Upload all models and assets for si (latest)

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README.md

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+---
+language: si
+language_name: Sinhala
+language_family: indoaryan_insular
+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-indoaryan_insular
+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: 4.567
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8359
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sinhala - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sinhala** 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** | 3.460x | 3.46 | 0.0794% | 1,490,772 |
+| **16k** | 3.888x | 3.89 | 0.0892% | 1,326,900 |
+| **32k** | 4.268x | 4.27 | 0.0979% | 1,208,595 |
+| **64k** | 4.567x 🏆 | 4.57 | 0.1047% | 1,129,426 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `බක් අව අටවක තිථියට අනුරූපී පෝය දවස බක් අව අටවක පෝය නම් වේ. මූලාශ්‍ර අටවක ඇ.1`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁බක් ▁අව ▁අටවක ▁තිථියට ▁අනුරූප ී ▁පෝය ▁දවස ▁බක් ▁අව ... (+10 more)` | 20 |
+| 16k | `▁බක් ▁අව ▁අටවක ▁තිථියට ▁අනුරූපී ▁පෝය ▁දවස ▁බක් ▁අව ▁අටවක ... (+9 more)` | 19 |
+| 32k | `▁බක් ▁අව ▁අටවක ▁තිථියට ▁අනුරූපී ▁පෝය ▁දවස ▁බක් ▁අව ▁අටවක ... (+9 more)` | 19 |
+| 64k | `▁බක් ▁අව ▁අටවක ▁තිථියට ▁අනුරූපී ▁පෝය ▁දවස ▁බක් ▁අව ▁අටවක ... (+9 more)` | 19 |
+
+**Sample 2:** `උපත් පිලිප් රජතුමා යනු බෙල්ජියමේ රජතුමා වේ. බෙල්ජියමේ රජ පවුල`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁උපත් ▁පිලිප් ▁රජතුමා ▁යනු ▁බෙල්ජිය මේ ▁රජතුමා ▁වේ . ▁බෙල්ජිය ... (+3 more)` | 13 |
+| 16k | `▁උපත් ▁පිලිප් ▁රජතුමා ▁යනු ▁බෙල්ජියමේ ▁රජතුමා ▁වේ . ▁බෙල්ජියමේ ▁රජ ... (+1 more)` | 11 |
+| 32k | `▁උපත් ▁පිලිප් ▁රජතුමා ▁යනු ▁බෙල්ජියමේ ▁රජතුමා ▁වේ . ▁බෙල්ජියමේ ▁රජ ... (+1 more)` | 11 |
+| 64k | `▁උපත් ▁පිලිප් ▁රජතුමා ▁යනු ▁බෙල්ජියමේ ▁රජතුමා ▁වේ . ▁බෙල්ජියමේ ▁රජ ... (+1 more)` | 11 |
+
+**Sample 3:** `වසාවාසි () යනු කුළු බඩු විශේෂයකි. මූලාශ්‍ර ආශ්‍රිත සගන්ධ තෙල් සාදික්කා බඩු`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁වස ාවා සි ▁() ▁යනු ▁කු ළු ▁බ ඩු ▁විශේෂයකි ... (+12 more)` | 22 |
+| 16k | `▁වස ාවා සි ▁() ▁යනු ▁කුළු ▁බඩු ▁විශේෂයකි . ▁මූලාශ්‍ර ... (+8 more)` | 18 |
+| 32k | `▁වසාවාසි ▁() ▁යනු ▁කුළු ▁බඩු ▁විශේෂයකි . ▁මූලාශ්‍ර ▁ආශ්‍රිත ▁සග ... (+4 more)` | 14 |
+| 64k | `▁වසාවාසි ▁() ▁යනු ▁කුළු ▁බඩු ▁විශේෂයකි . ▁මූලාශ්‍ර ▁ආශ්‍රිත ▁සගන්ධ ... (+3 more)` | 13 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.567x compression
+- **Lowest UNK Rate:** 8k with 0.0794% 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 | 91,979 | 16.49 | 262,122 | 6.4% | 17.0% |
