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README.md

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+---
+language: mni
+language_name: Manipuri
+language_family: tibetoburman_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-tibetoburman_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: 4.321
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6424
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Manipuri - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Manipuri** 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** | 3.393x | 3.40 | 0.2560% | 174,579 |
+| **16k** | 3.741x | 3.75 | 0.2824% | 158,309 |
+| **32k** | 4.017x | 4.02 | 0.3031% | 147,458 |
+| **64k** | 4.321x 🏆 | 4.33 | 0.3261% | 137,077 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ꯂꯥꯢꯔꯤꯛ ꯄꯔꯤꯡ (ꯕꯨꯛ ꯁꯤꯔꯤꯁ) ꯑꯁꯤ ꯑꯉꯥꯡꯁꯤꯡꯒꯤ ꯂꯥꯢꯔꯤꯛꯁꯤꯡꯒꯤ ꯃꯅꯨꯡꯗꯒꯤ ꯑꯃꯅꯤ ꯫ ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ ꯃ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꯂꯥꯢꯔꯤꯛ ▁ꯄꯔꯤꯡ ▁( ꯕꯨꯛ ▁ꯁꯤꯔꯤꯁ ) ▁ꯑꯁꯤ ▁ꯑꯉꯥꯡꯁꯤꯡꯒꯤ ▁ꯂꯥꯢꯔꯤꯛꯁꯤꯡꯒꯤ ▁ꯃꯅꯨꯡꯗꯒꯤ ... (+7 more)` | 17 |
+| 16k | `▁ꯂꯥꯢꯔꯤꯛ ▁ꯄꯔꯤꯡ ▁( ꯕꯨꯛ ▁ꯁꯤꯔꯤꯁ ) ▁ꯑꯁꯤ ▁ꯑꯉꯥꯡꯁꯤꯡꯒꯤ ▁ꯂꯥꯢꯔꯤꯛꯁꯤꯡꯒꯤ ▁ꯃꯅꯨꯡꯗꯒꯤ ... (+7 more)` | 17 |
+| 32k | `▁ꯂꯥꯢꯔꯤꯛ ▁ꯄꯔꯤꯡ ▁( ꯕꯨꯛ ▁ꯁꯤꯔꯤꯁ ) ▁ꯑꯁꯤ ▁ꯑꯉꯥꯡꯁꯤꯡꯒꯤ ▁ꯂꯥꯢꯔꯤꯛꯁꯤꯡꯒꯤ ▁ꯃꯅꯨꯡꯗꯒꯤ ... (+7 more)` | 17 |
+| 64k | `▁ꯂꯥꯢꯔꯤꯛ ▁ꯄꯔꯤꯡ ▁( ꯕꯨꯛ ▁ꯁꯤꯔꯤꯁ ) ▁ꯑꯁꯤ ▁ꯑꯉꯥꯡꯁꯤꯡꯒꯤ ▁ꯂꯥꯢꯔꯤꯛꯁꯤꯡꯒꯤ ▁ꯃꯅꯨꯡꯗꯒꯤ ... (+7 more)` | 17 |
+
+**Sample 2:** `ꯃꯁꯤꯡ ꯑꯁꯤ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ꯫ ꯃꯁꯤꯗꯒꯤ ꯃꯁꯤꯡ ꯱ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯅꯤ꯫ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꯃꯁꯤꯡ ▁ꯑꯁꯤ ▁ꯗꯒꯤ ▁ꯍꯦꯟꯅ ▁ꯆꯥꯎꯕ ▁ꯃꯁꯤꯡ ▁ꯑꯃꯅꯤ꯫ ▁ꯃꯁꯤꯗꯒꯤ ▁ꯃꯁꯤꯡ ▁꯱ ... (+11 more)` | 21 |
