---
language: bpy
language_name: Bishnupriya
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: 4.935
  - name: best_isotropy
    type: isotropy
    value: 0.6926
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Bishnupriya** 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** | 4.501x | 4.51 | 0.2384% | 99,847 |
| **16k** | 4.662x | 4.67 | 0.2469% | 96,404 |
| **32k** | 4.818x | 4.83 | 0.2551% | 93,284 |
| **64k** | 4.935x 🏆 | 4.95 | 0.2614% | 91,058 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `ইথাক বিষ্ণুপ্রিয়া মণিপুরী ঠারর অনিয়মিত পত্রিকা আহান, যেহান সংগ্রাম সিংহর সম্পা...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ই থ াক ▁বিষ্ণুপ্রিয়া ▁মণিপুরী ▁ঠারর ▁অ নি য় মি ... (+21 more)` | 31 |
| 16k | `▁ই থ াক ▁বিষ্ণুপ্রিয়া ▁মণিপুরী ▁ঠারর ▁অ নি য় মিত ... (+18 more)` | 28 |
| 32k | `▁ই থ াক ▁বিষ্ণুপ্রিয়া ▁মণিপুরী ▁ঠারর ▁অনি য়মিত ▁পত্রিকা ▁আহান ... (+13 more)` | 23 |
| 64k | `▁ইথাক ▁বিষ্ণুপ্রিয়া ▁মণিপুরী ▁ঠারর ▁অনিয়মিত ▁পত্রিকা ▁আহান , ▁যেহান ▁সংগ্রাম ... (+8 more)` | 18 |

**Sample 2:** `.এমও(.mo) এগ মাকাউর নাঙে লেপকরিসি চিঙপা ডমেইনগ (ccTLD)। মিলাপ আইএএনএ-র মাকাউর তথ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁. এম ও (. mo ) ▁এগ ▁মাকা উর ▁নাঙে ... (+23 more)` | 33 |
| 16k | `▁. এম ও (. mo ) ▁এগ ▁মাকা উর ▁নাঙে ... (+23 more)` | 33 |
| 32k | `▁. এম ও (. mo ) ▁এগ ▁মাকাউর ▁নাঙে ▁লেপকরিসি ... (+21 more)` | 31 |
| 64k | `▁. এম ও (. mo ) ▁এগ ▁মাকাউর ▁নাঙে ▁লেপকরিসি ... (+21 more)` | 31 |

**Sample 3:** `বাংলাদেশর স্থানীয় সরকারর সিজিলে আসেতাই জিলা পরিষদ সিটি কর্পোরেশন (৬গ) থানা বারো...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁বাংলাদেশর ▁স্ থান ীয় ▁সরকারর ▁সিজিল ে ▁আসেতাই ▁জিলা ▁পরিষ ... (+21 more)` | 31 |
| 16k | `▁বাংলাদেশর ▁স্থানীয় ▁সরকারর ▁সিজিল ে ▁আসেতাই ▁জিলা ▁পরিষদ ▁সিটি ▁কর্পোরেশন ... (+15 more)` | 25 |
| 32k | `▁বাংলাদেশর ▁স্থানীয় ▁সরকারর ▁সিজিল ে ▁আসেতাই ▁জিলা ▁পরিষদ ▁সিটি ▁কর্পোরেশন ... (+15 more)` | 25 |
| 64k | `▁বাংলাদেশর ▁স্থানীয় ▁সরকারর ▁সিজিল ে ▁আসেতাই ▁জিলা ▁পরিষদ ▁সিটি ▁কর্পোরেশন ... (+15 more)` | 25 |


