README.md

---
language: skr
language_name: Saraiki
language_family: indoaryan_central
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_central
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: 4.131
  - name: best_isotropy
    type: isotropy
    value: 0.8190
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Saraiki** 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** | 3.368x | 3.37 | 0.2577% | 539,682 |
| **16k** | 3.695x | 3.70 | 0.2827% | 492,033 |
| **32k** | 3.948x | 3.95 | 0.3021% | 460,447 |
| **64k** | 4.131x 🏆 | 4.13 | 0.3161% | 440,105 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `نتکاݨی سرائیکی بلوچ قبیلہ اے جیہڑا سوکڑ اچ آباد اے۔`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁نت ک اݨی ▁سرائیکی ▁بلوچ ▁قبیلہ ▁اے ▁جیہڑا ▁سوک ڑ ... (+3 more)` | 13 |
| 16k | `▁نت ک اݨی ▁سرائیکی ▁بلوچ ▁قبیلہ ▁اے ▁جیہڑا ▁سوک ڑ ... (+3 more)` | 13 |
| 32k | `▁نت ک اݨی ▁سرائیکی ▁بلوچ ▁قبیلہ ▁اے ▁جیہڑا ▁سوکڑ ▁اچ ... (+2 more)` | 12 |
| 64k | `▁نتکاݨی ▁سرائیکی ▁بلوچ ▁قبیلہ ▁اے ▁جیہڑا ▁سوکڑ ▁اچ ▁آباد ▁اے۔` | 10 |

**Sample 2:** `دائرہ دین پناہ ریلوے ٹیشݨ، پاکستان اچ واقع ہے۔ ایہ ٹیشݨ کوٹری-اٹک ریلوے لائن تے ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁دائر ہ ▁دین ▁پناہ ▁ریلوے ▁ٹیشݨ ، ▁پاکستان ▁اچ ▁واقع ... (+12 more)` | 22 |
| 16k | `▁دائرہ ▁دین ▁پناہ ▁ریلوے ▁ٹیشݨ ، ▁پاکستان ▁اچ ▁واقع ▁ہے۔ ... (+11 more)` | 21 |
| 32k | `▁دائرہ ▁دین ▁پناہ ▁ریلوے ▁ٹیشݨ ، ▁پاکستان ▁اچ ▁واقع ▁ہے۔ ... (+11 more)` | 21 |
| 64k | `▁دائرہ ▁دین ▁پناہ ▁ریلوے ▁ٹیشݨ ، ▁پاکستان ▁اچ ▁واقع ▁ہے۔ ... (+11 more)` | 21 |

**Sample 3:** `خالد حسین بھٹی ہک سرائیکی گلوکار ہے ڄم پل سردار گڑھ وچ پیدا تھئے جاہ ٹکاݨہ سردار...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁خالد ▁حسین ▁بھٹی ▁ہک ▁سرائیکی ▁گلوکار ▁ہے ▁ڄم ▁پل ▁سردار ... (+18 more)` | 28 |
| 16k | `▁خالد ▁حسین ▁بھٹی ▁ہک ▁سرائیکی ▁گلوکار ▁ہے ▁ڄم ▁پل ▁سردار ... (+17 more)` | 27 |
| 32k | `▁خالد ▁حسین ▁بھٹی ▁ہک ▁سرائیکی ▁گلوکار ▁ہے ▁ڄم ▁پل ▁سردار ... (+17 more)` | 27 |
| 64k | `▁خالد ▁حسین ▁بھٹی ▁ہک ▁سرائیکی ▁گلوکار ▁ہے ▁ڄم ▁پل ▁سردار ... (+16 more)` | 26 |