+| **2-gram** | Subword | 2,119 🏆 | 11.05 | 50,624 | 32.0% | 72.3% |
+| **3-gram** | Word | 150,233 | 17.20 | 288,151 | 3.5% | 11.6% |
+| **3-gram** | Subword | 20,524 | 14.33 | 333,353 | 10.5% | 33.2% |
+| **4-gram** | Word | 393,476 | 18.59 | 561,828 | 2.2% | 6.9% |
+| **4-gram** | Subword | 119,419 | 16.87 | 1,506,827 | 5.6% | 18.0% |
+| **5-gram** | Word | 312,338 | 18.25 | 419,011 | 2.5% | 7.2% |
+| **5-gram** | Subword | 385,462 | 18.56 | 3,075,495 | 3.4% | 11.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `වන අතර` | 18,056 |
+| 2 | `කරන ලදී` | 14,152 |
+| 3 | `කරන ලද` | 12,560 |
+| 4 | `වූ අතර` | 10,420 |
+| 5 | `අතර එය` | 8,750 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `වන අතර එය` | 2,889 |
+| 2 | `කරන ලද අතර` | 2,759 |
+| 3 | `කර ඇති අතර` | 1,579 |
+| 4 | `බවට පත් විය` | 1,565 |
+| 5 | `ප්‍රාදේශීය ලේකම් කොට්ඨාසය` | 1,405 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `සඳහා ප්‍රතිඵල අපේක්ෂකයාපක්ෂයසංකේතයඡන්ද සංඛ්‍යාව` | 919 |
+| 2 | `පාර්ලිමේන්තු මැතිවරණයෙහි මෙම මැතිවරණ` | 914 |
+| 3 | `ඡන්ද ඡන්ද ඡන්දදායක භාවිත` | 819 |
+| 4 | `ඡන්ද ඡන්ද ඡන්ද ඡන්දදායක` | 819 |
+| 5 | `ලංකාවේ ප්‍රාදේශීය ලේකම් කොට්ඨාස` | 649 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ඡන්ද ඡන්ද ඡන්ද ඡන්දදායක භාවිත` | 819 |
+| 2 | `ඡන්ද ඡන්ද ඡන්දදායක භාවිත කිරීමේ` | 555 |
+| 3 | `on wikidata using gadget wikiminiatlas` | 428 |
+| 4 | `ta m 1 5 3` | 418 |
+| 5 | `බැඳිය විසින් මුළු දින දසුන` | 415 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ය _` | 775,809 |
+| 2 | `න් _` | 649,429 |
+| 3 | `. _` | 564,248 |
+| 4 | `_ අ` | 537,926 |
+| 5 | `න _` | 506,185 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ස හ` | 149,125 |
+| 2 | `_ ප්‍ ර` | 144,256 |
+| 3 | `_ ක ර` | 142,975 |
+| 4 | `ස හ _` | 136,850 |
+| 5 | `ව න _` | 132,647 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ස හ _` | 136,177 |
+| 2 | `_ අ ත ර` | 100,547 |
+| 3 | `_ ව න _` | 79,031 |
+| 4 | `අ ත ර _` | 68,009 |
+| 5 | `_ ලෙ ස _` | 64,807 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ අ ත ර _` | 67,941 |
+| 2 | `_ ක ර න _` | 50,645 |
+| 3 | `_ t h e _` | 50,119 |
+| 4 | `_ ස ඳ හා _` | 46,525 |
+| 5 | `_ වි සි න් _` | 43,861 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 2,119
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~12% 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 | 0.8654 | 1.822 | 8.35 | 622,772 | 13.5% |
+| **1** | Subword | 0.9820 | 1.975 | 12.62 | 11,028 | 1.8% |
