Upload all models and assets for bbc (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: bbc
-language_name: BBC
+language_name: Batak Toba
 language_family: austronesian_batak
 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_batak
 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: 3.663
+    value: 3.662
   - name: best_isotropy
     type: isotropy
-    value: 0.8223
+    value: 0.8133
   - name: vocabulary_size
     type: vocab
     value: 0
 generated: 2026-01-03
 ---
 
-# BBC - Wikilangs Models
+# Batak Toba - Wikilangs Models
 ## Comprehensive Research Report & Full Ablation Study
 
-This repository contains NLP models trained and evaluated by Wikilangs, specifically on **BBC** Wikipedia data.
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Batak Toba** 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)
@@ -80,43 +90,43 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and
 
 | Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
 |------------|-------------|---------------|----------|--------------|
-| **8k** | 3.308x | 3.31 | 0.2131% | 1,665,136 |
-| **16k** | 3.527x | 3.53 | 0.2273% | 1,561,615 |
-| **32k** | 3.663x 🏆 | 3.66 | 0.2360% | 1,503,727 |
+| **8k** | 3.300x | 3.30 | 0.2266% | 1,666,856 |
+| **16k** | 3.529x | 3.53 | 0.2423% | 1,558,753 |
+| **32k** | 3.662x 🏆 | 3.66 | 0.2515% | 1,502,009 |
 
 ### Tokenization Examples
 
 Below are sample sentences tokenized with each vocabulary size:
 
-**Sample 1:** `Pedurungan i ma sada huta na adong di Kecamatan Taman, Kabupaten Pemalang, Propi...`
+**Sample 1:** `Janji i ma sada huta (desa) na adong di Kecamatan Siempat Nempu Hilir, Kabupaten...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁ped ur ungan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ... (+12 more)` | 22 |
-| 16k | `▁pedurungan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁taman ... (+10 more)` | 20 |
-| 32k | `▁pedurungan ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁taman ... (+10 more)` | 20 |
+| 8k | `▁janji ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ▁adong ... (+16 more)` | 26 |
+| 16k | `▁janji ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ▁adong ... (+16 more)` | 26 |
+| 32k | `▁janji ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ▁adong ... (+16 more)` | 26 |
 
-**Sample 2:** `Mulyoharjo i ma sada Kelurahan na adong di Kecamatan Pemalang, Kabupaten Pemalan...`
+**Sample 2:** `Siboras i ma sada huta (desa) na adong di Kecamatan Silima Pungga Pungga, Kabupa...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁mul y oharjo ▁i ▁ma ▁sada ▁kelurahan ▁na ▁adong ▁di ... (+12 more)` | 22 |
-| 16k | `▁mulyoharjo ▁i ▁ma ▁sada ▁kelurahan ▁na ▁adong ▁di ▁kecamatan ▁pemalang ... (+10 more)` | 20 |
-| 32k | `▁mulyoharjo ▁i ▁ma ▁sada ▁kelurahan ▁na ▁adong ▁di ▁kecamatan ▁pemalang ... (+10 more)` | 20 |
+| 8k | `▁sib oras ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ... (+16 more)` | 26 |
+| 16k | `▁siboras ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ▁adong ... (+15 more)` | 25 |
+| 32k | `▁siboras ▁i ▁ma ▁sada ▁huta ▁( desa ) ▁na ▁adong ... (+15 more)` | 25 |
 
-**Sample 3:** `Klegen i ma sada huta na adong di Kecamatan Comal, Kabupaten Pemalang, Propinsi ...`
+**Sample 3:** `Sukorejo i ma sada huta na adong di Kecamatan Ulujami, Kabupaten Pemalang, Propi...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁kl egen ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
-| 16k | `▁klegen ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁comal ... (+10 more)` | 20 |
-| 32k | `▁klegen ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁comal ... (+10 more)` | 20 |
+| 8k | `▁suk orejo ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ... (+11 more)` | 21 |
+| 16k | `▁sukorejo ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁ulujami ... (+10 more)` | 20 |
+| 32k | `▁sukorejo ▁i ▁ma ▁sada ▁huta ▁na ▁adong ▁di ▁kecamatan ▁ulujami ... (+10 more)` | 20 |
 
