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README.md

@@ -36,7 +36,7 @@ metrics:
     value: 4.925
   - name: best_isotropy
     type: isotropy
-    value: 0.5616
+    value: 0.4644
   - name: vocabulary_size
     type: vocab
     value: 0
@@ -99,32 +99,32 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and
 
 Below are sample sentences tokenized with each vocabulary size:
 
-**Sample 1:** `Propinsi Champasak nakeuh saboh propinsi di Laos. Nang nanggroejih nakeuh Pakse.`
+**Sample 1:** `Jonathan Alberto "John" Leguizamo – ) nakeuh sidroe aktor asay Amirika Syarikat.`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁propinsi ▁champ as ak ▁nakeuh ▁saboh ▁propinsi ▁di ▁laos . ... (+6 more)` | 16 |
-| 16k | `▁propinsi ▁champ asak ▁nakeuh ▁saboh ▁propinsi ▁di ▁laos . ▁nang ... (+5 more)` | 15 |
-| 32k | `▁propinsi ▁champasak ▁nakeuh ▁saboh ▁propinsi ▁di ▁laos . ▁nang ▁nanggroejih ... (+4 more)` | 14 |
-| 64k | `▁propinsi ▁champasak ▁nakeuh ▁saboh ▁propinsi ▁di ▁laos . ▁nang ▁nanggroejih ... (+3 more)` | 13 |
+| 8k | `▁jonathan ▁albert o ▁" john " ▁leg ui zam o ... (+9 more)` | 19 |
+| 16k | `▁jonathan ▁albert o ▁" john " ▁leg ui zam o ... (+9 more)` | 19 |
+| 32k | `▁jonathan ▁alberto ▁" john " ▁leg uizamo ▁– ▁) ▁nakeuh ... (+6 more)` | 16 |
+| 64k | `▁jonathan ▁alberto ▁" john " ▁leguizamo ▁– ▁) ▁nakeuh ▁sidroe ... (+5 more)` | 15 |
 
-**Sample 2:** `Mesjid Keumangan nakeuh gampông di Mutiara, Kabupatèn Pidie, Acèh. Lumbôi gampôn...`
+**Sample 2:** `Spencer Breslin nakeuh sidroe aktor asay Amirika Utara.`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁mesjid ▁keum angan ▁nakeuh ▁gampông ▁di ▁mutiara , ▁kabupatèn ▁pidie ... (+14 more)` | 24 |
-| 16k | `▁mesjid ▁keumangan ▁nakeuh ▁gampông ▁di ▁mutiara , ▁kabupatèn ▁pidie , ... (+13 more)` | 23 |
-| 32k | `▁mesjid ▁keumangan ▁nakeuh ▁gampông ▁di ▁mutiara , ▁kabupatèn ▁pidie , ... (+13 more)` | 23 |
-| 64k | `▁mesjid ▁keumangan ▁nakeuh ▁gampông ▁di ▁mutiara , ▁kabupatèn ▁pidie , ... (+13 more)` | 23 |
+| 8k | `▁sp en cer ▁br es lin ▁nakeuh ▁sidroe ▁aktor ▁asay ... (+3 more)` | 13 |
+| 16k | `▁sp en cer ▁br es lin ▁nakeuh ▁sidroe ▁aktor ▁asay ... (+3 more)` | 13 |
+| 32k | `▁spencer ▁br es lin ▁nakeuh ▁sidroe ▁aktor ▁asay ▁amirika ▁utara ... (+1 more)` | 11 |
+| 64k | `▁spencer ▁breslin ▁nakeuh ▁sidroe ▁aktor ▁asay ▁amirika ▁utara .` | 9 |
 
