---
language: bn
language_name: Bangla
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: 5.044
  - name: best_isotropy
    type: isotropy
    value: 0.8095
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-07
---

# Bangla - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Bangla** 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** | 3.770x | 3.77 | 0.0982% | 2,627,489 |
| **16k** | 4.281x | 4.28 | 0.1115% | 2,313,780 |
| **32k** | 4.713x | 4.71 | 0.1227% | 2,101,756 |
| **64k** | 5.044x 🏆 | 5.04 | 0.1313% | 1,964,118 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `ছেউড়িয়া কুষ্টিয়া শহরের পূর্ব দিকে অবস্থিত একটি এলাকা। লালন শাহের মাজার এই ছেউ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ছে উ ড়িয়া ▁কুষ্টিয়া ▁শহরের ▁পূর্ব ▁দিকে ▁অবস্থিত ▁একটি ▁এলাকা ... (+17 more)` | 27 |
| 16k | `▁ছে উ ড়িয়া ▁কুষ্টিয়া ▁শহরের ▁পূর্ব ▁দিকে ▁অবস্থিত ▁একটি ▁এলাকা ... (+15 more)` | 25 |
| 32k | `▁ছে উ ড়িয়া ▁কুষ্টিয়া ▁শহরের ▁পূর্ব ▁দিকে ▁অবস্থিত ▁একটি ▁এলাকা ... (+15 more)` | 25 |
| 64k | `▁ছে উ ড়িয়া ▁কুষ্টিয়া ▁শহরের ▁পূর্ব ▁দিকে ▁অবস্থিত ▁একটি ▁এলাকা ... (+15 more)` | 25 |

**Sample 2:** `বনী কেনানাহ () হল জর্ডানের ইরবিড গভর্নরেটের একটি জেলা। তথ্যসূত্র জেলা`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ব নী ▁কেন ান াহ ▁() ▁হল ▁জর্ ড ানের ... (+11 more)` | 21 |
| 16k | `▁ব নী ▁কেন ান াহ ▁() ▁হল ▁জর্ডানের ▁ইর বি ... (+7 more)` | 17 |
| 32k | `▁ব নী ▁কেন ান াহ ▁() ▁হল ▁জর্ডানের ▁ইর বি ... (+7 more)` | 17 |
| 64k | `▁বনী ▁কেন ানাহ ▁() ▁হল ▁জর্ডানের ▁ইর বিড ▁গভর্নরেটের ▁একটি ... (+4 more)` | 14 |

**Sample 3:** `উপভাষাতত্ত্ব () ভাষাবিজ্ঞানের একটি উপশাখা যেখানে ভাষার ভৌগোলিক বৈচিত্র্য নিয়ে গ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁উপ ভাষ াত ত্ত্ব ▁() ▁ভাষ াবি জ্ঞ ানের ▁একটি ... (+25 more)` | 35 |
| 16k | `▁উপ ভাষ াত ত্ত্ব ▁() ▁ভাষ াবিজ্ঞ ানের ▁একটি ▁উপ ... (+22 more)` | 32 |
| 32k | `▁উপভাষ াত ত্ত্ব ▁() ▁ভাষাবিজ্ঞানের ▁একটি ▁উপ শাখা ▁যেখানে ▁ভাষার ... (+17 more)` | 27 |
| 64k | `▁উপভাষ াতত্ত্ব ▁() ▁ভাষাবিজ্ঞানের ▁একটি ▁উপশাখা ▁যেখানে ▁ভাষার ▁ভৌগোলিক ▁বৈচিত্র্য ... (+15 more)` | 25 |


