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+visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text
+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: syl
+language_name: Sylheti
+language_family: indoaryan_eastern
+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_eastern
+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: 4.022
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2602
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sylheti - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sylheti** 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** | 3.222x | 3.23 | 0.1507% | 158,587 |
+| **16k** | 3.579x | 3.58 | 0.1674% | 142,736 |
+| **32k** | 4.022x 🏆 | 4.03 | 0.1881% | 127,036 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ꠀꠝ꠆ꠞꠣ ꠢꠇ꠆ꠇꠟ ꠍꠤꠟꠐꠤ ꠄꠉꠥ ꠍꠥꠟꠥꠉꠣꠘ ꠨ ꠗꠣꠞꠘꠣ ꠇꠞꠣ ꠅꠄ ꠁꠈꠣꠘ ꠎꠘꠙꠤꠞꠤꠅ ꠟꠥꠇ ꠝꠥꠈꠦ ꠢꠥꠘꠣ ꠉꠤꠔ ꠕꠘꠦ ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꠀꠝ꠆ ꠞꠣ ▁ꠢꠇ꠆ꠇꠟ ▁ꠍꠤꠟꠐꠤ ▁ꠄꠉꠥ ▁ꠍꠥꠟ ꠥ ꠉꠣꠘ ▁꠨ ▁ꠗꠣꠞꠘꠣ ... (+26 more)` | 36 |
+| 16k | `▁ꠀꠝ꠆ꠞꠣ ▁ꠢꠇ꠆ꠇꠟ ▁ꠍꠤꠟꠐꠤ ▁ꠄꠉꠥ ▁ꠍꠥꠟ ꠥ ꠉꠣꠘ ▁꠨ ▁ꠗꠣꠞꠘꠣ ▁ꠇꠞꠣ ... (+18 more)` | 28 |
+| 32k | `▁ꠀꠝ꠆ꠞꠣ ▁ꠢꠇ꠆ꠇꠟ ▁ꠍꠤꠟꠐꠤ ▁ꠄꠉꠥ ▁ꠍꠥꠟꠥꠉꠣꠘ ▁꠨ ▁ꠗꠣꠞꠘꠣ ▁ꠇꠞꠣ ▁ꠅꠄ ▁ꠁꠈꠣꠘ ... (+14 more)` | 24 |
+
+**Sample 2:** `ꠘꠣꠢꠤꠖ ꠁꠍꠟꠣꠝ ꠅ ꠛꠣꠋꠟꠣꠖꠦꠡꠞ ꠇꠥꠐꠣ ꠀꠘ꠆ꠖꠥꠟꠘꠞ ꠄꠇ ꠡꠝꠘ꠆ꠘꠄꠇꠞꠞꠣ ⁕ ꠉꠦꠟꠣꠞꠤꠔ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꠘꠣ ꠢꠤꠖ ▁ꠁꠍꠟꠣꠝ ▁ꠅ ▁ꠛꠣꠋꠟꠣꠖꠦꠡꠞ ▁ꠇꠥꠐꠣ ▁ꠀꠘ꠆ꠖꠥꠟꠘꠞ ▁ꠄꠇ ▁ꠡꠝꠘ꠆ꠘꠄꠇꠞꠞꠣ ▁⁕ ... (+1 more)` | 11 |
+| 16k | `▁ꠘꠣꠢꠤꠖ ▁ꠁꠍꠟꠣꠝ ▁ꠅ ▁ꠛꠣꠋꠟꠣꠖꠦꠡꠞ ▁ꠇꠥꠐꠣ ▁ꠀꠘ꠆ꠖꠥꠟꠘꠞ ▁ꠄꠇ ▁ꠡꠝꠘ꠆ꠘꠄꠇꠞꠞꠣ ▁⁕ ▁ꠉꠦꠟꠣꠞꠤꠔ` | 10 |
+| 32k | `▁ꠘꠣꠢꠤꠖ ▁ꠁꠍꠟꠣꠝ ▁ꠅ ▁ꠛꠣꠋꠟꠣꠖꠦꠡꠞ ▁ꠇꠥꠐꠣ ▁ꠀꠘ꠆ꠖꠥꠟꠘꠞ ▁ꠄꠇ ▁ꠡꠝꠘ꠆ꠘꠄꠇꠞꠞꠣ ▁⁕ ▁ꠉꠦꠟꠣꠞꠤꠔ` | 10 |
+
+**Sample 3:** `ꠇꠠꠤ ꠘꠣꠝꠣꠞ ꠙꠥꠕꠤ ꠍ꠆ꠞꠤꠢꠐ꠆ꠐ ꠕꠘꠦ ꠛꠣꠞꠅꠁꠍꠤꠟ ꠍ꠆ꠞꠤꠝꠢꠝ꠆ꠝꠖ ꠀꠛ꠆ꠖꠥꠟ ꠉꠘꠤ ꠄ ꠛꠣꠞ ꠇꠞ꠆ꠍꠤꠟꠣ ꠡꠘꠞ ꠛꠣꠄ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ꠇ ꠠꠤ ▁ꠘꠣꠝ ꠣꠞ ▁ꠙꠥꠕꠤ ▁ꠍ꠆ꠞꠤꠢꠐ꠆ꠐ ▁ꠕꠘꠦ ▁ꠛꠣꠞ ꠅꠁ ꠍꠤꠟ ... (+16 more)` | 26 |