+| 16k | `▁ꯃꯁꯤꯡ ▁ꯑꯁꯤ ▁ꯗꯒꯤ ▁ꯍꯦꯟꯅ ▁ꯆꯥꯎꯕ ▁ꯃꯁꯤꯡ ▁ꯑꯃꯅꯤ꯫ ▁ꯃꯁꯤꯗꯒꯤ ▁ꯃꯁꯤꯡ ▁꯱ ... (+11 more)` | 21 |
+| 32k | `▁ꯃꯁꯤꯡ ▁ꯑꯁꯤ ▁ꯗꯒꯤ ▁ꯍꯦꯟꯅ ▁ꯆꯥꯎꯕ ▁ꯃꯁꯤꯡ ▁ꯑꯃꯅꯤ꯫ ▁ꯃꯁꯤꯗꯒꯤ ▁ꯃꯁꯤꯡ ▁꯱ ... (+11 more)` | 21 |
+| 64k | `▁ꯃꯁꯤꯡ ▁ꯑꯁꯤ ▁ꯗꯒꯤ ▁ꯍꯦꯟꯅ ▁ꯆꯥꯎꯕ ▁ꯃꯁꯤꯡ ▁ꯑꯃꯅꯤ꯫ ▁ꯃꯁꯤꯗꯒꯤ ▁ꯃꯁꯤꯡ ▁꯱ ... (+11 more)` | 21 |
+
+**Sample 3:** `ꯑꯂꯤ ꯐꯥꯓꯜ ꯑꯁꯤ ꯏꯟꯗꯤꯌꯥꯒꯤ ꯍꯤꯟꯗꯤ ꯂꯣꯟꯒꯤ ꯕꯣꯜꯂꯤꯋꯨꯗ (ꯍꯤꯟꯗꯤ ꯃꯃꯤ ꯀꯨꯝꯃꯩ)ꯒꯤ ꯁꯛꯇꯝ ꯂꯥꯡꯕ ꯁꯤꯟꯂꯣꯢ ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꯑ ꯂꯤ ▁ꯐꯥ ꯓ ꯜ ▁ꯑꯁꯤ ▁ꯏꯟꯗꯤꯌꯥꯒꯤ ▁ꯍꯤꯟꯗꯤ ▁ꯂꯣꯟꯒꯤ ▁ꯕꯣꯜꯂꯤꯋꯨꯗ ... (+18 more)` | 28 |
+| 16k | `▁ꯑꯂꯤ ▁ꯐꯥ ꯓ ꯜ ▁ꯑꯁꯤ ▁ꯏꯟꯗꯤꯌꯥꯒꯤ ▁ꯍꯤꯟꯗꯤ ▁ꯂꯣꯟꯒꯤ ▁ꯕꯣꯜꯂꯤꯋꯨꯗ ▁( ... (+17 more)` | 27 |
+| 32k | `▁ꯑꯂꯤ ▁ꯐꯥ ꯓ ꯜ ▁ꯑꯁꯤ ▁ꯏꯟꯗꯤꯌꯥꯒꯤ ▁ꯍꯤꯟꯗꯤ ▁ꯂꯣꯟꯒꯤ ▁ꯕꯣꯜꯂꯤꯋꯨꯗ ▁( ... (+17 more)` | 27 |
+| 64k | `▁ꯑꯂꯤ ▁ꯐꯥꯓꯜ ▁ꯑꯁꯤ ▁ꯏꯟꯗꯤꯌꯥꯒꯤ ▁ꯍꯤꯟꯗꯤ ▁ꯂꯣꯟꯒꯤ ▁ꯕꯣꯜꯂꯤꯋꯨꯗ ▁( ꯍꯤꯟꯗꯤ ▁ꯃꯃꯤ ... (+15 more)` | 25 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.321x compression
+- **Lowest UNK Rate:** 8k with 0.2560% 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 | 1,742 | 10.77 | 10,310 | 35.2% | 72.4% |
+| **2-gram** | Subword | 1,226 🏆 | 10.26 | 15,112 | 41.9% | 79.3% |
+| **3-gram** | Word | 1,239 | 10.28 | 9,547 | 36.7% | 82.6% |
+| **3-gram** | Subword | 7,080 | 12.79 | 65,488 | 23.1% | 50.6% |
+| **4-gram** | Word | 1,700 | 10.73 | 18,256 | 32.5% | 80.1% |
+| **4-gram** | Subword | 22,325 | 14.45 | 195,890 | 16.2% | 37.5% |
+| **5-gram** | Word | 1,478 | 10.53 | 14,280 | 31.6% | 83.2% |
+| **5-gram** | Subword | 35,425 | 15.11 | 266,503 | 14.0% | 33.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ` | 9,094 |
+| 2 | `ꯃꯁꯤꯡ ꯑꯁꯤ` | 6,658 |
+| 3 | `ꯆꯥꯎꯕ ꯃꯁꯤꯡ` | 5,847 |