### Key Findings

- **Best Compression:** 64k achieves 5.044x compression
- **Lowest UNK Rate:** 8k with 0.0982% 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 | 291,514 | 18.15 | 1,574,708 | 4.7% | 13.8% |
| **2-gram** | Subword | 2,633 🏆 | 11.36 | 151,712 | 33.5% | 66.9% |
| **3-gram** | Word | 772,868 | 19.56 | 2,366,241 | 2.2% | 7.8% |
| **3-gram** | Subword | 26,877 | 14.71 | 1,149,281 | 12.1% | 33.1% |
| **4-gram** | Word | 1,492,191 | 20.51 | 3,512,891 | 1.8% | 5.9% |
| **4-gram** | Subword | 176,159 | 17.43 | 5,668,680 | 6.6% | 19.0% |
| **5-gram** | Word | 1,031,104 | 19.98 | 2,302,686 | 2.2% | 6.7% |
| **5-gram** | Subword | 672,872 | 19.36 | 12,813,291 | 4.0% | 12.3% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `করা হয়` | 178,320 |
| 2 | `তথ্যসূত্র বহিঃসংযোগ` | 62,509 |
| 3 | `করা হয়েছিল` | 55,266 |
| 4 | `করা হয়েছে` | 52,752 |
| 5 | `হয় এবং` | 47,516 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `থেকে সাল পর্যন্ত` | 15,509 |
| 2 | `করা হয় এবং` | 12,875 |
| 3 | `দায়িত্ব পালন করেন` | 11,918 |
| 4 | `উপর ভিত্তি করে` | 11,195 |
| 5 | `করা যেতে পারে` | 11,181 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `তথ্যসূত্র বহিঃসংযোগ জন্ম ব্যক্তি` | 6,636 |
| 2 | `সংসদ সদস্য সংসদ সদস্য` | 6,370 |
| 3 | `হিসেবে দায়িত্ব পালন করেন` | 5,513 |
| 4 | `এপ্রিল জুন জুলাই সেপ্টেম্বর` | 5,102 |
| 5 | `জুলাই সেপ্টেম্বর অক্টোবর ডিসেম্বর` | 5,100 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `জুন জুলাই সেপ্টেম্বর অক্টোবর ডিসেম্বর` | 5,049 |
| 2 | `এপ্রিল জুন জুলাই সেপ্টেম্বর অক্টোবর` | 5,048 |
| 3 | `মার্চ এপ্রিল জুন জুলাই সেপ্টেম্বর` | 5,040 |
| 4 | `জানুয়ারি মার্চ এপ্রিল জুন জুলাই` | 5,039 |
| 5 | `সদস্য সংসদ সদস্য সংসদ সদস্য` | 4,613 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `র _` | 10,460,613 |
| 2 | `_ এ` | 4,233,657 |
| 3 | `ন _` | 4,097,869 |
| 4 | `। _` | 3,608,688 |
| 5 | `_ ক` | 3,135,335 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ক রে` | 1,380,255 |
| 2 | `এ বং _` | 1,266,527 |
| 3 | `_ এ বং` | 1,265,068 |
| 4 | `_ এ ক` | 991,360 |
| 5 | `ন । _` | 910,746 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ এ বং _` | 1,263,055 |
| 2 | `_ এ ক টি` | 584,296 |
| 3 | `এ ক টি _` | 578,361 |
| 4 | `_ তি নি _` | 473,133 |
| 5 | `_ ক রা _` | 429,980 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ এ ক টি _` | 571,779 |
| 2 | `_ হ য় । _` | 358,749 |
| 3 | `র _ জ ন্য _` | 344,350 |
| 4 | `_ ক রা _ হ` | 325,163 |
| 5 | `_ ক রে ন ।` | 253,567 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 2,633
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~12% 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 | 0.8427 | 1.793 | 11.19 | 2,081,488 | 15.7% |
| **1** | Subword | 0.9831 | 1.977 | 14.53 | 30,042 | 1.7% |
| **2** | Word | 0.3490 | 1.274 | 2.13 | 23,273,031 | 65.1% |
| **2** | Subword | 0.7496 | 1.681 | 6.59 | 436,358 | 25.0% |
| **3** | Word | 0.1187 | 1.086 | 1.25 | 49,621,155 | 88.1% |
| **3** | Subword | 0.5931 | 1.508 | 4.11 | 2,877,364 | 40.7% |
| **4** | Word | 0.0412 🏆 | 1.029 | 1.07 | 61,780,303 | 95.9% |
| **4** | Subword | 0.5053 | 1.419 | 2.78 | 11,819,297 | 49.5% |

### Generated Text Samples (Word-based)

Below are text samples generated from each word-based Markov chain model:

**Context Size 1:**

1. `এবং পেনাল্টি শুট করা ঐতিহ্যগতভাবে মে তারিখে স্বাগতিক নিউজিল্যান্ড পুরুষ দীর্ঘ এবং ফোকসোনমি সালে u1 ১...`
2. `ও বিদ্রোহী দুর্গগুলির ধ্বংসাবশেষ এবং মহিলা ফুটবল ক্লাবের দৃশ্যের মিল মালিক মুম্বাইয়ে গুজরাটি ভাষায়...`
3. `হয় যা মামলুকের পদক্ষেপকে ইসরায়েল বেইট শেমেশের কাছে উন্মুক্ত এবং বাবা মাকে ডেকে পিছনে চার্জার কেইস`