 
 ### Key Findings
 
-- **Best Compression:** 32k achieves 3.663x compression
-- **Lowest UNK Rate:** 8k with 0.2131% unknown tokens
+- **Best Compression:** 32k achieves 3.662x compression
+- **Lowest UNK Rate:** 8k with 0.2266% unknown tokens
 - **Trade-off:** Larger vocabularies improve compression but increase model size
 - **Recommendation:** 32k vocabulary provides optimal balance for production use
 
@@ -133,12 +143,14 @@ 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 | 8,528 | 13.06 | 26,426 | 17.5% | 42.8% |
-| **2-gram** | Subword | 185 🏆 | 7.53 | 3,491 | 77.6% | 99.2% |
-| **3-gram** | Word | 22,579 | 14.46 | 43,165 | 8.3% | 25.2% |
-| **3-gram** | Subword | 1,219 | 10.25 | 18,183 | 38.1% | 83.2% |
-| **4-gram** | Word | 44,749 | 15.45 | 67,595 | 5.7% | 16.0% |
-| **4-gram** | Subword | 5,604 | 12.45 | 70,417 | 19.7% | 54.7% |
+| **2-gram** | Word | 8,503 | 13.05 | 26,404 | 17.5% | 42.9% |
+| **2-gram** | Subword | 185 🏆 | 7.53 | 3,447 | 77.7% | 99.2% |
+| **3-gram** | Word | 22,449 | 14.45 | 43,137 | 8.4% | 25.3% |
+| **3-gram** | Subword | 1,216 | 10.25 | 18,046 | 38.1% | 83.2% |
+| **4-gram** | Word | 44,360 | 15.44 | 67,584 | 5.9% | 16.2% |
+| **4-gram** | Subword | 5,587 | 12.45 | 70,061 | 19.7% | 54.7% |
+| **5-gram** | Word | 29,774 | 14.86 | 42,910 | 7.1% | 18.6% |
+| **5-gram** | Subword | 17,403 | 14.09 | 153,430 | 12.1% | 36.7% |
 
 ### Top 5 N-grams by Size
 
@@ -148,8 +160,8 @@ Below are sample sentences tokenized with each vocabulary size:
 |------|--------|-------|
 | 1 | `angka na` | 4,424 |
 | 2 | `dung i` | 4,327 |
-| 3 | `ni si` | 4,061 |
-| 4 | `i ma` | 3,622 |
+| 3 | `ni si` | 4,060 |
+| 4 | `i ma` | 3,682 |
 | 5 | `ni jahowa` | 2,892 |
 
 **3-grams (Word):**
@@ -157,57 +169,77 @@ Below are sample sentences tokenized with each vocabulary size:
 | Rank | N-gram | Count |
 |------|--------|-------|
 | 1 | `anak ni si` | 1,613 |
-| 2 | `dung i ninna` | 735 |
-| 3 | `i ma sada` | 728 |
-| 4 | `hata ni jahowa` | 703 |
-| 5 | `na adong di` | 690 |
+| 2 | `i ma sada` | 784 |
+| 3 | `na adong di` | 741 |
+| 4 | `dung i ninna` | 735 |
+| 5 | `hata ni jahowa` | 703 |
 
 **4-grams (Word):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
 | 1 | `on do hata ni` | 423 |
-| 2 | `songon on do hata` | 408 |
-| 3 | `i ma sada huta` | 363 |
-| 4 | `angka anak ni si` | 336 |
-| 5 | `na adong di kecamatan` | 297 |
+| 2 | `i ma sada huta` | 417 |
+| 3 | `songon on do hata` | 408 |
+| 4 | `na adong di kecamatan` | 353 |
+| 5 | `angka anak ni si` | 336 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `songon on do hata ni` | 406 |
+| 2 | `on do hata ni jahowa` | 250 |
+| 3 | `i ma sada huta na` | 215 |
+| 4 | `desa na adong di kecamatan` | 191 |
+| 5 | `km jala godang ni ruasna` | 175 |
 
