diff --git a/.gitattributes b/.gitattributes index 5128fe6dbab6ef0227c02cffa9ad5350ab6c4dd6..2c13c137350c43f6eb9bb5909fcbdeedaf89ac36 100644 --- a/.gitattributes +++ b/.gitattributes @@ -39,3 +39,4 @@ visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -t visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text visualizations/zipf_law.png filter=lfs diff=lfs merge=lfs -text +visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 26b301797f3793c0d256433fe34c1fbce01e0356..2ea2aea18b049d225b1560794e4792dca6199d61 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ --- language: ch -language_name: CH +language_name: Chamorro language_family: austronesian_oceanic_other tags: - wikilangs @@ -10,11 +10,21 @@ tags: - n-gram - markov - wikipedia + - feature-extraction + - sentence-similarity + - tokenization + - n-grams + - markov-chain + - text-mining + - fasttext + - babelvec + - vocabulous + - vocabulary - monolingual - family-austronesian_oceanic_other license: mit library_name: wikilangs -pipeline_tag: feature-extraction +pipeline_tag: text-generation datasets: - omarkamali/wikipedia-monthly dataset_info: @@ -23,20 +33,20 @@ dataset_info: metrics: - name: best_compression_ratio type: compression - value: 4.243 + value: 4.248 - name: best_isotropy type: isotropy - value: 0.0518 + value: 0.0563 - name: vocabulary_size type: vocab value: 0 generated: 2026-01-03 --- -# CH - Wikilangs Models +# Chamorro - Wikilangs Models ## Comprehensive Research Report & Full Ablation Study -This repository contains NLP models trained and evaluated by Wikilangs, specifically on **CH** Wikipedia data. +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Chamorro** Wikipedia data. We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings. ## 📋 Repository Contents @@ -60,7 +70,7 @@ 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. Morphological Analysis (Experimental)](#6-morphological-analysis) +- [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) @@ -80,39 +90,39 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and | Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens | |------------|-------------|---------------|----------|--------------| -| **8k** | 3.977x | 3.99 | 0.1019% | 38,272 | -| **16k** | 4.243x 🏆 | 4.26 | 0.1087% | 35,871 | +| **8k** | 3.977x | 3.99 | 0.0998% | 38,069 | +| **16k** | 4.248x 🏆 | 4.26 | 0.1066% | 35,644 | ### Tokenization Examples Below are sample sentences tokenized with each vocabulary size: -**Sample 1:** `Doerun, nasong-song gi Estados Unidos. GuĂ„ha 774 na tataogues na populasion i se...` +**Sample 1:** `+Afghanistan 125px Anthem: MillÄ« ŰłŰ±ÙˆŰŻ 300px Afghanistan capitat Kabul. GuĂ„ha na ...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁do er un , ▁nasong - song ▁gi ▁estados ▁unidos ... (+16 more)` | 26 | -| 16k | `▁doerun , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+14 more)` | 24 | +| 8k | `▁+ af ghanistan ▁ 1 2 5 px ▁anthem : ... (+21 more)` | 31 | +| 16k | `▁+ afghanistan ▁ 1 2 5 px ▁anthem : ▁millÄ« ... (+20 more)` | 30 | -**Sample 2:** `Newhalen, nasong-song gi Estados Unidos. GuĂ„ha 190 na tataogues na populasion i ...` +**Sample 2:** `Cartersville, nasong-song gi Estados Unidos. GuĂ„ha 19,731 na tataogues na popula...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁newha len , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+15 more)` | 25 | -| 16k | `▁newhalen , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+14 more)` | 24 | +| 8k | `▁carters ville , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+18 more)` | 28 | +| 16k | `▁cartersville , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+17 more)` | 27 | -**Sample 3:** `Larsen Bay, nasong-song gi Estados Unidos. GuĂ„ha 87 na tataogues na populasion i...` +**Sample 3:** `Waleska, nasong-song gi Estados Unidos. GuĂ„ha 644 na tataogues na populasion i s...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁larsen ▁bay , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+14 more)` | 24 | -| 16k | `▁larsen ▁bay , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+14 more)` | 24 | +| 8k | `▁wa les ka , ▁nasong - song ▁gi ▁estados ▁unidos ... (+16 more)` | 26 | +| 16k | `▁waleska , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+14 more)` | 24 | ### Key Findings -- **Best Compression:** 16k achieves 4.243x compression -- **Lowest UNK Rate:** 8k with 0.1019% unknown tokens +- **Best Compression:** 16k achieves 4.248x compression +- **Lowest UNK Rate:** 8k with 0.0998% unknown tokens - **Trade-off:** Larger vocabularies improve compression but increase model size - **Recommendation:** 32k vocabulary provides optimal balance for production use @@ -129,12 +139,14 @@ Below are sample sentences tokenized with each vocabulary size: | N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage | |--------|---------|------------|---------|----------------|------------------|-------------------| -| **2-gram** | Word | 181 | 7.50 | 496 | 68.1% | 100.0% | -| **2-gram** | Subword | 228 | 7.83 | 869 | 71.1% | 100.0% | -| **3-gram** | Word | 134 🏆 | 7.07 | 582 | 70.7% | 100.0% | -| **3-gram** | Subword | 1,281 | 10.32 | 4,543 | 36.5% | 79.7% | -| **4-gram** | Word | 158 | 7.30 | 842 | 66.6% | 100.0% | -| **4-gram** | Subword | 3,667 | 11.84 | 12,416 | 26.2% | 57.0% | +| **2-gram** | Word | 178 | 7.48 | 491 | 68.4% | 100.0% | +| **2-gram** | Subword | 227 | 7.83 | 866 | 71.1% | 100.0% | +| **3-gram** | Word | 133 | 7.06 | 577 | 70.8% | 100.0% | +| **3-gram** | Subword | 1,279 | 10.32 | 4,533 | 36.5% | 79.7% | +| **4-gram** | Word | 156 | 7.29 | 834 | 66.8% | 100.0% | +| **4-gram** | Subword | 3,664 | 11.84 | 12,412 | 26.2% | 57.0% | +| **5-gram** | Word | 102 🏆 | 6.67 | 583 | 72.6% | 100.0% | +| **5-gram** | Subword | 5,287 | 12.37 | 16,015 | 24.4% | 49.4% | ### Top 5 N-grams by Size @@ -153,8 +165,8 @@ Below are sample sentences tokenized with each vocabulary size: | Rank | N-gram | Count | |------|--------|-------| | 1 | `nu i senso` | 308 | -| 2 | `na tataogues na` | 304 | -| 3 | `na populasion i` | 304 | +| 2 | `na populasion i` | 304 | +| 3 | `na tataogues na` | 304 | | 4 | `tataogues na populasion` | 304 | | 5 | `i sengsong nu` | 299 | @@ -164,46 +176,66 @@ Below are sample sentences tokenized with each vocabulary size: |------|--------|-------| | 1 | `na tataogues na populasion` | 304 | | 2 | `tataogues na populasion i` | 303 | -| 3 | `na populasion i sengsong` | 299 | -| 4 | `sengsong nu i senso` | 299 | +| 3 | `sengsong nu i senso` | 299 | +| 4 | `i sengsong nu i` | 299 | | 5 | `populasion i sengsong nu` | 299 | +**5-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `na tataogues na populasion i` | 303 | +| 2 | `populasion i sengsong nu i` | 299 | +| 3 | `i sengsong nu i senso` | 299 | +| 4 | `na populasion i sengsong nu` | 299 | +| 5 | `tataogues na populasion i sengsong` | 298 | + **2-grams (Subword):** | Rank | N-gram | Count | |------|--------|-------| -| 1 | `a _` | 4,934 | -| 2 | `i _` | 4,206 | -| 3 | `n a` | 2,921 | -| 4 | `a n` | 2,812 | -| 5 | `_ i` | 2,769 | +| 1 | `a _` | 4,908 | +| 2 | `i _` | 4,194 | +| 3 | `n a` | 2,916 | +| 4 | `a n` | 2,801 | +| 5 | `_ i` | 2,765 | **3-grams (Subword):** | Rank | N-gram | Count | |------|--------|-------| -| 1 | `_ i _` | 2,254 | -| 2 | `_ n a` | 1,827 | +| 1 | `_ i _` | 2,248 | +| 2 | `_ n a` | 1,823 | | 3 | `n a _` | 1,562 | -| 4 | `_ g i` | 1,306 | -| 5 | `_ m a` | 1,153 | +| 4 | `_ g i` | 1,298 | +| 5 | `_ m a` | 1,144 | **4-grams (Subword):** | Rank | N-gram | Count | |------|--------|-------| -| 1 | `_ n a _` | 1,359 | -| 2 | `_ g i _` | 957 | -| 3 | `s o n g` | 950 | -| 4 | `_ i _ s` | 792 | -| 5 | `o n g _` | 757 | +| 1 | `_ n a _` | 1,357 | +| 2 | `_ g i _` | 959 | +| 3 | `s o n g` | 952 | +| 4 | `_ i _ s` | 793 | +| 5 | `o n g _` | 758 | + +**5-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ i _ s e` | 690 | +| 2 | `i _ s e n` | 687 | +| 3 | `s o n g _` | 653 | +| 4 | `_ u n i d` | 463 | +| 5 | `u n i d o` | 448 | ### Key Findings -- **Best Perplexity:** 3-gram (word) with 134 +- **Best Perplexity:** 5-gram (word) with 102 - **Entropy Trend:** Decreases with larger n-grams (more predictable) -- **Coverage:** Top-1000 patterns cover ~57% of corpus +- **Coverage:** Top-1000 patterns cover ~49% of corpus - **Recommendation:** 4-gram or 5-gram for best predictive performance --- @@ -219,14 +251,14 @@ Below are sample sentences tokenized with each vocabulary size: | Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability | |---------|---------|-------------|------------|------------------|-----------------|----------------| -| **1** | Word | 0.4921 | 1.406 | 2.63 | 5,466 | 50.8% | -| **1** | Subword | 1.0948 | 2.136 | 7.84 | 226 | 0.0% | -| **2** | Word | 0.1702 | 1.125 | 1.32 | 14,200 | 83.0% | -| **2** | Subword | 1.1295 | 2.188 | 5.29 | 1,769 | 0.0% | -| **3** | Word | 0.0593 | 1.042 | 1.09 | 18,551 | 94.1% | -| **3** | Subword | 0.7380 | 1.668 | 2.81 | 9,336 | 26.2% | -| **4** | Word | 0.0213 🏆 | 1.015 | 1.03 | 19,968 | 97.9% | -| **4** | Subword | 0.3911 | 1.311 | 1.72 | 26,134 | 60.9% | +| **1** | Word | 0.4903 | 1.405 | 2.61 | 5,477 | 51.0% | +| **1** | Subword | 1.0984 | 2.141 | 7.88 | 223 | 0.0% | +| **2** | Word | 0.1693 | 1.125 | 1.32 | 14,138 | 83.1% | +| **2** | Subword | 1.1342 | 2.195 | 5.32 | 1,755 | 0.0% | +| **3** | Word | 0.0592 | 1.042 | 1.09 | 18,443 | 94.1% | +| **3** | Subword | 0.7400 | 1.670 | 2.81 | 9,321 | 26.0% | +| **4** | Word | 0.0211 🏆 | 1.015 | 1.03 | 19,853 | 97.9% | +| **4** | Subword | 0.3920 | 1.312 | 1.72 | 26,122 | 60.8% | ### Generated Text Samples (Word-based) @@ -234,27 +266,27 @@ Below are text samples generated from each word-based Markov chain model: **Context Size 1:** -1. `i islan guĂ„han si nanĂ„ ña ti ha nĂ„ an i senso ine giya guĂ„han i` -2. `na tataogues na petsona siha manma Ă„ ñao i kotturan ñiha gi i dos botkan ni` -3. `gi sankattan na populasion i mina tres manggimen tuba ginen i taotao ya ma ganna i` +1. `i saddok segua ya siha gi i mayot maelihi gobietna i mundo ma li e societĂ ` +2. `na populasion i senso unidos guĂ„ha 296 na agronomia i senso bibliografia riferensia horst lehne and` +3. `gi i sengsong nu i patgon siha ma usa ginen i dos gi islan sumatra pekanbaru` **Context Size 2:** 1. `i sengsong nu i senso unidos` -2. `nu i proteksion i tano jesukristo gi fecha ni 25 disiembre` -3. `populasion i sengsong nu i senso unidos` +2. `nu i senso para i fondo gaige hĂ„lom hĂ„nom hao kalan guihan gue gi iya estados unidos` +3. `na