakota	-5945
-onáhne	-5946
-quickf	-5947
-tȧhéve	-5948
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-saneo	-6721
-yprus	-6722
-▁beli	-6723
-▁hide	-6724
-▁luxe	-6725
-▁mora	-6726
-▁rail	-6727
-▁suri	-6728
-▁volu	-6729
-aklike	-6730
-mbourg	-6731
-nassia	-6732
-nohtoa	-6733
-rctica	-6734
-tónovo	-6735
-voaxáa	-6736
-▁abies	-6737
-▁alber	-6738
-▁avañe	-6739
-▁bantu	-6740
-▁hóhtâ	-6741
-▁maria	-6742
-▁menea	-6743
-▁mille	-6744
-▁móstâ	-6745
-▁texas	-6746
-haeolog	-6747
-héhnove	-6748
-kôsáeve	-6749
-nāhuatl	-6750
-ocenter	-6751
-storyid	-6752
-óhtáéve	-6753
-ėstotse	-6754
-▁austin	-6755
-▁bascom	-6756
-▁belize	-6757
-▁cyprus	-6758
-▁debila	-6759
-▁forgan	-6760
-▁luther	-6761
-▁manito	-6762
-▁morais	-6763
-▁nodoka	-6764
-▁ohkeve	-6765
-▁volume	-6766
-aataemeo	-6767
-elephant	-6768
-êškónâšé	-6769
-▁alberta	-6770
-▁license	-6771
-▁limpopo	-6772
-▁looking	-6773
-▁ludność	-6774
-▁machine	-6775
-▁mandela	-6776
-▁masdeho	-6777
-▁mcmurdo	-6778
-▁meghpar	-6779
-▁mercury	-6780
-▁mexihco	-6781
-▁missing	-6782
-▁morocco	-6783
-▁motsêhé	-6784
-▁motâhko	-6785
-▁myanmar	-6786
-▁myspace	-6787
-▁mámáhta	-6788
-▁méonëva	-6789
-▁méstaéo	-6790
-▁méškéso	-6791
-▁môxéhéó	-6792
-▁naesóht	-6793
-▁nahkohe	-6794
-▁nanjing	-6795
-▁nannerl	-6796
-▁napanee	-6797
-▁nayarit	-6798
-▁nonétse	-6799
-how	-6800
-meu	-6801
-camp	-6802
-ilet	-6803
-iniw	-6804
-káse	-6805
-mekw	-6806
-ward	-6807
-▁add	-6808
-abril	-6809
-ahéne	-6810
-berry	-6811
-dress	-6812
-haber	-6813
-hahkö	-6814
-other	-6815
-▁cook	-6816
-▁kala	-6817
-▁koká	-6818
-▁león	-6819
-▁méná	-6820
-▁sung	-6821
-▁viva	-6822
-▁vung	-6823
-cheese	-6824
-hnóhkâ	-6825
-honóne	-6826
-ohvovó	-6827
-ôhease	-6828
-ôhvovó	-6829
-▁dress	-6830
-▁house	-6831
-▁johan	-6832
-▁netsė	-6833
-▁omeää	-6834
-▁urban	-6835
-▁vóóhe	-6836
-ference	-6837
-hohtóvá	-6838
-kâhonoo	-6839
-xaóeméá	-6840
-êstseää	-6841
-▁colors	-6842
-▁hhotse	-6843
-▁ininiw	-6844
-▁johnny	-6845
-▁manëhe	-6846
-▁onward	-6847
-▁sonora	-6848
-▁toilet	-6849
-▁táxemé	-6850
-▁étsêhe	-6851
-acomulco	-6852
-ameohtsé	-6853
-atistics	-6854
-néohvovó	-6855
-▁density	-6856
-▁hopping	-6857
-▁nubilus	-6858
-▁nâháóhe	-6859
-▁névááse	-6860
-▁nêhéóhe	-6861
-▁nēhilaw	-6862
-▁nēhinaw	-6863
-▁obesity	-6864
-▁odorata	-6865
-▁oeškëso	-6866
-▁omáǧažu	-6867
-▁ossetia	-6868
-▁outlets	-6869
-▁očhéthi	-6870
-▁pachuca	-6871
-▁paltoga	-6872
-▁pharōkh	-6873