+| 4 | `ꯍꯦꯟꯅ ꯆꯥꯎꯕ` | 5,424 |
+| 5 | `ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ` | 4,237 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ` | 5,408 |
+| 2 | `ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ` | 4,147 |
+| 3 | `ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ` | 3,732 |
+| 4 | `ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ` | 3,724 |
+| 5 | `ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ` | 2,348 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ` | 4,146 |
+| 2 | `ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ` | 3,723 |
+| 3 | `ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ` | 2,348 |
+| 4 | `ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ` | 2,348 |
+| 5 | `ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ` | 1,590 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ` | 2,348 |
+| 2 | `ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ` | 2,348 |
+| 3 | `ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ` | 1,585 |
+| 4 | `ꯑꯁꯤ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ` | 1,585 |
+| 5 | `ꯃꯁꯤꯡ ꯑꯁꯤ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ` | 1,582 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꯃ` | 104,012 |
+| 2 | `_ ꯑ` | 89,663 |
+| 3 | `ꯡ _` | 60,916 |
+| 4 | `ꯒꯤ _` | 53,149 |
+| 5 | `ꯁꯤ ꯡ` | 47,278 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꯑ ꯃ` | 33,170 |
+| 2 | `_ ꯃ ꯁꯤ` | 28,206 |
+| 3 | `ꯁꯤ ꯡ _` | 26,827 |
+| 4 | `_ ꯑ ꯁꯤ` | 22,140 |
+| 5 | `ꯃ ꯁꯤ ꯡ` | 19,797 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꯑ ꯁꯤ _` | 18,348 |
