README.md

---
language: bdr
language_name: West Coast Bajau
language_family: austronesian_other
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-austronesian_other
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: 4.803
  - name: best_isotropy
    type: isotropy
    value: 0.0390
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03
---

# West Coast Bajau - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **West Coast Bajau** 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** | 4.803x 🏆 | 4.82 | 0.1461% | 32,844 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `Dugal tu io akan bungkar pedih ni amun niak mangam buas dembangi , Dugal tu baya...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁dugal ▁tu ▁io ▁akan ▁bungkar ▁pedih ▁ni ▁amun ▁niak ▁mangam ... (+15 more)` | 25 |

**Sample 2:** `Bul (Ling Melayu: Bola) iyo dembua barang pinakai untuk besukan`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁bul ▁( ling ▁melayu : ▁bola ) ▁iyo ▁dembua ▁barang ... (+3 more)` | 13 |

**Sample 3:** `Tupi sungku tu sejenis tupi tradisional jomo sama. Tupi sungku pinakai untuk nge...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁tupi ▁sungku ▁tu ▁sejenis ▁tupi ▁tradisional ▁jomo ▁sama . ▁tupi ... (+10 more)` | 20 |


### Key Findings

- **Best Compression:** 8k achieves 4.803x compression
- **Lowest UNK Rate:** 8k with 0.1461% 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 | 287 | 8.16 | 401 | 53.3% | 100.0% |
| **2-gram** | Subword | 180 | 7.49 | 593 | 77.1% | 100.0% |
| **3-gram** | Word | 219 | 7.78 | 269 | 59.9% | 100.0% |
| **3-gram** | Subword | 1,136 | 10.15 | 3,407 | 32.8% | 85.1% |
| **4-gram** | Word | 272 | 8.09 | 345 | 51.2% | 100.0% |
| **4-gram** | Subword | 4,404 | 12.10 | 11,348 | 17.0% | 52.7% |
| **5-gram** | Word | 110 🏆 | 6.78 | 144 | 81.2% | 100.0% |
| **5-gram** | Subword | 8,815 | 13.11 | 18,466 | 12.6% | 37.5% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `tungan metelak` | 162 |
| 2 | `iyo no` | 137 |
| 3 | `iyo noh` | 69 |
| 4 | `iyo tu` | 68 |
| 5 | `bioso ni` | 45 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ma na ni` | 40 |
| 2 | `dewan undangan negeri` | 26 |
| 3 | `undangan negeri sabah` | 25 |
| 4 | `iyo tu dangan` | 19 |
| 5 | `tungan metelak dendo` | 18 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `dewan undangan negeri sabah` | 25 |
| 2 | `tungan metelak dendo malaysia` | 18 |
| 3 | `sama ma na ni` | 14 |
| 4 | `iyo no endangan jomo` | 12 |
| 5 | `no endangan jomo politik` | 12 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `no endangan jomo politik ta` | 12 |
| 2 | `iyo no endangan jomo politik` | 12 |
| 3 | `beliau tu ahli dewan undangan` | 11 |
| 4 | `malaysia beliau tu ahli dewan` | 11 |
| 5 | `ahli dewan undangan negeri sabah` | 11 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n` | 5,403 |
| 2 | `n _` | 3,715 |
| 3 | `n g` | 3,458 |
| 4 | `i _` | 3,000 |
| 5 | `_ t` | 2,981 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n _` | 2,433 |
| 2 | `a n g` | 1,567 |
| 3 | `n g _` | 1,349 |
| 4 | `_ t a` | 1,073 |
| 5 | `_ n i` | 983 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n g _` | 903 |
| 2 | `_ n i _` | 648 |
| 3 | `_ i y o` | 641 |
| 4 | `n g a n` | 618 |
| 5 | `g a n _` | 578 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `n g a n _` | 565 |
| 2 | `_ i y o _` | 504 |
| 3 | `y a n g _` | 410 |
| 4 | `_ y a n g` | 370 |
| 5 | `_ t a ' _` | 355 |


