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README.md

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+---
+language: sat
+language_name: Santali
+language_family: austroasiatic_other
+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-austroasiatic_other
+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: 4.334
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8573
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Santali - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Santali** 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** | 3.562x | 3.56 | 0.1107% | 614,914 |
+| **16k** | 3.887x | 3.89 | 0.1208% | 563,511 |
+| **32k** | 4.145x | 4.15 | 0.1289% | 528,448 |
+| **64k** | 4.334x 🏆 | 4.34 | 0.1347% | 505,414 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ᱛᱟᱥᱨᱤᱨ ᱛᱚᱵᱜᱮ ᱫᱚ ᱢᱤᱫᱴᱟᱝ ᱵᱷᱩᱴᱟᱱ ᱨᱤᱱᱤᱡ ᱯᱨᱚᱫᱷᱟᱱ ᱢᱚᱱᱛᱨᱤ ᱛᱟᱦᱮ ᱠᱟᱱᱟ᱾ ᱥᱟᱹᱠᱷᱭᱟᱹᱛ ᱵᱟᱦᱨᱮ ᱡᱚ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ᱛᱟᱥ ᱨᱤ ᱨ ▁ᱛ ᱚᱵ ᱜᱮ ▁ᱫᱚ ▁ᱢᱤᱫᱴᱟᱝ ▁ᱵᱷᱩᱴᱟᱱ ▁ᱨᱤᱱᱤᱡ ... (+7 more)` | 17 |
+| 16k | `▁ᱛᱟᱥ ᱨᱤ ᱨ ▁ᱛ ᱚᱵ ᱜᱮ ▁ᱫᱚ ▁ᱢᱤᱫᱴᱟᱝ ▁ᱵᱷᱩᱴᱟᱱ ▁ᱨᱤᱱᱤᱡ ... (+7 more)` | 17 |
+| 32k | `▁ᱛᱟᱥ ᱨᱤ ᱨ ▁ᱛᱚᱵ ᱜᱮ ▁ᱫᱚ ▁ᱢᱤᱫᱴᱟᱝ ▁ᱵᱷᱩᱴᱟᱱ ▁ᱨᱤᱱᱤᱡ ▁ᱯᱨᱚᱫᱷᱟᱱ ... (+6 more)` | 16 |
+| 64k | `▁ᱛᱟᱥ ᱨᱤᱨ ▁ᱛᱚᱵ ᱜᱮ ▁ᱫᱚ ▁ᱢᱤᱫᱴᱟᱝ ▁ᱵᱷᱩᱴᱟᱱ ▁ᱨᱤᱱᱤᱡ ▁ᱯᱨᱚᱫᱷᱟᱱ ▁ᱢᱚᱱᱛᱨᱤ ... (+5 more)` | 15 |
+
+**Sample 2:** `ᱡᱤᱭᱚᱛᱤ ᱫᱚ ᱢᱤᱫ ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ᱠᱟᱵᱟᱰᱤ ᱠᱷᱮᱞᱚᱸᱱᱰᱤᱭᱟᱹ ᱠᱟᱱᱟᱭ ᱾ ᱩᱱᱤ ᱫᱚ ᱮᱥᱤᱭᱟᱱ ᱜᱮᱢᱥ ᱨᱮ ᱥᱚᱱᱟ ᱢ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ᱡ ᱤᱭ ᱚᱛᱤ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ▁ᱠᱷᱮᱞᱚᱸᱱᱰ ᱤᱭᱟᱹ ▁ᱠᱟᱱᱟᱭ ... (+16 more)` | 26 |
