README.md

---
language: anp
language_name: ANP
language_family: indoaryan_central
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - monolingual
  - family-indoaryan_central
license: mit
library_name: wikilangs
pipeline_tag: feature-extraction
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: 4.233
  - name: best_isotropy
    type: isotropy
    value: 0.8322
  - name: vocabulary_size
    type: vocab
    value: 9567
generated: 2025-12-27
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **ANP** 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-gram)
- Markov chains (context of 1, 2, 3 and 4)
- Subword N-gram and Markov chains
- Embeddings in various sizes and dimensions
- 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. Summary & Recommendations](#6-summary--recommendations)
- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
- [Visualizations Index](#visualizations-index)

---
## 1. Tokenizer Evaluation

![Tokenizer Compression](visualizations/tokenizer_compression.png)

### Results

| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
|------------|-------------|---------------|----------|--------------|
| **8k** | 3.431x | 3.37 | 0.0938% | 441,405 |
| **16k** | 3.755x | 3.69 | 0.1026% | 403,338 |
| **32k** | 4.015x | 3.95 | 0.1098% | 377,196 |
| **64k** | 4.233x 🏆 | 4.16 | 0.1157% | 357,719 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `मंजूषा कला अंगप्रदेश के एक बहुचर्चित लोकगाथा बिहुला विषहरी पर आधारित छै।`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁म ंज ूष ा ▁कला ▁अंग प्र देश ▁के ▁एक ... (+16 more)` | 26 |
| 16k | `▁मंज ूष ा ▁कला ▁अंग प्रदेश ▁के ▁एक ▁बहु च ... (+13 more)` | 23 |
| 32k | `▁मंज ूष ा ▁कला ▁अंगप्रदेश ▁के ▁एक ▁बहु चर्चित ▁लोकगाथा ... (+7 more)` | 17 |
| 64k | `▁मंजूषा ▁कला ▁अंगप्रदेश ▁के ▁एक ▁बहुचर्चित ▁लोकगाथा ▁बिहुला ▁विष हरी ... (+4 more)` | 14 |

**Sample 2:** `कार्बन के रासायनिक तत्व छेकै। इ ठोस अवस्था मँ पैलौ जाय वाला अधातु छेकै।

एकरो दे...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁कार्बन ▁के ▁रासायनिक ▁तत्व ▁छेकै । ▁इ ▁ठोस ▁अवस्था ▁मँ ... (+13 more)` | 23 |
| 16k | `▁कार्बन ▁के ▁रासायनिक ▁तत्व ▁छेकै । ▁इ ▁ठोस ▁अवस्था ▁मँ ... (+11 more)` | 21 |
| 32k | `▁कार्बन ▁के ▁रासायनिक ▁तत्व ▁छेकै । ▁इ ▁ठोस ▁अवस्था ▁मँ ... (+11 more)` | 21 |
| 64k | `▁कार्बन ▁के ▁रासायनिक ▁तत्व ▁छेकै । ▁इ ▁ठोस ▁अवस्था ▁मँ ... (+11 more)` | 21 |

**Sample 3:** `ब्रह्मपुत्र 
श्रेणी:नद्दी`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ब्रह्म पुत्र ▁श्रेणी : न द्दी` | 6 |
| 16k | `▁ब्रह्मपुत्र ▁श्रेणी : न द्दी` | 5 |
| 32k | `▁ब्रह्मपुत्र ▁श्रेणी : नद्दी` | 4 |
| 64k | `▁ब्रह्मपुत्र ▁श्रेणी : नद्दी` | 4 |


### Key Findings

- **Best Compression:** 64k achieves 4.233x compression
- **Lowest UNK Rate:** 8k with 0.0938% 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 Coverage](visualizations/ngram_coverage.png)

### Results

| N-gram | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
|--------|------------|---------|----------------|------------------|-------------------|
| **2-gram** | 1,473 🏆 | 10.52 | 19,190 | 39.0% | 79.4% |
| **2-gram** | 611 🏆 | 9.26 | 5,088 | 50.3% | 92.6% |
| **3-gram** | 10,511 | 13.36 | 71,690 | 14.3% | 44.9% |
| **3-gram** | 4,774 | 12.22 | 39,446 | 20.6% | 57.0% |
| **4-gram** | 37,995 | 15.21 | 200,757 | 8.2% | 28.1% |
| **4-gram** | 20,752 | 14.34 | 155,528 | 10.6% | 34.2% |

### Top 5 N-grams by Size

**2-grams:**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `क े` | 45,157 |
| 2 | `ा र` | 27,669 |
| 3 | `् र` | 26,110 |
| 4 | `ि क` | 21,854 |
| 5 | `य ा` | 21,115 |

**3-grams:**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `म े ं` | 15,079 |
| 2 | `ि य ा` | 9,483 |
| 3 | `छ ै ।` | 7,624 |
| 4 | `् य ा` | 7,466 |
| 5 | `ा क े` | 7,405 |