### Key Findings

- **Best Compression:** 64k achieves 4.935x compression
- **Lowest UNK Rate:** 8k with 0.2384% 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 | 917 | 9.84 | 15,091 | 44.2% | 86.3% |
| **2-gram** | Subword | 598 🏆 | 9.22 | 14,901 | 51.1% | 92.9% |
| **3-gram** | Word | 1,565 | 10.61 | 31,633 | 38.0% | 79.5% |
| **3-gram** | Subword | 1,912 | 10.90 | 68,690 | 32.6% | 79.7% |
| **4-gram** | Word | 2,617 | 11.35 | 60,965 | 35.0% | 72.0% |
| **4-gram** | Subword | 3,535 | 11.79 | 166,549 | 26.1% | 72.8% |
| **5-gram** | Word | 3,304 | 11.69 | 65,705 | 33.6% | 68.3% |
| **5-gram** | Subword | 4,752 | 12.21 | 229,112 | 22.8% | 68.8% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `সাক্ষরতার হারহান` | 26,823 |
| 2 | `অতার মা` | 20,497 |
| 3 | `জনসংখ্যার উপাত্ত` | 19,704 |
| 4 | `জনসংখ্যা ইলাতাই` | 19,552 |
| 5 | `লোক গননা` | 19,533 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `মানুলেহা লোক গননা` | 19,527 |
| 2 | `মারির মানুলেহা লোক` | 19,526 |
| 3 | `অতার মা মুনি` | 16,569 |
| 4 | `গ অতার মা` | 15,694 |
| 5 | `লোক গননা অনুসারে` | 14,182 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `মারির মানুলেহা লোক গননা` | 19,525 |
| 2 | `গ অতার মা মুনি` | 15,620 |
| 3 | `মানুলেহা লোক গননা অনুসারে` | 14,181 |
| 4 | `অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই` | 9,366 |
| 5 | `মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ` | 9,315 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `মারির মানুলেহা লোক গননা অনুসারে` | 14,180 |
| 2 | `মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই` | 9,315 |
| 3 | `এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ` | 9,310 |
| 4 | `এহানর গড় উচ হান ইলতাই` | 6,096 |
| 5 | `মান্নাহাত্ত এহানর গড় উচ হান` | 6,096 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `র _` | 407,202 |
| 2 | `। _` | 163,086 |
| 3 | `হা ন` | 154,676 |
| 4 | `ন _` | 147,838 |
| 5 | `_ মা` | 138,460 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `র _ মা` | 95,254 |
| 2 | `হা ন _` | 94,536 |
| 3 | `_ বা রো` | 68,915 |
| 4 | `বা রো _` | 68,891 |
| 5 | `_ ই উ` | 64,643 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ বা রো _` | 68,886 |
| 2 | `_ ই উ নি` | 64,359 |
| 3 | `ই উ নি য়` | 55,648 |
| 4 | `উ নি য় ন` | 55,615 |
| 5 | `জ ন সং খ্যা` | 44,873 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ই উ নি য়` | 55,620 |
| 2 | `ই উ নি য় ন` | 55,614 |
| 3 | `_ জ ন সং খ্যা` | 44,868 |
| 4 | `_ উ পা ত্ত _` | 36,516 |
| 5 | `_ পৌ র স ভা` | 34,339 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 598
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~69% 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 | 0.7841 | 1.722 | 4.39 | 60,191 | 21.6% |
| **1** | Subword | 1.0505 | 2.071 | 11.75 | 3,037 | 0.0% |
| **2** | Word | 0.1820 | 1.134 | 1.54 | 262,172 | 81.8% |
| **2** | Subword | 0.6365 | 1.555 | 3.68 | 35,639 | 36.4% |
| **3** | Word | 0.0756 | 1.054 | 1.27 | 399,673 | 92.4% |
| **3** | Subword | 0.4888 | 1.403 | 2.43 | 130,940 | 51.1% |
| **4** | Word | 0.0494 🏆 | 1.035 | 1.19 | 504,719 | 95.1% |
| **4** | Subword | 0.3613 | 1.285 | 1.77 | 317,931 | 63.9% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `বারো জিলা বেয়াপা ১৫ ৪৪ ৮২৮ মিটার ফুট জনসংখ্যার উপাত্ত পৌরসভা আহান ভৌগলিক উপাত্ত ব্রাজিলর ঔয়াংমুঙ`
2. `ইউনিয়ন এগত গাঙ বারো ফুংগালাইরু বুলিয়া কিত্তাও নেই অহাত্তবারো এহার আয়তন লয়াহান ৪১৬ গ অতার মা`
3. `উপাত্ত শহর এহার আয়তন লয়াহান ৩৫৪ গ অতার মা হারহান ৫৯ ৫ অহানাত্ত এস নইচত জনসংখ্যার`