+| **2** | Word | 0.2799 | 1.214 | 1.70 | 5,190,673 | 72.0% |
+| **2** | Subword | 0.7847 | 1.723 | 5.98 | 139,154 | 21.5% |
+| **3** | Word | 0.0782 | 1.056 | 1.14 | 8,825,385 | 92.2% |
+| **3** | Subword | 0.5783 | 1.493 | 3.73 | 832,002 | 42.2% |
+| **4** | Word | 0.0239 🏆 | 1.017 | 1.03 | 9,999,542 | 97.6% |
+| **4** | Subword | 0.4793 | 1.394 | 2.50 | 3,101,075 | 52.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `සහ සැමුවෙල් බේකර් ඇල්ල හා මිනිස් ඇසුරින් මෙහිදී ඩිජිටල් අධ්‍යාපන අමාත්‍යාංශයේ නියෝජිතායතනයක් ද ඇගේ ක...`
+2. `අතර සංකීර්ණ ක්‍රම නිර්වචනය වන්නේ ඒවායේ කොටස් වලින් මෙම ද්විමණ්ඩල පාර්ලිමේන්තුව මත තීන්ත ඒවා සමහරක් ඇ...`
+3. `වන ඔහු අභියාචනාධිකරණයට අභියාචනා අධිකරණය විසින් නොවැම්බර් 21 උප්පත්තියෙන්ම ලබන පගසම් pagasam එකකි ඓති...`
+
+**Context Size 2:**
+
+1. `වන අතර මුස්ලිම් සංස්කෘතිය මාලදිවයිනේ පැලපදියම් වීමට නම් එය ලිංගික ප්‍රදේශ ස්පර්ශ කිරීමක් වීම ද සිදු ...`
+2. `කරන ලදී එහෙත් ඔඩිසි සහ ඉලියඩ් සඳහා පෙළඹීමද වූ බව පැවසේ එවක පැවති ඉංග්‍රීසි පාලකයන්ට විරුද්ධව අරගලයක`
+3. `කරන ලද වඩාත් අභිලාෂකාමී මූර්ති උත්සාහ කර ඇත එම සංකේතනය මඟින් අන්තර්ගතය පිටපත් කිරීම පිලිබඳ ජාතික කමි...`
+
+**Context Size 3:**
+
+1. `වන අතර එය මුලින් අයිරෝ වීල් ගුවන් වීල් සහ රොන් දණ්ඩ ලෙසද හැඳින්වේ රෝද නිර්මාණය විශාල රෝදය සමාන්තරව`
+2. `කරන ලද අතර එය මගින් ප්‍රාරම්භක අවස්ථාවේ අවහිර කරන ලද ගීතයන් ජර්මනියේ යූ ටියුබ් ප්‍රේක්ෂකයින්ට අලෙවි ...`
+3. `කර ඇති අතර සමාගම්වල ප්‍රතිලාභී හිමිකාරිත්ව තොරතුරු සත්‍යාපනය කර ඇති අතර එසේ වුවද ආණ්ඩුක්‍රම ව්‍යවස්ථ...`
+
+**Context Size 4:**
+
+1. `සඳහා ප්‍රතිඵල අපේක්ෂකයාපක්ෂයසංකේතයඡන්ද සංඛ්‍යාව ඒ එම් මොහමඩ් ජලාල්දීන්එක්සත් ජාතික කනගරත්නම්දෙමළ එක්...`
+2. `පාර්ලිමේන්තු මැතිවරණයෙහි මෙම මැතිවරණ කොට්ඨාසය සඳහා ප්‍රතිඵල අපේක්ෂකයාපක්ෂයසංකේතයඡන්ද සංඛ්‍යාව එම් සී...`
+3. `ඡන්ද ඡන්ද ඡන්ද ඡන්දදායක භාවිත කිරීමේ පාර්ලිමේන්තු මහා මැතිවරණය 5 අප්‍රේල් සහ 10 අප්‍රේල් කාලය අතරතුර...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_bsto_එහිමිදුසුවභාවර_`
+2. `යම,_සමාද්‍ය_පාසහඳු_ca`
+3. `වය"_nin_ත_සයි._රක්`
+
+**Context Size 2:**
+
+1. `ය_සමාන_ලබා_ඇත්තේ_සල්වැසි`
+2. `න්_සම_ක්‍රමය:_hows_m`
+3. `._වෙනත්_(හෙක්ටර්_ලාක්_සාග`
+
+**Context Size 3:**
+
+1. `_සහ_කවි_ඔට්ජොසොන්_අස්_වූ_`
+2. `_ප්‍රද��ශයේ_ජයග්‍රහලෝකයක්_ලැ`
+3. `_කරනු_ලැබේ._එසේ_පිහිටුවීමේ_`
+
+**Context Size 4:**
+
+1. `_සහ_සංවර්ධනය_දෙසැම්බර්_15`
+2. `_අතර,_ඊජිප්තුවේ_දෙවන_චීන_`
+3. `_වන_අතර,_කාලාන්තරය._ආර්`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (3,101,075 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 | 264,267 |
+| Total Tokens | 10,742,411 |