**Context Size 2:**

1. `করা হয় ১৫ নভেম্বর the day of francophonie ২০ মার্চ রাজ্য সরকার মাদুরাইয়ে দুটি আইটি ভিত্তিক সরঞ্জাম...`
2. `তথ্যসূত্র বহিঃসংযোগ উপজেলার ইউনিয়ন বিভাগের ইউনিয়ন জেলার ইউনিয়ন বিভাগের ইউনিয়ন জেলার ইউনিয়ন পরিষ...`
3. `করা হয়েছিল যে সামাজিক প্রভাবের প্রক্রিয়া যার মাধ্যমে গুগল টক ক্লায়েন্ট তৈরি করেন texier charles r...`

**Context Size 3:**

1. `থেকে সাল পর্যন্ত শাখাহার ইউনিয়নের পরপর পাঁচমেয়াদে নির্বাচিত চেয়ারম্যান ছিলেন তিনি থেকে সময়কালে অ...`
2. `করা হয় এবং এই ডকুমেন্ট সম্মেলনে আলোচনা হওয়ার পর সেখানেই একটি মাদরাসা প্রতিষ্ঠার ব্যাপারে অভিমত ব্য...`
3. `দায়িত্ব পালন করেন যেখানে তিনি দ্বিতীয় স্থান অধিকার করে সমালোচনামূলক প্রতিক্রিয়া রিভিউ অ্যাগ্রিগেট...`

**Context Size 4:**

1. `তথ্যসূত্র বহিঃসংযোগ জন্ম ব্যক্তি কোরীয় চলচ্চিত্র অভিনেত্রী কোরীয় নারী আইডল কোরীয় নারী মডেল কোরীয়...`
2. `সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য সংসদ সদস্য স...`
3. `হিসেবে দায়িত্ব পালন করেন জাতীয় সংসদের স্পিকার উপরাষ্ট্রপতি নিয়োগের বিধান না থাকায় রাষ্ট্রপতির অব...`


### Generated Text Samples (Subword-based)

Below are text samples generated from each subword-based Markov chain model:

**Context Size 1:**

1. `_প্রান্তে_সারকারীতামসদর_শের`
2. `র_আবহ_নের_থাকে_জেদেরজা`
3. `ন_সাইকে_স_পরিলাক্সিকাশ্মীর_`

**Context Size 2:**

1. `র_ভূপৃষ্ঠতলের_জনসংখ্যান_একা`
2. `_এবং_ডুবে_ঝুঁকিপূর্ণ_ছিলেন,_`
3. `ন_নাট্যধর্মীয়_লেজে_দৃশ্য_চলচ্চি`

**Context Size 3:**

1. `_করেন_এবং_সর্বোচ্চ_কানাডাব্যাপী_`
2. `এবং_এর_মাত্র_এবং_"প্রক্সিমালি"`
3. `_এবং_ওল্ফের_একটি_হল_৬৪-`

**Context Size 4:**

1. `_এবং_তাদের_তালিকা_১৬৩_±_০`
2. `_একটি_বিরোধ_দেখেন।_সাম্রাজ্যের_`
3. `একটি_কাজ_শুরু,_সালেই_প্রায়_৭`


### Key Findings

- **Best Predictability:** Context-4 (word) with 95.9% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (11,819,297 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 | 838,913 |
| Total Tokens | 71,898,290 |
| Mean Frequency | 85.70 |
| Median Frequency | 4 |
| Frequency Std Dev | 2805.67 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | এবং | 1,267,871 |
| 2 | ও | 702,980 |
| 3 | হয় | 618,329 |
| 4 | করে | 616,816 |
| 5 | একটি | 586,525 |
| 6 | তিনি | 495,350 |
| 7 | করা | 454,721 |
| 8 | থেকে | 424,445 |
| 9 | এই | 402,971 |
| 10 | তার | 388,104 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | সণ্ডিলা | 2 |
| 2 | শূকরক্ষেত | 2 |
| 3 | প্লীপেন | 2 |
| 4 | মস্‌ম্যান | 2 |
| 5 | শোরোশ | 2 |
| 6 | yohanna | 2 |
| 7 | katanacho | 2 |
| 8 | শোরোশের | 2 |
| 9 | ট্রাঞ্চবলের | 2 |
| 10 | হুলশফ | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.0269 |
| R² (Goodness of Fit) | 0.987733 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 23.9% |
| Top 1,000 | 50.1% |
| Top 5,000 | 71.3% |
| Top 10,000 | 78.8% |