**4-grams:**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `भ ा ष ा` | 4,716 |
| 2 | `प ् र ा` | 3,403 |
| 3 | `क े र ौ` | 3,367 |
| 4 | `ा ह ै ।` | 3,140 |
| 5 | `ै , ज े` | 3,079 |


### Key Findings

- **Best Perplexity:** 2-gram with 611
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~34% of corpus
- **Recommendation:** 4-gram or 5-gram for best predictive performance

---
## 3. Markov Chain Evaluation

![Markov Entropy](visualizations/markov_entropy.png)

![Markov Branching](visualizations/markov_branching.png)

### Results

| Context | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
|---------|-------------|------------|------------------|-----------------|----------------|
| **1** | 0.6716 | 1.593 | 6.02 | 20,822 | 32.8% |
| **1** | 1.3840 | 2.610 | 13.82 | 755 | 0.0% |
| **2** | 0.3861 | 1.307 | 2.68 | 125,337 | 61.4% |
| **2** | 1.1859 | 2.275 | 7.36 | 10,432 | 0.0% |
| **3** | 0.3331 | 1.260 | 2.02 | 335,781 | 66.7% |
| **3** | 0.8363 | 1.785 | 3.79 | 76,706 | 16.4% |
| **4** | 0.2418 🏆 | 1.182 | 1.55 | 678,169 | 75.8% |
| **4** | 0.5341 🏆 | 1.448 | 2.27 | 290,862 | 46.6% |

### Generated Text Samples

Below are text samples generated from each Markov chain model:

**Context Size 1:**

1. `ा ण ी कम ी ] स ा ) घ ो क ृ त ् रभ`
2. `् डल स ं ख ् ध ा य ु न े ण अक ा प`
3. `े म ँ स ू प ि त च ा जन ा न ह ै ज`

**Context Size 2:**

1. `क े प ु स ् थ अलग करत ी थ ी , ड ा इऑक ्`
2. `ा र अध ि न े अध ि क ा ल ा म द ि य ा`
3. `् र ो स ि मड े ग ा न , 2006 क ो खतर े क`

**Context Size 3:**

1. `म े ं क े द ु मक ा स ॑ ई बड ़ ऽ क ् ष`
2. `ि य ा च ि त ह ै और यह ी ं भ ी उनक े प ा`
3. `् य ा 1484 छ े ल ै । २८ नवम ् बर 1889 क ो और तन`

**Context Size 4:**

1. `भ ा ष ा स ि न ी क स ं स ् थ ा प ि त करन`
2. `प ् र ा प ् त छ ै । शब ् द - स ा धन इत ि`
3. `क े र ौ व ि भ ि न ् न - भ ि न ् न प ्`


### Key Findings

- **Best Predictability:** Context-4 with 75.8% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (290,862 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 | 9,567 |
| Total Tokens | 1,448,534 |
| Mean Frequency | 151.41 |
| Median Frequency | 4 |
| Frequency Std Dev | 2528.69 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | क | 136,096 |
| 2 | र | 92,258 |
| 3 | स | 77,006 |
| 4 | म | 59,103 |
| 5 | त | 56,261 |
| 6 | न | 54,426 |
| 7 | य | 53,099 |
| 8 | ल | 48,343 |
| 9 | प | 45,425 |
| 10 | ह | 44,325 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | css | 2 |
| 2 | zeros | 2 |
| 3 | ignored | 2 |
| 4 | dmy | 2 |
| 5 | mdy | 2 |
| 6 | paren | 2 |
| 7 | breaking | 2 |
| 8 | inserted | 2 |
| 9 | values | 2 |
| 10 | separator | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.4601 |
| R² (Goodness of Fit) | 0.993188 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 82.8% |
| Top 1,000 | 96.2% |
| Top 5,000 | 99.3% |
| Top 10,000 | 0.0% |

### Key Findings

- **Zipf Compliance:** R²=0.9932 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 82.8% of corpus
- **Long Tail:** -433 words needed for remaining 100.0% 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)

### Model Comparison

| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy |
|-------|------------|-----------|----------|----------|----------|
| **mono_32d** | 12,167 | 32 | 3.479 | 0.971 | 0.8322 🏆 |
| **mono_64d** | 12,167 | 64 | 3.809 | 0.905 | 0.7196 |
| **mono_128d** | 12,167 | 128 | 3.964 | 0.866 | 0.3647 |
| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8322 (more uniform distribution)
- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy
- **Vocabulary Coverage:** All models cover 12,167 words
- **Recommendation:** 100d for balanced semantic capture and efficiency

---
## 6. Summary & Recommendations

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

### Production Recommendations

| Component | Recommended | Rationale |
|-----------|-------------|-----------|
| Tokenizer | **32k BPE** | Best compression (4.23x) with low UNK rate |
| N-gram | **5-gram** | Lowest perplexity (611) |
| Markov | **Context-4** | Highest predictability (75.8%) |
| 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},
  publisher = {HuggingFace},
  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)
---
*Generated by Wikilangs Models Pipeline*

*Report Date: 2025-12-27 06:08:15*