+| Mean Frequency | 40.65 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 643.07 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | සහ | 137,360 |
+| 2 | අතර | 95,187 |
+| 3 | වන | 79,704 |
+| 4 | ලෙස | 67,370 |
+| 5 | හා | 59,489 |
+| 6 | වූ | 53,884 |
+| 7 | the | 52,310 |
+| 8 | විය | 51,836 |
+| 9 | කරන | 50,957 |
+| 10 | මෙම | 50,905 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | වොජික් | 2 |
+| 2 | ස්ලැට්කොයිච් | 2 |
+| 3 | ග්‍රැඩිස්කා | 2 |
+| 4 | ග්‍රැඩිෂ්කා | 2 |
+| 5 | ටෙසාන්ජ් | 2 |
+| 6 | bsp | 2 |
+| 7 | gdnp | 2 |
+| 8 | මිකොයාන් | 2 |
+| 9 | දැවිතෙල් | 2 |
+| 10 | ditwah | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9861 |
+| R² (Goodness of Fit) | 0.991091 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 22.3% |
+| Top 1,000 | 47.8% |
+| Top 5,000 | 69.0% |
+| Top 10,000 | 77.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9911 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 22.3% of corpus
+- **Long Tail:** 254,267 words needed for remaining 22.8% 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 | 0.8352 | 0.3629 | N/A | N/A |
+| **mono_64d** | 64 | 0.8359 | 0.2849 | N/A | N/A |
+| **mono_128d** | 128 | 0.7985 | 0.2254 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8352 | 0.3678 | 0.0600 | 0.2940 |
+| **aligned_64d** | 64 | 0.8359 🏆 | 0.2739 | 0.1220 | 0.4500 |
+| **aligned_128d** | 128 | 0.7985 | 0.2241 | 0.2100 | 0.5660 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_64d with 0.8359 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2898. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 21.0% 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 | **-0.378** | Low formulaic 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 |
+|--------|----------|
+| `-ස` | සිරගතකර, සැදුවේ, සාමාජිකයෙකුගෙන් |
+| `-ක` | කුසලතාපූර්ණ, කෙටවීම, කරණලදී |
+| `-ප` | පරිනත, පමුණවා, ප්‍රමාණන |
+| `-��` | මෙතර්ඩ්, මොංගල්වරු, මැතිනියට |
+| `-ව` | වුඞ්බරි, විලගෙදර, විචාරයෙන් |
+| `-අ` | අනුප්‍රාණේ, අපහසුම, අපසාරී |
+| `-බ` | බ්‍රහස්පති, බෝයගනේ, බාජන |
+| `-න` | නයිස්, නොපිළිගනී, නංවා |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ය` | ලෝකාන්තය, නොයෙදවිය, කෙරුනේය |
+| `-ට` | දෙවියාට, නිවෙසට, කොලොනියකරණයට |
+| `-s` | australias, chandras, wetas |
+| `-ව` | රජතුමන්ව, එක්ව, නාගමුව |
+| `-ම` | අපහසුම, කෙටවීම, කාව්‍යම |
+| `-e` | fertile, licence, clandestine |
+| `-ක` | ක‍්‍රමික, කුළුණක, කොයික |
+| `-a` | yulia, taifa, nacaduba |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ther` | 3.40x | 70 contexts | ether, thera, other |
+| `nter` | 3.32x | 49 contexts | unter, inter, enter |