### Key Findings

- **Zipf Compliance:** R²=0.9877 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 23.9% of corpus
- **Long Tail:** 828,913 words needed for remaining 21.2% 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 | 0.8095 🏆 | 0.3709 | N/A | N/A |
| **mono_64d** | 64 | 0.8011 | 0.2937 | N/A | N/A |
| **mono_128d** | 128 | 0.7560 | 0.2281 | N/A | N/A |
| **aligned_32d** | 32 | 0.8095 | 0.3802 | 0.0980 | 0.4600 |
| **aligned_64d** | 64 | 0.8011 | 0.2992 | 0.2280 | 0.6000 |
| **aligned_128d** | 128 | 0.7560 | 0.2319 | 0.3880 | 0.7640 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8095 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3007. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 38.8% 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 | **-0.452** | Low formulaic content | - |

### 6.2 Affix Inventory (Productive Units)

These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.

#### Productive Prefixes
| Prefix | Examples |
|--------|----------|

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-র` | মোহানপুর, গুরবানীর, শান্তির |
| `-ের` | সেরাদের, সাইট্রেটের, লিওঁনের |
| `-ার` | ভোজভোদিনার, স্পেকটার, মনোরোগবিদ্যার |
| `-কে` | দুঃস্বপ্নকে, ক্লাইনকে, হাংচৌকে |

### 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 |
|------|----------|------------------|----------|
| `ress` | 3.30x | 93 contexts | press, dress, cress |
| `nter` | 3.28x | 88 contexts | enter, unter, anter |
| `atio` | 3.33x | 77 contexts | ratio, ation, natio |
| `ctio` | 3.38x | 50 contexts | action, lectio, suction |
| `stor` | 2.96x | 87 contexts | astor, stora, stori |
| `mber` | 3.07x | 60 contexts | umber, ember, amber |
| `ence` | 3.40x | 37 contexts | pence, fence, bence |
| `ersi` | 3.11x | 43 contexts | ersin, persia, persie |
| `nati` | 3.22x | 34 contexts | natio, nativa, nation |
| `ical` | 3.23x | 33 contexts | epical, apical, micali |
| `ieve` | 3.35x | 25 contexts | sieve, lieve, pieve |
| `embe` | 3.34x | 20 contexts | ember, rember, embers |

### 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.

*No significant affix co-occurrences detected.*


### 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 |
|------|-----------------|------------|------|
| স্যাপারের | **`স্যাপ-ার-ের`** | 6.0 | `স্যাপ` |
| ক্রুসেডারের | **`ক্রুসেড-ার-ের`** | 6.0 | `ক্রুসেড` |
| পরিষদসমূহের | **`পরিষদসমূহ-ের`** | 4.5 | `পরিষদসমূহ` |
| তন্তুগুলিকে | **`তন্তুগুলি-কে`** | 4.5 | `তন্তুগুলি` |
| ইতালিয়াসের | **`ইতালিয়াস-ের`** | 4.5 | `ইতালিয়াস` |
| দ্বিতীয়কে | **`দ্বিতীয়-কে`** | 4.5 | `দ্বিতীয়` |
| অ্যাসপার্টের | **`অ্যাসপার্ট-ের`** | 4.5 | `অ্যাসপার্ট` |
| পেটারসেনের | **`পেটারসেন-ের`** | 4.5 | `পেটারসেন` |
| হার্জেগোভিনাকে | **`হার্জেগোভিনা-কে`** | 4.5 | `হার্জেগোভিনা` |
| অ্যাক্টিনের | **`অ্যাক্টিন-ের`** | 4.5 | `অ্যাক্টিন` |
| মাইগ্রেশনের | **`মাইগ্রেশন-ের`** | 4.5 | `মাইগ্রেশন` |
| এরদোয়ানকে | **`এরদোয়ান-কে`** | 4.5 | `এরদোয়ান` |
| ক্রীড়াঙ্গণের | **`ক্রীড়াঙ্গণ-ের`** | 4.5 | `ক্রীড়াঙ্গণ` |
| অ্যাপোপটোসিসের | **`অ্যাপোপটোসিস-ের`** | 4.5 | `অ্যাপোপটোসিস` |
| জার্নালকে | **`জার্নাল-কে`** | 4.5 | `জার্নাল` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Bangla shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

---
## 7. Summary & Recommendations

![Performance Dashboard](visualizations/performance_dashboard.png)

### Production Recommendations

| Component | Recommended | Rationale |
|-----------|-------------|-----------|
| Tokenizer | **64k BPE** | Best compression (5.04x) |
| N-gram | **2-gram** | Lowest perplexity (2,633) |
| Markov | **Context-4** | Highest predictability (95.9%) |
| 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: 2026-01-07 08:35:42*