populasion i sengsong nu i senso unidos` **Context Size 3:** -1. `na populasion i sengsong nu i senso unidos` -2. `na tataogues na populasion i sengsong nu i senso unidos` -3. `tataogues na populasion i sengsong nu i senso unidos` +1. `na tataogues na populasion i sengsong nu i senso unidos` +2. `na populasion i sengsong nu i senso website sanhiyong siha rome` +3. `tataogues na populasion i sengsong nu i senso yeet website sanhiyong siha commons coronel fabriciano` **Context Size 4:** 1. `na tataogues na populasion i sengsong nu i senso unidos` 2. `tataogues na populasion i sengsong nu i senso unidos` -3. `i sengsong nu i senso unidos` +3. `na populasion i sengsong nu i senso unidos` ### Generated Text Samples (Subword-based) @@ -263,34 +295,34 @@ Below are text samples generated from each subword-based Markov chain model: **Context Size 1:** -1. `_eyanso'_giterge` -2. `asipa_dinakoso_m` -3. `nsi_nandiki_u_pa` +1. `_yia_a_mesotinio` +2. `a_dorn._ikug._s_` +3. `nusot_fai_i_i_gs` **Context Size 2:** -1. `a_Ă„chokkas_na_kri` -2. `i_ta_magu_gi_i_ha` -3. `na'neho_"thunidos` +1. `a_para_ediu_nasto` +2. `i_me":_ki,_vĂ­cite` +3. `na'i_achamane_pĂ„s` **Context Size 3:** -1. `_i_caste_pies_dang` -2. `_na_populasifiku)_` -3. `na_tan_atten-ñiha_` +1. `_i_semak_senggen_c` +2. `_na_pat_gi_wikike'` +3. `na_taogues_na_gi_k` **Context Size 4:** -1. `_na_po'lu_na_aterit` -2. `_gi_estorio_ni'_kad` -3. `song-song_nu_i_akti` +1. `_na_populasion_yan_` +2. `_gi_para_u_matungo'` +3. `song_nu_i_sengsong_` ### Key Findings - **Best Predictability:** Context-4 (word) with 97.9% predictability - **Branching Factor:** Decreases with context size (more deterministic) -- **Memory Trade-off:** Larger contexts require more storage (26,134 contexts) +- **Memory Trade-off:** Larger contexts require more storage (26,122 contexts) - **Recommendation:** Context-3 or Context-4 for text generation --- @@ -306,64 +338,64 @@ Below are text samples generated from each subword-based Markov chain model: | Metric | Value | |--------|-------| -| Vocabulary Size | 1,918 | -| Total Tokens | 22,697 | -| Mean Frequency | 11.83 | +| Vocabulary Size | 1,919 | +| Total Tokens | 22,562 | +| Mean Frequency | 11.76 | | Median Frequency | 3 | -| Frequency Std Dev | 73.74 | +| Frequency Std Dev | 73.53 | ### Most Common Words | Rank | Word | Frequency | |------|------|-----------| -| 1 | i | 2,327 | -| 2 | na | 1,513 | -| 3 | gi | 972 | +| 1 | i | 2,319 | +| 2 | na | 1,511 | +| 3 | gi | 974 | | 4 | unidos | 448 | | 5 | yan | 436 | | 6 | sengsong | 370 | | 7 | guĂ„ha | 356 | -| 8 | ni | 339 | -| 9 | nu | 335 | -| 10 | populasion | 333 | +| 8 | nu | 335 | +| 9 | ni | 334 | +| 10 | populasion | 331 | ### Least Common Words (from vocabulary) | Rank | Word | Frequency | |------|------|-----------| -| 1 | av | 2 | -| 2 | berit | 2 | -| 3 | larsson | 2 | -| 4 | hemliga | 2 | -| 5 | tycker | 2 | -| 6 | att | 2 | -| 7 | var | 2 | -| 8 | rolig | 2 | -| 9 | ett | 2 | -| 10 | du | 2 | +| 1 | sĂ€ger | 2 | +| 2 | ett | 2 | +| 3 | sĂ„ | 2 | +| 4 | du | 2 | +| 5 | skate | 2 | +| 6 | med | 2 | +| 7 | smaskiga | 2 | +| 8 | löken | 2 | +| 9 | tychy | 2 | +| 10 | museon | 2 | ### Zipf's Law Analysis | Metric | Value | |--------|-------| -| Zipf Coefficient | 0.9581 | -| RÂČ (Goodness of Fit) | 0.986461 | +| Zipf Coefficient | 0.9547 | +| RÂČ (Goodness of Fit) | 0.986088 | | Adherence Quality | **excellent** | ### Coverage Analysis | Top N Words | Coverage | |-------------|----------| -| Top 100 | 63.1% | +| Top 100 | 63.2% | | Top 1,000 | 91.3% | | Top 5,000 | 0.0% | | Top 10,000 | 0.0% | ### Key Findings -- **Zipf Compliance:** RÂČ=0.9865 indicates excellent adherence to Zipf's law -- **High Frequency Dominance:** Top 100 words cover 63.1% of corpus -- **Long Tail:** -8,082 words needed for remaining 100.0% coverage +- **Zipf Compliance:** RÂČ=0.9861 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 63.2% of corpus +- **Long Tail:** -8,081 words needed for remaining 100.0% coverage --- ## 5. Word Embeddings Evaluation @@ -379,37 +411,40 @@ Below are text samples generated from each subword-based Markov chain model: ### 5.1 Cross-Lingual Alignment -> *Note: Multilingual alignment visualization not available for this language.* +![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 | 0.0518 🏆 | 0.6801 | N/A | N/A | -| **mono_64d** | 64 | 0.0071 | 0.8792 | N/A | N/A | -| **mono_128d** | 128 | 0.0017 | 0.8741 | N/A | N/A | +| **mono_32d** | 32 | 0.0563 🏆 | 0.6662 | N/A | N/A | +| **mono_64d** | 64 | 0.0067 | 0.8730 | N/A | N/A | +| **mono_128d** | 128 | 0.0017 | 0.8734 | N/A | N/A | +| **aligned_32d** | 32 | 0.0563 | 0.6862 | 0.0332 | 0.1848 | +| **aligned_64d** | 64 | 0.0067 | 0.8793 | 0.0095 | 0.1090 | +| **aligned_128d** | 128 | 0.0017 | 0.8561 | 0.0047 | 0.0853 | ### Key Findings -- **Best Isotropy:** mono_32d with 0.0518 (more uniform distribution) -- **Semantic Density:** Average pairwise similarity of 0.8111. Lower values indicate better semantic separation. -- **Alignment Quality:** No aligned models evaluated in this run. +- **Best Isotropy:** mono_32d with 0.0563 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.8057. Lower values indicate better semantic separation. +- **Alignment Quality:** Aligned