+| `atio` | 3.27x | 50 contexts | ratio, ratios, ration |
+| `inte` | 3.27x | 38 contexts | intel, inter, cintec |
+| `stor` | 3.25x | 36 contexts | stork, store, story |
+| `ctio` | 3.34x | 30 contexts | action, sectio, auction |
+| `pres` | 3.23x | 32 contexts | presl, press, preset |
+| `ical` | 3.42x | 25 contexts | comical, topical, musical |
+| `sion` | 3.38x | 26 contexts | fusion, vision, passion |
+| `indi` | 3.29x | 27 contexts | indii, indie, india |
+| `mber` | 3.33x | 24 contexts | amber, bomber, member |
+| `ence` | 3.27x | 23 contexts | pence, fence, sence |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ප` | `-ය` | 60 words | පුමානය, පීතෘවංශීය |
+| `-ප` | `-ට` | 47 words | පතිකුලයට, පීඩාවලට |
+| `-ස` | `-ය` | 47 words | ස්තූපය, සුභය |
+| `-ස` | `-ට` | 43 words | සුර්යාට, සංස්ලේෂණයට |
+| `-ව` | `-ට` | 41 words | විබෙදීමට, වාදයට |
+| `-ව` | `-ය` | 41 words | වුල්ෆ්ය, විශිෂ්ටය |
+| `-අ` | `-ය` | 36 words | අසබඩය, අභ්‍යන්තරාවරණය |
+| `-ක` | `-ය` | 34 words | කිරිමටය, කේතලය |
+| `-අ` | `-ට` | 31 words | අශ්වයන්ට, අභිචාරයන්ට |
+| `-ක` | `-ට` | 29 words | කලිමන්තන්ට, කවුන්සිලයට |
+
+### 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 |
+|------|-----------------|------------|------|
+| ප්‍රදානවල | **`ප්‍රදා-න-වල`** | 7.5 | `න` |
+| අනුපිළිවෙලටම | **`අනුපිළිවෙල-ට-ම`** | 7.5 | `ට` |
+| ති‍්‍රපිටක | **`ති‍්‍රපි-ට-ක`** | 7.5 | `ට` |
+| ජර්මනියටය | **`ජර්මනි-යට-ය`** | 6.0 | `ජර්මනි` |
+| සොයාගත්තේය | **`සොයාගත්තේ-ය`** | 4.5 | `සොයාගත්තේ` |
+| ව්‍යාපෘතිය | **`ව්‍යාපෘති-ය`** | 4.5 | `ව්‍යාපෘති` |
+| භූමිප්‍රදේශයන්ද | **`භූමිප්‍රදේශයන්-ද`** | 4.5 | `භූමිප්‍රදේශයන්` |
+| සංවේදකයකට | **`සංවේදකයක-ට`** | 4.5 | `සංවේදකයක` |
+| doctorate | **`doctorat-e`** | 4.5 | `doctorat` |
+| එරිත්‍රියාවට | **`එරිත්‍රියාව-ට`** | 4.5 | `එරිත්‍රියාව` |
+| ක්‍රමලේඛය | **`ක්‍රමලේඛ-ය`** | 4.5 | `ක්‍රමලේඛ` |
+| යුරේසියාවට | **`යුරේසියාව-ට`** | 4.5 | `යුරේසියාව` |
+| හදුනාගනීම | **`හදුනාගනී-ම`** | 4.5 | `හදුනාගනී` |
+| colombians | **`colombian-s`** | 4.5 | `colombian` |
+| parliamentarians | **`parliamentarian-s`** | 4.5 | `parliamentarian` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sinhala 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
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.57x) |
+| N-gram | **2-gram** | Lowest perplexity (2,119) |
+| Markov | **Context-4** | Highest predictability (97.6%) |
+| 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 21:32:02*

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