Upload all models and assets for cdo (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: cdo
-language_name: CDO
+language_name: Min Dong Chinese
 language_family: sinitic_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-sinitic_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: 2.892
+    value: 2.891
   - name: best_isotropy
     type: isotropy
-    value: 0.5551
+    value: 0.5099
   - name: vocabulary_size
     type: vocab
     value: 0
 generated: 2026-01-03
 ---
 
-# CDO - Wikilangs Models
+# Min Dong Chinese - Wikilangs Models
 ## Comprehensive Research Report & Full Ablation Study
 
-This repository contains NLP models trained and evaluated by Wikilangs, specifically on **CDO** Wikipedia data.
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Min Dong Chinese** 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 |
 |------------|-------------|---------------|----------|--------------|
-| **32k** | 2.757x | 2.76 | 0.1034% | 257,325 |
-| **64k** | 2.892x 🏆 | 2.90 | 0.1084% | 245,327 |
+| **32k** | 2.755x | 2.76 | 0.1043% | 256,064 |
+| **64k** | 2.891x 🏆 | 2.89 | 0.1094% | 244,079 |
 
 ### Tokenization Examples
 
 Below are sample sentences tokenized with each vocabulary size:
 
-**Sample 1:** `Bashkortostan sê Ngò̤-lò̤-sṳ̆ gì siŏh ciáh gê̤ṳng-huò-guók. gì gê̤ṳng-huò-guók`
+**Sample 1:** `Jessamine Gông (Ĭng-ngṳ̄: Jessamine County) sê Mī-guók Kentucky gì siŏh ciáh gôn...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 32k | `▁b ash k ort os tan ▁sê ▁ngò ̤- lò ... (+17 more)` | 27 |
-| 64k | `▁bash k ortos tan ▁sê ▁ngò ̤- lò ̤- sṳ̆ ... (+15 more)` | 25 |
+| 32k | `▁j ess am ine ▁gông ▁( ĭng - ngṳ̄ : ... (+18 more)` | 28 |
+| 64k | `▁jessamine ▁gông ▁( ĭng - ngṳ̄ : ▁jessamine ▁county ) ... (+12 more)` | 22 |
 
-**Sample 2:** `Montague Gông (Ĭng-ngṳ̄: Montague County) sê Mī-guók Texas gì siŏh ciáh gông. gì...`
+**Sample 2:** `2 nguŏk 1 hô̤ sê nùng-lĭk 2 nguŏk gì dâ̤ 1 gĕ̤ng. 2 nguŏk`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 32k | `▁mont a gue ▁gông ▁( ĭng - ngṳ̄ : ▁mont ... (+16 more)` | 26 |
-| 64k | `▁montague ▁gông ▁( ĭng - ngṳ̄ : ▁montague ▁county ) ... (+12 more)` | 22 |
+| 32k | `▁ 2 ▁nguŏk ▁ 1 ▁hô̤ ▁sê ▁nùng - lĭk ... (+12 more)` | 22 |
+| 64k | `▁ 2 ▁nguŏk ▁ 1 ▁hô̤ ▁sê ▁nùng - lĭk ... (+12 more)` | 22 |
 
-**Sample 3:** `Ochiltree Gông (Ĭng-ngṳ̄: Ochiltree County) sê Mī-guók Texas gì siŏh ciáh gông. ...`
+**Sample 3:** `McLean Gông (Ĭng-ngṳ̄: McLean County) sê Mī-guók Kentucky gì siŏh ciáh gông. gì ...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 32k | `▁o chi l t re e ▁gông ▁( ĭng - ... (+22 more)` | 32 |
-| 64k | `▁ochiltree ▁gông ▁( ĭng - ngṳ̄ : ▁ochiltree ▁county ) ... (+12 more)` | 22 |
+| 32k | `▁mclean ▁gông ▁( ĭng - ngṳ̄ : ▁mclean ▁county ) ... (+12 more)` | 22 |
+| 64k | `▁mclean ▁gông ▁( ĭng - ngṳ̄ : ▁mclean ▁county ) ... (+12 more)` | 22 |
 