-**Sample 3:** `Jurông Pandé nakeuh gampông di Geulumpang Tiga, Kabupatèn Pidie, Acèh. Lumbôi ga...`
+**Sample 3:** `Pasi Mali nakeuh saboh gampông nyang na lam keucamatan Woyla Barat, Kabupaten Ac...`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁jurông ▁pand é ▁nakeuh ▁gampông ▁di ▁geulumpang ▁tiga , ▁kabupatèn ... (+17 more)` | 27 |
-| 16k | `▁jurông ▁pandé ▁nakeuh ▁gampông ▁di ▁geulumpang ▁tiga , ▁kabupatèn ▁pidie ... (+16 more)` | 26 |
-| 32k | `▁jurông ▁pandé ▁nakeuh ▁gampông ▁di ▁geulumpang ▁tiga , ▁kabupatèn ▁pidie ... (+16 more)` | 26 |
-| 64k | `▁jurông ▁pandé ▁nakeuh ▁gampông ▁di ▁geulumpang ▁tiga , ▁kabupatèn ▁pidie ... (+16 more)` | 26 |
+| 8k | `▁pasi ▁mali ▁nakeuh ▁saboh ▁gampông ▁nyang ▁na ▁lam ▁keucamatan ▁woyla ... (+11 more)` | 21 |
+| 16k | `▁pasi ▁mali ▁nakeuh ▁saboh ▁gampông ▁nyang ▁na ▁lam ▁keucamatan ▁woyla ... (+11 more)` | 21 |
+| 32k | `▁pasi ▁mali ▁nakeuh ▁saboh ▁gampông ▁nyang ▁na ▁lam ▁keucamatan ▁woyla ... (+11 more)` | 21 |
+| 64k | `▁pasi ▁mali ▁nakeuh ▁saboh ▁gampông ▁nyang ▁na ▁lam ▁keucamatan ▁woyla ... (+11 more)` | 21 |
 
 
 ### Key Findings
@@ -176,7 +176,7 @@ Below are sample sentences tokenized with each vocabulary size:
 | 2 | `nyoe bak laman` | 3,694 |
 | 3 | `lumbôi gampông nyoe` | 3,567 |
 | 4 | `acèh lumbôi gampông` | 3,564 |
-| 5 | `lam data peumeurèntah` | 3,499 |
+| 5 | `nyoe lam data` | 3,499 |
 
 **4-grams (Word):**
 
@@ -184,16 +184,16 @@ Below are sample sentences tokenized with each vocabulary size:
 |------|--------|-------|
 | 1 | `gunong nyoe bak laman` | 3,694 |
 | 2 | `acèh lumbôi gampông nyoe` | 3,564 |
-| 3 | `nyoe lam data peumeurèntah` | 3,499 |
-| 4 | `lam data peumeurèntah nakeuh` | 3,499 |
+| 3 | `lam data peumeurèntah nakeuh` | 3,499 |
+| 4 | `nyoe lam data peumeurèntah` | 3,499 |
 | 5 | `gampông nyoe lam data` | 3,499 |
 
 **5-grams (Word):**
 
 | Rank | N-gram | Count |
 |------|--------|-------|
-| 1 | `gampông nyoe lam data peumeurèntah` | 3,499 |
-| 2 | `nyoe lam data peumeurèntah nakeuh` | 3,499 |
+| 1 | `nyoe lam data peumeurèntah nakeuh` | 3,499 |
+| 2 | `gampông nyoe lam data peumeurèntah` | 3,499 |
 | 3 | `lumbôi gampông nyoe lam data` | 3,498 |
 | 4 | `acèh lumbôi gampông nyoe lam` | 3,495 |
 | 5 | `lam data peumeurèntah nakeuh nè` | 3,489 |
@@ -274,27 +274,27 @@ Below are text samples generated from each word-based Markov chain model:
 
 **Context Size 1:**
 
-1. `di da irah bak wikidata data cuaca daerah gunong nyoe bak di acèh indonesia laos nang`
-2. `nakeuh saboh propinsi acèh timu burundi rwanda madagaskar nakeuh gampông lam data peumeurèntah nakeu...`
-3. `bak laman geonames data peumeurèntah nakeuh nè di gayo lues provinsi acèh barat pulo wèh lam`
+1. `di ateuh keude neulop ii dari mèssana strabô ngön sichuan jinoë sukèë calameae aseuli 苗族 haraih`
+2. `nakeuh saboh gampông nyoe bak wikidata data peumeurèntah nakeuh saboh spèsiès nibak volume 82 nibak ...`
+3. `bak laman sunrisesunset com di jeupun lé shogakkukan nè seuneubeuet bak laman sunrisesunset com di s...`
 
 **Context Size 2:**
 
-1. `bak laman nasa data matauroe teubiet teunom di da irah bak laman nasa data matauroe teubiet teunom`
-2. `gunong nyoe nakeuh bagian nibak inggréh pangiran maurits dari beulanda natom cit meukirém surat keu ...`
-3. `nyoe bak laman nasa data matauroe teubiet teunom di da irah bak laman sunrisesunset com di acèh`
+1. `bak laman nasa data matauroe teubiet teunom di da irah bak laman geonames data gunong nyoe bak`
+2. `gunong nyoe bak laman nasa data matauroe teubiet teunom di da irah ajyad 500 ngon 700 meté`
+3. `nyoe bak wikidata data cuaca daerah gunong nyoe bak wikidata data cuaca daerah gunong nyoe bak laman`
 
