README.md

---
language: as
language_name: Assamese
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: 4.542
  - name: best_isotropy
    type: isotropy
    value: 0.8547
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Assamese** 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** | 3.450x | 3.45 | 0.0757% | 1,416,711 |
| **16k** | 3.894x | 3.89 | 0.0855% | 1,255,391 |
| **32k** | 4.266x | 4.27 | 0.0937% | 1,145,685 |
| **64k** | 4.542x 🏆 | 4.54 | 0.0997% | 1,076,075 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `জয়নগৰ মজিলপুৰ ভাৰতৰ পশ্চিমবংগ ৰাজ্যৰ দক্ষিণ চব্বিশ পৰগনা জিলাত অৱস্থিত এখন চহৰ।...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁জ য়ন গৰ ▁মজ িল পুৰ ▁ভাৰতৰ ▁পশ্চিমব ংগ ▁ৰাজ্যৰ ... (+14 more)` | 24 |
| 16k | `▁জ য়ন গৰ ▁মজ িল পুৰ ▁ভাৰতৰ ▁পশ্চিমবংগ ▁ৰাজ্যৰ ▁দক্ষিণ ... (+12 more)` | 22 |
| 32k | `▁জয়ন গৰ ▁মজ িল পুৰ ▁ভাৰতৰ ▁পশ্চিমবংগ ▁ৰাজ্যৰ ▁দক্ষিণ ▁চব্বিশ ... (+8 more)` | 18 |
| 64k | `▁জয়নগৰ ▁মজ িল পুৰ ▁ভাৰতৰ ▁পশ্চিমবংগ ▁ৰাজ্যৰ ▁দক্ষিণ ▁চব্বিশ ▁পৰগনা ... (+7 more)` | 17 |

**Sample 2:** `হাবুং মৈদাম হৈছে আহোমসকলৰ পঞ্চমৰাজধানী হাবুংৰ টাইভেটিত অৱস্থিত দুটা প্ৰাচীন মৈদা...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁হাব ু ং ▁মৈ দ াম ▁হৈছে ▁আহোম সকলৰ ▁পঞ্চম ... (+31 more)` | 41 |
| 16k | `▁হাব ুং ▁মৈ দাম ▁হৈছে ▁আহোম সকলৰ ▁পঞ্চম ৰাজ ধান ... (+26 more)` | 36 |
| 32k | `▁হাব ুং ▁মৈদাম ▁হৈছে ▁আহোমসকলৰ ▁পঞ্চম ৰাজ ধানী ▁হাব ুং ... (+21 more)` | 31 |
| 64k | `▁হাবুং ▁মৈদাম ▁হৈছে ▁আহোমসকলৰ ▁পঞ্চম ৰাজধানী ▁হাবুং ৰ ▁টাই ভেটিত ... (+16 more)` | 26 |

**Sample 3:** `ভাৰতীয় ন্যায় সংহিতা (IAST: Bhāratīya Nyāya Saṃhitā), ভাৰতীয় গণৰাজ্যৰ অপৰাধ সং...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ভাৰতীয় ▁ন্যায় ▁সংহ িতা ▁( i ast : ▁bh ā ... (+27 more)` | 37 |
| 16k | `▁ভাৰতীয় ▁ন্যায় ▁সংহিতা ▁( i ast : ▁bh ā rat ... (+23 more)` | 33 |
| 32k | `▁ভাৰতীয় ▁ন্যায় ▁সংহিতা ▁( iast : ▁bh ā rat ī ... (+20 more)` | 30 |
| 64k | `▁ভাৰতীয় ▁ন্যায় ▁সংহিতা ▁( iast : ▁bh ā rat īya ... (+18 more)` | 28 |