 
 ### Key Findings
 
-- **Best Compression:** 64k achieves 2.892x compression
-- **Lowest UNK Rate:** 32k with 0.1034% unknown tokens
+- **Best Compression:** 64k achieves 2.891x compression
+- **Lowest UNK Rate:** 32k with 0.1043% 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 | 3,105 | 11.60 | 11,679 | 27.7% | 59.2% |
-| **2-gram** | Subword | 342 🏆 | 8.42 | 6,912 | 63.5% | 95.8% |
-| **3-gram** | Word | 4,698 | 12.20 | 17,954 | 23.8% | 52.2% |
-| **3-gram** | Subword | 1,659 | 10.70 | 21,000 | 36.1% | 75.8% |
-| **4-gram** | Word | 8,483 | 13.05 | 30,938 | 18.6% | 45.4% |
-| **4-gram** | Subword | 5,744 | 12.49 | 69,193 | 23.7% | 55.8% |
+| **2-gram** | Word | 3,139 | 11.62 | 11,777 | 27.5% | 59.0% |
+| **2-gram** | Subword | 341 🏆 | 8.41 | 6,920 | 63.6% | 95.8% |
+| **3-gram** | Word | 4,753 | 12.21 | 18,116 | 23.7% | 52.0% |
+| **3-gram** | Subword | 1,655 | 10.69 | 21,022 | 36.1% | 75.9% |
+| **4-gram** | Word | 8,558 | 13.06 | 31,134 | 18.5% | 45.2% |
+| **4-gram** | Subword | 5,737 | 12.49 | 69,190 | 23.7% | 55.8% |
+| **5-gram** | Word | 7,101 | 12.79 | 23,547 | 17.3% | 48.1% |
+| **5-gram** | Subword | 13,084 | 13.68 | 106,632 | 16.4% | 41.9% |
 
 ### Top 5 N-grams by Size
 
@@ -142,18 +154,18 @@ Below are sample sentences tokenized with each vocabulary size:
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `gì siŏh` | 6,258 |
-| 2 | `siŏh ciáh` | 6,232 |
-| 3 | `mī guók` | 3,385 |
-| 4 | `sê mī` | 3,191 |
+| 1 | `gì siŏh` | 6,261 |
+| 2 | `siŏh ciáh` | 6,233 |
+| 3 | `mī guók` | 3,384 |
+| 4 | `sê mī` | 3,190 |
 | 5 | `gì gông` | 3,000 |
 
 **3-grams (Word):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `gì siŏh ciáh` | 5,413 |
-| 2 | `sê mī guók` | 3,173 |
+| 1 | `gì siŏh ciáh` | 5,415 |
+| 2 | `sê mī guók` | 3,172 |
 | 3 | `siŏh ciáh gông` | 3,000 |
 | 4 | `ciáh gông gì` | 2,557 |
 | 5 | `gông gì gông` | 2,557 |
@@ -163,47 +175,67 @@ Below are sample sentences tokenized with each vocabulary size:
 | Rank | N-gram | Count |
 |------|--------|-------|
 | 1 | `gì siŏh ciáh gông` | 3,000 |
-| 2 | `ciáh gông gì gông` | 2,557 |
-| 3 | `siŏh ciáh gông gì` | 2,557 |
+| 2 | `siŏh ciáh gông gì` | 2,557 |
+| 3 | `ciáh gông gì gông` | 2,557 |
 | 4 | `county sê mī guók` | 1,971 |
 | 5 | `gông sê mī guók` | 1,029 |
 
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `siŏh ciáh gông gì gông` | 2,557 |
+| 2 | `gì siŏh ciáh gông gì` | 2,557 |
+| 3 | `diē sié gì siŏh ciáh` | 390 |
+| 4 | `ìng mìng gê̤ṳng huò guók` | 385 |
+| 5 | `dâi chók sié guó sié` | 348 |
+
 **2-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `n g` | 146,797 |
-| 2 | `_ g` | 59,970 |
-| 3 | `g -` | 55,946 |
-| 4 | `g _` | 55,139 |
-| 5 | `_ s` | 41,311 |
+| 1 | `n g` | 148,099 |
+| 2 | `_ g` | 60,261 |
+| 3 | `g -` | 56,437 |
+| 4 | `g _` | 55,736 |
+| 5 | `_ s` | 41,503 |
 
 **3-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `n g -` | 55,920 |
-| 2 | `n g _` | 55,025 |
-| 3 | `_ g ì` | 23,090 |
-| 4 | `g ì _` | 22,312 |
-| 5 | `_ s i` | 14,134 |
+| 1 | `n g -` | 56,411 |
+| 2 | `n g _` | 55,623 |
+| 3 | `_ g ì` | 23,145 |
+| 4 | `g ì _` | 22,365 |
+| 5 | `_ s i` | 14,188 |
 
