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+visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text
+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language: lo
+language_name: Lao
+language_family: taikadai_southwestern
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-taikadai_southwestern
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.276
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7907
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Lao - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lao** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [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-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.254x | 3.29 | 0.1618% | 425,150 |
+| **16k** | 3.649x | 3.69 | 0.1814% | 379,183 |
+| **32k** | 4.002x | 4.05 | 0.1990% | 345,735 |
+| **64k** | 4.276x 🏆 | 4.32 | 0.2126% | 323,587 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `.gf ແມ່ນໂດເມນອິນເຕີເນັດລະດັບສູງສຸດຕາມລະຫັດປະເທດ (ccTLD) ສໍາລັບປະເທດເຟນົເກີຍ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁. g f ▁ແມ່ນ ໂດເມນອິນເຕີເນັດ ລະດັບສູງສຸດ ຕາມລະຫັດປະເທດ ▁( cctld ) ... (+6 more)` | 16 |
+| 16k | `▁. g f ▁ແມ່ນ ໂດເມນອິນເຕີເນັດ ລະດັບສູງສຸດ ຕາມລະຫັດປະເທດ ▁( cctld ) ... (+6 more)` | 16 |
+| 32k | `▁. g f ▁ແມ່ນ ໂດເມນອິນເຕີເນັດ ລະດັບສູງສຸດ ຕາມລະຫັດປະເທດ ▁( cctld ) ... (+5 more)` | 15 |
+| 64k | `▁. gf ▁ແມ່ນ ໂດເມນອິນເຕີເນັດ ລະດັບສູງສຸດ ຕາມລະຫັດປະເທດ ▁( cctld ) ▁ສໍາລັບປະເທດ ... (+2 more)` | 12 |
+
+**Sample 2:** `6 ສິງຫາ ເປນວັນທີ່ 218 ຊອງປີຕາມປະຕິທິນສຸລິຍະຄະຕິ ເຫດການສຳຄັນ ວັນເກີດ ວັນເສຍຊີວິດ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 6 ▁ສິງຫາ ▁ເປນວັນທີ່ ▁ 2 1 8 ▁ຊອງປີຕາມປະຕິທິນ ສຸລິຍະຄະຕິ ... (+3 more)` | 13 |
+| 16k | `▁ 6 ▁ສິງຫາ ▁ເປນວັນທີ່ ▁ 2 1 8 ▁ຊອງປີຕາມປະຕິທິນ ສຸລິຍະຄະຕິ ... (+3 more)` | 13 |
+| 32k | `▁ 6 ▁ສິງຫາ ▁ເປນວັນທີ່ ▁ 2 1 8 ▁ຊອງປີຕາມປະຕິທິນ ສຸລິຍະຄະຕິ ... (+3 more)` | 13 |
+| 64k | `▁ 6 ▁ສິງຫາ ▁ເປນວັນທີ່ ▁ 2 1 8 ▁ຊອງປີຕາມປະຕິທິນ ສຸລິຍະຄະຕິ ... (+3 more)` | 13 |
+
+**Sample 3:** `ສຸດທິດາ ອະນຸສິນ (ຊື່ຫຼິ້ນ:ມິມີ່) ເປັນ��າງງາມ,ນາງແບບຊາວລາວ ແລະເປັນຕົວແທນຈາກແຂວງສະຫ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ສຸດ ທິດາ ▁ອະນຸ ສິນ ▁( ຊື່ຫຼິ້ນ : ມິ ມີ ່ ... (+18 more)` | 28 |
+| 16k | `▁ສຸດ ທິດາ ▁ອະນຸ ສິນ ▁( ຊື່ຫຼິ້ນ : ມິ ມີ່ ) ... (+14 more)` | 24 |
+| 32k | `▁ສຸດທິດາ ▁ອະນຸສິນ ▁( ຊື່ຫຼິ້ນ : ມິ ມີ່ ) ▁ເປັນນາງງາມ , ... (+12 more)` | 22 |
+| 64k | `▁ສຸດທິດາ ▁ອະນຸສິນ ▁( ຊື່ຫຼິ້ນ : ມິ ມີ່ ) ▁ເປັນນາງງາມ , ... (+10 more)` | 20 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.276x compression