+| 16k | `▁ꠇ ꠠꠤ ▁ꠘꠣꠝ ꠣꠞ ▁ꠙꠥꠕꠤ ▁ꠍ꠆ꠞꠤꠢꠐ꠆ꠐ ▁ꠕꠘꠦ ▁ꠛꠣꠞ ꠅꠁꠍꠤꠟ ▁ꠍ꠆ꠞꠤ ... (+12 more)` | 22 |
+| 32k | `▁ꠇꠠꠤ ▁ꠘꠣꠝꠣꠞ ▁ꠙꠥꠕꠤ ▁ꠍ꠆ꠞꠤꠢꠐ꠆ꠐ ▁ꠕꠘꠦ ▁ꠛꠣꠞꠅꠁꠍꠤꠟ ▁ꠍ꠆ꠞꠤꠝꠢꠝ꠆ꠝꠖ ▁ꠀꠛ꠆ꠖꠥꠟ ▁ꠉꠘꠤ ▁ꠄ ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.022x compression
+- **Lowest UNK Rate:** 8k with 0.1507% 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 | 691 🏆 | 9.43 | 884 | 33.5% | 100.0% |
+| **2-gram** | Subword | 1,332 | 10.38 | 5,973 | 36.8% | 77.2% |
+| **3-gram** | Word | 836 | 9.71 | 1,105 | 30.9% | 94.7% |
+| **3-gram** | Subword | 8,364 | 13.03 | 21,498 | 13.7% | 39.9% |
+| **4-gram** | Word | 2,379 | 11.22 | 3,031 | 17.4% | 53.0% |
+| **4-gram** | Subword | 24,708 | 14.59 | 50,570 | 7.4% | 23.8% |
+| **5-gram** | Word | 2,151 | 11.07 | 2,640 | 17.3% | 54.6% |
+| **5-gram** | Subword | 31,205 | 14.93 | 51,776 | 5.2% | 19.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ` | 73 |
+| 2 | `ꠟꠂꠀ ꠛꠦꠡ` | 73 |
+| 3 | `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ` | 73 |
+| 4 | `ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ` | 73 |
+| 5 | `of the` | 65 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ` | 73 |
+| 2 | `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ` | 73 |
+| 3 | `ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ` | 73 |
+| 4 | `ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ` | 51 |
+| 5 | `ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ` | 51 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ` | 73 |
+| 2 | `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ` | 73 |
+| 3 | `ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ ꠘꠣꠄ` | 51 |
+| 4 | `ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ` | 51 |