### Key Findings

- **Best Compression:** 64k achieves 4.131x compression
- **Lowest UNK Rate:** 8k with 0.2577% 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 | 45,660 | 15.48 | 231,941 | 10.2% | 26.3% |
| **2-gram** | Subword | 378 🏆 | 8.56 | 12,976 | 62.1% | 96.6% |
| **3-gram** | Word | 102,229 | 16.64 | 425,070 | 8.5% | 19.9% |
| **3-gram** | Subword | 3,269 | 11.67 | 89,050 | 25.5% | 64.4% |
| **4-gram** | Word | 239,684 | 17.87 | 840,242 | 6.4% | 15.3% |
| **4-gram** | Subword | 17,995 | 14.14 | 430,953 | 12.6% | 36.8% |
| **5-gram** | Word | 221,106 | 17.75 | 720,153 | 6.6% | 15.3% |
| **5-gram** | Subword | 67,629 | 16.05 | 1,137,504 | 7.5% | 23.9% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `میں تبدیلی` | 29,929 |
| 2 | `کی خاصیت` | 29,929 |
| 3 | `ڈیٹا پر` | 29,928 |
| 4 | `خاصیت میں` | 29,928 |
| 5 | `link ڈیٹا` | 29,917 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `کی خاصیت میں` | 29,928 |
| 2 | `خاصیت میں تبدیلی` | 29,928 |
| 3 | `link ڈیٹا پر` | 29,917 |
| 4 | `دے بارے وچ` | 13,487 |
| 5 | `دے طور تے` | 10,710 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `کی خاصیت میں تبدیلی` | 29,928 |
| 2 | `ڈیٹا پر کی خاصیت` | 5,324 |
| 3 | `پر کی خاصیت میں` | 5,324 |
| 4 | `link ڈیٹا پر کی` | 5,318 |
| 5 | `ترمیم link دستاویز دیکھیے` | 4,438 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `پر کی خاصیت میں تبدیلی` | 5,324 |
| 2 | `ڈیٹا پر کی خاصیت میں` | 5,324 |
| 3 | `link ڈیٹا پر کی خاصیت` | 5,318 |
| 4 | `کریںدرستی ترمیم link دستاویز دیکھیے` | 4,044 |
| 5 | `میں تبدیلی کریںدرستی ترمیم link` | 4,044 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ے _` | 1,556,008 |
| 2 | `ی _` | 1,545,137 |
| 3 | `ں _` | 1,200,841 |
| 4 | `_ ا` | 1,095,456 |
| 5 | `_ د` | 927,450 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ا ں _` | 548,452 |
| 2 | `د ے _` | 441,590 |
| 3 | `ت ے _` | 424,921 |
| 4 | `و ں _` | 357,389 |
| 5 | `د ی _` | 351,671 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ د ے _` | 323,952 |
| 2 | `_ ت ے _` | 280,298 |
| 3 | `_ د ی _` | 257,801 |
| 4 | `_ و چ _` | 196,466 |
| 5 | `ک و ں _` | 161,726 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ک و ں _` | 135,465 |
| 2 | `ن ہ ا ں _` | 107,621 |
| 3 | `_ ا ن ہ ا` | 94,382 |
| 4 | `ا ن ہ ا ں` | 93,812 |
| 5 | `_ ن ا ل _` | 84,209 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 378
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~24% 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 | 0.9010 | 1.867 | 9.46 | 290,564 | 9.9% |
| **1** | Subword | 1.0083 | 2.012 | 10.84 | 3,108 | 0.0% |
| **2** | Word | 0.3654 | 1.288 | 2.09 | 2,745,718 | 63.5% |
| **2** | Subword | 0.8287 | 1.776 | 5.65 | 33,673 | 17.1% |
| **3** | Word | 0.1336 | 1.097 | 1.26 | 5,735,986 | 86.6% |
| **3** | Subword | 0.7147 | 1.641 | 4.06 | 190,176 | 28.5% |
| **4** | Word | 0.0542 🏆 | 1.038 | 1.09 | 7,215,131 | 94.6% |
| **4** | Subword | 0.5922 | 1.508 | 2.94 | 772,001 | 40.8% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `دے نشان بݨیا ہویا سمجھا ویندا ایں تذکرے دے آدر ہا ݙوجھے دا استعمال دے نال`
2. `تے پِچھوں ونڄݨ اُتّے قبضہ گیر نے سنسکرت تے خوشحال ہے ہک ڄہاڑے رات دھم ویسی`
3. `دی لکھتاں اوں ݙکھایا کہ خون دا نِیں تاں تھیسے نِت نفی کیتی ناہید شاہد علی`

**Context Size 2:**

1. `میں تبدیلی کریںخاندانزرداری خاندان بھٹو خاندانمناصبخاتون اول پاکستان دے خلاف جنگ دا مقصد اینگلو سیکس...`
2. `کی خاصیت میں تبدیلی کریںآغاز منصب مارچ کی قومی اسمبلیآغاز منصب 13 august of the dolls اتے`
3. `ڈیٹا پر p19 کی خاصیت میں تبدیلی کریںشریک حیاتایم کے منی سوامیاولادبندوماء پیوٹی پی دامودرن گوریعملی ...`