+| 2 | `_ ꯃ ꯁꯤ ꯡ` | 16,785 |
+| 3 | `ꯃ ꯁꯤ ꯡ _` | 16,509 |
+| 4 | `ꯁꯤ ꯡ _ ꯑ` | 14,961 |
+| 5 | `_ ꯑ ꯃ ꯅꯤ` | 11,342 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꯃ ꯁꯤ ꯡ _` | 13,768 |
+| 2 | `ꯃ ꯁꯤ ꯡ _ ꯑ` | 11,210 |
+| 3 | `ꯑ ꯃ ꯁꯨ ꯡ _` | 10,162 |
+| 4 | `_ ꯑ ꯃ ꯁꯨ ꯡ` | 10,153 |
+| 5 | `_ ꯃ ꯇꯦ ꯡ _` | 9,972 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 1,226
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~33% 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 | 0.7036 | 1.629 | 3.89 | 86,410 | 29.6% |
+| **1** | Subword | 1.2295 | 2.345 | 13.46 | 2,717 | 0.0% |
+| **2** | Word | 0.1754 | 1.129 | 1.33 | 335,779 | 82.5% |
+| **2** | Subword | 0.8032 | 1.745 | 4.49 | 36,564 | 19.7% |
+| **3** | Word | 0.0432 | 1.030 | 1.06 | 446,350 | 95.7% |
+| **3** | Subword | 0.5398 | 1.454 | 2.69 | 164,127 | 46.0% |
+| **4** | Word | 0.0126 🏆 | 1.009 | 1.02 | 471,596 | 98.7% |
+| **4** | Subword | 0.3671 | 1.290 | 1.81 | 440,820 | 63.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ꯑꯁꯤ ꯕꯤ ꯗꯤ ꯑꯦꯂꯦꯝꯕꯔꯇꯀꯤ ꯑꯦꯟꯁꯥꯏꯛꯂꯣꯄꯤꯗꯤ ꯑꯃꯁꯨꯡ ꯂꯂꯣꯟ ꯏꯇꯤꯛ ꯇꯧꯅꯕꯥ ꯅꯥꯠꯀꯤ ꯑꯣꯏꯕ ꯋꯥꯊꯣꯛ ꯂꯝꯖꯤꯡ ꯂꯝꯇꯥꯛꯄ ꯑꯗꯨꯗꯤ ꯑꯍꯨꯝꯁꯨꯕ`
+2. `ꯃꯁꯤꯡ ꯍꯥꯏꯄꯇꯤ ꯆꯩꯁꯤꯡ ꯃꯁꯤꯡ ꯑꯃꯅꯦ ꯃꯊꯪꯀꯤ ꯃꯁꯤꯡ ꯸꯹꯹ ꯐꯥꯎꯕ ꯂꯩꯕ ꯆꯩꯁꯤꯡ ꯑꯁꯤ ꯃꯤꯇꯩꯀꯤ ꯆꯩꯁꯤꯡ ꯏꯌꯦꯛꯅ ꯁꯦꯝꯄꯅꯦ`
+3. `ꯑꯃꯅꯤ ꯃꯁꯤꯗꯒꯤ ꯃꯁꯤꯡ ꯍꯥꯏꯄꯇꯤ ꯆꯩꯁꯤꯡ ꯏꯌꯦꯛꯅ ꯁꯦꯝꯄꯅꯤ ꯃꯁꯤꯡ ꯸꯰꯰ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯃꯄꯥꯟꯒ ꯁꯝꯅꯕꯁꯤꯡ out in ukrainian`
+
+**Context Size 2:**
+
+1. `ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯃꯃꯤ ꯀꯨꯝꯃꯩ ꯁꯛꯇꯝ ꯂꯥꯡꯕꯤ ꯑꯣꯢꯈꯤ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯏꯁꯩꯁꯤꯡ`
+2. `ꯃꯁꯤꯡ ꯑꯁꯤ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ ꯃꯁꯤꯡ ꯑꯁꯤ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯅꯤ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ`
+3. `ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯅꯤ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯗ ꯌꯦꯡꯂꯗꯤ ꯃꯁꯤꯡ ꯑꯁꯤ ꯃꯤꯇꯩꯀꯤ ꯆꯩꯁꯤꯡ ꯏꯌꯦꯛꯅ ꯁꯦꯝꯄꯅꯦ ꯃꯁꯤꯡ ꯑꯁꯤ`
+
+**Context Size 3:**
+
+1. `ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ ꯀꯥꯈꯟ ꯴ ꯂꯩꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ ꯃꯁꯤꯁꯨ`
+2. `ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯂꯥꯢꯔꯤꯛꯁꯤꯡ khamlangba erengba puwaree neinarol by yaima lamgdum kakching ha...`
+3. `ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯄꯅꯥꯁꯤꯡ ꯄꯅꯥ`
+
+**Context Size 4:**
+
+1. `ꯃꯁꯤꯁꯨ ꯌꯦꯡꯕꯤꯌꯨ ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯃꯌꯥꯝꯒꯤ ꯑꯁꯨꯞꯄ ꯆꯠꯅ ꯀꯥꯡꯂꯣꯟꯁꯤꯡ`
+2. `ꯃꯇꯦꯡ ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ ꯃꯁꯤꯗꯒꯤ ꯃꯁꯤꯡ ꯱ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯅꯤ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯗ ꯌꯦꯡꯂꯗꯤ`
+3. `ꯂꯧꯔꯛꯐꯝ ꯗꯒꯤ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯑꯃꯅꯤ ꯃꯁꯤꯗꯒꯤ ꯃꯁꯤꯡ ꯱ ꯍꯦꯟꯅ ꯆꯥꯎꯕ ꯃꯁꯤꯡ ꯗꯤ ꯅꯤ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯆꯩꯁꯤꯡꯂꯣꯟ ꯗ ꯌꯦꯡꯂꯗꯤ ꯃꯁꯤꯡ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_꯱_ꯂꯧꯁꯤꯅ_ꯑꯃꯁꯤꯡꯄꯤ_ꯅꯣꯐ_`
+2. `ꯃꯆꯠꯇꯦꯝꯊꯣꯛꯀꯤ_ꯃꯗꯤꯂꯣꯟ_ꯃꯛ`
+3. `ꯡ_ꯃꯥ_ꯐꯣꯟ_ꯆꯥꯎꯆꯦꯟꯅꯔꯤꯕ_ꯆꯥ`
+
+**Context Size 2:**
+
+1. `_ꯃꯌꯦꯛꯇꯤ_"le_phe_ger`
+2. `_ꯑꯁꯤ_ꯑꯣꯗꯤꯁꯤꯡꯒꯤ_ꯍꯧꯔꯀꯈꯤ_꯫_`
+3. `ꯡ_ꯑꯆꯧꯄ_ꯀꯪꯂꯩꯄꯥꯛ_ꯑꯃꯃꯁꯤꯁꯨ_`
+
+**Context Size 3:**
+
+1. `_ꯑꯃꯁꯨ_ꯌꯦꯡꯕꯤꯌꯨ_ꯃꯇꯦꯡ_ꯂꯧꯔꯛꯐ`
+2. `_ꯃꯁꯤꯁꯨ_ꯌꯦꯡꯉꯨ_꯴꯰_(ꯆꯥꯍꯨꯝ_(`
+3. `ꯁꯤꯡ_ꯑꯃꯗꯒꯤ_ꯃꯍꯨꯠꯇ,_ꯍꯦꯗꯤꯁꯇ`
+
+**Context Size 4:**
+
+1. `_ꯑꯁꯤ_ꯑꯆꯧꯕ_ꯑꯆꯥꯄꯣꯠ_ꯑꯃꯗꯥ_ꯑꯣꯄꯦ`
+2. `ꯃꯁꯤꯡ_ꯑꯣꯏꯅ_ꯊꯝꯕ_ꯌꯥꯏ,_ꯃꯅꯥ-`