 **4-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `_ g ì _` | 22,161 |
-| 2 | `_ s ê _` | 13,231 |
-| 3 | `n g _ g` | 11,336 |
-| 4 | `i ŏ h _` | 10,632 |
-| 5 | `_ s i ŏ` | 9,391 |
+| 1 | `_ g ì _` | 22,216 |
+| 2 | `_ s ê _` | 13,258 |
+| 3 | `n g _ g` | 11,418 |
+| 4 | `i ŏ h _` | 10,678 |
+| 5 | `_ s i ŏ` | 9,423 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ s i ŏ h` | 9,171 |
+| 2 | `_ g ô n g` | 9,066 |
+| 3 | `s i ŏ h _` | 8,474 |
+| 4 | `_ g ì _ s` | 8,113 |
+| 5 | `i ŏ h _ c` | 7,536 |
 
 
 ### Key Findings
 
-- **Best Perplexity:** 2-gram (subword) with 342
+- **Best Perplexity:** 2-gram (subword) with 341
 - **Entropy Trend:** Decreases with larger n-grams (more predictable)
-- **Coverage:** Top-1000 patterns cover ~56% of corpus
+- **Coverage:** Top-1000 patterns cover ~42% 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 | 0.4882 | 1.403 | 4.73 | 29,670 | 51.2% |
-| **1** | Subword | 0.3461 | 1.271 | 2.92 | 25,622 | 65.4% |
-| **2** | Word | 0.3187 | 1.247 | 1.80 | 139,308 | 68.1% |
-| **2** | Subword | 0.2753 | 1.210 | 1.79 | 74,780 | 72.5% |
-| **3** | Word | 0.1201 | 1.087 | 1.23 | 249,012 | 88.0% |
-| **3** | Subword | 0.2343 | 1.176 | 1.69 | 133,665 | 76.6% |
-| **4** | Word | 0.0526 🏆 | 1.037 | 1.09 | 301,670 | 94.7% |
-| **4** | Subword | 0.2290 | 1.172 | 1.53 | 225,577 | 77.1% |
+| **1** | Word | 0.4885 | 1.403 | 4.74 | 29,717 | 51.2% |
+| **1** | Subword | 0.3463 | 1.271 | 2.92 | 25,650 | 65.4% |
+| **2** | Word | 0.3200 | 1.248 | 1.81 | 139,964 | 68.0% |
+| **2** | Subword | 0.2749 | 1.210 | 1.79 | 74,833 | 72.5% |
+| **3** | Word | 0.1204 | 1.087 | 1.23 | 250,754 | 88.0% |
+| **3** | Subword | 0.2342 | 1.176 | 1.69 | 133,597 | 76.6% |
+| **4** | Word | 0.0528 🏆 | 1.037 | 1.09 | 303,909 | 94.7% |
+| **4** | Subword | 0.2293 | 1.172 | 1.54 | 225,426 | 77.1% |
 
 ### 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. `gì hiŏng 蘆洋鄉 bìng dēng có̤i kăi sṳ̄ gó nâ háng nè̤ng gó̤ lō̤ 法老 gái`
-2. `sê dṳ̆ng huà ìng chê 邢臺市 lòng cĭ 退之 sê ĕng diŏh adelaide ô 2 nguŏk`
-3. `siŏh bĭk cék éng sáuk īng lĭk â̤ dé̤ṳng buŏng nàng áng cê sê turkic ngṳ̄`
+1. `gì siŏh déng bĭng giàng guó mī guók gì kó găk hók ciŭ gì siŏh gă`
+2. `sê mī guók sì dâi chók sirens nièng gáu huòng 閩江公園 dê lī hŏk â dā̤`
+3. `siŏh cṳ̄ng ī gì céng sī mò̤ siū ăng gô iók hâng săng sê mī guók`
 
 **Context Size 2:**
 
-1. `gì siŏh cṳ̄ng lòi ĭng ôi sĭng ô 79 ciáh mŭk sṳ̆ 佳木斯 sê dṳ̆ng guók sĭng`
-2. `siŏh ciáh chê hăk kṳ̆ 市轄區 lĭk sṳ̄ diē sié sê siŏh cṳ̄ng ciŏng muòng dò̤ lā̤`
-3. `mī guók montana gì siŏh ciáh gông gì gông`
+1. `gì siŏh ciáh gông gì gông`
+2. `siŏh ciáh mìng cŭk iâ sê giū cê̤ṳ sìng bŏng gá ĭ sá̤ bò̤ dìng uòng 陳垣`
+3. `mī guók tennessee gì siŏh cṳ̄ng â̤ buŏi gì sèng dău cê mō̤ gì dâ̤ 140 ôi`
 