 **Context Size 3:**
 
-1. `gunong nyoe bak laman nasa data matauroe teubiet teunom di da irah bak laman sunrisesunset com di ac...`
-2. `nyoe bak laman nasa data matauroe teubiet teunom di da irah bak laman sunrisesunset com di acèh`
-3. `lumbôi gampông nyoe lam data peumeurèntah nakeuh nè di acèh timu jernih acèh timu`
+1. `gunong nyoe bak wikidata data cuaca daerah gunong nyoe bak laman nasa data matauroe teubiet teunom d...`
+2. `nyoe bak laman geonames data gunong nyoe bak laman geonames data gunong nyoe bak wikidata data cuaca...`
+3. `lumbôi gampông nyoe lam data peumeurèntah nakeuh nè di pidie pidie`
 
 **Context Size 4:**
 
 1. `gunong nyoe bak laman nasa data matauroe teubiet teunom di da irah bak laman sunrisesunset com di ac...`
-2. `acèh lumbôi gampông nyoe lam data peumeurèntah nakeuh nè di acèh barôh acèh barôh`
-3. `gampông nyoe lam data peumeurèntah nakeuh nè di acèh rayek kawan peukan bada acèh rayek ngön nan awa...`
+2. `acèh lumbôi gampông nyoe lam data peumeurèntah nakeuh nè di acèh rayek acèh rayek`
+3. `gampông nyoe lam data peumeurèntah nakeuh nè di acèh seulatan raja acèh seulatan`
 
 
 ### Generated Text Samples (Subword-based)
@@ -303,27 +303,27 @@ Below are text samples generated from each subword-based Markov chain model:
 
 **Context Size 1:**
 
-1. `_onirastak_lh_ak`
-2. `ansa_pônng_39_n.`
-3. `naneum_l_()._dam`
+1. `_peulopôt_onohoo`
+2. `acoeuh_dd_teumph`
+3. `nta'ôn_1,_ba),_b`
 
 **Context Size 2:**
 
-1. `euh_aoyatèktiong_`
-2. `_nya_droë:_teukeu`
-3. `an_ak_di_istreng_`
+1. `eurènteuh_nè_deuh`
+2. `_nakeuneuropinak_`
+3. `an_acilife_39_nya`
 
 **Context Size 3:**
 
-1. `ng_geukheungui_gam`
-2. `_na_data_pranté_ab`
-3. `_bak_da'irahmada_u`
+1. `ng_di_daerah_cuaca`
+2. `_najôh,_sha_peunaw`
+3. `_bagoë_di_kabupatè`
 
 **Context Size 4:**
 
-1. `euh_gampông_na_di_a`
-2. `bak_encyclopedia_of`
-3. `_di_tunong_nyoë,_bh`
+1. `euh_babah_la'èn_nya`
+2. `bak_jijak_ulee_stud`
+3. `_di_muhammouaneuh'e`
 
 
 ### Key Findings
@@ -428,18 +428,18 @@ Below are text samples generated from each subword-based Markov chain model:
 
 | Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
 |-------|-----------|----------|------------------|---------------|----------------|
-| **mono_32d** | 32 | 0.5616 🏆 | 0.3940 | N/A | N/A |
-| **mono_64d** | 64 | 0.2087 | 0.3984 | N/A | N/A |
-| **mono_128d** | 128 | 0.0274 | 0.4044 | N/A | N/A |
-| **aligned_32d** | 32 | 0.5616 | 0.4083 | 0.0220 | 0.1860 |
-| **aligned_64d** | 64 | 0.2087 | 0.4071 | 0.0460 | 0.2660 |
-| **aligned_128d** | 128 | 0.0274 | 0.4087 | 0.0440 | 0.2760 |
+| **mono_32d** | 32 | 0.4644 | 0.4250 | N/A | N/A |
+| **mono_64d** | 64 | 0.1432 | 0.4182 | N/A | N/A |
+| **mono_128d** | 128 | 0.0251 | 0.4207 | N/A | N/A |
+| **aligned_32d** | 32 | 0.4644 🏆 | 0.4392 | 0.0240 | 0.1600 |
+| **aligned_64d** | 64 | 0.1432 | 0.4223 | 0.0340 | 0.2120 |
+| **aligned_128d** | 128 | 0.0251 | 0.4223 | 0.0540 | 0.2900 |
 