+3. `ꯁꯤꯡ_ꯑꯁꯤꯁꯨ_ꯌꯥꯝꯅ_ꯁꯨꯔꯨꯡꯁꯤꯡꯗ_ꯅ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (440,820 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 | 35,928 |
+| Total Tokens | 676,105 |
+| Mean Frequency | 18.82 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 209.83 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ꯃꯁꯤꯡ | 18,949 |
+| 2 | ꯑꯁꯤ | 18,391 |
+| 3 | ꯑꯃꯅꯤ | 11,341 |
+| 4 | ꯑꯃꯁꯨꯡ | 10,185 |
+| 5 | ꯃꯇꯦꯡ | 10,150 |
+| 6 | ꯂꯧꯔꯛꯐꯝ | 9,121 |
+| 7 | ꯍꯦꯟꯅ | 6,892 |
+| 8 | ꯆꯥꯎꯕ | 6,104 |
+| 9 | ꯃꯁꯤꯁꯨ | 5,695 |
+| 10 | ꯌꯦꯡꯕꯤꯌꯨ | 4,565 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ꯖꯡꯁꯟ | 2 |
+| 2 | ꯁ꯭ꯀꯣꯜꯀꯣꯞ | 2 |
+| 3 | ꯃꯥꯏꯀ꯭ꯔꯣꯋꯦꯚ | 2 |
+| 4 | ꯁ꯭ꯋꯔꯠ | 2 |
+| 5 | ꯃꯤꯆꯤꯌꯁ | 2 |
+| 6 | ꯅꯥꯀꯃꯨꯔꯥ | 2 |
+| 7 | ꯁꯔꯀ꯭ꯌꯨꯠ | 2 |
+| 8 | ꯇꯟꯅꯦꯂꯤꯡ | 2 |
+| 9 | ꯁꯥꯏꯀꯃꯣꯔ | 2 |
+| 10 | ꯗꯦꯕꯥꯏꯁ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0805 |
+| R² (Goodness of Fit) | 0.996289 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 35.5% |
+| Top 1,000 | 65.4% |
+| Top 5,000 | 82.5% |
+| Top 10,000 | 88.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9963 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 35.5% of corpus
+- **Long Tail:** 25,928 words needed for remaining 11.1% 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 | 0.6424 | 0.3709 | N/A | N/A |
+| **mono_64d** | 64 | 0.3014 | 0.3657 | N/A | N/A |
+| **mono_128d** | 128 | 0.0542 | 0.3495 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6424 🏆 | 0.3667 | 0.0080 | 0.0540 |
+| **aligned_64d** | 64 | 0.3014 | 0.3759 | 0.0060 | 0.0480 |