models achieve up to 3.3% R@1 in cross-lingual retrieval. - **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 | - | +| Productivity Index | **3.506** | High morphological productivity | Reliable analysis | +| Idiomaticity Gap | **1.025** | High formulaic/idiomatic content | - | ### 6.2 Affix Inventory (Productive Units) @@ -418,17 +453,17 @@ These are the most productive prefixes and suffixes identified by sampling the v #### Productive Prefixes | Prefix | Examples | |--------|----------| -| `-ma` | matematika, matai, mansen | +| `-ma` | manamerikanu, maisang, manmafa | #### Productive Suffixes | Suffix | Examples | |--------|----------| -| `-a` | matematika, kana, bĂ„nda | -| `-n` | sanhayan, monhayan, fan | -| `-on` | organisasion, aplikasion, adelanton | -| `-an` | sanhayan, monhayan, fan | -| `-ia` | bibliografia, termania, indonesia | -| `-ion` | organisasion, aplikasion, atministrasion | +| `-a` | sina, finta, nangga | +| `-n` | ayman, guguan, direchon | +| `-on` | direchon, mision, museon | +| `-an` | ayman, guguan, geran | +| `-ia` | iglesia, cecilia, diktionaria | +| `-ion` | mision, administration, nasion | ### 6.3 Bound Stems (Lexical Roots) @@ -443,10 +478,10 @@ This table shows which prefixes and suffixes most frequently co-occur on the sam | Prefix | Suffix | Frequency | Examples | |--------|--------|-----------|----------| -| `-ma` | `-a` | 17 words | matematika, manfa | -| `-ma` | `-n` | 13 words | mansen, mandarin | -| `-ma` | `-an` | 6 words | manguayan, man | -| `-ma` | `-on` | 4 words | matutuhon, madison | +| `-ma` | `-a` | 17 words | manmafa, mafana | +| `-ma` | `-n` | 13 words | mangginen, manmatutuhon | +| `-ma` | `-an` | 6 words | masasangan, maneran | +| `-ma` | `-on` | 4 words | manmatutuhon, matutuhon | | `-ma` | `-ia` | 1 words | malaysia, maria | ### 6.5 Recursive Morpheme Segmentation @@ -456,25 +491,27 @@ Using **Recursive Hierarchical Substitutability**, we decompose complex words in | Word | Suggested Split | Confidence | Stem | |------|-----------------|------------|------| | makonsidera | **`ma-konsidera`** | 4.5 | `konsidera` | +| manmatutuhon | **`ma-nmatutuh-on`** | 3.0 | `nmatutuh` | | matutuhon | **`ma-tutuh-on`** | 3.0 | `tutuh` | -| manguayan | **`ma-nguay-an`** | 3.0 | `nguay` | +| masasangan | **`ma-sasang-an`** | 3.0 | `sasang` | | pennsylvania | **`pennsylv-an-ia`** | 3.0 | `pennsylv` | -| manmatutuhon | **`ma-nmatutuh-on`** | 3.0 | `nmatutuh` | -| machulijan | **`ma-chulij-an`** | 3.0 | `chulij` | | manofisinan | **`ma-nofisin-an`** | 3.0 | `nofisin` | -| masasangan | **`ma-sasang-an`** | 3.0 | `sasang` | -| matematika | **`ma-tematika`** | 1.5 | `tematika` | -| organisasion | **`organisas-ion`** | 1.5 | `organisas` | -| aplikasion | **`aplikas-ion`** | 1.5 | `aplikas` | -| adelanton | **`adelant-on`** | 1.5 | `adelant` | -| bibliografia | **`bibliograf-ia`** | 1.5 | `bibliograf` | +| manguayan | **`ma-nguay-an`** | 3.0 | `nguay` | +| machulijan | **`ma-chulij-an`** | 3.0 | `chulij` | | manamerikanu | **`ma-namerikanu`** | 1.5 | `namerikanu` | -| indonesia | **`indones-ia`** | 1.5 | `indones` | +| diktionaria | **`diktionar-ia`** | 1.5 | `diktionar` | +| administration | **`administrat-ion`** | 1.5 | `administrat` | +| misionarion | **`misionar-ion`** | 1.5 | `misionar` | +| mangginen | **`ma-ngginen`** | 1.5 | `ngginen` | +| toneladan | **`tonelad-an`** | 1.5 | `tonelad` | +| wikimedia | **`wikimed-ia`** | 1.5 | `wikimed` | ### 6.6 Linguistic Interpretation > **Automated Insight:** -The language CH 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. +The language Chamorro shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding. + +> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts. --- ## 7. Summary & Recommendations @@ -485,8 +522,8 @@ The language CH appears to be more isolating or has a highly fixed vocabulary. W | Component | Recommended | Rationale | |-----------|-------------|-----------| -| Tokenizer | **16k BPE** | Best compression (4.24x) | -| N-gram | **3-gram** | Lowest perplexity (134) | +| Tokenizer | **16k BPE** | Best compression (4.25x) | +| N-gram | **5-gram** | Lowest perplexity (102) | | Markov | **Context-4** | Highest predictability (97.9%) | | Embeddings | **100d** | Balanced semantic capture and isotropy | @@ -701,4 +738,4 @@ MIT License - Free for academic and commercial use. --- *Generated by Wikilangs Models Pipeline* -*Report Date: 2026-01-03 10:06:23* +*Report Date: 2026-01-03 20:18:48* diff --git a/models/embeddings/aligned/ch_128d.bin b/models/embeddings/aligned/ch_128d.bin new file mode 100644 index 0000000000000000000000000000000000000000..ffd147f1a7934cd749603dff6b367a6d562e9daf --- /dev/null +++ b/models/embeddings/aligned/ch_128d.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c101c6b6b1879eb87cc2d607ecdf473db825b5ff529674fab69c62998c083fe +size 1024531495 diff --git a/models/embeddings/aligned/ch_128d.meta.json b/models/embeddings/aligned/ch_128d.meta.json new file mode 100644 index 0000000000000000000000000000000000000000..576953146df06075b765a8c9fa2a7392adf75c57 --- /dev/null +++ b/models/embeddings/aligned/ch_128d.meta.json @@ -0,0 +1 @@ +{"lang": "ch", "dim": 128, "max_seq_len": 512, "is_aligned": true} \ No newline at end of file diff --git a/models/embeddings/aligned/ch_128d.projection.npy b/models/embeddings/aligned/ch_128d.projection.npy new file mode 100644 index 0000000000000000000000000000000000000000..eb9c559e8e8d2f7a83264626024749e83504601d --- /dev/null +++ b/models/embeddings/aligned/ch_128d.projection.