 **2-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `a _` | 206,904 |
-| 2 | `a n` | 205,541 |
-| 3 | `n g` | 154,122 |
-| 4 | `i _` | 143,001 |
-| 5 | `n a` | 122,611 |
+| 1 | `a _` | 206,965 |
+| 2 | `a n` | 205,323 |
+| 3 | `n g` | 154,062 |
+| 4 | `i _` | 142,882 |
+| 5 | `n a` | 122,548 |
 
 **3-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `a n g` | 81,985 |
-| 2 | `_ m a` | 76,327 |
-| 3 | `n a _` | 58,974 |
-| 4 | `_ n a` | 53,547 |
-| 5 | `a n _` | 51,343 |
+| 1 | `a n g` | 81,918 |
+| 2 | `_ m a` | 76,355 |
+| 3 | `n a _` | 58,981 |
+| 4 | `_ n a` | 53,557 |
+| 5 | `a n _` | 51,287 |
 
 **4-grams (Subword):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `_ n i _` | 34,971 |
-| 2 | `_ n a _` | 33,600 |
-| 3 | `_ d i _` | 25,982 |
-| 4 | `a n g k` | 24,957 |
-| 5 | `_ m a _` | 23,771 |
+| 1 | `_ n i _` | 34,904 |
+| 2 | `_ n a _` | 33,621 |
+| 3 | `_ d i _` | 25,919 |
+| 4 | `a n g k` | 24,948 |
+| 5 | `_ m a _` | 23,827 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n g k a` | 19,235 |
+| 2 | `_ a n g k` | 17,946 |
+| 3 | `n g k a _` | 17,765 |
+| 4 | `_ j a l a` | 14,671 |
+| 5 | `j a l a _` | 14,594 |
 
 
 ### Key Findings
 
 - **Best Perplexity:** 2-gram (subword) with 185
 - **Entropy Trend:** Decreases with larger n-grams (more predictable)
-- **Coverage:** Top-1000 patterns cover ~55% of corpus
+- **Coverage:** Top-1000 patterns cover ~37% of corpus
 - **Recommendation:** 4-gram or 5-gram for best predictive performance
 
 ---
@@ -223,14 +255,14 @@ Below are sample sentences tokenized with each vocabulary size:
 
 | Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
 |---------|---------|-------------|------------|------------------|-----------------|----------------|
-| **1** | Word | 0.9188 | 1.890 | 6.44 | 50,697 | 8.1% |
-| **1** | Subword | 0.9378 | 1.916 | 7.17 | 1,435 | 6.2% |
-| **2** | Word | 0.3742 | 1.296 | 2.02 | 325,909 | 62.6% |
-| **2** | Subword | 0.7095 | 1.635 | 4.04 | 10,290 | 29.0% |
-| **3** | Word | 0.1535 | 1.112 | 1.28 | 658,447 | 84.6% |
-| **3** | Subword | 0.6445 | 1.563 | 3.15 | 41,529 | 35.5% |
-| **4** | Word | 0.0590 🏆 | 1.042 | 1.09 | 840,046 | 94.1% |
-| **4** | Subword | 0.5191 | 1.433 | 2.39 | 130,734 | 48.1% |
+| **1** | Word | 0.9199 | 1.892 | 6.44 | 50,491 | 8.0% |
+| **1** | Subword | 0.9288 | 1.904 | 7.09 | 1,431 | 7.1% |
+| **2** | Word | 0.3746 | 1.296 | 2.02 | 324,952 | 62.5% |
+| **2** | Subword | 0.7034 | 1.628 | 4.04 | 10,144 | 29.7% |
+| **3** | Word | 0.1537 | 1.112 | 1.28 | 656,964 | 84.6% |
+| **3** | Subword | 0.6472 | 1.566 | 3.17 | 40,950 | 35.3% |
+| **4** | Word | 0.0591 🏆 | 1.042 | 1.09 | 838,369 | 94.1% |
+| **4** | Subword | 0.5206 | 1.435 | 2.40 | 129,601 | 47.9% |
 