**Context Size 2:**

1. `সাক্ষরতার হারহান ৫৯ ৫ অহানাত্ত গঞ্জাম এহানর সাক্ষরতার হারহান ৭২ মুনির মা সাক্ষরতার হারহান ৬৫ বারো হু...`
2. `অতার মা মুনি ৫০ বারো জিলা বেয়াপা এরে পৌরসভার মানু শহরেদে বারো ১১ ৭৩৬গ গাঙেদে থাইতারা হারি`
3. `জনসংখ্যার উপাত্ত ভারতর মারির মানুলেহা লোক গননা অনুসারে আলসটের কাউন্টি ইংরেজি oglethorpe county এহান ...`

**Context Size 3:**

1. `মানুলেহা লোক গননা অনুসারে বার্বোসা পৌরসভাহানর জনসংখ্যা ইলাতাই ১০ ৪২৫ গ অতার মা মুনি ৫০ বারো জিলা বেয...`
2. `মারির মানুলেহা লোক গননা অনুসারে পালেসটিনা ডে গোয়াস পর্তুগীজ santa bárbara de goiás এহান ব্রাজিলর হম...`
3. `অতার মা মুনি ৫১ বারো জেলা বেয়াপা ৪৯ এহানাত সাক্ষরতার হারহান ৭৩ বারো জেলার মা হারহান ৬৮ আস্তা`

**Context Size 4:**

1. `মারির মানুলেহা লোক গননা অনুসারে টের্রেবোন পারিশ র জনসংখ্যা ইলাতাই ৮৭ ৯০৪ গ ৩২ ৭৩২গ ঘরর ইউনিট আসে হার...`
2. `গ অতার মা মুনি ৫২ বারো জিলা বেয়াপা এরে পৌরসভার মানু ৪২৩গ শহরেদে বারো গাঙেদে থাইতারা হারি বর্গ কিলোম...`
3. `মানুলেহা লোক গননা অনুসারে ক্লাবেরাস কাউন্টি র জনসংখ্যা ইলাতাই ১৮ ৫৬১ গ ঘরর ইউনিট আসে চৌদ্দাহান মুঙেদ...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_মারিসিতার_ঔয়াঙেদে:_মুনিয়`
2. `রসভা_সাক্ষর_শহর_পৌর_ই`
3. `নর_অক্টোবসভার_হানিয়ন।_`

**Context Size 2:**

1. `র_সাই_৬৬%।_ঔয়াঙেদে_থা_`
2. `।_অনুসারে_৩১তম_বিয়া_জিলা`
3. `হান_এহান_ইউনিয়নর_সান্টা_`

**Context Size 3:**

1. `র_মা_সাক্ষরতার_হারহান_৫৯.`
2. `হান_৭৯%,_অতার_হারহান_(`
3. `_বারো_গাঙেদে_থাইতারা।_হারি_ব`

**Context Size 4:**

1. `_বারো_জিলা/বেয়াপা_(১৫-৪৪_ব`
2. `_ইউনিট_আসে।_চৌদ্দাহান_মুঙেদে:`
3. `ইউনিয়ন_আগ।_ভৌগলিক_উপাত্ত_`


### Key Findings

- **Best Predictability:** Context-4 (word) with 95.1% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (317,931 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 | 32,965 |
| Total Tokens | 2,030,616 |
| Mean Frequency | 61.60 |
| Median Frequency | 3 |
| Frequency Std Dev | 897.18 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | বারো | 68,888 |
| 2 | ইউনিয়ন | 42,535 |
| 3 | উপাত্ত | 36,516 |
| 4 | হারহান | 31,910 |
| 5 | মা | 31,022 |
| 6 | মানু | 30,460 |
| 7 | সাক্ষরতার | 26,839 |
| 8 | গ | 26,421 |
| 9 | অতার | 25,584 |
| 10 | জনসংখ্যার | 24,823 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | সুখর | 2 |
| 2 | পরিত্যাগ | 2 |
| 3 | মালতী | 2 |
| 4 | আকগও | 2 |
| 5 | ক্ষনিক | 2 |
| 6 | সযন্তে | 2 |
| 7 | কণ্টক | 2 |
| 8 | পরিহার | 2 |
| 9 | বিরোধিতা | 2 |
| 10 | অপরাপর | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.3137 |
| R² (Goodness of Fit) | 0.980288 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 62.6% |
| Top 1,000 | 89.9% |
| Top 5,000 | 95.0% |
| Top 10,000 | 96.8% |