### Key Findings

- **Best Compression:** 64k achieves 4.542x compression
- **Lowest UNK Rate:** 8k with 0.0757% 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 | 62,472 | 15.93 | 206,764 | 8.3% | 21.4% |
| **2-gram** | Subword | 2,308 🏆 | 11.17 | 63,567 | 34.0% | 69.4% |
| **3-gram** | Word | 109,754 | 16.74 | 237,526 | 5.0% | 14.6% |
| **3-gram** | Subword | 20,939 | 14.35 | 371,943 | 13.3% | 35.5% |
| **4-gram** | Word | 247,178 | 17.92 | 371,701 | 2.3% | 7.7% |
| **4-gram** | Subword | 113,780 | 16.80 | 1,515,602 | 7.8% | 20.9% |
| **5-gram** | Word | 182,489 | 17.48 | 239,039 | 1.9% | 7.3% |
| **5-gram** | Subword | 319,720 | 18.29 | 2,664,609 | 5.1% | 14.4% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `কৰা হয়` | 29,188 |
| 2 | `কৰা হৈছিল` | 12,508 |
| 3 | `হ ল` | 11,276 |
| 4 | `লাভ কৰে` | 10,608 |
| 5 | `কৰা হৈছে` | 10,201 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ব্যৱহাৰ কৰা হয়` | 3,336 |
| 2 | `হ ব পাৰে` | 3,197 |
| 3 | `বুলি কোৱা হয়` | 3,190 |
| 4 | `গণ্য কৰা হয়` | 2,309 |
| 5 | `ডিগ্ৰী লাভ কৰে` | 2,043 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `তথ্য সংগ্ৰহ বাহ্যিক সংযোগ` | 1,641 |
| 2 | `বুলি গণ্য কৰা হয়` | 1,265 |
| 3 | `স্নাতক ডিগ্ৰী লাভ কৰে` | 864 |
| 4 | `হিচাপে গণ্য কৰা হয়` | 801 |
| 5 | `তথ্য উৎস বাহ্যিক সংযোগ` | 782 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `archived from the original on` | 423 |
| 2 | `অভিনেত্ৰী চলচ্চিত্ৰৰ অভিনেত্ৰী চলচ্চিত্ৰৰ অভিনেত্ৰী` | 245 |
| 3 | `দিনটোত ঘটা কেইটামান উল্লেখযোগ্য ঘটনা` | 244 |
| 4 | `এই দিনটোত ঘটা কেইটামান উল্লেখযোগ্য` | 237 |
| 5 | `প্ৰাৰম্ভিক জীৱন আৰু শিক্ষা চনৰ` | 214 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ৰ _` | 1,309,192 |
| 2 | `ত _` | 645,783 |
| 3 | `_ আ` | 585,970 |
| 4 | `। _` | 462,800 |
| 5 | `_ ক` | 454,528 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `আ ৰু _` | 247,371 |
| 2 | `_ আ ৰু` | 247,194 |
| 3 | `_ ক ৰি` | 139,299 |
| 4 | `_ তে ওঁ` | 136,655 |
| 5 | `ন ৰ _` | 124,787 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ আ ৰু _` | 246,758 |
| 2 | `ছি ল । _` | 101,454 |
| 3 | `_ ক ৰা _` | 90,139 |
| 4 | `_ এ ই _` | 64,252 |
| 5 | `_ তে ওঁ _` | 64,067 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ হ য় । _` | 58,520 |
| 2 | `_ ক ৰে । _` | 51,805 |
| 3 | `_ ক ৰি ছি ল` | 51,692 |
| 4 | `ৰ _ বা বে _` | 47,193 |
| 5 | `_ চ ন ত _` | 47,011 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 2,308
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~14% 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 | 0.8450 | 1.796 | 7.84 | 550,295 | 15.5% |
| **1** | Subword | 0.8398 | 1.790 | 12.10 | 15,252 | 16.0% |
| **2** | Word | 0.2695 | 1.205 | 1.71 | 4,311,912 | 73.1% |
| **2** | Subword | 0.7069 | 1.632 | 5.33 | 184,530 | 29.3% |
| **3** | Word | 0.0827 | 1.059 | 1.15 | 7,360,379 | 91.7% |
| **3** | Subword | 0.5599 | 1.474 | 3.49 | 984,248 | 44.0% |
| **4** | Word | 0.0276 🏆 | 1.019 | 1.04 | 8,480,563 | 97.2% |
| **4** | Subword | 0.4373 | 1.354 | 2.27 | 3,437,009 | 56.3% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `আৰু লেখিকা বুলি সকলোৱে উৎসাহিত কৰাৰ উদ্দেশ্যে চনত বামুণপাৰা বালিপাৰা ষ্টীম কুকাৰতে খাদ্যৰ ৬৫ মিলিয়ন...`
2. `কৰা এক শিক্ষা প্ৰদানৰ বিষয় হিচাপে কাৰ্যনিৰ্বাহ কৰিছিল মৃত্যু চনত হোমেন বৰগোহাঞিৰ এখন মেল পাতে আৰু`
3. `হয় আমেৰিকা যুক্তৰাষ্টৰ প্ৰথম উপাচাৰ্য আছিল অনুমান কৰা পুৰণি অট্টালিকাবোৰত মধ্যমীয়া চৰিত্ৰত অভিনয় ...`