 **Context Size 3:**
 
-1. `gì siŏh ciáh duâi kṳ̆ gì duâi kṳ̆`
-2. `sê mī guók kansas gì siŏh ciáh cê mō̤ diŏh eta gì piăng âu gâe̤ng sigma gì sèng`
+1. `gì siŏh ciáh gáu puái céng tūng puái nêng dêng sê siŏh ciáh bìng nièng tàu gĕ̤ng sê`
+2. `sê mī guók dâ̤ 19 êng gáu huòng 310 nièng gáu 314 nièng câi ôi nièng hô̤ tái`
 3. `siŏh ciáh gông gì gông`
 
 **Context Size 4:**
 
 1. `gì siŏh ciáh gông gì gông`
 2. `siŏh ciáh gông gì gông`
-3. `county sê mī guók nebraska gì siŏh ciáh gông gì gông`
+3. `county sê mī guók georgia gì siŏh ciáh gông gì gông`
 
 
 ### 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. `_ônià_hô̤_sênty)_`
-2. `gì_ciônièng,_sṳ̄:`
-3. `ngăngôner_g-ccáh`
+1. `_7_g_sê-ngì-gì_s`
+2. `g_cīng_(ĭngṳ̄_sēn`
+3. `nerotŭ_sê_g_sê-m`
 
 **Context Size 2:**
 
-1. `nguô_ka_cĭ_gì_dâ̤_`
-2. `_gì_sié-sê_hŏk-cê`
-3. `g-hĭ_(獨聯體)_gông_n`
+1. `ngiù_hâiu-gáu-sī“`
+2. `_guô-hô̤_gāi_gôngu`
+3. `g-gă_dìng_coung-h`
 
 **Context Size 3:**
 
-1. `ng-hèng_biéng,_mac`
-2. `ng_adahoma_gì_(兩個聲`
-3. `_gì_siōng-dĕ̤ng-ŭk_`
+1. `ng-huá-hŏk-pŭng-cŭ`
+2. `ng_siàng_gâe̤ng_(埃及`
+3. `_gì_pàng,_ĭ_mĕ̤k-ci`
 
 **Context Size 4:**
 
-1. `_gì_«sṳ̀ng-kṳ̆_dĕk-bi`
-2. `_sê_„發現更大的世界“)_có̤_c`
-3. `ng_găk_chăng-muò_(𧋘`
+1. `_gì_siŏh_ciáh_dĭng_`
+2. `_sê_mī-guók-nè̤ng_nè̤`
+3. `ng_gék-cĭu_gó_ô_sié`
 
 
 ### Key Findings
 
 - **Best Predictability:** Context-4 (word) with 94.7% predictability
 - **Branching Factor:** Decreases with context size (more deterministic)
-- **Memory Trade-off:** Larger contexts require more storage (225,577 contexts)
+- **Memory Trade-off:** Larger contexts require more storage (225,426 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 | 9,559 |
-| Total Tokens | 467,385 |
-| Mean Frequency | 48.89 |
+| Vocabulary Size | 9,566 |
+| Total Tokens | 470,049 |
+| Mean Frequency | 49.14 |
 | Median Frequency | 3 |
-| Frequency Std Dev | 395.71 |
+| Frequency Std Dev | 396.77 |
 
 ### Most Common Words
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | gì | 23,295 |
-| 2 | sê | 14,068 |
-| 3 | siŏh | 9,247 |
+| 1 | gì | 23,347 |
+| 2 | sê | 14,101 |
+| 3 | siŏh | 9,273 |
 | 4 | gông | 9,087 |
-| 5 | guók | 8,549 |
-| 6 | ciáh | 7,131 |
-| 7 | nièng | 5,854 |
-| 8 | ngṳ̄ | 5,277 |
-| 9 | sié | 4,616 |
-| 10 | gáu | 4,179 |
+| 5 | guók | 8,556 |
+| 6 | ciáh | 7,148 |
+| 7 | nièng | 5,899 |
+| 8 | ngṳ̄ | 5,273 |
+| 9 | sié | 4,623 |
+| 10 | gáu | 4,196 |
 
 ### Least Common Words (from vocabulary)
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | woolridge | 2 |
-| 2 | imperiyası | 2 |
-| 3 | abş | 2 |
-| 4 | çox | 2 |
-| 5 | dünyada | 2 |
-| 6 | bütün | 2 |
-| 7 | 嘉祿 | 2 |
-| 8 | 六一路 | 2 |
-| 9 | 神壇樹 | 2 |
-| 10 | 신단수 | 2 |
+| 1 | 小天王國 | 2 |
+| 2 | baidu | 2 |
+| 3 | 宋在康 | 2 |
+| 4 | woolridge | 2 |
+| 5 | 六一路 | 2 |
+| 6 | 神壇樹 | 2 |
+| 7 | 신단수 | 2 |
+| 8 | 날 | 2 |
+| 9 | kbo | 2 |
+| 10 | 우주항공청 | 2 |
 