+- **Lowest UNK Rate:** 8k with 0.1618% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 3,552 | 11.79 | 7,139 | 20.1% | 49.9% |
+| **2-gram** | Subword | 2,184 🏆 | 11.09 | 20,562 | 30.0% | 71.8% |
+| **3-gram** | Word | 5,061 | 12.31 | 8,179 | 15.3% | 43.3% |
+| **3-gram** | Subword | 18,283 | 14.16 | 110,536 | 10.7% | 33.7% |
+| **4-gram** | Word | 15,243 | 13.90 | 20,416 | 8.0% | 22.3% |
+| **4-gram** | Subword | 79,610 | 16.28 | 344,446 | 5.8% | 18.2% |
+| **5-gram** | Word | 12,635 | 13.63 | 15,847 | 8.0% | 21.2% |
+| **5-gram** | Subword | 160,221 | 17.29 | 489,758 | 3.6% | 12.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ຄ ສ` | 3,689 |
+| 2 | `พ ศ` | 1,013 |
+| 3 | `ພ ສ` | 932 |
+| 4 | `of the` | 838 |
+| 5 | `ປະ ເທດ` | 375 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ສ ຄ ສ` | 245 |
+| 2 | `ສາ ທາ ລະ` | 193 |
+| 3 | `ທາ ລະ ນະ` | 186 |
+| 4 | `ພ ສ ຄ` | 185 |
+| 5 | `ຊອງປີຕາມປະຕິທິນສຸລິຍະຄະຕິ ເຫດການສຳຄັນ ວັນເກີດ` | 169 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ພ ສ ຄ ສ` | 185 |
+| 2 | `ສາ ທາ ລະ ນະ` | 183 |
+| 3 | `ທາ ລະ ນະ ລັດ` | 164 |
+| 4 | `ຊອງປີຕາມປະຕິທິນສຸລິຍະຄະຕິ ເຫດການສຳຄັນ ວັນເກີດ ວັນເສຍຊີວິດ` | 164 |
+| 5 | `ສະບັບຄົ້ນຄວ້າ ສົມມະນາ ການພິມ ສປປ` | 139 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ສາ ທາ ລະ ນະ ລັດ` | 163 |
+| 2 | `ສະບັບຄົ້ນຄວ້າ ສົມມະນາ ການພິມ ສປປ ລາວ` | 139 |
+| 3 | `ສ ສະບັບຄົ້ນຄວ້າ ສົມມະນາ ການພິມ ສປປ` | 139 |
+| 4 | `ຄ ສ ສະບັບຄົ້ນຄວ້າ ສົມມະນາ ການພິມ` | 139 |
+| 5 | `ພ ສ ຄ ສ ສະບັບຄົ້ນຄວ້າ` | 139 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `າ ນ` | 59,706 |
+| 2 | `ອ ງ` | 44,102 |
+| 3 | `ນ _` | 39,271 |
+| 4 | `ກ າ` | 37,742 |
+| 5 | `ລ ະ` | 34,362 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ກ າ ນ` | 28,652 |
+| 2 | `ແ ລ ະ` | 21,130 |
+| 3 | `ຂ ອ ງ` | 19,934 |
+| 4 | `ເ ປັ ນ` | 16,912 |
+| 5 | `_ ແ ລ` | 15,559 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ແ ລ ະ` | 15,503 |
+| 2 | `ແ ລ ະ _` | 10,398 |
+| 3 | `ຄ ວ າ ມ` | 6,185 |
+| 4 | `ະ ເ ທ ດ` | 5,750 |
+| 5 | `ປ ະ ເ ທ` | 5,748 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ແ ລ ະ _` | 9,660 |
+| 2 | `ປ ະ ເ ທ ດ` | 5,720 |
+| 3 | `_ t h e _` | 3,835 |
+| 4 | `ຄ . ສ . _` | 3,241 |
+| 5 | `_ ຄ . ສ .` | 2,982 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 2,184
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~13% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.3040 | 1.235 | 2.14 | 178,626 | 69.6% |
+| **1** | Subword | 1.0554 | 2.078 | 12.72 | 4,475 | 0.0% |
+| **2** | Word | 0.1004 | 1.072 | 1.19 | 378,257 | 90.0% |