**Context Size 2:**

1. `কৰা হয় চনৰ জানুৱাৰী মাহত অম্বা বহোৰা নামৰ এগৰাকী যুৱতীক তেওঁৰ স্বামী টোপনি যোৱালৈকে অপেক্ষা কৰাটো প...`
2. `কৰা হৈছিল কলহোৰা শাসকসকলৰ সমাধিস্থলত ফুল আৰু প্ৰসাদেৰে তুলসীক পূজা কৰা ধৰণৰ তাৰতম্য আছিল তথাপি ধৰ্মে...`
3. `হ ল পদ্মভূষণ ভাৰতৰ তৃতীয় সৰ্বোচ্চ অসামৰিক সন্মান পদ্মশ্ৰী লাভ কৰে তেখেতে অভিনয় কৰে চনত তেওঁৰ নিজাক...`

**Context Size 3:**

1. `ব্যৱহাৰ কৰা হয় msa এ smtp প্ৰটোকলত প্ৰদান কৰা গন্তব্যস্থানৰ ঠিকনা নিৰ্ধাৰণ কৰে বাৰ্তা হেডাৰৰ পৰা নহ...`
2. `হ ব পাৰে অসমৰ কবি লেখক জীৱন নৰহে আত্মজীৱনীমূলক গ্ৰন্থখনক নতুন প্ৰজন্মৰ সাহসৰ দলিল বুলি অভিহিত কৰে অৰ...`
3. `বুলি কোৱা হয় ৰাক্ষসসকলক প্ৰায় পৰাধীন সৈনিকৰ ৰূপত দেখুৱা হৈছিল পিছে কিছু ৰাক্ষসে অত্যন্ত বল অৰ্জন ক...`

**Context Size 4:**

1. `তথ্য সংগ্ৰহ বাহ্যিক সংযোগ cornell university e book library of classic texts on mechanical design an...`
2. `বুলি গণ্য কৰা হয় আৰু কোমল গ আৰু কোমল ধ স্বৰসমূহ কম্পনৰ সৈতে অন্দোলিত পৰিবেশিত হয় সকলো পাঁচটা স্বৰ`
3. `স্নাতক ডিগ্ৰী লাভ কৰে সেই একেই দীন দয়াল উপাধ্যায় কলেজৰ পৰা সামাজিক কাম চনত ১৯ বছৰ বয়সত ছেম অল্টমে...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_ধান_লা_শক্তি।_হাসিদ্ধ_তাকা`
2. `ৰক্ষ_নিজনগোৱাহালচ্চিত্ৰ_মবাবে`
3. `কথাই_ইছিল_জীৱন_ডিয়_বা`

**Context Size 2:**

1. `ৰ_আৰু_লাউ।_অসংখ্যক_ভাৰত`
2. `ত_জ্ঞানৰ_কৃপ,_কেন্দ্ৰটোৰ_পৰা`
3. `_আৰু_মোৰ_পৰা_উৰুলিয়ান_শা`

**Context Size 3:**

1. `আৰু_বীৰেন্দ্ৰ_মোডীৰ_নিগমৰ_প্ৰয়া`
2. `_আৰু_অৰ্থ।_দেৱালয়খনৰ_পিছ`
3. `_কৰিবলৈ_অস্বীকাৰ_হোৱা_মতবাদ`

**Context Size 4:**

1. `_আৰু_পাম_তেল_আৰু_পিপিপি_আৰু`
2. `ছিল।_কুমাৰীত্ব_পৰীক্ষাৰ_অংগ_আৰু`
3. `_কৰা_দুখৰ_আৰু_তেওঁৰ_ছিলভাৰ`


### Key Findings

- **Best Predictability:** Context-4 (word) with 97.2% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (3,437,009 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 | 225,407 |
| Total Tokens | 9,007,362 |
| Mean Frequency | 39.96 |
| Median Frequency | 4 |
| Frequency Std Dev | 792.95 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | আৰু | 247,463 |
| 2 | কৰা | 93,923 |
| 3 | হয় | 87,716 |
| 4 | কৰে | 78,599 |
| 5 | এই | 64,931 |
| 6 | তেওঁ | 64,613 |
| 7 | পৰা | 53,636 |
| 8 | কৰিছিল | 51,623 |
| 9 | বাবে | 50,799 |
| 10 | চনত | 49,149 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | পাণ্ডুবংশী | 2 |
| 2 | মনুমেণ্টছ | 2 |
| 3 | ছিৰপুৰৰ | 2 |
| 4 | swfl | 2 |
| 5 | manhunt | 2 |
| 6 | megamodel | 2 |
| 7 | গ্লেডৰেগ্‌চ | 2 |
| 8 | কিস | 2 |
| 9 | পদাইভীৰণ | 2 |
| 10 | বিগিল | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.0094 |
| R² (Goodness of Fit) | 0.989782 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 25.5% |
| Top 1,000 | 50.9% |
| Top 5,000 | 71.9% |
| Top 10,000 | 79.7% |