 ### Generated Text Samples (Word-based)
 
@@ -238,27 +270,27 @@ Below are text samples generated from each word-based Markov chain model:
 
 **Context Size 1:**
 
-1. `ni harbangan asa dioloi si ferdinand lumban gaol boi manampung 1 11 3 naung disunat 2`
-2. `na marumur sataon na humaliang 5 000 m2 hira hira songon sondang ni raja iii diida`
-3. `i gok daupa sian si daud sian pangasammu do sarita pardapot ni na sampuludua i 22`
+1. `ni tano naung leleng on marupaya maningkathon kesadaran masarakat na pauli pintu ni si hannas dohot`
+2. `na talup do angka naposongku alai anggo raoanna nang jahudi tubu ni halak batak di tongatongamu`
+3. `i si arni anak ni harangan na mengatur istimewa dok gumodang sian saluhut na nidabuna i`
 
 **Context Size 2:**
 
-1. `angka na niuhir dohot na tarulang angka bagasnasida jala ndang marnalemba 10 38 ingkon mago roham ba...`
-2. `dung i ninna jesus ma siseanna i ninna parompuan na mabalu disi marmudu ho 17 2 dung`
-3. `ni si beor pangarunding i dibunu halak daniel 6 6 1 hamu pe ditanda sada pangituai parheheon`
+1. `angka na di ginjang ni angka ompunami umbahen manjadi angka i tu ahu do jahowa molo ahu`
+2. `dung i ro di salelenglelengna psalmen 94 94 1 ale anaha sai parateatehon hamu panariason ni bibirhon`
+3. `ni si jakkob anak ni si rehabeam di jerusalem 7 17 dua lombu lima birubiru tunggal sada`
 
 **Context Size 3:**
 
-1. `anak ni si jaasia si beno 24 27 ia angka anak ni si aron ma mudar i jala`
-2. `dung i ninna ibana tu ahu hombar tu bagabagam 119 171 sai marbulakkon pujipujian ma angka bibirhu al...`
-3. `i ma sada kecamatan na adong di kecamatan petarukan kabupaten pemalang propinsi jawa tonga indonesia...`
+1. `anak ni si aron hahanasida i marhalado di joro ni jahowa tungkan jolo ni rimberimbe i 40 27`
+2. `i ma sada nagara na maringanan di lobu panjang`
+3. `na adong di halak batak toba tombur tarbahen sian sibuk ni manuk na dibumbui`
 
 **Context Size 4:**
 
-1. `on do hata ni tuhan jahowa ida ma ahu sandiri pahehehon tu nasida hahisaron dohot hamalumon jala pam...`
-2. `songon on do hata ni jahowa zebaot tu hamu malim angka na palea goarhu hape lam didok hamu do`
-3. `i ma sada huta na maringanan di kecamatan tarutung kabupaten tapanuli utara propinsi sumatera utara ...`
+1. `on do hata ni tuhan jahowa nunga pola hupatoltol tanganku maruari ingkon lehononku do i tu ompumuna ...`
+2. `i ma sada huta na adong di kecamatan silima pungga pungga kabupaten dairi propinsi sumatera utara in...`
+3. `songon on do hata ni tuhan jahowa hape so tutu jahowa mandok 22 29 ia situan na torop isi`
 
 
 ### Generated Text Samples (Subword-based)
@@ -267,34 +299,34 @@ Below are text samples generated from each subword-based Markov chain model:
 
 **Context Size 1:**
 
-1. `_nobedi_anoa?_hi`
-2. `akoni_jarin_a_ng`
-3. `nabomaholalowap_`
+1. `_man_i_sa_nina_s`
+2. `amai_palalaseu_n`
+3. `ndi_ᯔ_no_pa_de_d`
 