+| 5 | `ꠀꠝꠦꠞꠤꠇꠣ ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ` | 31 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ` | 73 |
+| 2 | `ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ ꠘꠣꠄ` | 51 |
+| 3 | `ꠀꠝꠦꠞꠤꠇꠣ ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ` | 31 |
+| 4 | `ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠅ ꠢꠣꠟ ꠇꠅꠀ ꠎꠣꠄ ꠘꠣ` | 30 |
+| 5 | `ꠢꠣꠟ ꠇꠅꠀ ꠎꠣꠄ ꠘꠣ ꠇꠤꠔꠣ` | 30 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ꠞ _` | 12,277 |
+| 2 | `_ ꠀ` | 6,142 |
+| 3 | `ꠘ _` | 5,686 |
+| 4 | `_ ꠅ` | 4,509 |
+| 5 | `⁕ _` | 3,764 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ⁕ _` | 2,981 |
+| 2 | `ꠀ ꠞ _` | 2,292 |
+| 3 | `_ ꠨ _` | 2,256 |
+| 4 | `_ ꠀ ꠞ` | 2,193 |
+| 5 | `_ ꠅ ꠁ` | 1,323 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꠀ ꠞ _` | 1,762 |
+| 2 | `_ ꠅ ꠄ _` | 505 |
+| 3 | `_ ꠍꠤ ꠟ ꠐ` | 445 |
+| 4 | `ꠄ _ ⁕ _` | 441 |
+| 5 | `_ ꠝꠣ ꠔ _` | 432 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ꠍꠤ ꠟ ꠐꠤ _` | 332 |
+| 2 | `_ ꠛꠣꠋ ꠟꠣ ꠖꠦ ꠡ` | 328 |
+| 3 | `_ t h e _` | 326 |
+| 4 | `_ ꠍꠤ ꠟ ꠐ _` | 284 |
+| 5 | `_ ꠅ ꠄ _ ⁕` | 272 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (word) with 691
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~19% 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 | 0.5935 | 1.509 | 2.79 | 23,596 | 40.6% |
+| **1** | Subword | 1.2552 | 2.387 | 11.97 | 1,427 | 0.0% |
+| **2** | Word | 0.0932 | 1.067 | 1.13 | 65,510 | 90.7% |
+| **2** | Subword | 0.7555 | 1.688 | 3.82 | 17,071 | 24.4% |
+| **3** | Word | 0.0199 | 1.014 | 1.03 | 73,767 | 98.0% |
+| **3** | Subword | 0.4929 | 1.407 | 2.25 | 65,171 | 50.7% |
+| **4** | Word | 0.0085 🏆 | 1.006 | 1.01 | 75,181 | 99.2% |
