---
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*