+| 16k | `▁ᱡᱤᱭ ᱚᱛᱤ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ▁ᱠᱷᱮᱞᱚᱸᱱᱰᱤᱭᱟᱹ ▁ᱠᱟᱱᱟᱭ ▁᱾ ▁ᱩᱱᱤ ... (+14 more)` | 24 |
+| 32k | `▁ᱡᱤᱭ ᱚᱛᱤ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ▁ᱠᱷᱮᱞᱚᱸᱱᱰᱤᱭᱟᱹ ▁ᱠᱟᱱᱟᱭ ▁᱾ ▁ᱩᱱᱤ ... (+14 more)` | 24 |
+| 64k | `▁ᱡᱤᱭ ᱚᱛᱤ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ▁ᱠᱷᱮᱞᱚᱸᱱᱰᱤᱭᱟᱹ ▁ᱠᱟᱱᱟᱭ ▁᱾ ▁ᱩᱱᱤ ... (+14 more)` | 24 |
+
+**Sample 3:** `ᱯᱩᱡᱟ ᱱᱚᱨᱣᱟᱞ (ᱡᱟᱱᱟᱢ ᱑᱕ ᱢᱟᱨᱪ ᱫᱚ ᱢᱤᱫ ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ᱠᱟᱵᱟᱰᱤ ᱠᱷᱮᱞᱚᱸᱰᱤᱭᱟ. ᱠᱟᱱᱟᱭ ᱾ ᱩᱱᱤ ᱫᱚ ᱮᱥ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ᱯᱩᱡᱟ ▁ᱱᱚᱨ ᱣᱟᱞ ▁( ᱡᱟᱱᱟᱢ ▁᱑᱕ ▁ᱢᱟᱨᱪ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ... (+20 more)` | 30 |
+| 16k | `▁ᱯᱩᱡᱟ ▁ᱱᱚᱨ ᱣᱟᱞ ▁( ᱡᱟᱱᱟᱢ ▁᱑᱕ ▁ᱢᱟᱨᱪ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ... (+20 more)` | 30 |
+| 32k | `▁ᱯᱩᱡᱟ ▁ᱱᱚᱨᱣᱟᱞ ▁( ᱡᱟᱱᱟᱢ ▁᱑᱕ ▁ᱢᱟᱨᱪ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ... (+19 more)` | 29 |
+| 64k | `▁ᱯᱩᱡᱟ ▁ᱱᱚᱨᱣᱟᱞ ▁( ᱡᱟᱱᱟᱢ ▁᱑᱕ ▁ᱢᱟᱨᱪ ▁ᱫᱚ ▁ᱢᱤᱫ ▁ᱥᱤᱧᱚᱛᱤᱭᱟᱹ ▁ᱠᱟᱵᱟᱰᱤ ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.334x compression
+- **Lowest UNK Rate:** 8k with 0.1107% 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 | 20,084 | 14.29 | 97,087 | 14.0% | 34.6% |
+| **2-gram** | Subword | 373 🏆 | 8.54 | 7,442 | 61.2% | 97.5% |
+| **3-gram** | Word | 54,503 | 15.73 | 165,587 | 7.3% | 21.9% |
+| **3-gram** | Subword | 2,810 | 11.46 | 55,355 | 27.5% | 67.4% |
+| **4-gram** | Word | 106,952 | 16.71 | 264,198 | 4.3% | 16.9% |
+| **4-gram** | Subword | 13,742 | 13.75 | 288,409 | 15.4% | 42.0% |
+| **5-gram** | Word | 75,915 | 16.21 | 180,244 | 5.1% | 19.6% |