+| **2** | Subword | 0.7301 | 1.659 | 5.16 | 56,905 | 27.0% |
+| **3** | Word | 0.0354 | 1.025 | 1.06 | 447,523 | 96.5% |
+| **3** | Subword | 0.5034 | 1.418 | 2.77 | 293,770 | 49.7% |
+| **4** | Word | 0.0171 🏆 | 1.012 | 1.02 | 468,823 | 98.3% |
+| **4** | Subword | 0.3413 | 1.267 | 1.85 | 811,856 | 65.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ແລະ ຄວາມຊົ່ວ ຫລື ໃນຊາຕເທົ່ານັ້ນບໍ່ແມ່ນເອົາສຫວັນເມື່ອຕາຍແລ້ວ ແລະ ລາງ ເຊັ່ນດຽວກັນກັບຫ້ອງໂຖງສະຖານີທີ່ສາ...`
+2. `ສ ເຖິງ 12 07 ມັງກອນ ເປັນ ລູກ ດ້ວຍ ເຖົ້າ ພໍກະ ເທີນ ເຖົ້າ ກະ ສານ ຂອງ ກະຊວງກະສິກຳແລະປ່າໄມ້`
+3. `the summation takes ເພື່ອປ້ອງກັນບໍ່ໃຫ້ສະ ກຸນເງິນເອີໂຣ ລົ້ມສະຫລາຍ ໃນປີ ນະຄອນ ທີ່ມີຕັ້ງແຕ່ປະມານສະຕະວັດ...`
+
+**Context Size 2:**
+
+1. `ຄ ສ ເມຖຸນາ ຄ ສ ຫາ ໂດຍມີກຸງສີອະຍຸດທະຍາເປັນນະຄອນຫຼວງ ອານາຈັກອະຍຸດທະຍານັບວ່າຈະເລີນຮຸ່ງເຮືອງຮັ່ງມີທີ່ສຸດ...`
+2. `พ ศ ราชกิจจานุเบกษา สืบค้นวันที่ 25 มิถุนายน ຕໍ່ມາວັນທີ 17 ພະຈິກ ທີ່ເມືອງກາມາວ ແຂວງກາມາວ ການສຶກສາ ຈົ...`
+3. `of the 20th century fox ອ້າງອີງ ລິ້ງຄ໌ພາຍນອກ sukhoi su 27 ທີ່ສຸດ ມັກຈະ ມີແມງວັນ ຄະ ດີກວ່າ ອາກາດ ແຕ່`
+
+**Context Size 3:**
+
+1. `ສ ຄ ສ ສະບັບຄົ້ນຄວ້າ ສົມມະນາ ການພິມ ສປປ ລາວ ເປັນລະດູອັນມີອາກາດໜາວທີ່ສຸດໃນປີ ລະດູໜາວໃນເຂດອົບອຸ່ນແລະເຂດ...`
+2. `ສາ ທາ ລະ ນະ ລັດ ເຢ ເມນ 45 ໝູ່ເກາະເຄ ແມນ 46 ການາດາ 47 ສາ ທາ ລະ ນະ ຂອງ`
+3. `ທາ ລະ ນະ ລັດ ປະ ຊາ ຊົນ ຈີນ 48 ຈີນ ໄທ ເປ ໄຕ້ ຫວັນ 49 ອາ ນາ ເຂດ`
+
+**Context Size 4:**
+
+1. `ສາ ທາ ລະ ນະ ລັດ ປະ ຊາ ທິ ປະ ໄຕ ແລະ ການ ປົກ ຄອງ ໂດຍ ກົງ ຂອງ ມົງ ກຸດ`
+2. `ພ ສ ຄ ສ ຕະຫຼອດໄລຍະເວລາທີລາຍການລະເບີດເຖິດເທິງອອກອາກາດຈົນຮອດປະຈຸບັນ ຊ່ວງຊິດຄອມລະເບີດເຖິດເທິງໃນຍຸກຕໍ່ໆມ...`
+3. `ທາ ລະ ນະ ລັດ ບູ ຣຸນ ດີ 97 ສາ ທາ ລະ ນະ ຂອງ ອັ ຟ ກາ ນິ ສ ຖານ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_vens_ໄຊາ​ປະຊ_sh`
+2. `ານ_ຂ້ອຍ​ສ້າມວົງກາວລ`
+3. `ນ​ກລັດທຶນ​ອົງປີ_ລະວົງ`
+
+**Context Size 2:**
+
+1. `ານງ​ໂດຍໄປນີ້_ເພື່ອເຈຍ`
+2. `ອງຢ້ຽມເຕະແຂວງ_3100`
+3. `ນ_ໆ_ເປັນເມືອງຍ່ອຍຮັບປ`
+
+**Context Size 3:**
+
+1. `ການເລື່ອງທີ່ມີຊີວິດແຕ່ພາເວ`
+2. `ແລະ_ກຸງຣາຊະຖາບັນເທົາ_`
+3. `ຂອງກ້ອນມົກກະພັດຍີ່ປຸ່ນຈະຕຸ`
+
+**Context Size 4:**
+
+1. `_ແລະ_ຕຸ໊ກກີ້_ຊິງຮ້ອຍຊິງລ້ານ`
+2. `ແລະ_ປັບປຸງເພື່ອພະສົບແລ້ວ,`
+3. `ຄວາມຮັກສາພະອົງເຈົ້າພະຍາ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (811,856 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 36,378 |
+| Total Tokens | 382,077 |
+| Mean Frequency | 10.50 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 92.90 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ແລະ | 10,966 |
+| 2 | ສ | 5,253 |
+| 3 | the | 4,001 |
+| 4 | ຄ | 3,718 |
+| 5 | 1 | 3,286 |
+| 6 | of | 3,039 |
+| 7 | ຂອງ | 2,967 |
+| 8 | ໃນ | 2,847 |
+| 9 | 2 | 2,607 |