 ### Key Findings
 
-- **Best Isotropy:** mono_32d with 0.5616 (more uniform distribution)
-- **Semantic Density:** Average pairwise similarity of 0.4035. Lower values indicate better semantic separation.
-- **Alignment Quality:** Aligned models achieve up to 4.6% R@1 in cross-lingual retrieval.
+- **Best Isotropy:** aligned_32d with 0.4644 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4246. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 5.4% R@1 in cross-lingual retrieval.
 - **Recommendation:** 128d aligned for best cross-lingual performance
 
 ---
@@ -461,18 +461,18 @@ These are the most productive prefixes and suffixes identified by sampling the v
 #### Productive Prefixes
 | Prefix | Examples |
 |--------|----------|
-| `-me` | meusuci, meutapi, meukheuluk |
-| `-meu` | meusuci, meutapi, meukheuluk |
-| `-ge` | geuôseuha, geuplueng, geupeuhu |
-| `-geu` | geuôseuha, geuplueng, geupeuhu |
-| `-pe` | peuneujeutneuh, peutinggai, pelelangan |
+| `-ge` | geudapeuta, geuseutöt, geutanyoe |
+| `-me` | meuubah, meuasai, meupawôt |
+| `-geu` | geudapeuta, geuseutöt, geutanyoe |
+| `-meu` | meuubah, meuasai, meupawôt |
+| `-pe` | perdagangan, peunténg, peuradaban |
 
 #### Productive Suffixes
 | Suffix | Examples |
 |--------|----------|
-| `-ng` | geuplueng, loyang, berperang |
-| `-an` | pelelangan, onekotan, kerobokan |
-| `-ah` | ketukah, beulasah, beudarah |
+| `-ng` | lambéng, peunténg, gadông |
+| `-an` | perdagangan, azerbaijan, pikeran |
+| `-ah` | pamarèntah, meuubah, bhah |
 
 ### 6.3 Bound Stems (Lexical Roots)
 
@@ -480,18 +480,18 @@ Bound stems are high-frequency subword units that are semantically cohesive but
 
 | Stem | Cohesion | Substitutability | Examples |
 |------|----------|------------------|----------|
-| `eung` | 1.41x | 64 contexts | meung, reung, jeung |
-| `uneu` | 1.70x | 28 contexts | runeu, uneun, meuneu |
-| `euen` | 1.53x | 38 contexts | meuen, leuen, eueng |
-| `euna` | 1.35x | 60 contexts | beuna, keuna, peuna |
-| `ubeu` | 1.43x | 22 contexts | ubeut, neubeu, keubeu |
-| `umeu` | 1.40x | 23 contexts | jumeu, jeumeu, geumeu |
-| `meur` | 1.59x | 15 contexts | meuri, meurô, meurah |
-| `neub` | 1.58x | 14 contexts | neuba, neubôk, neubut |
-| `teun` | 1.31x | 25 contexts | uteun, ateung, teuntè |
-| `beue` | 1.49x | 16 contexts | beuet, tabeue, abeuek |
-| `anga` | 1.31x | 23 contexts | langa, panga, manga |
-| `eune` | 1.61x | 12 contexts | jeuneh, meuneu, geuneu |
+| `eung` | 1.43x | 64 contexts | reung, jeung, meung |
+| `uneu` | 1.75x | 28 contexts | uneun, runeu, meuneu |
+| `euna` | 1.43x | 60 contexts | keuna, beuna, peuna |
+| `euen` | 1.53x | 38 contexts | leuen, eueng, meuen |
+| `ubeu` | 1.48x | 22 contexts | ubeut, neubeu, keubeu |
+| `umeu` | 1.43x | 23 contexts | jumeu, geumeu, jeumeu |
+| `meur` | 1.61x | 15 contexts | meurô, meuri, meurak |
+| `beue` | 1.55x | 16 contexts | beuet, rabeue, abeuek |
+| `teun` | 1.34x | 25 contexts | uteun, ateung, teuntè |
+| `neub` | 1.61x | 14 contexts | neuba, neubeu, neubôh |
+| `eune` | 1.65x | 12 contexts | meuneu, seuneu, jeuneh |
+| `anga` | 1.33x | 23 contexts | langa, manga, panga |
 