-▁philoso	-6874
-▁picasso	-6875
-▁pirahán	-6876
-▁poesono	-6877
-▁praeger	-6878
-▁prepare	-6879
-▁product	-6880
-▁profile	-6881
-▁project	-6882
-▁republi	-6883
-▁resurre	-6884
-▁russell	-6885
-▁sagaing	-6886
-▁sapporo	-6887
-▁schwarz	-6888
-▁society	-6889
-▁solomon	-6890
-▁squarro	-6891
-▁steambo	-6892
-▁suicide	-6893
-▁sukhumi	-6894
-▁sumatra	-6895
-▁sverige	-6896
-▁séhpató	-6897
-▁tabasco	-6898
-▁tactics	-6899
-whc	-6900
-)"),	-6901
-alau	-6902
-arid	-6903
-ebla	-6904
-iêka	-6905
-jima	-6906
-ohta	-6907
-ulum	-6908
-óvéé	-6909
-▁are	-6910
-▁era	-6911
-eaéna	-6912
-eéxoo	-6913
-fruit	-6914
-heohk	-6915
-ivers	-6916
-korea	-6917
-kéeho	-6918
-mámaa	-6919
-pilai	-6920
-ridae	-6921
-salix	-6922
-éotóá	-6923
-▁area	-6924
-▁came	-6925
-▁east	-6926
-▁hoad	-6927
-▁kent	-6928
-▁nǐst	-6929
-▁path	-6930
-▁rain	-6931
-▁soto	-6932
-▁tequ	-6933
-asiaun	-6934
-estaha	-6935
-french	-6936
-horsey	-6937
-kemeno	-6938
-nanohe	-6939
-ohtama	-6940
-pirahã	-6941
-pónoma	-6942
-ringed	-6943
-sétove	-6944
-thicus	-6945
-éestsé	-6946
-óvééno	-6947
-▁berne	-6948
-▁dairy	-6949
-▁links	-6950
-▁muddy	-6951
-▁palau	-6952
-▁paris	-6953
-▁phili	-6954
-▁sétov	-6955
-▁voice	-6956
-chicago	-6957
-hahpenó	-6958
-heóvêsé	-6959
-koneame	-6960
-ometres	-6961
-rosebud	-6962
-šeméheo	-6963
-▁puebla	-6964
-▁rafael	-6965
-▁sahade	-6966
-▁slovak	-6967
-▁social	-6968
-▁valery	-6969
-hkooheme	-6970
-hoemâhöö	-6971
-hovávemá	-6972
-hämêstse	-6973
-thailand	-6974
-ôhkevánó	-6975
-▁fringed	-6976
-▁reivers	-6977
-▁taxidea	-6978
-▁thurlow	-6979
-▁tianjin	-6980
-▁timothy	-6981
-▁tooilak	-6982
-▁trojany	-6983
-▁trpinja	-6984
-▁trudell	-6985
-▁tsénéma	-6986
-▁tsépêhé	-6987
-▁tsévovó	-6988
-▁tsêheta	-6989
-▁táeotsé	-6990
-▁uruguay	-6991
-▁vananda	-6992
-▁vanuatu	-6993
-▁vatican	-6994
-▁vehicle	-6995
-▁vovóeóó	-6996
-▁véstahe	-6997
-▁walking	-6998
-▁warfare	-6999
-div	-7000
-ahéo	-7001
-esee	-7002
-hōng	-7003
-itch	-7004
-kstâ	-7005
-oveo	-7006
-sôhk	-7007
-▁liv	-7008
-▁séa	-7009
-▁vcu	-7010
-chool	-7011
-dalay	-7012
-hills	-7013
-james	-7014
-nonom	-7015
-north	-7016
-phers	-7017
-sétov	-7018
-êsémo	-7019
-▁bear	-7020
-▁fiji	-7021
-▁hóvo	-7022
-▁kind	-7023
-▁naah	-7024
-▁sino	-7025
-▁vôhe	-7026
-abkhaz	-7027
-acheva	-7028
-empire	-7029
-eöhtse	-7030
-malaya	-7031
-mbodia	-7032
-navajo	-7033
-némené	-7034
-vóaxaa	-7035
-éšeëva	-7036
-ôhkêhó	-7037