 **Context Size 2:**
 
-1. `a_sia_ahaan_rohot`
-2. `anahu:_tana_raela`
-3. `ngon_nak_i._10:2_`
+1. `a_lamar_na._jalut`
+2. `ani_ni_ahit_bando`
+3. `ng_dongkop_hot_ad`
 
 **Context Size 3:**
 
-1. `anggo_ia_ingka_jor`
-2. `_marik_marhabus;_a`
-3. `na_5_menjangkup_he`
+1. `angitlawa_rajai,_d`
+2. `_marhalahite_hite_`
+3. `na_sapangku_imbolo`
 
 **Context Size 4:**
 
-1. `_ni_angka_halak_juj`
-2. `_na_hian_gabe_manan`
-3. `_di_bagaska_indones`
+1. `_ni_jahowa_hamu_ang`
+2. `_na_marsaro_mameuth`
+3. `_di_jeremia_7_novem`
 
 
 ### Key Findings
 
 - **Best Predictability:** Context-4 (word) with 94.1% predictability
 - **Branching Factor:** Decreases with context size (more deterministic)
-- **Memory Trade-off:** Larger contexts require more storage (130,734 contexts)
+- **Memory Trade-off:** Larger contexts require more storage (129,601 contexts)
 - **Recommendation:** Context-3 or Context-4 for text generation
 
 ---
@@ -310,48 +342,48 @@ Below are text samples generated from each subword-based Markov chain model:
 
 | Metric | Value |
 |--------|-------|
-| Vocabulary Size | 24,970 |
-| Total Tokens | 972,166 |
-| Mean Frequency | 38.93 |
+| Vocabulary Size | 24,923 |
+| Total Tokens | 971,594 |
+| Mean Frequency | 38.98 |
 | Median Frequency | 4 |
-| Frequency Std Dev | 557.36 |
+| Frequency Std Dev | 557.86 |
 
 ### Most Common Words
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | ni | 35,042 |
-| 2 | na | 33,939 |
-| 3 | i | 32,856 |
-| 4 | ma | 26,602 |
-| 5 | di | 26,003 |
-| 6 | tu | 20,420 |
-| 7 | do | 19,118 |
-| 8 | angka | 17,417 |
-| 9 | jala | 14,585 |
-| 10 | dohot | 13,546 |
+| 1 | ni | 34,971 |
+| 2 | na | 33,958 |
+| 3 | i | 32,913 |
+| 4 | ma | 26,658 |
+| 5 | di | 25,940 |
+| 6 | tu | 20,429 |
+| 7 | do | 19,116 |
+| 8 | angka | 17,411 |
+| 9 | jala | 14,584 |
+| 10 | dohot | 13,515 |
 
 ### Least Common Words (from vocabulary)
 
 | Rank | Word | Frequency |
 |------|------|-----------|
-| 1 | continua | 2 |
-| 2 | giuseppe | 2 |
-| 3 | mamutuskan | 2 |
-| 4 | disidang | 2 |
-| 5 | disuspensi | 2 |
-| 6 | formula | 2 |
-| 7 | dibenarkan | 2 |
-| 8 | pidana | 2 |
-| 9 | piazza | 2 |
-| 10 | fontana | 2 |
+| 1 | ᯇᯔᯒᯪᯉ᯲ᯖ | 2 |
+| 2 | kayo | 2 |
+| 3 | uttar | 2 |
+| 4 | ltr | 2 |
+| 5 | font | 2 |
+| 6 | ebrima | 2 |
+| 7 | border | 2 |
+| 8 | cellpadding | 2 |
+| 9 | td | 2 |
+| 10 | align | 2 |
 
 ### Zipf's Law Analysis
 
 | Metric | Value |
 |--------|-------|
-| Zipf Coefficient | 1.1798 |
-| R² (Goodness of Fit) | 0.997075 |
+| Zipf Coefficient | 1.1806 |
+| R² (Goodness of Fit) | 0.997033 |
 | Adherence Quality | **excellent** |
 