**Context Size 3:**

1. `خاصیت میں تبدیلی کریںوالیں دا رنگسرخ link ڈیٹا پر p40 کی خاصیت میں تبدیلی کریںکمسیاست دان link ڈیٹا`
2. `کی خاصیت میں تبدیلی کریںدرستی ترمیم link دستاویز دیکھیے عظمیٰ خان اردو عظمی اسلم خان کنوں لاہور وِچ`
3. `link ڈیٹا پر p172 کی خاصیت میں تبدیلی نومبر 83 person id بنام chandrika person id بنام anna`

**Context Size 4:**

1. `کی خاصیت میں تبدیلی پر صفحہ link ڈیٹا پر p345 کی خاصیت میں تبدیلی کریںکماداکارہماء ٻولیانگریزی link ...`
2. `ڈیٹا پر کی خاصیت میں تبدیلی کریںاکھیں دا رنگبھورا link ڈیٹا پر کی خاصیت میں تبدیلی کریںشریک mcmahon ...`
3. `پر کی خاصیت میں تبدیلی کریںدرستی ترمیم link دستاویز دیکھیے ریٹا کوٹھاری پیدائش 30 جولائی گجرات ہندوس...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_احق_موزکوعموڑار`
2. `انہے_شعلف_بلم_وں`
3. `ی_ت_dars_203)توں`

**Context Size 2:**

1. `ے_ناللہ_اوݨ_واربا`
2. `ی_رضیاں_کر_ݙٹھار_`
3. `ں_چھپاکے_کوشخص_تے`

**Context Size 3:**

1. `اں_اُتھاں_بہتر_ہے_سَ`
2. `دے_جنگ_کرݨ_تھی_سر_`
3. `تے_ٻیا_تاری_صلیت_ر`

**Context Size 4:**

1. `_دے_درخواست_دریاواں`
2. `_تے_ٹیشݨیں_گھر_وچ_س`
3. `_دی_ترجیحات_کوں_اہم`


### Key Findings

- **Best Predictability:** Context-4 (word) with 94.6% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (772,001 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 | 131,771 |
| Total Tokens | 10,177,640 |
| Mean Frequency | 77.24 |
| Median Frequency | 4 |
| Frequency Std Dev | 1854.94 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | دے | 324,588 |
| 2 | تے | 282,107 |
| 3 | دی | 259,436 |
| 4 | وچ | 199,515 |
| 5 | دا | 159,949 |
| 6 | کوں | 136,075 |
| 7 | ہے | 119,241 |
| 8 | انہاں | 93,620 |
| 9 | نال | 85,114 |
| 10 | ہک | 74,333 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | عرفیّت | 2 |
| 2 | زکریاؒمزار | 2 |
| 3 | رفتم | 2 |
| 4 | المرندی | 2 |
| 5 | مرند | 2 |
| 6 | مُرمّت | 2 |
| 7 | اَپَڑیا | 2 |
| 8 | ٻِلھایا | 2 |
| 9 | حموی | 2 |
| 10 | ݙیوسی | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.1368 |
| R² (Goodness of Fit) | 0.987501 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 37.7% |
| Top 1,000 | 64.9% |
| Top 5,000 | 83.8% |
| Top 10,000 | 89.5% |

### Key Findings

- **Zipf Compliance:** R²=0.9875 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 37.7% of corpus
- **Long Tail:** 121,771 words needed for remaining 10.5% 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 | 0.8190 | 0.3676 | N/A | N/A |
| **mono_64d** | 64 | 0.8078 | 0.2776 | N/A | N/A |
| **mono_128d** | 128 | 0.7900 | 0.2128 | N/A | N/A |
| **aligned_32d** | 32 | 0.8190 🏆 | 0.3872 | 0.0200 | 0.1900 |
| **aligned_64d** | 64 | 0.8078 | 0.2841 | 0.0620 | 0.2760 |
| **aligned_128d** | 128 | 0.7900 | 0.2169 | 0.1300 | 0.3860 |

### Key Findings

- **Best Isotropy:** aligned_32d with 0.8190 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2910. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 13.0% 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 | **-0.360** | 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 |
|--------|----------|
| `-ا` | اکارینا, اگواڑ, اےعبدالواحدصابر |
| `-م` | مورر, منین٘دے, مہیلا |
| `-ک` | کلمتی, کمبوڈیا, کُرسیاں |
| `-س` | سرزنش, سئییں, سسک |
| `-ب` | بوخنوالڈ, بنْاونْ, بلکدے |
| `-پ` | پھپھڑ, پُڈوکوٹئیلیرندھو, پرچھانویں |
| `-ال` | السوس, الکبریٰ, الیکسس |
| `-و` | وَہی, وَڈی, وائٹ |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ں` | ڳیئوں, کُرسیاں, ٻُہوں |
| `-ی` | کلمتی, لودھی, کنٹری |
| `-اں` | کُرسیاں, چھتّاں, ٹِکانواں |
| `-ن` | بریڈیکائنن, کیتئین, ݙیوین |
| `-ا` | اکارینا, کمبوڈیا, مہیلا |
| `-ے` | جترے, منین٘دے, ڈولچے |
| `-ر` | مورر, اےعبدالواحدصابر, اچار |
| `-یں` | بینکیں, سئییں, پرچھانویں |

