Upload all models and assets for ch (latest)

.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

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. Guåha 774 na tataogues na populasion i se...`
+**Sample 1:** `+Afghanistan 125px Anthem: Millī سرود 300px Afghanistan capitat Kabul. Guåha na ...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁do er un , ▁nasong - song ▁gi ▁estados ▁unidos ... (+16 more)` | 26 |
-| 16k | `▁doerun , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guåha ... (+14 more)` | 24 |
+| 8k | `▁+ af ghanistan ▁ 1 2 5 px ▁anthem : ... (+21 more)` | 31 |
+| 16k | `▁+ afghanistan ▁ 1 2 5 px ▁anthem : ▁millī ... (+20 more)` | 30 |
 
-**Sample 2:** `Newhalen, nasong-song gi Estados Unidos. Guåha 190 na tataogues na populasion i ...`
+**Sample 2:** `Cartersville, nasong-song gi Estados Unidos. Guåha 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 . ▁guåha ... (+14 more)` | 24 |
+| 8k | `▁carters ville , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+18 more)` | 28 |
+| 16k | `▁cartersville , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guåha ... (+17 more)` | 27 |
 
-**Sample 3:** `Larsen Bay, nasong-song gi Estados Unidos. Guåha 87 na tataogues na populasion i...`
+**Sample 3:** `Waleska, nasong-song gi Estados Unidos. Guåha 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 . ▁guåha ... (+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 guåhan si nanå ña ti ha nå an i senso ine giya guåhan i`
-2. `na tataogues na petsona siha manma å 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 società`
+2. `na populasion i senso unidos guåha 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 hålom hånom 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_åchokkas_na_kri`
-2. `i_ta_magu_gi_i_ha`
-3. `na'neho_"thunidos`
+1. `a_para_ediu_nasto`
+2. `i_me":_ki,_vícite`
+3. `na'i_achamane_pås`
 
 **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 | guåha | 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 | säger | 2 |
+| 2 | ett | 2 |
+| 3 | så | 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 |
-| R² (Goodness of Fit) | 0.986461 |
+| Zipf Coefficient | 0.9547 |
+| R² (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:** R²=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:** R²=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, bånda |
-| `-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*

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