+| **4** | Subword | 0.2650 | 1.202 | 1.49 | 146,673 | 73.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ꠀꠞ ꠄꠡꠤꠀ ꠄꠘ꠆ꠒꠤꠀ ꠘꠞꠅꠦ ꠄꠘ꠆ꠒꠤꠀꠞꠦ ꠅꠞ꠆ꠕꠘꠤꠔꠤꠇ ꠃꠘ꠆ꠘꠔꠤꠞ ꠟꠣꠉꠤ ꠄꠇꠐꠣ ꠖꠤꠙꠇ꠆ꠇꠤ ꠟꠄ ꠘꠞꠅꠦꠞ ꠡꠣꠋꠡ꠆ꠇ꠆ꠞꠤꠔꠤꠇ ꠅꠂꠔꠤꠎ꠆ꠎꠎꠞ ꠄꠉꠥ...`
+2. `ꠅꠄ ꠀꠞ ꠇꠥꠟꠡꠤ ꠀ ꠇꠣꠞ ꠟꠣꠉꠣꠁꠟ ꠎꠦꠇꠥꠘꠥ ꠡꠤꠇ꠆ꠞꠤꠔ ꠌꠣꠞ꠆ꠐꠤꠚꠤꠇꠦꠡꠘ ꠅꠒꠤꠐ ꠡꠚꠟꠇꠞꠤ ꠢꠦꠡ july ꠡꠦꠙ꠆ꠐꠦꠝ꠆ꠛꠞ γ0l9 ꠝꠣꠔ`
+3. `ꠁ ꠡꠣꠋꠡ꠆ꠇ꠆ꠞꠤꠔꠤꠇ ꠅꠂꠔꠤꠎ꠆ꠎꠎꠞ ꠄꠉꠥ ꠚꠥꠞꠣꠝ ꠎꠣ ꠀꠞ꠆ꠎꠣꠔ ꠟꠦꠈꠣꠞ ꠖꠣꠄ ꠈꠁꠘ ꠄꠈꠡꠝꠄ ꠛꠤꠀꠘꠤꠛꠣꠎꠣꠞꠞ ꠘꠣꠝ ꠔꠣꠞꠣꠞ ꠡꠣꠁꠎ꠆ꠎ ꠡꠢꠎꠥꠉꠤ...`
+
+**Context Size 2:**
+
+1. `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ vishavan ꠝꠣꠔ ꠄꠡꠤꠀ ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ haitian vodoun cultu...`
+2. `ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ guaicaro ꠝꠣꠔ ꠖꠇ꠆ꠘꠞ ꠀꠝꠦꠞꠤꠇꠣ ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠅ ꠢꠣꠟ ꠇꠅꠀ ꠎꠣꠄ ꠘꠣ ꠇꠤꠔꠣ gaya ꠝꠣꠔ ꠄꠡꠤꠀ`
+3. `ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ kwʼadza ꠝꠣꠔ ꠀꠚ꠆ꠞꠤꠇꠣ ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ ꠘꠣꠄ yugul ꠝꠣꠔ ꠅꠍꠤꠀꠘꠤꠀ ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ...`
+
+**Context Size 3:**
+
+1. `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ north picene ꠝꠣꠔ ꠁꠃꠞꠥꠙ ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ ꠘꠣꠄ jiamao ꠝꠣꠔ ꠄꠡꠤ...`
+2. `ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ mangree ꠝꠣꠔ ꠀꠚ꠆ꠞꠤꠇꠣ ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠅ ꠢꠣꠟ ꠇꠅꠀ ꠎꠣꠄ ꠘꠣ ꠇꠤꠔꠣ paleo european ꠝꠣꠔ ꠁꠃꠞꠥꠙ ling...`
+3. `ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ kwʼadza ꠝꠣꠔ ꠀꠚ꠆ꠞꠤꠇꠣ ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠞ ꠢꠣꠟ ꠍꠣꠚ ꠘꠣꠄ karami ꠝꠣꠔ ꠅꠍꠤꠀꠘꠤꠀ ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠅ ꠇꠦꠟꠣꠡꠤꠚꠤꠇ...`
+
+**Context Size 4:**
+
+1. `ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ mangree ꠝꠣꠔ ꠀꠚ꠆ꠞꠤꠇꠣ ꠜꠣꠡꠣꠛꠤꠉ꠆ꠉꠣꠘꠅ ꠢꠣꠟ ꠇꠅꠀ ꠎꠣꠄ ꠘꠣ ꠇꠤꠔꠣ oblo ꠝꠣꠔ ꠀꠚ꠆ꠞꠤꠇꠣ ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ...`