### Key Findings

- **Best Perplexity:** 5-gram (word) with 110
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~37% 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 | 0.8062 | 1.749 | 3.61 | 5,206 | 19.4% |
| **1** | Subword | 1.4517 | 2.735 | 11.24 | 101 | 0.0% |
| **2** | Word | 0.1664 | 1.122 | 1.26 | 18,482 | 83.4% |
| **2** | Subword | 1.2091 | 2.312 | 5.80 | 1,130 | 0.0% |
| **3** | Word | 0.0377 | 1.026 | 1.05 | 22,853 | 96.2% |
| **3** | Subword | 0.8020 | 1.744 | 3.16 | 6,542 | 19.8% |
| **4** | Word | 0.0104 🏆 | 1.007 | 1.01 | 23,441 | 99.0% |
| **4** | Subword | 0.5453 | 1.459 | 2.09 | 20,556 | 45.5% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `ni tak sekolah kebangsanaan puteri ngerujuk ta dikau bumbung laat atau pan buli kinurban iyo no`
2. `tu pan kuleh status teralap malaysia diom arena seni soro tungan metelak politik malaysia iko pinaka...`
3. `iyo menjadi budaya bajau sama ngeruo elau ule bedagang tradisi boi penenakan tak taun iyo pan`

**Context Size 2:**

1. `iyo no dangan jomo mediom menjogo keselamatan ko kestabilan masyarakat nuut ta diom undang undang lu...`
2. `iyo noh tun dr hasmah binti haji mohamad ali nganak 12 julai hasmah iyono doktor dendo yang`
3. `iyo tu pelego pemuzik kok pelakun dendo malaysia iyo tekilo kok watak ni lua kawasan asahan sumatera`

**Context Size 3:**

1. `ma na ni dediki bana`
2. `dewan undangan negeri sabah dewan undangan negeri sabah betiru`
3. `undangan negeri sabah boi nilantik lua 8 oktober beliau betiru ngentan jewatan ketua parti islam se ...`

**Context Size 4:**

1. `dewan undangan negeri sabah betiru`
2. `sama ma na ni telampau oyo antawa oyo bana`
3. `ahli dewan undangan negeri sabah dewan undangan negeri sabah ta kewasan matunggong lua tungan metela...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_amagri_dintutha`
2. `a'_tandartino_il`
3. `ntan_bik_di_a_bi`

**Context Size 2:**

1. `angerangerebini_p`
2. `n_tau_us_amungine`
3. `ng._tan_fa_langha`

**Context Size 3:**

1. `an_ole_ta'_mapas_d`
2. `ang_ma'na_tang_di_`
3. `ng_un_pan_atley_ma`

**Context Size 4:**

1. `ang_semek_regisin,_`
2. `_ni_untuk_pelbagas_`
3. `_iyo_no_un_pakai_bi`


### Key Findings

- **Best Predictability:** Context-4 (word) with 99.0% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (20,556 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 | 2,333 |
| Total Tokens | 23,243 |
| Mean Frequency | 9.96 |
| Median Frequency | 3 |
| Frequency Std Dev | 33.24 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ni | 758 |
| 2 | tu | 583 |
| 3 | iyo | 548 |
| 4 | ta | 452 |
| 5 | yang | 381 |
| 6 | boi | 353 |
| 7 | pan | 303 |
| 8 | kok | 280 |
| 9 | jomo | 273 |
| 10 | tungan | 250 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | pelikat | 2 |
| 2 | avi | 2 |
| 3 | me | 2 |
| 4 | jewatan | 2 |
| 5 | michael | 2 |
| 6 | joseph | 2 |
| 7 | ho | 2 |
| 8 | ny | 2 |
| 9 | pembunuh | 2 |
| 10 | mundu | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.9534 |
| R² (Goodness of Fit) | 0.984288 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 45.6% |
| Top 1,000 | 85.7% |
| Top 5,000 | 0.0% |
| Top 10,000 | 0.0% |