 ### Coverage Analysis
@@ -359,15 +391,15 @@ Below are text samples generated from each subword-based Markov chain model:
 | Top N Words | Coverage |
 |-------------|----------|
 | Top 100 | 53.7% |
-| Top 1,000 | 78.4% |
+| Top 1,000 | 78.5% |
 | Top 5,000 | 91.4% |
 | Top 10,000 | 95.7% |
 
 ### Key Findings
 
-- **Zipf Compliance:** R²=0.9971 indicates excellent adherence to Zipf's law
+- **Zipf Compliance:** R²=0.9970 indicates excellent adherence to Zipf's law
 - **High Frequency Dominance:** Top 100 words cover 53.7% of corpus
-- **Long Tail:** 14,970 words needed for remaining 4.3% coverage
+- **Long Tail:** 14,923 words needed for remaining 4.3% coverage
 
 ---
 ## 5. Word Embeddings Evaluation
@@ -383,37 +415,40 @@ 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 | 0.8223 🏆 | 0.3239 | N/A | N/A |
-| **mono_64d** | 64 | 0.7605 | 0.2710 | N/A | N/A |
-| **mono_128d** | 128 | 0.4652 | 0.2352 | N/A | N/A |
+| **mono_32d** | 32 | 0.8133 | 0.3464 | N/A | N/A |
+| **mono_64d** | 64 | 0.7715 | 0.2725 | N/A | N/A |
+| **mono_128d** | 128 | 0.4709 | 0.2523 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8133 🏆 | 0.3386 | 0.0140 | 0.1240 |
+| **aligned_64d** | 64 | 0.7715 | 0.2780 | 0.0560 | 0.2460 |
+| **aligned_128d** | 128 | 0.4709 | 0.2525 | 0.1340 | 0.3160 |
 
 ### Key Findings
 
-- **Best Isotropy:** mono_32d with 0.8223 (more uniform distribution)
-- **Semantic Density:** Average pairwise similarity of 0.2767. Lower values indicate better semantic separation.
-- **Alignment Quality:** No aligned models evaluated in this run.
+- **Best Isotropy:** aligned_32d with 0.8133 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2900. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 13.4% 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 | **-0.493** | Low formulaic content | - |
 
 ### 6.2 Affix Inventory (Productive Units)
 
@@ -422,26 +457,26 @@ These are the most productive prefixes and suffixes identified by sampling the v
 #### Productive Prefixes
 | Prefix | Examples |
 |--------|----------|
-| `-pa` | paunsathon, paraguay, panimbukbuk |
-| `-ma` | matan, marsogotna, marimbang |
-| `-di` | dihalungunhon, dipajonok, dikunjungi |
-| `-mar` | marsogotna, marimbang, marsuhat |
-| `-si` | sintuana, simalolongna, siotihotik |
-| `-man` | manongoshon, maneat, manongos |
-| `-par` | paraguay, paransis, parmaraan |
-| `-ha` | haro, hadoboon, hakristenon |
+| `-ma` | mangain, manuhati, mamingkiri |
+| `-pa` | pangir, pahosing, parsonduk |
+| `-di` | disiorhon, didege, diri |
+| `-man` | mangain, manuhati, mangkasiholi |
+| `-mar` | marilah, marhabanhaban, marnioli |
+| `-ha` | hapistaranmuna, harajaon, hanna |
+| `-par` | parsonduk, partalianta, parnidaan |
+| `-si` | sitorus, sitalutuk, sinimpan |
 
 #### Productive Suffixes
 | Suffix | Examples |
 |--------|----------|
-| `-n` | matan, kanan, paunsathon |
-| `-a` | bulanda, tanomanmuna, musikna |
-| `-on` | paunsathon, manongoshon, dihalungunhon |
-| `-na` | tanomanmuna, musikna, marsogotna |
-| `-an` | matan, kanan, tibetan |
-| `-ng` | marimbang, palding, lumeleng |
-| `-hon` | paunsathon, manongoshon, dihalungunhon |
-| `-nna` | anginna, binoanna, parumaenna |
+| `-n` | disiorhon, mangain, getasan |
+| `-a` | acara, opatsa, hapistaranmuna |
+| `-on` | disiorhon, harajaon, mandaon |
+| `-an` | getasan, nangkohan, bulanan |
+| `-na` | hapistaranmuna, etonganna, utamana |
+| `-hon` | disiorhon, hinungkuphon, ditoishon |
+| `-ng` | humosing, pahosing, taretong |
+| `-nna` | etonganna, hanna, salpuanna |
 