+| **aligned_128d** | 128 | 0.0542 | 0.3530 | 0.0040 | 0.0620 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.6424 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3636. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 0.8% 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 | **1.511** | 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 |
+|--------|----------|
+| `-ꯑ` | ꯑꯜꯒꯥ, ꯑꯍꯨꯃꯁꯨꯕꯥ, ꯑꯣꯢꯕꯤ |
+| `-ꯃ` | ꯃꯄꯨꯔꯣꯏꯕꯅ, ꯃꯤꯠꯅꯥ, ꯃꯔꯨꯞꯁꯤꯡꯅ |
+| `-ꯁ` | ꯁꯥꯗ꯭ꯗꯥꯝꯕꯨ, ꯁ꯭ꯇꯥꯝꯞ, ꯁ꯭ꯇꯥꯐ |
+| `-ꯂ` | ꯂꯩꯔꯝꯕ, ꯂꯤꯊ꯭ꯋꯥꯅꯤꯌꯥꯟ, ꯂꯥꯟꯃꯤꯒꯤ |
+| `-ꯀ` | ꯀꯧꯁꯤꯀꯥ, ꯀꯥꯡꯕ, ꯀꯅꯕꯤꯒꯅꯤ |
+| `-ꯄ` | ꯄꯨꯔꯥꯛꯂꯦ, ꯄꯤꯖꯕ, ꯄꯦꯒꯦꯠ |
+| `-ꯍ` | ꯍꯧꯖꯤꯛꯁꯨ, ꯍꯨꯝꯐꯨꯇꯔꯥꯍꯨꯝꯊꯣꯏ, ꯍꯣꯡꯗꯣꯛꯇꯨꯅ |
+| `-ꯇ` | ꯇꯥꯛꯅꯕꯒꯤ, ꯇꯃꯊꯤꯕꯥ, ꯇꯪꯈꯥꯏꯗꯥ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ꯡ` | ꯈꯣꯡ, ꯆꯤꯊꯤꯁꯤꯡ, ꯇꯃꯦꯡꯂꯣꯡ |
+| `-ꯟ` | ꯂꯤꯊ꯭ꯋꯥꯅꯤꯌꯥꯟ, ꯌꯥꯡꯈꯩꯃꯥꯄꯟ, ꯑꯣꯐꯤꯌꯣꯟ |
+| `-ꯅ` | ꯃꯄꯨꯔꯣꯏꯕꯅ, ꯂꯩꯇꯕꯅ, ꯃꯔꯨꯞꯁꯤꯡꯅ |
+| `-ꯗ` | ꯹ꯗ, ꯋꯥꯔꯜꯗꯋꯥꯏꯗ, ꯃꯤꯇꯩꯂꯣꯟꯗ |
+| `-ꯕ` | ꯂꯩꯔꯝꯕ, ꯎꯞꯂꯕ, ꯄꯤꯖꯕ |
+| `-s` | epirus, chris, andreas |
+| `-ꯝ` | ꯂꯝꯇꯥꯛꯂꯣꯢꯁꯛꯇꯝ, ꯉꯥꯉꯝ, ꯁꯤꯟꯂꯝ |
+| `-ꯛ` | ꯑꯥꯏꯀꯣꯅꯣꯒ꯭ꯔꯥꯐꯤꯛ, ꯊꯕꯛꯁꯤꯡꯒꯤꯗꯃꯛ, ꯇꯧꯕꯒꯤꯗꯃꯛ |
+
+### 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` | 2.36x | 17 contexts | uther, other, there |
+| `tion` | 2.34x | 15 contexts | nation, action, motion |
+| `atio` | 2.35x | 10 contexts | ratio, nation, nations |
+| `ꯍꯜꯂꯛ` | 1.80x | 19 contexts | ꯍꯜꯂꯛꯄ, ꯍꯜꯂꯛꯏ, ꯍꯜꯂꯛꯄꯒ |
+| `ꯝꯅꯃꯛ` | 1.89x | 12 contexts | ꯄꯨꯝꯅꯃꯛ, ꯄꯨꯝꯅꯃꯛꯅ, ꯄꯨꯝꯅꯃꯛꯇ |
+| `ꯔꯛꯐꯝ` | 1.60x | 11 contexts | ꯂꯔꯛꯐꯝ, ꯂꯧꯔꯛꯐꯝ, ꯍꯧꯔꯛꯐꯝ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ꯂ` | `-ꯕ` | 27 words | ꯂꯝꯀꯣꯏꯕ, ꯂꯥꯢꯔꯦꯝꯕ |
+| `-ꯁ` | `-ꯡ` | 26 words | ꯁꯔꯨꯀꯁꯤꯡ, ꯁꯥꯆꯨꯡ |
+| `-ꯑ` | `-ꯗ` | 26 words | ꯑꯗꯨꯋꯥꯏꯗ, ꯑꯥꯏꯂꯦꯟꯗ |
+| `-ꯂ` | `-ꯡ` | 24 words | ꯂꯥꯡꯂꯤꯕꯁꯤꯡ, ꯂꯦꯝꯍꯧꯕꯁꯤꯡ |