+| **5-gram** | Subword | 43,676 | 15.41 | 734,127 | 10.4% | 30.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱩᱱᱤ ᱫᱚ` | 27,097 |
+| 2 | `ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ` | 24,265 |
+| 3 | `ᱡᱟᱦᱟᱸ ᱫᱚ` | 11,415 |
+| 4 | `ᱨᱮ ᱢᱮᱱᱟᱜᱼᱟ` | 9,610 |
+| 5 | `ᱫᱚ ᱢᱤᱫ` | 8,714 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ` | 6,636 |
+| 2 | `ᱥᱟᱶᱛᱟ ᱩᱛᱷᱱᱟᱹᱣ ᱵᱚᱱᱚᱛ` | 5,033 |
+| 3 | `ᱥᱟᱹᱠᱷᱭᱟᱹᱛ ᱵᱟᱦᱨᱮ ᱡᱚᱱᱚᱲ` | 4,990 |
+| 4 | `ᱨᱮ ᱩᱱᱤ ᱫᱚ` | 4,504 |
+| 5 | `ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ` | 3,803 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ` | 3,279 |
+| 2 | `ᱦᱚᱲ ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ` | 2,960 |
+| 3 | `ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ ᱞᱮᱠᱟᱛᱮ` | 2,711 |
+| 4 | `ᱥᱟᱞ ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ` | 2,039 |
+| 5 | `ᱥᱟᱶᱛᱟ ᱩᱛᱷᱱᱟᱹᱣ ᱵᱚᱱᱚᱛ ᱨᱮ` | 1,482 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ ᱞᱮᱠᱟᱛᱮ` | 2,560 |
+| 2 | `ᱥᱟᱞ ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ` | 2,014 |
+| 3 | `ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱚᱸᱰᱮ ᱠᱷᱚᱱ` | 639 |
+| 4 | `ᱦᱚᱲ ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱚᱸᱰᱮ` | 622 |
+| 5 | `ᱨᱮᱱᱟᱜ ᱥᱟᱞ ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ` | 599 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱟ _` | 532,897 |
+| 2 | `_ ᱠ` | 452,845 |
+| 3 | `_ ᱨ` | 441,511 |
+| 4 | `ᱨ ᱮ` | 427,576 |
+| 5 | `ᱮ _` | 424,447 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ᱨ ᱮ` | 359,020 |
+| 2 | `ᱟ ᱜ _` | 216,961 |
+| 3 | `ᱨ ᱮ _` | 206,913 |
+| 4 | `_ ᱫ ᱚ` | 193,101 |
