---
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*