### 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 |
|------|----------|------------------|----------|
| `ریند` | 1.87x | 142 contexts | ریندا, کریند, کریندی |
| `ھیند` | 1.69x | 229 contexts | تھیند, گھیندن, تھیندی |
| `ائیک` | 1.73x | 114 contexts | ہائیک, ائیکی, گائیک |
| `اکار` | 2.13x | 44 contexts | ڈاکار, اکارس, اداکار |
| `لتان` | 2.34x | 25 contexts | التان, ملتان, مُلتان |
| `زندگ` | 3.29x | 8 contexts | زندگی, زندگي, زندگیکم |
| `ندگی` | 2.45x | 18 contexts | زندگی, ذندگی, گندگی |
| `سرائ` | 2.05x | 31 contexts | سرائی, سرائے, سرائیک |
| `داکا` | 2.53x | 14 contexts | اداکار, صداکار, بوداکا |
| `یاتی` | 1.79x | 38 contexts | حیاتی, زیاتی, رویاتی |
| `ائنس` | 2.12x | 19 contexts | بائنس, جائنس, لائنس |
| `ردار` | 1.53x | 60 contexts | کردار, سردار, زردار |

### 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 |
|--------|--------|-----------|----------|
| `-ا` | `-ی` | 57 words | اپٹی, اوستی |
| `-ا` | `-ں` | 52 words | اشلوکیں, انساں |
| `-م` | `-ں` | 47 words | مُلکاں, مملوکاں |
| `-پ` | `-ں` | 46 words | پُراݨیاں, پکیساں |
| `-ا` | `-ن` | 45 words | القران, الجھن |
| `-ک` | `-ں` | 41 words | کھمباں, کیتھائیں |
| `-س` | `-ں` | 32 words | سامݨھیں, ساہاں |
| `-م` | `-ی` | 30 words | محاکاتی, مکتی |
| `-ت` | `-ں` | 30 words | تازیاں, ترئےویں |
| `-ا` | `-اں` | 30 words | انساں, اَلانواں |

### 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 |
|------|-----------------|------------|------|
| سرائیکیانہ | **`سرائیکی-ان-ہ`** | 7.5 | `ان` |
| آبسٹیٹرکس | **`آبسٹیٹ-ر-کس`** | 7.5 | `ر` |
| اتھاباسکا | **`اتھاباس-ک-ا`** | 7.5 | `ک` |
| سن٘بھالیاں | **`سن٘بھال-ی-اں`** | 6.0 | `سن٘بھال` |
| کالیایندا | **`کالیا-ین-دا`** | 6.0 | `کالیا` |
| مُنجھاریاں | **`مُنجھا-ری-اں`** | 6.0 | `مُنجھا` |
| انھاندیاں | **`انھان-دی-اں`** | 6.0 | `انھان` |
| فوٹوگرافراں | **`فوٹوگرافر-اں`** | 4.5 | `فوٹوگرافر` |
| ڈیموگرافرز | **`ڈیموگرافر-ز`** | 4.5 | `ڈیموگرافر` |
| ٹیکنالوجیاں | **`ٹیکنالوجی-اں`** | 4.5 | `ٹیکنالوجی` |
| اسٹیبلشمنٹ | **`ا-سٹیبلشمنٹ`** | 4.5 | `سٹیبلشمنٹ` |
| فلوروسینس | **`فلوروسین-س`** | 4.5 | `فلوروسین` |
| پرائمیٹاں | **`پرائمیٹ-اں`** | 4.5 | `پرائمیٹ` |
| سیاستدانیں | **`سیاستدان-یں`** | 4.5 | `سیاستدان` |
| کھوکھلیاں | **`کھوکھلی-اں`** | 4.5 | `کھوکھلی` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Saraiki 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 (4.13x) |
| N-gram | **2-gram** | Lowest perplexity (378) |
| Markov | **Context-4** | Highest predictability (94.6%) |
| 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 21:16:40*