### Key Findings

- **Zipf Compliance:** R²=0.9843 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 45.6% of corpus
- **Long Tail:** -7,667 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)


### 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 | 0.0390 🏆 | 0.8823 | N/A | N/A |
| **mono_64d** | 64 | 0.0242 | 0.9395 | N/A | N/A |
| **mono_128d** | 128 | 0.0071 | 0.9393 | N/A | N/A |
| **aligned_32d** | 32 | 0.0390 | 0.8940 | 0.0078 | 0.0667 |
| **aligned_64d** | 64 | 0.0242 | 0.9410 | 0.0039 | 0.0627 |
| **aligned_128d** | 128 | 0.0071 | 0.9401 | 0.0039 | 0.0627 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.0390 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.9227. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 0.8% 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 | **2.767** | High morphological productivity | Reliable analysis |
| Idiomaticity Gap | **1.669** | High formulaic/idiomatic 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 |
|--------|----------|
| `-pe` | petaling, pekakasan, pesat |
| `-se` | sepanjang, serupo, seri |
| `-ke` | keempat, kempen, kemudahan |
| `-te` | tena, teposok, terbaik |
| `-me` | melodi, mencakup, menduo |
| `-be` | bege, begiang, been |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-n` | sangkan, intan, kempen |
| `-an` | sangkan, intan, pekakasan |
| `-ng` | suang, sepanjang, petaling |
| `-ang` | suang, sepanjang, begiang |
| `-ah` | tanah, buah, majalah |

### 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 |
|--------|--------|-----------|----------|
| `-pe` | `-n` | 55 words | pekakasan, pernikahan |
| `-pe` | `-an` | 49 words | pekakasan, pernikahan |
| `-ke` | `-n` | 42 words | kempen, kemudahan |
| `-ke` | `-an` | 36 words | kemudahan, keadilan |
| `-se` | `-n` | 11 words | semimon, sembilan |
| `-se` | `-ng` | 9 words | sepanjang, sesambung |
| `-te` | `-n` | 9 words | temban, teniman |
| `-me` | `-n` | 9 words | mekitoon, mesakan |
| `-pe` | `-ng` | 7 words | petaling, perang |
| `-se` | `-an` | 7 words | sembilan, sebahagian |

### 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 |
|------|-----------------|------------|------|
| kebenyakan | **`ke-be-nyak-an`** | 7.5 | `nyak` |
| kebudayaan | **`ke-budaya-an`** | 6.0 | `budaya` |
| keramaian | **`ke-ramai-an`** | 6.0 | `ramai` |
| kepulauan | **`ke-pulau-an`** | 6.0 | `pulau` |
| keputeraan | **`ke-putera-an`** | 6.0 | `putera` |
| kelahiran | **`ke-lahir-an`** | 6.0 | `lahir` |
| kelebihan | **`ke-lebih-an`** | 6.0 | `lebih` |
| kebangsaan | **`ke-bangsa-an`** | 6.0 | `bangsa` |
| pernikahan | **`pe-rnik-ah-an`** | 4.5 | `rnik` |
| pertandingan | **`pe-rtandi-ng-an`** | 4.5 | `rtandi` |
| pelancongan | **`pe-lanco-ng-an`** | 4.5 | `lanco` |
| persembahan | **`pe-rsemb-ah-an`** | 4.5 | `rsemb` |
| kemerdekaan | **`ke-me-rdeka-an`** | 4.5 | `rdeka` |
| keselamatan | **`ke-se-lamat-an`** | 4.5 | `lamat` |
| sedembila | **`se-dembila`** | 4.5 | `dembila` |

### 6.6 Linguistic Interpretation

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

> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.

---
## 7. Summary & Recommendations

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

### Production Recommendations

| Component | Recommended | Rationale |
|-----------|-------------|-----------|
| Tokenizer | **8k BPE** | Best compression (4.80x) |
| N-gram | **5-gram** | Lowest perplexity (110) |
| Markov | **Context-4** | Highest predictability (99.0%) |
| 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 18:34:47*