### Key Findings

- **Zipf Compliance:** R²=0.9803 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 62.6% of corpus
- **Long Tail:** 22,965 words needed for remaining 3.2% 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 | 0.6926 🏆 | 0.3671 | N/A | N/A |
| **mono_64d** | 64 | 0.5161 | 0.3444 | N/A | N/A |
| **mono_128d** | 128 | 0.2440 | 0.3266 | N/A | N/A |
| **aligned_32d** | 32 | 0.6926 | 0.3703 | 0.0100 | 0.0740 |
| **aligned_64d** | 64 | 0.5161 | 0.3426 | 0.0240 | 0.1200 |
| **aligned_128d** | 128 | 0.2440 | 0.3276 | 0.0380 | 0.1340 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.6926 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3465. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 3.8% 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 | **-0.006** | 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 |
|--------|----------|
| `-কা` | কানেডো, কাইতলী, কানিনা |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-া` | বারুইয়া, খানা, বুললা |
| `-র` | ০০০র, চাটমোহর, ফুর |
| `-়া` | বারুইয়া, বেলেয়া, ভরাপাড়া |
| `-য়া` | বারুইয়া, বেলেয়া, বড়হাতিয়া |
| `-ুর` | ফুর, গোপালপুর, সরদারপুর |
| `-পুর` | গোপালপুর, সরদারপুর, কুতবউল্লাপুর |
| `-িয়া` | বড়হাতিয়া, বাসুন্দিয়া, ঘাটলোদিয়া |
| `-রা` | ভাদ্রা, ভাটারা, মোরেইরা |

### 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 |
|--------|--------|-----------|----------|
| `-কা` | `-া` | 44 words | কারোবা, কাটাৱাবা |
| `-কা` | `-র` | 41 words | কামর, কান্নানুর |
| `-কা` | `-ুর` | 15 words | কান্নানুর, কাজীপুর |
| `-কা` | `-়া` | 15 words | কাদিরপাড়া, কালকরিয়া |
| `-কা` | `-য়া` | 10 words | কালকরিয়া, কালাবাড়িয়া |
| `-কা` | `-িয়া` | 10 words | কালকরিয়া, কালাবাড়িয়া |
| `-কা` | `-পুর` | 5 words | কাজীপুর, কালিদাসপুর |
| `-কা` | `-রা` | 5 words | কাংরা, কাকৈরগরা |

### 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 |
|------|-----------------|------------|------|
| জাঙ্গালিয়া | **`জাঙ্গাল-িয়া`** | 4.5 | `জাঙ্গাল` |
| মাখদুমপুর | **`মাখদুম-পুর`** | 4.5 | `মাখদুম` |
| স্লোভাকিয়া | **`স্লোভাক-িয়া`** | 4.5 | `স্লোভাক` |
| বাল্লাপুর | **`বাল্লা-পুর`** | 4.5 | `বাল্লা` |
| ওসমানীয়া | **`ওসমানী-য়া`** | 4.5 | `ওসমানী` |
| কাসকালহেইরা | **`কা-সকালহেই-রা`** | 3.0 | `সকালহেই` |
| কারুপ্পুর | **`কা-রুপ্-পুর`** | 3.0 | `রুপ্` |
| বাহাদুরপুর | **`বাহাদ-ুর-পুর`** | 3.0 | `বাহাদ` |
| কাফেলান্ডিয়া | **`কা-ফেলান্ড-িয়া`** | 3.0 | `ফেলান্ড` |
| ইটাকোয়াটিয়ারা | **`ইটাকোয়াট-িয়া-রা`** | 3.0 | `ইটাকোয়াট` |
| পীরযাত্রাপুর | **`পীরযাত্-রা-পুর`** | 3.0 | `পীরযাত্` |
| কাসসিলান্ডিয়া | **`কা-সসিলান্ড-িয়া`** | 3.0 | `সসিলান্ড` |
| কাশালিয়া | **`কা-শালি-য়া`** | 3.0 | `শালি` |
| কাউন্দিয়া | **`কা-উন্দ-িয়া`** | 3.0 | `উন্দ` |
| কান্নানুর | **`কা-ন্নান-ুর`** | 3.0 | `ন্নান` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Bishnupriya 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 (4.94x) |
| N-gram | **2-gram** | Lowest perplexity (598) |
| Markov | **Context-4** | Highest predictability (95.1%) |
| 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: 2026-01-03 19:21:34*