+| `-ꯑ` | `-ꯟ` | 21 words | ꯑꯦꯁ꯭ꯇꯣꯟ, ꯑꯦꯗꯃꯟ |
+| `-ꯄ` | `-ꯡ` | 21 words | ꯄꯟꯅꯨꯡ, ꯄꯔꯦꯡ |
+| `-ꯃ` | `-ꯡ` | 19 words | ꯃꯆꯥꯅꯨꯄꯥꯁꯤꯡ, ꯃꯌꯨꯡ |
+| `-ꯑ` | `-ꯡ` | 19 words | ꯑꯦꯜꯕꯝꯁꯤꯡ, ꯑꯍꯂꯁꯤꯡ |
+| `-ꯑ` | `-ꯁ` | 19 words | ꯑꯥꯔꯀꯥꯟꯁꯥꯁ, ꯑꯣꯒꯁ |
+| `-ꯑ` | `-ꯛ` | 18 words | ꯑꯁꯨꯛ, ꯑꯗꯨꯈꯛꯇꯃꯛ |
+
+### 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 |
+|------|-----------------|------------|------|
+| ꯇꯤꯎꯁꯅꯒꯤꯗꯃꯛ | **`ꯇꯤꯎꯁꯅꯒꯤ-ꯗ-ꯃꯛ`** | 7.5 | `ꯗ` |
+| ꯄꯨꯊꯣꯛꯄꯒꯤꯗꯃꯛ | **`ꯄꯨꯊꯣꯛꯄꯒꯤ-ꯗ-ꯃꯛ`** | 7.5 | `ꯗ` |
+| ꯏꯊꯤꯌꯣꯄꯤꯌꯥꯟ | **`ꯏꯊꯤꯌꯣꯄꯤꯌꯥ-ꯟ`** | 4.5 | `ꯏꯊꯤꯌꯣꯄꯤꯌꯥ` |
+| ꯄꯨꯟꯁꯤꯟꯈꯤꯕ | **`ꯄꯨꯟꯁꯤꯟꯈꯤ-ꯕ`** | 4.5 | `ꯄꯨꯟꯁꯤꯟꯈꯤ` |
+| ꯚꯤꯗꯤꯑꯣꯁꯤꯡꯗ | **`ꯚꯤꯗꯤꯑꯣꯁꯤꯡ-ꯗ`** | 4.5 | `ꯚꯤꯗꯤꯑꯣꯁꯤꯡ` |
+| ꯍꯦꯟꯒꯠꯂꯛꯂꯤꯕ | **`ꯍꯦꯟꯒꯠꯂꯛꯂꯤ-ꯕ`** | 4.5 | `ꯍꯦꯟꯒꯠꯂꯛꯂꯤ` |
+| ꯐꯨꯡꯒꯥꯋꯥꯔꯤꯗ | **`ꯐꯨꯡꯒꯥꯋꯥꯔꯤ-ꯗ`** | 4.5 | `ꯐꯨꯡꯒꯥꯋꯥꯔꯤ` |
+| ꯍꯣꯁ꯭ꯄꯤꯇꯥꯜꯗ | **`ꯍꯣꯁ꯭ꯄꯤꯇꯥꯜ-ꯗ`** | 4.5 | `ꯍꯣꯁ꯭ꯄꯤꯇꯥꯜ` |
+| ꯄꯨꯔꯣꯍꯤꯠꯁꯤꯡꯅ | **`ꯄꯨꯔꯣꯍꯤꯠꯁꯤꯡ-ꯅ`** | 4.5 | `ꯄꯨꯔꯣꯍꯤꯠꯁꯤꯡ` |
+| ꯌꯨꯅꯤꯚꯔꯁꯤꯇꯤꯗ | **`ꯌꯨꯅꯤꯚꯔꯁꯤꯇꯤ-ꯗ`** | 4.5 | `ꯌꯨꯅꯤꯚꯔꯁꯤꯇꯤ` |
+| ꯅꯤꯟꯍꯨꯔꯁꯥꯒꯅ | **`ꯅꯤꯟꯍꯨꯔꯁꯥꯒ-ꯅ`** | 4.5 | `ꯅꯤꯟꯍꯨꯔꯁꯥꯒ` |
+| ꯅꯨꯃꯤꯗꯥꯡꯋꯥꯏꯔꯝꯗ | **`ꯅꯨꯃꯤꯗꯥꯡꯋꯥꯏꯔꯝ-ꯗ`** | 4.5 | `ꯅꯨꯃꯤꯗꯥꯡꯋꯥꯏꯔꯝ` |
+| relationships | **`relationship-s`** | 4.5 | `relationship` |
+| ꯍꯣꯔꯥꯏꯖꯣꯟꯁ | **`ꯍ-ꯣꯔꯥꯏꯖꯣꯟ-ꯁ`** | 3.0 | `ꯣꯔꯥꯏꯖꯣꯟ` |
+| ꯁꯃꯨꯗ꯭ꯔꯁꯤꯡꯗꯥ | **`ꯁ-ꯃ-ꯨꯗ꯭ꯔꯁꯤꯡꯗꯥ`** | 3.0 | `ꯨꯗ꯭ꯔꯁꯤꯡꯗꯥ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Manipuri 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 (4.32x) |
+| N-gram | **2-gram** | Lowest perplexity (1,226) |
+| Markov | **Context-4** | Highest predictability (98.7%) |
+| 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 12:16:30*

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