+| 10 | ການ | 2,393 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ປະຕິເສດຄວາມຮັບຜິດຊອບແລະຖືກຕໍາຫຼວດຈັບຕົວເພື່ອສອບຖາມໃນວັນທີ | 2 |
+| 2 | ຜູ້ເຄາະຮ້າຍ | 2 |
+| 3 | ຢ່າງນ້ອຍ | 2 |
+| 4 | ຍ້ອນການສົງໄສວ່າ | 2 |
+| 5 | ດື່ມເຄື່ອງດື່ມທີ່ມີສານ | 2 |
+| 6 | ແລະອີກ | 2 |
+| 7 | ເຈັບປ່ວຍຈາກການບໍລິໂພກເຫຼົ້າປົນເປື້ອນນັ້ນ | 2 |
+| 8 | ໂຣມາຍາ | 2 |
+| 9 | ເຈເຣັດ | 2 |
+| 10 | ລະດູຮ້ອນໃນເມືອງແມ່ນຮ້ອນແລະຊຸ່ມຊື່ນ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9640 |
+| R² (Goodness of Fit) | 0.996857 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 28.8% |
+| Top 1,000 | 54.5% |
+| Top 5,000 | 74.0% |
+| Top 10,000 | 82.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9969 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 28.8% of corpus
+- **Long Tail:** 26,378 words needed for remaining 17.5% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![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 | 0.7907 🏆 | 0.3413 | N/A | N/A |
+| **mono_64d** | 64 | 0.4812 | 0.3220 | N/A | N/A |
+| **mono_128d** | 128 | 0.1381 | 0.3128 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7907 | 0.3454 | 0.0400 | 0.2780 |
+| **aligned_64d** | 64 | 0.4812 | 0.3164 | 0.0760 | 0.3560 |
+| **aligned_128d** | 128 | 0.1381 | 0.3154 | 0.1140 | 0.4340 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7907 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3256. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 11.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+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 | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.675** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-ເ` | ເມືອງນາກາຍ, ເສ, ເບສດີໄຊເນີ |
+| `-ສ` | ສິງເສນ, ສະຖານີສາມຢ່ານ, ສົມເດັດພະສີພັດຊະລິນທາບໍລົມລາຊິນີນາດ |
+| `-s` | skants, stage, scandinavia |
+| `-ອ` | ອິງຟ້າ, ອະນຸສາວະລີ, ອະດຸນຍະສັກອັກຄະນະເຣດສະຣາທິບໍດີ |
+| `-a` | ashgabat, at, alison |
+| `-ການ` | ການສຸ່ມກາຊາ, ການທີ��ຊາວບ້ານຍັງເຊື່ອຖືສິ່ງດັ່ງກ່າວແມ່ນບໍ່ໄດ້ຖື, ການປະຕິວັດເດືອນຕຸລາ |
+| `-ກ` | ກຽດຕິຍົດຄົນທີວີ, ການສຸ່ມກາຊາ, ການທີ່ຊາວບ້ານຍັງເຊື່ອຖືສິ່ງດັ່ງກ່າວແມ່ນບໍ່ໄດ້ຖື |
+| `-m` | men, martini, mans |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ນ` | ສິງເສນ, ປະຍູນພັນ, ໄລຍະທາງປະມານ |
+| `-ງ` | ນະຄອນຫລວງ, ຕະວັນພົງ, ພຽງແມ່ນ້ຳຂອງ |
+| `-ດ` | ຕົ້ນກຳເນີດ, ບ້ານຄຳສະຫວາດ, ແຄ່ຄົນໂທຜິດ |
+| `-າ` | ແຄນນາດາ, ອິງຟ້າ, ດາ |
+| `-s` | skants, mans, holdings |
+| `-ານ` | ໄລຍະທາງປະມານ, ເຄີດິສຖານ, ສະຖານີສາມຢ່ານ |
+| `-ກ` | ຄາລສະເບີກ, ນຄນປລັນໂລກ, ລັອກ |
+| `-e` | treble, stage, unicode |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `tion` | 2.20x | 21 contexts | nation, notion, motion |