+| 5 | `ᱫ ᱚ _` | 184,355 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ᱨ ᱮ _` | 183,663 |
+| 2 | `_ ᱫ ᱚ _` | 173,539 |
+| 3 | `ᱮ ᱱ ᱟ ᱜ` | 121,241 |
+| 4 | `ᱟ _ ᱾ _` | 118,531 |
+| 5 | `_ ᱟ ᱨ _` | 109,370 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᱮ ᱱ ᱟ ᱜ _` | 88,897 |
+| 2 | `_ ᱠ ᱟ ᱱ ᱟ` | 77,004 |
+| 3 | `ᱨ ᱮ ᱱ ᱟ ᱜ` | 76,395 |
+| 4 | `_ ᱨ ᱮ ᱱ ᱟ` | 76,338 |
+| 5 | `ᱠ ᱟ ᱱ ᱟ _` | 56,559 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 373
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% 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 | 0.7025 | 1.627 | 5.73 | 274,818 | 29.8% |
+| **1** | Subword | 0.8387 | 1.788 | 5.63 | 5,505 | 16.1% |
+| **2** | Word | 0.2957 | 1.228 | 1.89 | 1,572,360 | 70.4% |
+| **2** | Subword | 0.6641 | 1.585 | 4.27 | 30,957 | 33.6% |
+| **3** | Word | 0.1263 | 1.091 | 1.26 | 2,962,389 | 87.4% |
+| **3** | Subword | 0.7552 | 1.688 | 3.97 | 132,005 | 24.5% |
+| **4** | Word | 0.0549 🏆 | 1.039 | 1.09 | 3,737,893 | 94.5% |
+| **4** | Subword | 0.6689 | 1.590 | 2.92 | 523,754 | 33.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ᱨᱮ ᱚᱠᱷᱟ ᱫᱚ ᱯᱚᱱᱚᱛ ᱯᱚᱪᱷᱤᱢ ᱵᱟᱝᱞᱟ ᱨᱮ ᱟᱭᱢᱟ ᱠᱟ ᱥᱱᱤ ᱵᱚᱠᱠᱷᱟᱞᱤ ᱫᱚ ᱥᱮᱨᱢᱟ ᱨᱮᱭᱟᱜ ᱟᱢᱮᱨᱤᱠᱟᱱ ᱮᱠᱥᱯᱨᱮᱥ`
+2. `ᱫᱚ ᱢᱤᱴᱚᱨ ᱮᱱᱴᱨᱤ ᱞᱮᱠᱟᱛᱮ ᱱᱚᱸᱰᱮ ᱱᱟᱜ ᱥᱟᱥᱚᱱ ᱨᱮᱱᱟᱜ ᱪᱮᱛᱟᱱ ᱨᱮᱠᱚ ᱨᱚᱲᱼᱟ ᱡᱚᱨᱡᱽ ᱢᱟᱭᱨᱤᱱ ᱟᱜ ᱡᱟᱱᱟᱢ ᱞᱮᱱᱟᱭ`
+3. `ᱟᱨ ᱰᱤᱨᱮᱠᱴᱚᱨ ᱞᱟᱹᱜᱤᱫ ᱛᱮ ᱜᱮᱲᱤᱭᱟᱠᱚᱞᱟ ᱟᱹᱛᱩ ᱠᱟᱱᱟ ᱮᱱᱤᱢᱮᱥᱚᱱ ᱨᱮ ᱛᱮᱭᱟᱨ ᱞᱮᱫᱟ ᱟᱭᱢᱟ ᱫᱤᱱ ᱠᱚ ᱵᱟᱰᱟᱭ ᱛᱟᱦᱮᱸᱫ`
+
+**Context Size 2:**
+
+1. `ᱩᱱᱤ ᱫᱚ ᱰᱤᱨᱮᱠᱴᱚᱨ ᱰᱟᱭᱱᱟ ᱞᱳᱨᱮᱱ ᱥᱟᱶ ᱢᱤᱫ ᱥᱟᱹᱜᱟᱹᱭ ᱢᱮᱱᱟᱜ ᱟ ᱫᱚ ᱱᱤᱭᱟᱹ ᱥᱤᱧᱚᱛ ᱨᱮ ᱦᱳᱢᱤᱭᱳᱯᱮᱛᱷᱤ ᱨᱮᱭᱟᱜ ᱮᱛᱦᱚᱵ`
+2. `ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱦᱟᱣᱲᱟ ᱱᱤᱭᱩ ᱡᱚᱞᱯᱟᱭᱜᱩᱲᱤ ᱵᱚᱱᱫᱮ ᱵᱷᱟᱨᱚᱛ ᱮᱠᱥᱯᱨᱮᱥ ᱞᱮᱠᱟᱛᱮ ᱪᱟᱞᱟᱣ ᱞᱮᱱᱟ ᱛᱟᱦᱮᱸᱫ ᱥᱟᱹᱠᱷᱭᱟᱹᱛ ᱵᱟᱦᱨᱮ ᱡᱚᱱᱚᱲ...`