-▁htené	-7038
-▁mahla	-7039
-▁nėhpa	-7040
-▁peace	-7041
-▁riley	-7042
-▁tones	-7043
-▁zhōng	-7044
-charles	-7045
-chinese	-7046
-geocode	-7047
-hahkeso	-7048
-hövaéve	-7049
-itchish	-7050
-million	-7051
-xetôhoo	-7052
-▁birney	-7053
-▁formed	-7054
-▁hesénô	-7055
-▁homôse	-7056
-▁hévemé	-7057
-▁maahéo	-7058
-▁mitsuo	-7059
-▁mounir	-7060
-▁ronald	-7061
-▁school	-7062
-▁sásôhk	-7063
-htsenáno	-7064
-medicago	-7065
-▁háeohéo	-7066
-▁hótsêha	-7067
-▁mȧhtóht	-7068
-▁washita	-7069
-▁wielkop	-7070
-▁xaserho	-7071
-▁yucatán	-7072
-▁zealand	-7073
-▁âxeetoo	-7074
-▁éestseé	-7075
-▁éhaahpo	-7076
-▁énéméne	-7077
-▁évevéhé	-7078
-▁óestome	-7079
-▁ókôhkeo	-7080
-▁ónestnó	-7081
-▁óoetane	-7082
-▁ôhtséše	-7083
-▁łódźkie	-7084
-▁šakówiŋ	-7085
-aatahotse	-7086
-adistaani	-7087
-aehevôtse	-7088
-apartheid	-7089
-appaloosa	-7090
-aqhohamma	-7091
-arapongas	-7092
-argentina	-7093
-bcyahttps	-7094
-blackfire	-7095
-bouteloua	-7096
-buquerque	-7097
-calientes	-7098
-caribbean	-7099
-',	-7100
-▁dc	-7101
-area	-7102
-egon	-7103
-egus	-7104
-feed	-7105
-konö	-7106
-reau	-7107
-▁aye	-7108
-▁bip	-7109
-crata	-7110
-farro	-7111
-hairy	-7112
-quito	-7113
-table	-7114
-tokyo	-7115
-ónéto	-7116
-česká	-7117
-▁bitc	-7118
-▁book	-7119
-▁fair	-7120
-▁höva	-7121
-▁jump	-7122
-▁legg	-7123
-▁loco	-7124
-▁sega	-7125
-▁zibe	-7126
-batini	-7127
-oregon	-7128
-tavâho	-7129
-xénéhe	-7130
-▁about	-7131
-▁aymar	-7132
-▁camps	-7133
-▁dinar	-7134
-▁files	-7135
-▁shoko	-7136
-▁today	-7137
-▁tokyo	-7138
-▁years	-7139
-albania	-7140
-arizona	-7141
-farrokh	-7142
-▁bureau	-7143
-▁comune	-7144
-▁makaji	-7145
-ononôtse	-7146
-scolding	-7147
-slovakia	-7148
-énochief	-7149
-ôhévetoo	-7150
-▁advance	-7151
-▁kôsáeho	-7152
-▁kôsáevo	-7153
-▁sinaloa	-7154
-crataegus	-7155
-dalstrong	-7156
-difficult	-7157
-downriver	-7158
-echinacea	-7159
-eeséotsee	-7160
-enomóvóhe	-7161
-entalpost	-7162
-eseeséheo	-7163
-eseonôtse	-7164
-executive	-7165
-eétaheéve	-7166
-finimenti	-7167
-furniture	-7168
-gallinago	-7169
-grindelia	-7170
-haneheéve	-7171
-hanémâhéó	-7172
-haéheséeo	-7173
-hemövâtse	-7174
-henóvetse	-7175
-hestáotse	-7176
-hestâhéhe	-7177
-hestȧhese	-7178
-historywi	-7179
-hkoetohko	-7180
-hnaesohto	-7181
-hohtâhévê	-7182
-hotóhkeve	-7183
-htsēhȯtse	-7184
-háahéhame	-7185
-héamehnée	-7186
-hémenôtse	-7187
-héstȧhese	-7188
-hóxovôhoo	-7189
-ification	-7190