+2. `ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ pre arawakan ꠝꠣꠔ of the greater antilles ꠃꠔ꠆ꠞꠞ ꠀꠝꠦꠞꠤꠇꠣ linguistic ꠇꠦꠟ...`
+3. `ꠇꠥꠘꠥ ꠘꠤꠞꠖꠤꠡ꠆ꠐ ꠇꠥꠘ꠆ꠔꠣ ꠛꠣꠔꠣꠁꠟ ꠘꠣꠄ cayuse ꠝꠣꠔ ꠃꠔ꠆ꠞꠞ ꠀꠝꠦꠞꠤꠇꠣ ꠇꠦꠟꠣꠡꠤꠚꠤꠇꠦꠡꠘ ꠟꠂꠀ ꠛꠦꠡ ꠔꠂꠔ꠆ꠔ ꠘꠣꠄ bhariati ꠄꠡꠤ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ꠎ_ꠝꠞꠣꠈꠣꠝ।"_ꠀꠍꠦꠞꠦ_(মে`
+2. `ꠞ_ꠀꠁꠢꠣꠍꠣꠠꠣ_⁕_ꠒꠥꠎꠞꠥ_ꠔꠣ_`
+3. `ꠀꠎꠣꠔ_ꠀꠞ_lasher/ꠡꠦꠡ`
+
+**Context Size 2:**
+
+1. `ꠞ_(bood_iporly,_ꠛꠦ`
+2. `_ꠀꠟꠣꠖꠣ_ꠉꠣꠘꠞꠅꠦꠞ_ꠀꠞ_ꠀꠟ_`
+3. `ꠘ_ꠎꠣꠔꠘ꠆ꠔ꠆ꠞ-ꠇꠕꠣ_॥_ꠔꠣꠞꠣꠞ_`
+
+**Context Size 3:**
+
+1. `_⁕_'ꠛꠁꠅ_ꠁꠋꠟꠤꠡ:_provk`
+2. `ꠀꠞ_ꠍꠤꠟꠐ_ꠛ꠆ꠞꠤꠐꠤꠡ_ꠎꠣꠔꠤ_ꠍꠤꠟꠐꠤ`
+3. `_꠨_ꠀꠘꠣꠙꠣꠄꠖꠣꠞ_(ꠙꠥꠛ_ꠜꠣꠟꠣ_ꠔꠥ`
+
+**Context Size 4:**
+
+1. `_ꠀꠞ_ꠍꠤꠟꠐ_ꠙ꠆ꠞꠌꠥꠞ_ꠙꠞꠤꠛꠦꠡꠅ_`
+2. `_ꠅꠄ_ꠘꠣ_ꠇꠤꠔꠣ_vazimba_=_`
+3. `_ꠍꠤꠟꠐ_ꠅꠘ꠆ꠌꠟ_ꠛꠤꠐꠤꠡ_ꠞꠣꠎ_ꠀꠍꠤ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (146,673 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 | 8,518 |
+| Total Tokens | 68,093 |
+| Mean Frequency | 7.99 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 28.79 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ꠀꠞ | 1,780 |
+| 2 | ꠅꠄ | 671 |
+| 3 | ꠁ | 569 |
+| 4 | ꠝꠣꠔ | 478 |
+| 5 | ꠅ | 408 |
+| 6 | ꠍꠤꠟꠐꠤ | 361 |
+| 7 | the | 354 |
+| 8 | ꠍꠤꠟꠐ | 347 |
+| 9 | ꠅꠞ | 303 |
+| 10 | ꠄꠉꠥ | 283 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ꠟꠤꠍ꠆ꠐ | 2 |
+| 2 | ꠔꠝꠤꠎ | 2 |
+| 3 | ꠇꠤꠞꠘ | 2 |
+| 4 | ꠍꠐꠇꠥ | 2 |
+| 5 | ꠞꠢꠡ꠆ꠡ | 2 |
+| 6 | ꠀꠍꠣꠝꠤ | 2 |
+| 7 | ꠢꠔ꠆ꠔꠣ | 2 |
+| 8 | ꠘꠤꠞ꠆ꠖꠦꠡ | 2 |
+| 9 | ꠅꠎ | 2 |
+| 10 | ꠌꠟꠌ꠆ꠌꠤꠔ꠆ꠞ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8800 |
+| R² (Goodness of Fit) | 0.982770 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 25.5% |
+| Top 1,000 | 59.3% |
+| Top 5,000 | 89.5% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9828 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 25.5% of corpus