+3. `ᱡᱟᱦᱟᱸ ᱫᱚ ᱡᱮᱜᱮᱫ ᱵᱤᱨᱫᱟᱹᱜᱟᱲ ᱨᱮᱭᱟᱜ ᱯᱷᱮᱠᱟᱞᱴᱤ ᱚᱯᱷ ᱟᱨᱴᱥ ᱮ ᱯᱩᱨᱟᱹᱣ ᱞᱮᱫ ᱛᱟᱦᱮᱸᱫ ᱨᱮ ᱟᱫᱽᱨᱟ ᱵᱷᱮᱫᱩᱣᱟᱥᱳᱞ ᱥᱮᱠᱴᱚᱨ ᱟᱨ`
+
+**Context Size 3:**
+
+1. `ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱠᱮᱱᱰᱟᱨᱤ ᱤᱥᱠᱩᱞ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱦᱚᱲᱢᱚ ᱥᱟᱶᱟᱨ ᱵᱟᱱᱫᱳᱣᱟᱱ ᱨᱮ ᱓᱐ ᱜᱚᱴᱟᱝ ᱵᱮᱰ ᱟᱜ ᱵᱮᱞᱠᱩᱨᱤ`
+2. `ᱥᱟᱶᱛᱟ ᱩᱛᱷᱱᱟᱹᱣ ᱵᱚᱱᱚᱛ ᱨᱤᱱ ᱜᱩᱴ ᱦᱚᱲ ᱮᱞ ᱨᱤᱱ ᱑᱗ ᱐᱔ ᱠᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ ᱱᱚᱸᱰᱮ ᱠᱚ ᱛᱟᱦᱮᱸᱱ ᱠᱟᱱ ᱵᱟᱡᱟᱨ`
+3. `ᱨᱮ ᱩᱱᱤ ᱫᱚ ᱟᱯᱱᱟ ᱥᱟᱯᱱᱟ ᱢᱚᱱᱤ ᱢᱚᱱᱤ ᱯᱷᱤᱞᱢ ᱨᱮ ᱠᱟ ᱠᱚᱱᱱᱚᱰᱟ ᱨᱮ ᱑᱐᱐ ᱜᱚᱴᱟᱝ ᱚᱱᱚᱞ ᱢᱮᱱᱟᱜᱼᱟ ᱱᱟᱜᱟᱢ ᱡᱟᱨᱢᱟᱱ`
+
+**Context Size 4:**
+
+1. `ᱨᱮᱱᱟᱜ ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱱᱚᱣᱟ ᱥᱟᱞ ᱨᱮ ᱵᱟᱲᱦᱟᱣ ᱠᱟᱛᱮ ᱦᱚᱲ ᱠᱚ ᱦᱩᱭ ᱮᱱᱟ ᱟᱨ ᱟᱨ ᱛᱟᱞᱟ ᱨᱮ`
+2. `ᱦᱚᱲ ᱠᱚ ᱛᱟᱦᱮᱸ ᱠᱟᱱᱟ ᱚᱸᱰᱮ ᱠᱷᱚᱱ ᱒᱙ ᱘᱒᱗ ᱗᱙ ᱔᱖ ᱠᱚᱲᱟ ᱟᱨ ᱑᱘ ᱙᱖᱙ ᱔᱙ ᱓᱒ ᱛᱤᱨᱞᱟᱹ ᱠᱚ ᱛᱟᱦᱮᱸ`
+3. `ᱦᱚᱲ ᱞᱮᱠᱷᱟ ᱡᱚᱠᱷᱟ ᱞᱮᱠᱟᱛᱮ ᱨᱟᱭᱯᱩᱨ ᱕ ᱔᱗᱐ ᱥᱩᱠᱫᱚᱞ ᱑᱓ ᱐᱙᱓ ᱟᱨ ᱵᱚᱰ ᱵᱚᱰ ᱔ ᱕᱕᱘ ᱜᱚᱞᱥᱤ ᱑ ᱥᱟᱶᱛᱟ ᱩᱛᱷᱱᱟᱹᱣ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ᱜ_,_ᱨ_ᱡ_ᱩᱴᱤᱫᱚᱣ_`
+2. `ᱟᱨᱩᱜ_ᱢ_ᱠᱴᱤᱨᱤᱞ_ᱠᱷ`
+3. `ᱚᱦᱚ_᱒_ᱢᱤ_ᱠᱷᱟᱨᱮᱸᱠ`
+
+**Context Size 2:**
+
+1. `ᱟ_ᱞᱟᱹ_ᱨᱮ_ᱱᱤᱭᱟᱱ_ᱠᱚ`
+2. `_ᱠᱷᱟᱣ_ᱨᱟᱸᱦᱮ',_ᱵᱟᱡ`
+3. `_ᱨᱮ_ᱟᱨ_᱖_ᱠᱚ_ᱪᱟᱞ,_`
+
+**Context Size 3:**
+
+1. `_ᱨᱮᱱᱟᱜᱼᱟ_bum_ᱵᱤᱥᱟᱱ`
+2. `ᱟᱜ_ᱯᱟᱹᱨᱤ_ᱢᱟᱞᱟᱜ_ᱢᱟᱨ`
+3. `ᱨᱮ_᱑᱒0,᱖᱔᱐_ᱟᱜ_ᱠᱟᱱ_`
+
+**Context Size 4:**
+
+1. `_ᱨᱮ_ᱯᱷᱮᱰ_ᱠᱚ_ᱚᱲᱟᱜ_ᱨᱚ`
+2. `_ᱫᱚ_ᱵᱤᱫᱷᱟᱱᱤ_ᱡᱟᱦᱟᱸ_ᱫ`
+3. `ᱮᱱᱟᱜ_ᱦᱚᱸ_ᱨᱤᱱ_ᱠᱟᱱ_ᱫᱷ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (523,754 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 | 104,851 |
+| Total Tokens | 4,586,629 |
+| Mean Frequency | 43.74 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 1084.86 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ᱨᱮ | 194,411 |