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e6551433039f7f6017e26301151ff5ce6ef6bb5af9ee9a3ef6361945fda8609 +size 65664 diff --git a/models/embeddings/aligned/ch_128d_metadata.json b/models/embeddings/aligned/ch_128d_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..525cdd03315fcf5f26185e6c7542cdfc3c38bbe4 --- /dev/null +++ b/models/embeddings/aligned/ch_128d_metadata.json @@ -0,0 +1,8 @@ +{ + "language": "ch", + "dimension": 128, + "version": "aligned", + "hub_language": "en", + "seed_vocab_size": 211, + "vocab_size": 511 +} \ No newline at end of file diff --git a/models/embeddings/aligned/ch_32d.bin b/models/embeddings/aligned/ch_32d.bin new file mode 100644 index 0000000000000000000000000000000000000000..a321b083d966b4c0d3a21a29f81c9bb48d3ac2e6 --- /dev/null +++ b/models/embeddings/aligned/ch_32d.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e584860b58a7721475d5055a783bbe800416c805994e8a64fd732ee68bba136e +size 256139047 diff --git a/models/embeddings/aligned/ch_32d.meta.json b/models/embeddings/aligned/ch_32d.meta.json new file mode 100644 index 0000000000000000000000000000000000000000..281269a92661a959515e82679dbef90d68653703 --- /dev/null +++ b/models/embeddings/aligned/ch_32d.meta.json @@ -0,0 +1 @@ +{"lang": "ch", "dim": 32, "max_seq_len": 512, "is_aligned": true} \ No newline at end of file diff --git a/models/embeddings/aligned/ch_32d.projection.npy b/models/embeddings/aligned/ch_32d.projection.npy new file mode 100644 index 0000000000000000000000000000000000000000..15aec8214af851073f7e22f9836ea7d626b87c10 --- /dev/null +++ b/models/embeddings/aligned/ch_32d.projection.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59e52077ca54e97043ac5ba55c6408418b379f87be81a6105ff4d5d066c60c7d +size 4224 diff --git a/models/embeddings/aligned/ch_32d_metadata.json b/models/embeddings/aligned/ch_32d_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c8d75c4011ac06e13303e4ca123204d81c93f0d8 --- /dev/null +++ b/models/embeddings/aligned/ch_32d_metadata.json @@ -0,0 +1,8 @@ +{ + "language": "ch", + "dimension": 32, + "version": "aligned", + "hub_language": "en", + "seed_vocab_size": 211, + "vocab_size": 511 +} \ No newline at end of file diff --git a/models/embeddings/aligned/ch_64d.bin b/models/embeddings/aligned/ch_64d.bin new file mode 100644 index 0000000000000000000000000000000000000000..ac6d74f9219dc5d80a0f0647ef709a8311f6fe7f --- /dev/null +++ b/models/embeddings/aligned/ch_64d.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0507af78a2b20fc4f03d79877e45dd543f3b031b8c6e976e439754e45c8cb54 +size 512269863 diff --git a/models/embeddings/aligned/ch_64d.meta.json b/models/embeddings/aligned/ch_64d.meta.json new file mode 100644 index 0000000000000000000000000000000000000000..0cbf870f8f98f73457cf9a9436c2ff04996aaa23 --- /dev/null +++ b/models/embeddings/aligned/ch_64d.meta.json @@ -0,0 +1 @@ +{"lang": "ch", "dim": 64, "max_seq_len": 512, "is_aligned": true} \ No newline at end of file diff --git a/models/embeddings/aligned/ch_64d.projection.npy b/models/embeddings/aligned/ch_64d.projection.npy new file mode 100644 index 0000000000000000000000000000000000000000..2a42e500caa72be5e48cd8efd138a9f06a7ca16c --- /dev/null +++ b/models/embeddings/aligned/ch_64d.projection.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:981b1cfca3191dda47c139109f2b8557af78006455e1d6c19a9bb02bf8ba66e2 +size 16512 diff --git a/models/embeddings/aligned/ch_64d_metadata.json b/models/embeddings/aligned/ch_64d_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9b66ba0b86f27b19ef3ee00c3dd9781ef703b2e1 --- /dev/null +++ b/models/embeddings/aligned/ch_64d_metadata.json @@ -0,0 +1,8 @@ +{ + "language": "ch", + "dimension": 64, + "version": "aligned", + "hub_language": "en", + "seed_vocab_size": 211, + "vocab_size": 511 +} \ No newline at end of file diff --git a/models/embeddings/monolingual/ch_128d.bin b/models/embeddings/monolingual/ch_128d.bin index 2bd44c5203ee1c93df2e60e05553cb348f2fb19b..ffd147f1a7934cd749603dff6b367a6d562e9daf 100644 --- a/models/embeddings/monolingual/ch_128d.bin +++ b/models/embeddings/monolingual/ch_128d.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0df9cc84b978b1e115c6cee71e84a836b069e938ed89ba0990d33fb6e76d469f -size 1024535659 +oid sha256:2c101c6b6b1879eb87cc2d607ecdf473db825b5ff529674fab69c62998c083fe +size 1024531495 diff --git a/models/embeddings/monolingual/ch_128d_metadata.json b/models/embeddings/monolingual/ch_128d_metadata.json index 59def8914a7f3c6906fc533cc9e655496f54253c..b431c6624a56da78259c290dbf04fa9a60fb65cf 100644 --- a/models/embeddings/monolingual/ch_128d_metadata.json +++ b/models/embeddings/monolingual/ch_128d_metadata.json @@ -11,5 +11,5 @@ "encoding_method": "rope", "dim": 128 }, - "vocab_size": 515 + "vocab_size": 511 } \ No newline at end of file diff --git a/models/embeddings/monolingual/ch_32d.bin b/models/embeddings/monolingual/ch_32d.bin index 9f0c07a02502ca7032369f7011ad5c931d18c55b..a321b083d966b4c0d3a21a29f81c9bb48d3ac2e6 100644 --- a/models/embeddings/monolingual/ch_32d.bin +++ b/models/embeddings/monolingual/ch_32d.