-indonesia	-7191
-ketanóéve	-7192
-komâhonoo	-7193
-konéneéše	-7194
-konôhtáve	-7195
-kóhtâxová	-7196
-kôhtávahe	-7197
-leporidae	-7198
-livestock	-7199
-lodgepole	-7200
-mónêškeho	-7201
-nomáhtseo	-7202
-nêhestôhá	-7203
-onóvevone	-7204
-parnassia	-7205
-phaseolus	-7206
-pituophis	-7207
-pomorskie	-7208
-póevónáne	-7209
-pėhévoneo	-7210
-religions	-7211
-sciuridae	-7212
-senetâhev	-7213
-stapleton	-7214
-stooestse	-7215
-sturnella	-7216
-swaziland	-7217
-sėstanove	-7218
-taheanéve	-7219
-templates	-7220
-tofagasta	-7221
-tonôseméo	-7222
-ungariket	-7223
-velarglot	-7224
-vésehóhtȧ	-7225
-vêstséavó	-7226
-vóóhéhéve	-7227
-wordpress	-7228
-âhestôtsé	-7229
-émôxêšéne	-7230
-éotsêstse	-7231
-êhnémâhéó	-7232
-êsevotoná	-7233
-êstaestse	-7234
-êstonâheo	-7235
-êstotôtse	-7236
-êšeeonáhe	-7237
-ësenotová	-7238
-óévaohtsé	-7239
-ôhkemahpe	-7240
-ôhkeénohe	-7241
-ôhtavetoo	-7242
-ôhtávetoo	-7243
-ôhtávoome	-7244
-ôhtávôheo	-7245
-ėsesétove	-7246
-šenetâhev	-7247
-ȯhohtȧhnā	-7248
-ирлеппоси	-7249
-▁accepted	-7250
-▁aglukark	-7251
-▁agricult	-7252
-▁airplane	-7253
-▁algerian	-7254
-▁allmusic	-7255
-▁alphabet	-7256
-▁amazonas	-7257
-▁amebantu	-7258
-▁amehesto	-7259
-▁amehoham	-7260
-▁ameháahp	-7261
-▁amóvôhtó	-7262
-▁anatoliy	-7263
-▁aneohtsé	-7264
-▁angustif	-7265
-▁apishapa	-7266
-▁argentea	-7267
-▁aseohtsé	-7268
-▁atlantic	-7269
-▁attempts	-7270
-▁banlirut	-7271
-▁barbados	-7272
-▁bathroom	-7273
-▁bhumibol	-7274
-▁billings	-7275
-▁bosporus	-7276
-▁bringing	-7277
-▁buddhism	-7278
-▁bulletin	-7279
-▁caballus	-7280
-▁cambodia	-7281
-▁capitals	-7282
-▁carvalho	-7283
-▁children	-7284
-▁cimarron	-7285
-▁coahuila	-7286
-▁comments	-7287
-▁commonly	-7288
-▁communis	-7289
-▁congress	-7290
-▁controls	-7291
-▁counties	-7292
-▁curitiba	-7293
-▁delaware	-7294
-▁delicata	-7295
-▁division	-7296
-▁dominica	-7297
-▁enoièmxe	-7298
-▁eswatini	-7299
-▁european	-7300
-▁explorer	-7301
-▁external	-7302
-▁feminism	-7303
-▁floridum	-7304
-▁gaborone	-7305
-▁gujarati	-7306
-▁haaninin	-7307
-▁hangzhou	-7308
-▁hathaway	-7309
-▁heheséve	-7310
-▁helsinki	-7311
-▁henenéva	-7312
-▁herdsmen	-7313
-▁hetanevo	-7314
-▁himalaya	-7315
-▁hinduism	-7316
-▁hohnóhkâ	-7317
-▁honduras	-7318
-▁honóvôhk	-7319
-▁háestôhe	-7320
-▁hévȯhóné	-7321
-▁hóheeheo	-7322
-▁hóhtséme	-7323
-▁hóhtâhna	-7324
-▁hótoanáo	-7325
-▁hóxovôhó	-7326
-▁illinois	-7327
-▁istanbul	-7328