+| 2 | ᱫᱚ | 174,300 |
+| 3 | ᱟᱨ | 110,495 |
+| 4 | ᱨᱮᱱᱟᱜ | 75,922 |
+| 5 | ᱠᱚ | 74,024 |
+| 6 | ᱠᱟᱱᱟ | 64,170 |
+| 7 | ᱠᱷᱚᱱ | 46,273 |
+| 8 | ᱩᱱᱤ | 40,257 |
+| 9 | ᱢᱤᱫ | 40,250 |
+| 10 | ᱨᱮᱭᱟᱜ | 38,160 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ᱜᱟᱲᱤᱢᱟᱭ | 2 |
+| 2 | ᱜᱨᱟᱱᱰᱤᱝ | 2 |
+| 3 | ᱟᱯᱚᱫᱟ | 2 |
+| 4 | ᱵᱮᱵᱚᱥᱛᱟᱯᱚᱱᱟ | 2 |
+| 5 | ᱢᱩᱦᱟᱹᱱᱟᱹ | 2 |
+| 6 | estuary | 2 |
+| 7 | ᱢᱚᱸᱜᱨᱚᱵᱷ | 2 |
+| 8 | ᱦᱚᱸᱥᱟ | 2 |
+| 9 | ᱞᱮᱛᱤᱯᱩᱨ | 2 |
+| 10 | ᱴᱮᱨᱟᱠᱚᱴᱟ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1879 |
+| R² (Goodness of Fit) | 0.996295 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 42.8% |
+| Top 1,000 | 71.1% |
+| Top 5,000 | 84.6% |
+| Top 10,000 | 89.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9963 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 42.8% of corpus
+- **Long Tail:** 94,851 words needed for remaining 10.9% 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 | 0.8573 | 0.3536 | N/A | N/A |
+| **mono_64d** | 64 | 0.8443 | 0.2821 | N/A | N/A |
+| **mono_128d** | 128 | 0.7962 | 0.2213 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8573 🏆 | 0.3640 | 0.0320 | 0.1660 |
+| **aligned_64d** | 64 | 0.8443 | 0.2836 | 0.0440 | 0.2060 |
+| **aligned_128d** | 128 | 0.7962 | 0.2203 | 0.0800 | 0.2960 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8573 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2875. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 8.0% 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 | **-0.348** | 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 |