 ### 6.4 Affix Compatibility (Co-occurrence)
 
@@ -499,15 +499,15 @@ This table shows which prefixes and suffixes most frequently co-occur on the sam
 
 | Prefix | Suffix | Frequency | Examples |
 |--------|--------|-----------|----------|
-| `-pe` | `-an` | 53 words | peureumponan, pertahanan |
-| `-ge` | `-ng` | 52 words | geumeugabong, geutamöng |
-| `-me` | `-ng` | 33 words | meuulang, meunatang |
-| `-pe` | `-ng` | 27 words | peunayông, peudong |
-| `-ge` | `-ah` | 21 words | geupeuleumah, geupisah |
-| `-me` | `-ah` | 17 words | meubatah, meuseudeukah |
-| `-pe` | `-ah` | 15 words | peuneugah, peumerintah |
-| `-me` | `-an` | 13 words | meukawan, mediterranian |
-| `-ge` | `-an` | 4 words | geurakan, gerakan |
+| `-ge` | `-ng` | 64 words | geumeujuang, geulumpang |
+| `-pe` | `-an` | 54 words | permulaan, peumeréntahan |
+| `-me` | `-ng` | 27 words | meuteureubang, meugang |
+| `-pe` | `-ng` | 27 words | peuseunang, peujuang |
+| `-me` | `-ah` | 21 words | meriah, meutuwah |
+| `-ge` | `-ah` | 20 words | geuminah, geujajah |
+| `-pe` | `-ah` | 15 words | pemerintah, peumeuréntah |
+| `-me` | `-an` | 14 words | mediterranian, meurakan |
+| `-ge` | `-an` | 6 words | geuritan, geulawan |
 
 ### 6.5 Recursive Morpheme Segmentation
 
@@ -515,21 +515,21 @@ Using **Recursive Hierarchical Substitutability**, we decompose complex words in
 
 | Word | Suggested Split | Confidence | Stem |
 |------|-----------------|------------|------|
-| geumeujuang | **`geu-meu-juang`** | 6.0 | `juang` |
 | geulumbang | **`geu-lumba-ng`** | 6.0 | `lumba` |
-| geumeunarit | **`geu-meu-narit`** | 6.0 | `narit` |
-| geumeuripèe | **`geu-meu-ripèe`** | 6.0 | `ripèe` |
+| geutanyong | **`geu-tanyo-ng`** | 6.0 | `tanyo` |
 | geumeupakat | **`geu-meu-pakat`** | 6.0 | `pakat` |
-| geumeusipheuët | **`geu-meu-sipheuët`** | 6.0 | `sipheuët` |
-| geumeuduëk | **`geu-meu-duëk`** | 6.0 | `duëk` |
+| geulanggang | **`geu-langga-ng`** | 6.0 | `langga` |
+| gelombang | **`ge-lomba-ng`** | 6.0 | `lomba` |
+| meupangkat | **`meu-pangkat`** | 4.5 | `pangkat` |
+| meuhubôngan | **`meu-hubô-ng-an`** | 4.5 | `hubô` |
+| meujangeun | **`meu-jangeun`** | 4.5 | `jangeun` |
+| meuneunguy | **`meu-neunguy`** | 4.5 | `neunguy` |
+| meusayeuëp | **`meu-sayeuëp`** | 4.5 | `sayeuëp` |
+| meupapeuen | **`meu-papeuen`** | 4.5 | `papeuen` |
+| geupeuleumah | **`geu-pe-uleum-ah`** | 4.5 | `uleum` |
 | meubintéh | **`meu-bintéh`** | 4.5 | `bintéh` |
-| geutanyöe | **`geu-tanyöe`** | 4.5 | `tanyöe` |
-| geupeuriwang | **`geu-pe-uriwa-ng`** | 4.5 | `uriwa` |
-| meuadaptasi | **`meu-adaptasi`** | 4.5 | `adaptasi` |
-| geumigrasi | **`geu-migrasi`** | 4.5 | `migrasi` |
-| geutimbak | **`geu-timbak`** | 4.5 | `timbak` |
-| geupageuë | **`geu-pageuë`** | 4.5 | `pageuë` |
-| meutugaih | **`meu-tugaih`** | 4.5 | `tugaih` |
+| meupoliték | **`meu-politék`** | 4.5 | `politék` |
+| meuteukeubi | **`meu-teukeubi`** | 4.5 | `teukeubi` |
 
 ### 6.6 Linguistic Interpretation
 
@@ -763,4 +763,4 @@ MIT License - Free for academic and commercial use.
 ---
 *Generated by Wikilangs Models Pipeline*
 
-*Report Date: 2026-01-03 14:04:07*
+*Report Date: 2026-01-03 16:16:20*

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