-▁jaltenco	-7329
-▁jonathan	-7330
-▁kangaroo	-7331
-▁kinshasa	-7332
-▁kiribati	-7333
-▁knuckles	-7334
-▁koroleva	-7335
-▁kujawsko	-7336
-▁language	-7337
-▁lassaono	-7338
-▁leggings	-7339
-▁leontiev	-7340
-▁litefoot	-7341
-▁mandalay	-7342
-▁manitoba	-7343
-▁manôhéve	-7344
-▁maritime	-7345
-▁marshall	-7346
-▁maryland	-7347
-▁mashonee	-7348
-▁medicine	-7349
-▁merchant	-7350
-▁military	-7351
-▁missouri	-7352
-▁monopoly	-7353
-▁montaner	-7354
-▁mosquito	-7355
-▁motšêške	-7356
-▁muscogee	-7357
-▁mâhtovoo	-7358
-▁méonôtse	-7359
-▁mótšêške	-7360
-▁môhtáeve	-7361
-▁nahuatla	-7362
-▁nanomóne	-7363
-▁nauseosa	-7364
-▁nebraska	-7365
-▁neglecta	-7366
-▁netrebko	-7367
-▁névhoham	-7368
-▁nøtterøy	-7369
-▁nīhithaw	-7370
-▁outhouse	-7371
-▁oévemana	-7372
-▁paradisi	-7373
-▁paraguay	-7374
-▁pehenene	-7375
-▁pomifera	-7376
-▁pretoria	-7377
-▁quintana	-7378
-▁racemosa	-7379
-▁railroad	-7380
-▁ratibida	-7381
-▁religion	-7382
-▁renminbi	-7383
-▁rotation	-7384
-▁saaklike	-7385
-▁salvador	-7386
-▁sanguine	-7387
-▁scouting	-7388
-▁security	-7389
-▁shanghai	-7390
-▁shenzhen	-7391
-▁shetland	-7392
-▁shinjuku	-7393
-▁singular	-7394
-▁spelling	-7395
-▁stampede	-7396
-▁strategy	-7397
-▁suitcase	-7398
-▁suriname	-7399
-▁tanzania	-7400
-▁tlaxcala	-7401
-▁together	-7402
-▁tonôhené	-7403
-▁toyohama	-7404
-▁turnpike	-7405
-▁táxemôxe	-7406
-▁tónovêšé	-7407
-▁ultimate	-7408
-▁vaotseva	-7409
-▁veracruz	-7410
-▁vetšêške	-7411
-▁vulgaris	-7412
-▁vóetséna	-7413
-▁vóhkoohé	-7414
-▁vóhpeeva	-7415
-▁walfisch	-7416
-▁wastaneo	-7417
-▁wolfgang	-7418
-▁xamaevee	-7419
-▁xamaevée	-7420
-▁yokohama	-7421
-▁zanzibar	-7422
-▁zhōnghuá	-7423
-▁âxeohöva	-7424
-▁éhestotó	-7425
-▁ésevonan	-7426
-▁éseváohe	-7427
-▁éstánové	-7428
-aestȯtsean	-7429
-algonquian	-7430
-ameotseéve	-7431
-amêškévonó	-7432
-anėstóoseo	-7433
-atrocities	-7434
-britannica	-7435
-chordeiles	-7436
-circassian	-7437
-concepción	-7438
-designated	-7439
-ericameria	-7440
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diff --git a/models/vocabulary/chy_vocabulary.parquet b/models/vocabulary/chy_vocabulary.parquet
index d3250908dac1000b045a1613c86c8979658ce153..5f3009ad677a72a74348a5993bf8c804d65599ae 100644
--- a/models/vocabulary/chy_vocabulary.parquet
+++ b/models/vocabulary/chy_vocabulary.parquet
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:470592a0b112b9a3a65f69af1557219cccb407f27f3a4236e60e71c7c2f40313