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5ddf21da0a6cf2c7c20309473f6133a07aa8e95d376e57c53cba79dae0c66d67 -size 256140139 +oid sha256:e584860b58a7721475d5055a783bbe800416c805994e8a64fd732ee68bba136e +size 256139047 diff --git a/models/embeddings/monolingual/ch_32d_metadata.json b/models/embeddings/monolingual/ch_32d_metadata.json index 2206a769030f3373904be8ca4a50c55fcb23ab1d..dab5fba3dc61c1704cedc2fb60f77425753b746c 100644 --- a/models/embeddings/monolingual/ch_32d_metadata.json +++ b/models/embeddings/monolingual/ch_32d_metadata.json @@ -11,5 +11,5 @@ "encoding_method": "rope", "dim": 32 }, - "vocab_size": 515 + "vocab_size": 511 } \ No newline at end of file diff --git a/models/embeddings/monolingual/ch_64d.bin b/models/embeddings/monolingual/ch_64d.bin index b94ca96d56af86ed73fed4c3ae42e4bd10d5c595..ac6d74f9219dc5d80a0f0647ef709a8311f6fe7f 100644 --- a/models/embeddings/monolingual/ch_64d.bin +++ b/models/embeddings/monolingual/ch_64d.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:765adc5bcc8f79ac3c37ad1e8f88947431aa2cfc778fa7b216fa71374b23bb88 -size 512271979 +oid sha256:d0507af78a2b20fc4f03d79877e45dd543f3b031b8c6e976e439754e45c8cb54 +size 512269863 diff --git a/models/embeddings/monolingual/ch_64d_metadata.json b/models/embeddings/monolingual/ch_64d_metadata.json index 872106a0391667df470b11926d725b8e081d99b6..2a8aeccdf48744d133a2fb8a064bbc0be206fdc6 100644 --- a/models/embeddings/monolingual/ch_64d_metadata.json +++ b/models/embeddings/monolingual/ch_64d_metadata.json @@ -11,5 +11,5 @@ "encoding_method": "rope", "dim": 64 }, - "vocab_size": 515 + "vocab_size": 511 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx1_subword.parquet 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"unique_contexts": 223, + "total_transitions": 150847 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx2_subword.parquet b/models/subword_markov/ch_markov_ctx2_subword.parquet index 5f12bd59189bfb5892b74cffb6e5c53175e09ea3..12f78bfd85915c9d16458ee7479452be9d8fd078 100644 --- a/models/subword_markov/ch_markov_ctx2_subword.parquet +++ b/models/subword_markov/ch_markov_ctx2_subword.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:264f038954ff6478301dcba734b78924744419ec3051afb803ea4a1d438a05f0 -size 67586 +oid sha256:a36d550286c9422c2ffb82e99c3b81ca35bb4735060577a35057479732a4a101 +size 67152 diff --git a/models/subword_markov/ch_markov_ctx2_subword_metadata.json b/models/subword_markov/ch_markov_ctx2_subword_metadata.json index 906328ee423a033d42487505374d99168df345c3..16519e7b6fa2f5b1c38bfdfa68215e659ea10664 100644 --- a/models/subword_markov/ch_markov_ctx2_subword_metadata.json +++ b/models/subword_markov/ch_markov_ctx2_subword_metadata.json @@ -2,6 +2,6 @@ "context_size": 2, "variant": "subword", "language": "ch", - "unique_contexts": 1769, - "total_transitions": 151064 + "unique_contexts": 1755, + "total_transitions": 150287 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx3_subword.parquet b/models/subword_markov/ch_markov_ctx3_subword.parquet index d722bb8bd3f8a3e6cbe2855a52e16bfca8b40349..1e4e8084d4f15d140926f1b0c7a159ee7a95b3d8 100644 --- a/models/subword_markov/ch_markov_ctx3_subword.parquet +++ b/models/subword_markov/ch_markov_ctx3_subword.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:84fdaafcb5e52af0cd9e249ff651ea983749cc925557218121f0d7a85653c797 -size 194392 +oid sha256:1820466b4f1fdc1254d29c47d12e951b540c0b83db5d9509ae8f3d38d1cb77a9 +size 186375 diff --git a/models/subword_markov/ch_markov_ctx3_subword_metadata.json b/models/subword_markov/ch_markov_ctx3_subword_metadata.json index 71cf9400931b9541b42a478fd0428990ddb4ae80..2f96a4706ef4c8ef4a01c61edaf8ea8a7624d8a5 100644 --- a/models/subword_markov/ch_markov_ctx3_subword_metadata.json +++ b/models/subword_markov/ch_markov_ctx3_subword_metadata.json @@ -2,6 +2,6 @@ "context_size": 3, "variant": "subword", "language": "ch", - "unique_contexts": 9336, - "total_transitions": 150504 + "unique_contexts": 9321, + "total_transitions": 149727 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx4_subword.parquet b/models/subword_markov/ch_markov_ctx4_subword.parquet index 5644b98f879b6a50bbb42f90ed0f2bb23d951b0c..949883e22d76b89c4c44aaea3cfc10f31c0ae78a 100644 --- a/models/subword_markov/ch_markov_ctx4_subword.parquet +++ b/models/subword_markov/ch_markov_ctx4_subword.