 ### 6.3 Bound Stems (Lexical Roots)
 
@@ -449,18 +484,18 @@ Bound stems are high-frequency subword units that are semantically cohesive but
 
 | Stem | Cohesion | Substitutability | Examples |
 |------|----------|------------------|----------|
-| `anga` | 1.65x | 126 contexts | angan, sanga, langa |
-| `angk` | 1.46x | 153 contexts | angka, rangka, dangka |
-| `mang` | 1.72x | 61 contexts | amang, damang, mangae |
-| `ngka` | 1.53x | 87 contexts | angka, rangka, dangka |
-| `ngko` | 1.76x | 41 contexts | ingkon, angkot, tingko |
-| `onga` | 1.75x | 36 contexts | longa, tonga, dongan |
-| `angg` | 1.39x | 75 contexts | anggo, anggi, anggia |
-| `anna` | 1.73x | 31 contexts | hanna, manna, annai |
-| `bahe` | 1.78x | 26 contexts | bahen, dibahe, ibahen |
-| `ingk` | 1.43x | 59 contexts | ingkon, tingki, lingka |
-| `ngan` | 1.37x | 65 contexts | ingan, angan, dongan |
-| `ndan` | 1.68x | 25 contexts | ndang, pandan, undang |
+| `anga` | 1.61x | 127 contexts | angan, langa, sanga |
+| `angk` | 1.53x | 157 contexts | angka, bangko, angkal |
+| `ngka` | 1.56x | 89 contexts | angka, bungka, engkau |
+| `mang` | 1.64x | 61 contexts | amang, mangan, memang |
+| `ngko` | 1.70x | 42 contexts | bangko, ingkon, angkot |
+| `bang` | 1.45x | 72 contexts | bange, abang, bangis |
+| `ingk` | 1.48x | 60 contexts | lingka, ingkau, ingkon |
+| `onga` | 1.68x | 36 contexts | tonga, longa, bongal |
+| `bahe` | 1.79x | 26 contexts | bahen, dibahe, ibahen |
+| `ngan` | 1.40x | 65 contexts | angan, ingan, mangan |
+| `ongo` | 1.62x | 36 contexts | longo, kongo, rongom |
+| `angg` | 1.31x | 78 contexts | anggi, anggo, angguk |
 
 ### 6.4 Affix Compatibility (Co-occurrence)
 
@@ -468,16 +503,16 @@ This table shows which prefixes and suffixes most frequently co-occur on the sam
 
 | Prefix | Suffix | Frequency | Examples |
 |--------|--------|-----------|----------|
-| `-pa` | `-n` | 372 words | paboaon, pangalelaon |
-| `-ma` | `-n` | 227 words | malungun, mambahen |
-| `-pa` | `-on` | 208 words | paboaon, pangalelaon |
-| `-pa` | `-a` | 196 words | pangkeannasida, padanna |
-| `-pa` | `-an` | 162 words | pamalian, parmiahan |
-| `-di` | `-n` | 135 words | diparsahitan, dison |
-| `-ma` | `-on` | 127 words | mangkalungunhon, mangkasogohon |
-| `-pa` | `-na` | 124 words | padanna, pandokna |
-| `-ha` | `-n` | 124 words | harun, hamulian |
-| `-di` | `-on` | 113 words | dison, ditahbishon |
+| `-pa` | `-n` | 358 words | parsapataan, partingkian |
+| `-ma` | `-n` | 206 words | marpadanpadan, marharajaon |
+| `-pa` | `-on` | 200 words | patoltolhon, paimbarhon |
+| `-pa` | `-a` | 184 words | pallawa, pasalihonsa |
+| `-pa` | `-an` | 157 words | parsapataan, partingkian |
+| `-di` | `-n` | 156 words | disiaphon, dilembagahon |
+| `-di` | `-on` | 134 words | disiaphon, dilembagahon |
+| `-ha` | `-n` | 128 words | hasundatan, hasusaan |
+| `-pa` | `-na` | 119 words | parsuhatonmuna, pabalionna |
+| `-ma` | `-on` | 116 words | marharajaon, mangaluhon |
 