+- **Long Tail:** -1,482 words needed for remaining 100.0% 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 | 0.2602 | 0.4837 | N/A | N/A |
+| **mono_64d** | 64 | 0.0664 | 0.4652 | N/A | N/A |
+| **mono_128d** | 128 | 0.0110 | 0.4986 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2602 🏆 | 0.4847 | 0.0040 | 0.0920 |
+| **aligned_64d** | 64 | 0.0664 | 0.4845 | 0.0080 | 0.1160 |
+| **aligned_128d** | 128 | 0.0110 | 0.5055 | 0.0120 | 0.1160 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.2602 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4870. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.2% 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 | **1.860** | 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 |
+|--------|----------|
+| `-ꠛ` | ꠛꠣꠅꠝꠙꠥꠞꠤ, ꠛꠣꠠꠣꠞ, ꠛꠞ꠆ꠝꠤ |
+| `-ꠡ` | ꠡꠤꠡꠐꠝ, ꠡꠢꠤꠖ, ꠡꠥꠛꠤꠗꠣ |
+| `-ꠀ` | ꠀꠝꠞꠣꠞꠦ, ꠀꠡꠟ, ꠀꠞꠣꠝꠞ |
+| `-ꠝ` | ꠝꠘꠞ, ꠝꠤꠟꠣꠁꠀ, ꠝꠤꠟ |
+| `-ꠇ` | ꠇꠦ, ꠇꠘ꠆ꠐꠞꠟ, ꠇꠘ꠆ꠘꠣꠖꠣꠘ |
+| `-ꠙ` | ꠙꠥꠞꠣꠘ, ꠙ꠆ꠞꠡꠣꠡꠘꠤꠇꠜꠣꠛꠦ, ꠙꠣꠁꠟꠐꠣꠁꠘ꠆ꠔꠞ |
+| `-ꠎ` | ꠎꠂꠘ꠆ꠔꠣ, ꠎꠦꠝꠟꠣ, ꠎꠣꠖꠛꠙꠥꠞ |
+| `-ꠚ` | ꠚꠤꠚꠣꠞ, ꠚꠥꠞꠣꠘꠣ, ꠚꠦꠍꠛꠥꠇ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ꠞ` | ꠚꠤꠚꠣꠞ, ꠝꠘꠞ, ꠗꠞꠝꠞ |
+| `-ꠘ` | ꠙꠥꠞꠣꠘ, ꠃꠖꠎꠣꠙꠘ, ꠢꠤꠘ꠆ꠖꠥꠡ꠆ꠔꠣꠘ |
+| `-ꠔ` | ꠙꠤꠔꠤꠛꠤꠔ, ꠎꠣꠇꠣꠔ, ꠖꠞꠉꠣꠔ |
+| `-ꠟ` | ꠀꠡꠟ, ꠛꠟ, ꠛꠣꠟꠥꠟ |
+| `-ꠇ` | ꠚꠦꠍꠛꠥꠇ, ꠡꠝ꠆ꠙꠞ꠆ꠇ, ꠄꠇꠣꠗꠤꠇ |
+| `-ꠔꠞ` | ꠙꠣꠁꠟꠐꠣꠁꠘ꠆ꠔꠞ, ꠎꠦꠉꠣꠁꠘ꠆ꠔꠞ, ꠡꠢꠞꠣꠁꠘ꠆ꠔꠞ |
+| `-ꠁꠘ` | ꠛꠤꠡ꠆ꠡꠣꠍꠤꠘꠔꠣꠁꠘ, ꠁꠍ꠆ꠙꠦꠁꠘ, ꠖꠤꠍꠁꠘ |
+| `-ꠘꠞ` | ꠝꠘꠞ, ꠅꠛꠖꠣꠘꠞ, ꠡꠋꠉꠑꠘꠞ |
+
+### 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.
+
+*No significant bound stems detected.*
+
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ꠛ` | `-ꠞ` | 52 words | ꠛꠣꠠꠣꠞ, ꠛꠣꠋꠉꠟꠣꠖꠦꠡꠞ |
+| `-ꠝ` | `-ꠞ` | 51 words | ꠝꠘꠞ, ꠝꠘ꠆ꠒꠟꠞ |
+| `-ꠡ` | `-ꠞ` | 35 words | ꠡꠣꠢꠞꠤꠀꠞ, ꠡꠣꠢꠎꠣꠟꠣꠟꠦꠞ |
+| `-ꠇ` | `-ꠞ` | 35 words | ꠇꠝ꠆ꠙꠤꠃꠐꠣꠞ, ꠇꠣꠃꠘ꠆ꠡꠤꠟꠞ |
+| `-ꠙ` | `-ꠞ` | 33 words | ꠙꠣꠁꠟꠐꠣꠁꠘ꠆ꠔꠞ, ꠙꠥꠞꠥꠡ꠆ꠇꠣꠞ |
+| `-ꠎ` | `-ꠞ` | 31 words | ꠎꠣꠖꠛꠙꠥꠞ, ꠎꠤꠀꠃꠞ |