### Key Findings

- **Zipf Compliance:** R²=0.9898 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 25.5% of corpus
- **Long Tail:** 215,407 words needed for remaining 20.3% 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 | 0.8458 | 0.3637 | N/A | N/A |
| **mono_64d** | 64 | 0.8547 🏆 | 0.2742 | N/A | N/A |
| **mono_128d** | 128 | 0.8359 | 0.2093 | N/A | N/A |
| **aligned_32d** | 32 | 0.8458 | 0.3735 | 0.0580 | 0.3060 |
| **aligned_64d** | 64 | 0.8547 | 0.2836 | 0.1180 | 0.3960 |
| **aligned_128d** | 128 | 0.8359 | 0.2075 | 0.1480 | 0.4820 |

### Key Findings

- **Best Isotropy:** mono_64d with 0.8547 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2853. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 14.8% 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 | **-0.495** | 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 |
|--------|----------|
| `-ৰ` | কেথলিকসকলৰ, স্বপ্ৰচাৰ, ৱিণ্টাৰ |
| `-াৰ` | স্বপ্ৰচাৰ, ৱিণ্টাৰ, অফকাটাৰ |

### 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` | 3.36x | 64 contexts | theri, there, other |
| `ress` | 3.34x | 44 contexts | press, dress, duress |
| `nter` | 3.33x | 38 contexts | inter, enter, wynter |
| `vers` | 3.15x | 47 contexts | verso, versa, verse |
| `atio` | 3.32x | 37 contexts | ratio, fatio, nation |
| `indi` | 3.24x | 39 contexts | hindi, indie, india |
| `ment` | 3.24x | 38 contexts | cement, moment, mental |
| `stor` | 3.25x | 35 contexts | storm, jstor, story |
| `ctio` | 3.33x | 32 contexts | action, auction, faction |
| `iver` | 3.16x | 26 contexts | liver, giver, river |
| `ersi` | 3.21x | 20 contexts | persia, persie, yersin |
| `mber` | 3.17x | 18 contexts | amber, number, member |

### 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 |
|------|-----------------|------------|------|
| চেটেছুয়াৰাৰ | **`চেটেছুয়-াৰ-াৰ`** | 3.0 | `চেটেছুয়` |
| সীতাৰামায়াৰ | **`সীতাৰামায়-াৰ`** | 1.5 | `সীতাৰামায়` |
| ৰাজ্কুমাৰ | **`ৰাজ্কুম-াৰ`** | 1.5 | `ৰাজ্কুম` |
| প্ৰতিৰক্ষাৰ | **`প্ৰতিৰক্ষ-াৰ`** | 1.5 | `প্ৰতিৰক্ষ` |
| দত্তবৰুৱাৰ | **`দত্তবৰুৱ-াৰ`** | 1.5 | `দত্তবৰুৱ` |
| বিষ্ণুৰাভাৰ | **`বিষ্ণুৰাভ-াৰ`** | 1.5 | `বিষ্ণুৰাভ` |
| বদৌপায়াৰ | **`বদৌপায়-াৰ`** | 1.5 | `বদৌপায়` |
| হাছলেংগাৰ | **`হাছলেংগ-াৰ`** | 1.5 | `হাছলেংগ` |
| চিজাৰিয়াৰ | **`চিজাৰিয়-াৰ`** | 1.5 | `চিজাৰিয়` |
| কুকুৰাঝাৰ | **`কুকুৰাঝ-াৰ`** | 1.5 | `কুকুৰাঝ` |
| মন্দাৱস্থাৰ | **`মন্দাৱস্থ-াৰ`** | 1.5 | `মন্দাৱস্থ` |
| আত্মপ্ৰতিষ্ঠাৰ | **`আত্মপ্ৰতিষ্ঠ-াৰ`** | 1.5 | `আত্মপ্ৰতিষ্ঠ` |
| কেঁচাগোল্লাৰ | **`কেঁচাগোল্ল-াৰ`** | 1.5 | `কেঁচাগোল্ল` |
| ফ্ৰণ্টিয়াৰ | **`ফ্ৰণ্টিয়-াৰ`** | 1.5 | `ফ্ৰণ্টিয়` |
| যিহোচূৱাৰ | **`যিহোচূৱ-াৰ`** | 1.5 | `যিহোচূৱ` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Assamese 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 (4.54x) |
| N-gram | **2-gram** | Lowest perplexity (2,308) |
| Markov | **Context-4** | Highest predictability (97.2%) |
| 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: 2026-01-03 17:31:44*