 ### 6.5 Recursive Morpheme Segmentation
 
@@ -485,26 +520,26 @@ Using **Recursive Hierarchical Substitutability**, we decompose complex words in
 
 | Word | Suggested Split | Confidence | Stem |
 |------|-----------------|------------|------|
-| dipahatahata | **`di-pa-ha-ta-hata`** | 9.0 | `hata` |
-| marhatomanon | **`mar-ha-toman-on`** | 7.5 | `toman` |
-| panimbangan | **`pan-imba-ng-an`** | 7.5 | `imba` |
-| patongonhon | **`pa-tong-on-hon`** | 7.5 | `tong` |
-| hatigoranku | **`ha-tigor-an-ku`** | 7.5 | `tigor` |
-| pargogoanku | **`par-gogo-an-ku`** | 7.5 | `gogo` |
-| hagaleonku | **`ha-gale-on-ku`** | 7.5 | `gale` |
-| dipatongon | **`di-pa-tong-on`** | 7.5 | `tong` |
-| taparrohahon | **`ta-par-roha-hon`** | 7.5 | `roha` |
-| parhaporseaon | **`par-ha-porsea-on`** | 7.5 | `porsea` |
-| hamuliaonku | **`ha-mulia-on-ku`** | 7.5 | `mulia` |
-| paluhutonku | **`pa-luhut-on-ku`** | 7.5 | `luhut` |
-| hamateanna | **`ha-ma-tean-na`** | 7.5 | `tean` |
-| silehononku | **`si-lehon-on-ku`** | 7.5 | `lehon` |
-| patoltolonku | **`pa-toltol-on-ku`** | 7.5 | `toltol` |
+| pabotohononku | **`pa-boto-hon-on-ku`** | 9.0 | `boto` |
+| paradiananku | **`par-adian-an-ku`** | 7.5 | `adian` |
+| sipasahaton | **`si-pa-sahat-on`** | 7.5 | `sahat` |
+| marparmangsian | **`mar-par-mang-sian`** | 7.5 | `sian` |
+| panailingku | **`pan-aili-ng-ku`** | 7.5 | `aili` |
+| pardonganan | **`par-dong-an-an`** | 7.5 | `dong` |
+| marhamuliaon | **`mar-ha-mulia-on`** | 7.5 | `mulia` |
+| diparsiajari | **`di-par-si-ajari`** | 7.5 | `ajari` |
+| sipaingotna | **`si-pa-ingot-na`** | 7.5 | `ingot` |
+| sipatudoson | **`si-pa-tudos-on`** | 7.5 | `tudos` |
+| dipangasahon | **`di-pan-gasa-hon`** | 7.5 | `gasa` |
+| situtungon | **`si-tutu-ng-on`** | 7.5 | `tutu` |
+| pasahaton | **`pa-sahat-on`** | 6.0 | `sahat` |
+| parbungkason | **`par-bungkas-on`** | 6.0 | `bungkas` |
+| dipajomba | **`di-pa-jomba`** | 6.0 | `jomba` |
 
 ### 6.6 Linguistic Interpretation
 
 > **Automated Insight:**
-The language BBC 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 Batak Toba 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
@@ -731,4 +766,4 @@ MIT License - Free for academic and commercial use.
 ---
 *Generated by Wikilangs Models Pipeline*
 
-*Report Date: 2026-01-03 06:19:26*
+*Report Date: 2026-01-03 18:37:11*

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