+| `atio` | 2.22x | 15 contexts | ratio, nation, nations |
+| `ະເທດ` | 1.87x | 12 contexts | ຮະເທດ, ປະເທດ, ປີປະເທດ |
+| `ະການ` | 1.71x | 13 contexts | ມະການ, ປະການ, ແລະການ |
+| `ປະເທ` | 1.78x | 10 contexts | ປະເທດ, ປີປະເທດ, ປະເທດໄທ |
+| `ນການ` | 1.94x | 8 contexts | ໃນການ, ບວນການ, ແຜນການ |
+| `ປະກອ` | 1.65x | 12 contexts | ປະກອບ, ປະກອນ, ຄຳປະກອບ |
+| `ານປະ` | 1.99x | 7 contexts | ການປະກອບ, ການປະສູດ, ການປະກັນ |
+| `ສະຖາ` | 2.02x | 6 contexts | ສະຖານ, ສະຖານະ, ສະຖານີ |
+| `ງປະເ` | 2.17x | 5 contexts | ຂອງປະເທດ, ແດງປະເສີດ, ແຂວງປະເວດ |
+| `ງຈາກ` | 1.83x | 7 contexts | ຮອງຈາກ, ບາງຈາກ, ຫຼັງຈາກ |
+| `ທະຍາ` | 2.05x | 5 contexts | ວິທະຍາ, ພັດທະຍາ, ວິທະຍາໄລ |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-ເ` | `-ນ` | 97 words | ເມືອງຕຸ້ມລານ, ເພື່ອຄັດຄ້ານ |
+| `-ສ` | `-ນ` | 72 words | ສັດຈະກຸນ, ສ້າງອາຄານຮຽນ |
+| `-ເ` | `-ງ` | 67 words | ເພາະຫຍັງ, ເຂດສັມພັນທະວົງ |
+| `-ສ` | `-ດ` | 55 words | ສີດ, ສູນພັດທະນາຊົນນະບົດ |
+| `-ແ` | `-ນ` | 52 words | ແກນ, ແຂວງສາລະວັນ |
+| `-ອ` | `-ນ` | 48 words | ອຶນ, ອັສຈັນ |
+| `-ເ` | `-ດ` | 47 words | ເມືອງຊົນນະບົດ, ເຟສບຸກແຟນເຜດ |
+| `-ແ` | `-ງ` | 45 words | ແບັງ, ແມ່ນນັກການເມືອງ |
+| `-ກ` | `-ນ` | 41 words | ກຽກກີດສະຖານ, ກິດຕິກອນຈະເລີນ |
+| `-ເ` | `-າ` | 39 words | ເມືອງໄຊເຊດຖາ, ເຈົ້າເກດສະດາ |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| ວັດສີພຸດທະບາດ | **`ວັດສີພຸດທະ-ບ-າດ`** | 7.5 | `ບ` |
+| detectors | **`detector-s`** | 4.5 | `detector` |
+| ການສືບສວນ | **`ການ-ສືບສວນ`** | 4.5 | `ສືບສວນ` |
+| ການເຄື່ອນໄຫວ | **`ການ-ເຄື່ອນໄຫວ`** | 4.5 | `ເຄື່ອນໄຫວ` |
+| olympians | **`olympian-s`** | 4.5 | `olympian` |
+| recommended | **`recommend-ed`** | 4.5 | `recommend` |
+| ministers | **`minister-s`** | 4.5 | `minister` |
+| ການລົງທຶນ | **`ການ-ລົງທຶນ`** | 4.5 | `ລົງທຶນ` |
+| ການເດີນທາງ | **`ການ-ເດີນທາງ`** | 4.5 | `ເດີນທາງ` |
+| ການວິເຄ��ະ | **`ການ-ວິເຄາະ`** | 4.5 | `ວິເຄາະ` |
+| ການໂຄສະນາ | **`ການ-ໂຄສະນາ`** | 4.5 | `ໂຄສະນາ` |
+| republican | **`republic-an`** | 4.5 | `republic` |
+| ໃນກາງຊຸມປີ | **`ໃນ-ກາງຊຸມປີ`** | 4.5 | `ກາງຊຸມປີ` |
+| ອະທິນນາທານາ | **`ອະທິນນາທ-ານ-າ`** | 3.0 | `ອະທິນນາທ` |
+| ການໄດ້ຍິນ | **`ການ-ໄດ້ຍິ-ນ`** | 3.0 | `ໄດ້ຍິ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lao 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
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.28x) |
+| N-gram | **2-gram** | Lowest perplexity (2,184) |
+| Markov | **Context-4** | Highest predictability (98.3%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 11:21:00*

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