 ### Zipf's Law Analysis
 
 | Metric | Value |
 |--------|-------|
-| Zipf Coefficient | 1.3995 |
-| R² (Goodness of Fit) | 0.957431 |
+| Zipf Coefficient | 1.4007 |
+| R² (Goodness of Fit) | 0.957225 |
 | Adherence Quality | **excellent** |
 
 ### Coverage Analysis
 
 | Top N Words | Coverage |
 |-------------|----------|
-| Top 100 | 52.2% |
-| Top 1,000 | 91.7% |
+| Top 100 | 52.1% |
+| Top 1,000 | 91.8% |
 | Top 5,000 | 98.0% |
 | Top 10,000 | 0.0% |
 
 ### Key Findings
 
-- **Zipf Compliance:** R²=0.9574 indicates excellent adherence to Zipf's law
-- **High Frequency Dominance:** Top 100 words cover 52.2% of corpus
-- **Long Tail:** -441 words needed for remaining 100.0% coverage
+- **Zipf Compliance:** R²=0.9572 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 52.1% of corpus
+- **Long Tail:** -434 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 | 0.5551 🏆 | 0.4156 | N/A | N/A |
-| **mono_64d** | 64 | 0.1856 | 0.4055 | N/A | N/A |
-| **mono_128d** | 128 | 0.0279 | 0.4128 | N/A | N/A |
+| **mono_32d** | 32 | 0.5099 | 0.4122 | N/A | N/A |
+| **mono_64d** | 64 | 0.2128 | 0.3926 | N/A | N/A |
+| **mono_128d** | 128 | 0.0308 | 0.3921 | N/A | N/A |
+| **aligned_32d** | 32 | 0.5099 🏆 | 0.4223 | 0.0120 | 0.1260 |
+| **aligned_64d** | 64 | 0.2128 | 0.3730 | 0.0280 | 0.2380 |
+| **aligned_128d** | 128 | 0.0308 | 0.3804 | 0.0380 | 0.2160 |
 
 ### Key Findings
 
-- **Best Isotropy:** mono_32d with 0.5551 (more uniform distribution)
-- **Semantic Density:** Average pairwise similarity of 0.4113. Lower values indicate better semantic separation.
-- **Alignment Quality:** No aligned models evaluated in this run.
+- **Best Isotropy:** aligned_32d with 0.5099 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3954. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 3.8% 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 | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.147** | Low formulaic content | - |
 
 ### 6.2 Affix Inventory (Productive Units)
 
@@ -424,10 +459,10 @@ Bound stems are high-frequency subword units that are semantically cohesive but
 
 | Stem | Cohesion | Substitutability | Examples |
 |------|----------|------------------|----------|
-| `áung` | 1.97x | 9 contexts | táung, láung, dáung |
-| `âung` | 1.96x | 9 contexts | câung, bâung, hâung |
-| `iăng` | 1.80x | 7 contexts | hiăng, siăng, giăng |
-| `iāng` | 1.55x | 8 contexts | liāng, biāng, ciāng |
+| `áung` | 2.01x | 9 contexts | dáung, sáung, gáung |
+| `âung` | 1.99x | 9 contexts | hâung, dâung, lâung |
+| `iăng` | 1.88x | 7 contexts | siăng, hiăng, tiăng |
+| `iāng` | 1.54x | 8 contexts | niāng, biāng, tiāng |
 
 ### 6.4 Affix Compatibility (Co-occurrence)
 
@@ -446,7 +481,7 @@ Using **Recursive Hierarchical Substitutability**, we decompose complex words in
 ### 6.6 Linguistic Interpretation
 
 > **Automated Insight:**
-The language CDO 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 Min Dong Chinese shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
 
 ---
 ## 7. Summary & Recommendations
@@ -458,7 +493,7 @@ The language CDO appears to be more isolating or has a highly fixed vocabulary.
 | Component | Recommended | Rationale |
 |-----------|-------------|-----------|
 | Tokenizer | **64k BPE** | Best compression (2.89x) |
-| N-gram | **2-gram** | Lowest perplexity (342) |
+| N-gram | **2-gram** | Lowest perplexity (341) |
 | Markov | **Context-4** | Highest predictability (94.7%) |
 | Embeddings | **100d** | Balanced semantic capture and isotropy |
 
@@ -673,4 +708,4 @@ MIT License - Free for academic and commercial use.
 ---
 *Generated by Wikilangs Models Pipeline*
 
-*Report Date: 2026-01-03 09:43:04*
+*Report Date: 2026-01-03 20:07:11*

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