+|--------|----------|
+| `-ᱥ` | ᱥᱵᱫᱚ, ᱥᱨᱚᱵᱷᱟᱱ, ᱥᱤᱧᱟᱛ |
+| `-ᱠ` | ᱠᱷᱮᱢᱟᱨ, ᱠᱳᱲᱚ, ᱠᱟᱞᱮ |
+| `-ᱵ` | ᱵᱤᱨᱫᱟᱹᱜᱟᱲᱤᱭᱩᱱᱤᱵᱷᱟᱨᱥᱤᱴᱮᱴ, ᱵᱷᱳ, ᱵᱟᱫᱽᱞᱟ |
+| `-ᱥᱟ` | ᱥᱟᱠᱩᱱᱛᱚᱞᱟ, ᱥᱟᱭᱟᱱᱟ, ᱥᱟᱵᱽᱢᱟᱨᱥᱟᱞ |
+| `-ᱟ` | ᱟᱣᱚ, ᱟᱵᱽᱫᱩᱞᱟᱦ, ᱟᱭᱹ |
+| `-ᱠᱟ` | ᱠᱟᱞᱮ, ᱠᱟᱨᱟᱭᱠᱟᱞ, ᱠᱟᱨᱠᱟᱛ |
+| `-ᱵᱟ` | ᱵᱟᱫᱽᱞᱟ, ᱵᱟᱯᱞᱟᱱᱤᱡ, ᱵᱟᱴᱚᱢ |
+| `-ᱯ` | ᱯᱟᱱᱛᱷᱟᱠᱚ, ᱯᱷᱤᱠᱟᱨᱰ, ᱯᱷᱟᱭᱡᱟᱵᱟᱫᱽ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ᱟ` | ᱛᱷᱩᱱᱤᱜᱟ, ᱵᱟᱫᱽᱞᱟ, ᱢᱟᱱᱤᱥᱟ |
+| `-ᱤ` | ᱜᱳᱢᱚᱛᱤ, ᱡᱚᱱᱢᱟ���ᱴᱚᱢᱤ, ᱡᱤᱵᱩᱴᱤ |
+| `-ᱨ` | ᱠᱷᱮᱢᱟᱨ, ᱪᱮᱱᱫᱩᱨ, ᱠᱣᱟᱰᱨᱮᱝᱜᱩᱞᱟᱨ |
+| `-ᱱ` | ᱥᱨᱚᱵᱷᱟᱱ, ᱥᱮᱬᱟᱦᱟᱱ, ᱟᱯᱩᱱ |
+| `-ᱚᱱ` | ᱰᱤᱵᱷᱤᱡᱚᱱ, ᱥᱨᱤᱱᱤᱠᱮᱛᱚᱱ, ᱡᱟᱢᱚᱱ |
+| `-ᱟᱨ` | ᱠᱷᱮᱢᱟᱨ, ᱠᱣᱟᱰᱨᱮᱝᱜᱩᱞᱟᱨ, ᱥᱴᱮᱞᱟᱨ |
+| `-ᱟᱱ` | ᱥᱨᱚᱵᱷᱟᱱ, ᱥᱮᱬᱟᱦᱟᱱ, ᱨᱟᱡᱽᱟᱹᱨᱤᱭᱟᱱ |
+| `-ᱞ` | ᱳᱝᱜᱳᱞ, ᱥᱟᱵᱽᱢᱟᱨᱥᱟᱞ, ᱠᱟᱨᱟᱭᱠᱟᱞ |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ᱟᱦᱮᱸ` | 2.13x | 43 contexts | ᱪᱟᱦᱮᱸ, ᱠᱟᱦᱮᱸ, ᱴᱟᱦᱮᱸ |
+| `ᱟᱹᱨᱥ` | 2.33x | 28 contexts | ᱯᱟᱹᱨᱥ, ᱟᱹᱨᱥᱤ, ᱠᱟᱹᱨᱥᱤ |
+| `ᱟᱹᱜᱤ` | 2.06x | 41 contexts | ᱛᱟᱹᱜᱤ, ᱜᱟᱹᱜᱤ, ᱞᱟᱹᱜᱤ |
+| `ᱮᱥᱚᱱ` | 1.90x | 47 contexts | ᱠᱮᱥᱚᱱ, ᱴᱮᱥᱚᱱ, ᱡᱮᱥᱚᱱ |
+| `ᱞᱟᱹᱜ` | 2.30x | 23 contexts | ᱞᱟᱹᱜᱽ, ᱞᱟᱹᱜᱤ, ᱞᱟᱹᱜᱫ |
+| `ᱹᱨᱥᱤ` | 2.40x | 19 contexts | ᱟᱹᱨᱥᱤ, ᱯᱹᱨᱥᱤ, ᱠᱟᱹᱨᱥᱤ |
+| `ᱮᱱᱟᱣ` | 2.03x | 33 contexts | ᱢᱮᱱᱟᱣ, ᱵᱮᱱᱟᱣ, ᱞᱮᱱᱟᱣ |
+| `ᱷᱤᱞᱢ` | 2.47x | 15 contexts | 0ᱷᱤᱞᱢ, ᱳᱷᱤᱞᱢ, ᱯᱷᱤᱞᱢ |
+| `ᱱᱟᱜᱼ` | 2.18x | 20 contexts | ᱟᱱᱟᱜᱼ, ᱮᱱᱟᱜᱼᱟ, ᱟᱱᱟᱜᱼᱟ |
+| `ᱹᱜᱤᱫ` | 2.38x | 15 contexts | ᱟᱹᱜᱤᱫ, ᱞᱟᱹᱜᱤᱫ, ᱯᱟᱹᱜᱤᱫ |
+| `ᱮᱠᱟᱛ` | 2.15x | 20 contexts | ᱞᱮᱠᱟᱛ, ᱪᱮᱠᱟᱛᱮ, ᱞᱮᱠᱟᱛᱮ |
+| `ᱟᱦᱟᱸ` | 1.70x | 45 contexts | ᱦᱟᱦᱟᱸ, ᱛᱟᱦᱟᱸ, ᱨᱟᱦᱟᱸ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ᱵ` | `-ᱤ` | 74 words | ᱵᱤᱥᱥᱚᱼᱵᱷᱟᱨᱚᱛᱤ, ᱵᱚᱨᱠᱤ |
+| `-ᱵ` | `-ᱟ` | 73 words | ᱵᱷᱟᱫᱩᱨᱟ, ᱵᱤᱛᱟ |
+| `-ᱥ` | `-ᱟ` | 70 words | ᱥᱚᱨᱚᱱᱠᱷᱚᱞᱟ, ᱥᱞᱮᱥᱢᱟ |