-size 29068
+oid sha256:557659dcccab98922c4fc45c22d401a01f92a33330e2d06cbdbafd9e3d8f37d2
+size 22392
diff --git a/models/vocabulary/chy_vocabulary_metadata.json b/models/vocabulary/chy_vocabulary_metadata.json
index 33d754655c8c37b5913c6babe30cef985f13ed0f..929cdaba95adcb0396ddbbe9a9cb203f3f0535eb 100644
--- a/models/vocabulary/chy_vocabulary_metadata.json
+++ b/models/vocabulary/chy_vocabulary_metadata.json
@@ -1,14 +1,15 @@
 {
   "language": "chy",
-  "vocabulary_size": 1659,
+  "vocabulary_size": 1237,
+  "variant": "full",
   "statistics": {
-    "type_token_ratio": 0.24974895150333748,
+    "type_token_ratio": 0.32707120045087357,
     "coverage": {
-      "top_100": 0.4891015417331207,
-      "top_1000": 0.7703939984641739
+      "top_100": 0.4324628968626714,
+      "top_1000": 0.7445989103888785
     },
-    "hapax_count": 2569,
-    "hapax_ratio": 0.6076158940397351,
-    "total_documents": 825
+    "hapax_count": 2245,
+    "hapax_ratio": 0.644744399770247,
+    "total_documents": 459
   }
 }
\ No newline at end of file
diff --git a/models/word_markov/chy_markov_ctx1_word.parquet b/models/word_markov/chy_markov_ctx1_word.parquet
index 08a4a7d9d7f03e6128afff8c7c6eaa2ee082effd..8acf149959f72ea8c4e48203f71cfd6c798bdfce 100644
--- a/models/word_markov/chy_markov_ctx1_word.parquet
+++ b/models/word_markov/chy_markov_ctx1_word.parquet
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6fb77d185c917a1f8ece3debd54279262bbc2e1a01799fcb691731a7561bee31
-size 113562
+oid sha256:386ae58f4271bb09f2a7300c1ffb43cb66268f9329c798213791212b7375c544
+size 86902
diff --git a/models/word_markov/chy_markov_ctx1_word_metadata.json b/models/word_markov/chy_markov_ctx1_word_metadata.json
index 5cdc3d6fc43a79066182c32df25b830eabb01291..79f793a05930f1a4c24f54487e1287ece9e416e5 100644
--- a/models/word_markov/chy_markov_ctx1_word_metadata.json
+++ b/models/word_markov/chy_markov_ctx1_word_metadata.json
@@ -2,6 +2,6 @@
   "context_size": 1,
   "variant": "word",
   "language": "chy",
-  "unique_contexts": 4255,
-  "total_transitions": 27559
+  "unique_contexts": 3383,
+  "total_transitions": 10187
 }
\ No newline at end of file
diff --git a/models/word_markov/chy_markov_ctx2_word.parquet b/models/word_markov/chy_markov_ctx2_word.parquet
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