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6d71078f4f1468532db5ba657998c8efdbdd211b304e67e2ca77d88635a671f7 -size 397638 +oid sha256:2ff258330d3bfed1d6305ca0cd26a119b91efa1ab927068fb949b7f8fdad2308 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-▁vladimir -9294 -▁votkinsk -9295 -▁wellness -9296 -▁wikinews -9297 -▁wolfgang -9298 -▁yahayaya -9299 -▁yahĂ„yaya -9300 -▁yayammak -9301 -▁yinammak -9302 -▁yinaoyao -9303 -▁yokohama -9304 -▁youtuber -9305 -alentisque -9306 -astrunomia -9307 -bangladesh -9308 -castellano -9309 -corporated -9310 -estruktura -9311 -fusianismo -9312 -geskapande -9313 -influensia -9314 -mageftungo -9315 -micronesia -9316 -ningsemail -9317 -ponibiliza -9318 -populasion -9319 -rosinensis -9320 -rĂ©publique -9321 -singkuenta -9322 -utauguiyes -9323 -▁agrochemi -9324 -▁agronomia -9325 -▁aguianasi -9326 -▁aleknagik -9327 -▁alexander -9328 -▁alexandre -9329 -▁aliquippa -9330 -▁allakaket -9331 -▁altissima -9332 -▁americana -9333 -▁amsterdam -9334 -▁anchorage -9335 -▁andalucĂ­a -9336 -▁apartheid -9337 -▁arenillas -9338 -▁atkitektu -9339 -▁atlantiku -9340 -▁attleboro -9341 -▁australia -9342 -▁authority -9343 -▁balfourii -9344 -▁baltimore -9345 -▁baptistii -9346 -▁bastienne -9347 -▁beautiful -9348 -▁beethoven -9349 -▁beginning -9350 -▁benavides -9351 -▁bendision -9352 -▁bengtsson -9353 -▁biddeford -9354 -▁bilingual -9355 -▁bollinger -9356 -▁byggvaror -9357 -▁camilinho -9358 -▁capuchino -9359 -▁caravelle -9360 -▁carolinas -9361 -▁carrierña -9362 -▁catherine -9363 -▁cañaveral -9364 -▁cedartown -9365 -▁chapanesu -9366 -▁chefornak -9367 -▁commodore -9368 -▁comunidad -9369 -▁concertos -9370 -▁conoideus -9371 -▁countries -9372 -▁cristiana -9373 -▁cristiano -9374 -▁dahlonega -9375 -▁davisboro -9376 -▁decipiens -9377 -▁decumbens -9378 -▁dellacova -9379 -▁demonstra -9380 -▁denamarka -9381 -▁deportiva -9382 -▁dialekton -9383 -▁diayakeun -9384 -▁difefendi -9385 -▁digitales -9386 -▁dimaluade -9387 -▁dipositun -9388 -▁direcciĂłn -9389 -▁direksion -9390 -▁distritun -9391 -▁dokumento -9392 -▁domestica -9393 -▁doraville -9394 -▁dumedeggo -9395 -▁dĂ„nsalĂ„tu -9396 -▁ebidensia -9397 -▁ebolusion -9398 -▁edificios -9399 -▁ekolĂ„hiya -9400 -▁ellaville -9401 -▁englatera -9402 -▁enhinieru -9403 -▁ensegidas -9404 -▁enstudiĂ„t -9405 -▁episĂłdios -9406 -▁eslobakia -9407 -▁estocolmo -9408 -▁europeano -9409 -▁exclusive -9410 -▁fairbanks -9411 -▁falabella -9412 -▁fanechong -9413 -▁fanggimen -9414 -▁fanhĂ„gaga -9415 -▁federated -9416 -▁filĂłsofos -9417 -▁fondasion -9418 -▁fotbollen -9419 -▁frameless -9420 -▁française -9421 -▁fĂ€rggrann -9422 -▁gaipachot -9423 -▁ginefsaga -9424 -▁gobienton -9425 -▁gobietnan -9426 -▁gobietnun -9427 -▁gobitenan -9428 -▁grabaciĂłn -9429 -▁greenwood -9430 -▁gressitii -9431 -▁grovetown -9432 -▁grĂ„matika -9433 -▁guaiyayon -9434 -▁guinaguat -9435 -▁guinaiyan -9436 -▁gumalilek -9437 -▁gumbranch -9438 -▁halleorum -9439 -▁hapeville -9440 -▁hempshall -9441 -▁hinduismo -9442 -▁holĂ­stico -9443 -▁holĂ­stika -9444 -▁holĂ­stiko -9445 -▁howlstead -9446 -▁humĂ„nanao -9447 -▁identidat -9448 -▁imperadot -9449 -▁inanokcha -9450 -▁indalecio -9451 -▁indonesai -9452 -▁indonesio -9453 -▁institute -9454 -▁instituto -9455 -▁islamabad -9456 -▁jĂŹnxĂ­ngqǔ -9457 -▁kabiseran -9458 -▁karolinas -9459 -▁kategoria -9460 -▁katherine -9461 -▁ketchikan -9462 -▁kinahahat -9463 -▁kinibatde -9464 -▁kinientos -9465 -▁kinonsuma -9466 -▁kolombiya -9467 -▁komprende -9468 -▁kongreson -9469 -▁konstverk -9470 -▁konteksto -9471 -▁kontekstu -9472 -▁kontinuha -9473 -▁krĂłlewski -9474 -▁kumeekuĂ„p -9475 -▁kumuentos -9476 -▁kupreanof -9477 -▁kustumbre -9478 -▁lancheria -9479 -▁languages -9480 -▁linatuñgo -9481 -▁linguistu -9482 -▁litterĂ€ra -9483 -▁ljubljana -9484 -▁lovelines -9485 -▁lumilista -9486 -▁madiklĂ„ra -9487 -▁mafañagon -9488 -▁mafañÄgue -9489 -▁maheffong -9490 -▁mampiniti -9491 -▁mamplĂĄnta -9492 -▁mamprueba -9493 -▁manaligao -9494 -▁manalisay -9495 -▁manatatte -9496 -▁mandibidi -9497 -▁mandinaña -9498 -▁manestaba -9499 -▁mangifera -9500 -▁manhebreo -9501 -▁mankompet -9502 -▁manmafana -9503 -▁manmalĂ„te -9504 -▁manmamaki -9505 -▁manmumumu -9506 -▁manobispo -9507 -▁manokotak -9508 -▁manotohge -9509 -▁mansanita -9510 -▁mantaotao -9511 -▁mantienes -9512 -▁mantituge -9513 -▁manĂĄkhilo -9514 -▁maprotehi -9515 -▁mapublica -9516 -▁mapĂșpulan -9517 -▁masculino -9518 -▁matdision -9519 -▁matemaika -9520 -▁materials -9521 -▁mañottera -9522 -▁medinilla -9523 -▁menghapus -9524 -▁mexicanos -9525 -▁middlesex -9526 -▁militaryo -9527 -▁minahgong -9528 -▁mitridate -9529 -▁montagner -9530 -▁mumalĂ„ngu -9531 -▁municipat -9532 -▁munisipĂ„t -9533 -▁muttfield -9534 -▁mĂ€rkvĂ€rda -9535 -▁mĂ„nggĂ„gĂ„o -9536 -▁mĂ­talento -9537 -▁nabegĂ„nte -9538 -▁napaskiak -9539 -▁nederland -9540 -▁nesisario -9541 -▁nightmute -9542 -▁nikonovna -9543 -▁nisisidat -9544 -▁nobilidat -9545 -▁nondalton -9546 -▁numanguyi -9547 -▁nutrisiĂłn -9548 -▁ofisiales -9549 -▁onorapble -9550 -▁palembang -9551 -▁palustris -9552 -▁participa -9553 -▁patterson -9554 -▁pekanbaru -9555 -▁pentandra -9556 -▁pestisida -9557 -▁pichuleco -9558 -▁pierluigi -9559 -▁pigazzano -9560 -▁pinipilan -9561 -▁pitsilemu -9562 -▁pizzicato -9563 -▁politikat -9564 -▁popoddong -9565 -▁pottugues -9566 -▁presensia -9567 -▁prinsipio -9568 -▁prinsipĂ„t -9569 -▁prohekton -9570 -▁propiedĂ„t -9571 -▁prudukton -9572 -▁prufesiha -9573 -▁prumomote -9574 -▁prusision -9575 -▁pseudonym -9576 -▁pulitikat -9577 -▁pulitikĂ„t -9578 -▁pumoddong -9579 -▁quinhagak -9580 -▁ramirinho -9581 -▁relijioso -9582 -▁reykjavik -9583 -▁ripopotta -9584 -▁roseicapi -9585 -▁saccharum -9586 -▁satbasion -9587 -▁screaming -9588 -▁sebastian -9589 -▁secondary -9590 -▁selective -9591 -▁semesters -9592 -▁sergeevna -9593 -▁setbision -9594 -▁siamensis -9595 -▁sientista -9596 -▁simplĂ­cio -9597 -▁sinentĂ„nu -9598 -▁siniseden -9599 -▁sinkuenta -9600 -▁sistemang -9601 -▁slovenija -9602 -▁statistik -9603 -▁stevenson -9604 -▁streamers -9605 -▁sumanhaya -9606 -▁sumanlagu -9607 -▁sustansia -9608 -▁taiyaoyao -9609 -▁talbotton -9610 -▁tectorius -9611 -▁teksturan -9612 -▁tennessee -9613 -▁teritoriu -9614 -▁thomensis -9615 -▁thumbnail -9616 -▁tiberianu -9617 -▁tidningen -9618 -▁tiliaceus -9619 -▁tinayuyot -9620 -▁tinitihon -9621 -▁transferi -9622 -▁tumutuhon -9623 -▁valquĂ­ria -9624 -▁vasilyeva -9625 -▁verĂ­ssimo -9626 -▁waterbury -9627 -▁wentworth -9628 -▁wikibooks -9629 -▁wikiquote -9630 -▁ÄterstĂ€ll -9631 -afghanistan -9632 -aidentifika -9633 -byrinthicus -9634 -estrangheru -9635 -garticphone -9636 -jumhĆ«riyyah -9637 -kalendarion -9638 -positifolia -9639 -saiklopidia -9640 -sionatmente -9641 -tĂ„dusĂșnidus -9642 -▁achataotao -9643 -▁alentisque -9644 -▁alpharetta -9645 -▁apologized -9646 -▁asociaciĂłn -9647 -▁atkitektun -9648 -▁atrocarpus -9649 -▁attapulgus -9650 -▁ballantyne -9651 -▁banggladis -9652 -▁behavioral -9653 -▁bengalense -9654 -▁bioloyista -9655 -▁bnideoñiha -9656 -▁boninensis -9657 -▁braunstein -9658 -▁bridgeport -9659 -▁buonarroti -9660 -▁burlington -9661 -▁california -9662 -▁castellano -9663 -▁catapretra -9664 -▁catesismon -9665 -▁cavalcanti -9666 -▁chelminski -9667 -▁chimpanzee -9668 -▁commission -9669 -▁concurrent -9670 -▁copelandii -9671 -▁corallinus -9672 -▁croileusci -9673 -▁decastigma -9674 -▁democratic -9675 -▁deribasion -9676 -▁derivative -9677 -▁difirensia -9678 -▁dillingham -9679 -▁dimokratĂ­a -9680 -▁dumiririhi -9681 -▁dumĂ„ngkulu -9682 -▁ebanghelio -9683 -▁eddukasion -9684 -▁eksakatibu -9685 -▁eksklusibu -9686 -▁ekstension -9687 -▁engsigidas -9688 -▁espesifiku -9689 -▁espisifiku -9690 -▁estrukturĂ„ -9691 -▁evangĂ©lica -9692 -▁evangĂ©lika -9693 -▁federativa -9694 -▁gaitalento -9695 -▁generasion -9696 -▁geological -9697 -▁gillsville -9698 -▁government -9699 -▁grzeszczak -9700 -▁gymnopĂ©die -9701 -▁harrisburg -9702 -▁hindustani -9703 -▁hinesville -9704 -▁homerville -9705 -▁humanistic -9706 -▁hyacinthus -9707 -▁hĂ„ftaimanu -9708 -▁imbentadot -9709 -▁impottĂ„nte -9710 -▁inatulaika -9711 -▁industrian -9712 -▁ineksplika -9713 -▁infinitibo -9714 -▁infleksion -9715 -▁inisiasion -9716 -▁innovasion -9717 -▁internista -9718 -▁jesukristu -9719 -▁journalist -9720 -▁kategoriya -9721 -▁kedanchiyi -9722 -▁kensington -9723 -▁komposistĂ„ -9724 -▁kompositor -9725 -▁konsensian -9726 -▁kontinente -9727 -▁kontradisi -9728 -▁kosmunauta -9729 -▁kumokontra -9730 -▁kustumbren -9731 -▁laibirihan -9732 -▁liberation -9733 -▁lingguista -9734 -▁lingguĂ„hen -9735 -▁litchfield -9736 -▁luzonensis -9737 -▁macrocarpa -9738 -▁madeskribi -9739 -▁madestrosa -9740 -▁madibibide -9741 -▁mafunhayan -9742 -▁magapokpok -9743 -▁mageftungo -9744 -▁magueresse -9745 -▁mamensiona -9746 -▁mamfabulas -9747 -▁manapattao -9748 -▁manareklan -9749 -▁mandadanña -9750 -▁mandesponi -9751 -▁manengheng -9752 -▁manespañot -9753 -▁manggeftao -9754 -▁manguentos -9755 -▁manmalingu -9756 -▁manmamanda -9757 -▁manmamatto -9758 -▁manmapetdi -9759 -▁manmapresu -9760 -▁manmatungo -9761 -▁manmañugon -9762 -▁maprobecha -9763 -▁marehistra -9764 -▁marikonisa -9765 -▁masĂ„sĂ„ngan -9766 -▁matakpangi -9767 -▁matgaritan -9768 -▁matimatiko -9769 -▁matrimonia -9770 -▁mayengyong -9771 -▁mckeesport -9772 -▁meansville -9773 -▁meditasion -9774 -▁midwestern -9775 -▁minaneneha -9776 -▁minataknga -9777 -▁ministeriu -9778 -▁montbrison -9779 -▁movimiento -9780 -▁nepalensis -9781 -▁neurologĂ­a -9782 -▁nonchalant -9783 -▁otganisayi -9784 -▁otginasion -9785 -▁parotikova -9786 -▁peninsulan -9787 -▁petmanente -9788 -▁philippine -9789 -▁pilgrimage -9790 -▁pittsburgh -9791 -▁pittsfield -9792 -▁populasiĂłn -9793 -▁population -9794 -▁porterdale -9795 -▁posteriori -9796 -▁principion -9797 -▁principles -9798 -▁propositun -9799 -▁proteksion -9800 -▁ptilinopus -9801 -▁qerrulliik -9802 -▁quincheros -9803 -▁representa -9804 -▁rigulasion -9805 -▁rolihlahla -9806 -▁rumikokohi -9807 -▁sakrifisiu -9808 -▁schauspiel -9809 -▁schumacher -9810 -▁seguidores -9811 -▁sentimetru -9812 -▁shaktoolik -9813 -▁shintoismo -9814 -▁shishmaref -9815 -▁sientifiku -9816 -▁sinahanzza -9817 -▁sinembatgo -9818 -▁sinembĂ„tgo -9819 -▁snellville -9820 -▁spontaneum -9821 -▁statesboro -9822 -▁stravinsky -9823 -▁supervisor -9824 -▁swainsboro -9825 -▁talcahuano -9826 -▁teritoriat -9827 -▁teritorion -9828 -▁teritoriun -9829 -▁tremouroux -9830 -▁trimesters -9831 -▁tubefilter -9832 -▁unalakleet -9833 -▁upplevelse -9834 -▁valenciana -9835 -▁valladolid -9836 -▁variations -9837 -▁venerasion -9838 -▁verecundus -9839 -▁verrocchio -9840 -▁webformats -9841 -▁wikijunior -9842 -▁wikipedian -9843 -▁wikisource -9844 -▁wiktionary -9845 -▁woonsocket -9846 -▁yegoryevsk -9847 -▁yeographia -9848 -▁yeogrĂ„fiku -9849 -▁yumayammak -9850 -▁österreich -9851 -â–ÎŽÎ·ÎŒÎżÎșÏÎ±Ï„ÎŻÎ± -9852 -dokumentĂ„riu -9853 -magyarorszĂĄg -9854 -ofisiatmente -9855 -▁adairsville -9856 -▁alagatapeha -9857 -▁antibiotiku -9858 -▁aquaculture -9859 -▁archdiocese -9860 -▁arijansensĂș -9861 -▁association -9862 -▁astronomika -9863 -▁balochistan -9864 -▁barkapellas -9865 -▁bibliotecas -9866 -▁bokabulario -9867 -▁bokabulariu -9868 -▁brandenburg -9869 -▁bucaramanga -9870 -▁carlinville -9871 -▁carnesville -9872 -▁chiguayante -9873 -▁chuathbaluk -9874 -▁cinquentona -9875 -▁cosmoglotta -9876 -▁debonisario -9877 -▁deskrikribi -9878 -▁deskubridot -9879 -▁destinasion -9880 -▁diksionĂ„riu -9881 -▁diplomatiku -9882 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