+| `-ᱠ` | `-ᱟ` | 66 words | ᱠᱷᱩᱫᱟ, ᱠᱷᱟᱞᱮᱫᱟ |
+| `-ᱥ` | `-ᱤ` | 60 words | ᱥᱳᱱᱤ, ᱥᱤᱝᱡᱤ |
+| `-ᱠ` | `-ᱤ` | 58 words | ᱠᱟᱣᱮᱨᱤ, ᱠᱩᱱᱴᱤ |
+| `-ᱯ` | `-ᱟ` | 57 words | ᱯᱚᱞᱥᱩᱸᱰᱟ, ᱯᱩᱸᱪᱟ |
+| `-ᱵ` | `-ᱨ` | 54 words | ᱵᱷᱚᱣᱟᱱᱤᱯᱩᱨ, ᱵᱷᱤᱴᱤᱨ |
+| `-ᱵ` | `-ᱱ` | 52 words | ᱵᱨᱤᱱᱫᱟᱣᱟᱱ, ᱵᱚᱸᱜᱟᱛᱷᱟᱱ |
+| `-ᱯ` | `-ᱤ` | 50 words | ᱯᱟᱹᱫᱽᱨᱤ, ᱯᱨᱚᱡᱟᱛᱚᱱᱛᱨᱤ |
+
+### 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 |
+|------|-----------------|------------|------|
+| ᱫᱟᱫᱮᱝᱜᱤᱨᱤ | **`ᱫᱟᱫᱮᱝᱜ-ᱤ-ᱨᱤ`** | 7.5 | `ᱤ` |
+| ᱵᱚᱨᱟᱵᱟᱡᱟᱨ | **`ᱵᱚ-ᱨᱟ-ᱵᱟᱡᱟᱨ`** | 7.5 | `ᱵᱟᱡᱟᱨ` |
+| ᱥᱟᱬᱮᱸᱥᱤᱭᱟ | **`ᱥᱟᱬᱮᱸᱥ-ᱤ-ᱭᱟ`** | 7.5 | `ᱤ` |
+| ᱜᱚᱢᱠᱮᱭᱟᱱᱤ | **`ᱜᱚᱢᱠᱮ-ᱭᱟ-ᱱᱤ`** | 6.0 | `ᱜᱚᱢᱠᱮ` |
+| ᱢᱮᱠᱟᱱᱤᱠᱮᱞ | **`ᱢᱮ-ᱠᱟ-ᱱᱤᱠᱮᱞ`** | 6.0 | `ᱱᱤᱠᱮᱞ` |
+| ᱥᱟᱵᱰᱤᱵᱤᱡᱚᱱ | **`ᱥᱟ-ᱵ-ᱰᱤᱵᱤᱡᱚᱱ`** | 6.0 | `ᱰᱤᱵᱤᱡᱚᱱ` |
+| ᱨᱟᱡᱟᱵᱟᱡᱟᱨ | **`ᱨᱟ-ᱡᱟ-ᱵᱟᱡᱟᱨ`** | 6.0 | `ᱵᱟᱡᱟᱨ` |
+| strangers | **`stranger-s`** | 4.5 | `stranger` |
+| proposals | **`proposal-s`** | 4.5 | `proposal` |
+| ᱨᱤᱯᱷᱟᱭᱤᱱᱰ | **`ᱨᱤᱯᱷᱟᱭᱤᱱ-ᱰ`** | 4.5 | `ᱨᱤᱯᱷᱟᱭᱤᱱ` |
+| ᱟᱹᱠᱷᱨᱤᱧᱟᱱ | **`ᱟᱹᱠᱷᱨᱤᱧ-ᱟᱱ`** | 4.5 | `ᱟᱹᱠᱷᱨᱤᱧ` |
+| ᱯᱨᱚᱠᱨᱤᱛᱤᱥ | **`ᱯᱨᱚᱠᱨᱤᱛᱤ-ᱥ`** | 4.5 | `ᱯᱨᱚᱠᱨᱤᱛᱤ` |
+| instituted | **`institute-d`** | 4.5 | `institute` |
+| ᱯᱨᱳᱰᱟᱠᱥᱟᱱᱥ | **`ᱯᱨᱳᱰᱟᱠᱥᱟᱱ-ᱥ`** | 4.5 | `ᱯᱨᱳᱰᱟᱠᱥᱟᱱ` |
+| quarterfinals | **`quarterfinal-s`** | 4.5 | `quarterfinal` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Santali 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 (4.33x) |
+| N-gram | **2-gram** | Lowest perplexity (373) |
+| Markov | **Context-4** | Highest predictability (94.5%) |
+| 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 19:38:19*

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