+| `-ꠀ` | `-ꠞ` | 23 words | ꠀꠞꠣꠝꠞ, ꠀꠝꠤꠞ |
+| `-ꠛ` | `-ꠘ` | 20 words | ꠛꠤꠡ꠆ꠡꠣꠍꠤꠘꠔꠣꠁꠘ, ꠛꠣꠉꠣꠘ |
+| `-ꠙ` | `-ꠘ` | 19 words | ꠙꠥꠞꠣꠘ, ꠙ꠆ꠞꠍ꠆ꠘ |
+| `-ꠚ` | `-ꠞ` | 19 words | ꠚꠤꠚꠣꠞ, ꠚꠟꠣꠘꠤꠞ |
+
+### 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 |
+|------|-----------------|------------|------|
+| ꠚꠣꠃꠘ꠆ꠒꠦꠡꠘꠞ | **`ꠚꠣꠃꠘ꠆ꠒꠦꠡ-ꠘ-ꠞ`** | 7.5 | `ꠘ` |
+| ꠅꠡ꠆ꠐꠦꠟꠤꠀꠘ | **`ꠅꠡ꠆ꠐꠦꠟꠤꠀ-ꠘ`** | 4.5 | `ꠅꠡ꠆ꠐꠦꠟꠤꠀ` |
+| ꠛꠣꠋꠟꠣꠖꠦꠡꠅꠞ | **`ꠛꠣꠋꠟꠣꠖꠦꠡꠅ-ꠞ`** | 4.5 | `ꠛꠣꠋꠟꠣꠖꠦꠡꠅ` |
+| ꠝꠣꠐ꠆ꠐꠥꠝꠣꠞ | **`ꠝꠣꠐ꠆ꠐꠥꠝꠣ-ꠞ`** | 4.5 | `ꠝꠣꠐ꠆ꠐꠥꠝꠣ` |
+| ꠝꠥꠢꠣꠝ꠆ꠝꠣꠖꠞ | **`ꠝꠥꠢꠣꠝ꠆ꠝꠣꠖ-ꠞ`** | 4.5 | `ꠝꠥꠢꠣꠝ꠆ꠝꠣꠖ` |
+| ꠖꠤꠙꠙꠥꠘ꠆ꠎꠔ | **`ꠖꠤꠙꠙꠥꠘ꠆ꠎ-ꠔ`** | 4.5 | `ꠖꠤꠙꠙꠥꠘ꠆ꠎ` |
+| ꠞꠛꠤꠘ꠆ꠖ꠆ꠞꠘꠣꠕꠞ | **`ꠞꠛꠤꠘ꠆ꠖ꠆ꠞꠘꠣꠕ-ꠞ`** | 4.5 | `ꠞꠛꠤꠘ꠆ꠖ꠆ꠞꠘꠣꠕ` |
+| ꠎꠘꠡꠋꠈ꠆ꠎꠣꠞ | **`ꠎꠘꠡꠋꠈ꠆ꠎꠣ-ꠞ`** | 4.5 | `ꠎꠘꠡꠋꠈ꠆ꠎꠣ` |
+| ꠀꠝꠥꠀꠔꠤꠢꠞꠚ | **`ꠀ-ꠝꠥꠀꠔꠤꠢꠞꠚ`** | 4.5 | `ꠝꠥꠀꠔꠤꠢꠞꠚ` |
+| ꠚꠤꠘꠟꠦꠘ꠆ꠒꠞ | **`ꠚꠤꠘꠟꠦꠘ꠆ꠒ-ꠞ`** | 4.5 | `ꠚꠤꠘꠟꠦꠘ꠆ꠒ` |
+| ꠌꠘ꠆ꠖꠞꠝꠥꠈꠤꠞ | **`ꠌꠘ꠆ꠖꠞꠝꠥꠈꠤ-ꠞ`** | 4.5 | `ꠌꠘ꠆ꠖꠞꠝꠥꠈꠤ` |
+| ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣꠘ | **`ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣ-ꠘ`** | 4.5 | `ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣ` |
+| ꠙꠣꠘ꠆ꠒꠥꠟꠤꠙꠤꠞ | **`ꠙꠣꠘ꠆ꠒꠥꠟꠤꠙꠤ-ꠞ`** | 4.5 | `ꠙꠣꠘ꠆ꠒꠥꠟꠤꠙꠤ` |
+| ꠡꠛ꠆ꠖꠣꠁꠘ꠆ꠔꠞ | **`ꠡꠛ꠆ꠖꠣꠁꠘ꠆ꠔ-ꠞ`** | 4.5 | `ꠡꠛ꠆ꠖꠣꠁꠘ꠆ꠔ` |
+| ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣꠞ | **`ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣ-ꠞ`** | 4.5 | `ꠙ꠆ꠞꠔꠤꠡ꠆ꠑꠣ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sylheti 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 | **32k BPE** | Best compression (4.02x) |
+| N-gram | **2-gram** | Lowest perplexity (691) |
+| Markov | **Context-4** | Highest predictability (99.2%) |
+| 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 23:59:58*

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