README.md

---
language: dtp
language_name: Central Dusun
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: 4.962
  - name: best_isotropy
    type: isotropy
    value: 0.8679
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-04
---

# Central Dusun - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Central Dusun** 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** | 4.024x | 4.03 | 0.1643% | 595,784 |
| **16k** | 4.420x | 4.42 | 0.1805% | 542,287 |
| **32k** | 4.736x | 4.74 | 0.1934% | 506,176 |
| **64k** | 4.962x 🏆 | 4.96 | 0.2026% | 483,109 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `Boros Murut Timugon nopo nga boros di gunoon do Tulun Murut id Borneo. Sukuon`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁boros ▁murut ▁tim ug on ▁nopo ▁nga ▁boros ▁di ▁gunoon ... (+7 more)` | 17 |
| 16k | `▁boros ▁murut ▁tim ug on ▁nopo ▁nga ▁boros ▁di ▁gunoon ... (+7 more)` | 17 |
| 32k | `▁boros ▁murut ▁tim ugon ▁nopo ▁nga ▁boros ▁di ▁gunoon ▁do ... (+6 more)` | 16 |
| 64k | `▁boros ▁murut ▁timugon ▁nopo ▁nga ▁boros ▁di ▁gunoon ▁do ▁tulun ... (+5 more)` | 15 |

**Sample 2:** `Suminundu nopo nga sinawaan di Kinoingan.Kitanak yolo do songulun tondu tolumis ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁sumin undu ▁nopo ▁nga ▁sin awaan ▁di ▁kino ingan . ... (+14 more)` | 24 |
| 16k | `▁sumin undu ▁nopo ▁nga ▁sinawaan ▁di ▁kinoingan . k itanak ... (+11 more)` | 21 |
| 32k | `▁sumin undu ▁nopo ▁nga ▁sinawaan ▁di ▁kinoingan . k itanak ... (+10 more)` | 20 |
| 64k | `▁suminundu ▁nopo ▁nga ▁sinawaan ▁di ▁kinoingan . kitanak ▁yolo ▁do ... (+8 more)` | 18 |

**Sample 3:** `Mongintob nopo nga nunu nopo iri kokomoi do ginumu, ginayo, sinodu toi winagat.`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁mongin tob ▁nopo ▁nga ▁nunu ▁nopo ▁iri ▁kokomoi ▁do ▁ginumu ... (+7 more)` | 17 |
| 16k | `▁mongintob ▁nopo ▁nga ▁nunu ▁nopo ▁iri ▁kokomoi ▁do ▁ginumu , ... (+6 more)` | 16 |
| 32k | `▁mongintob ▁nopo ▁nga ▁nunu ▁nopo ▁iri ▁kokomoi ▁do ▁ginumu , ... (+6 more)` | 16 |
| 64k | `▁mongintob ▁nopo ▁nga ▁nunu ▁nopo ▁iri ▁kokomoi ▁do ▁ginumu , ... (+6 more)` | 16 |


### Key Findings

- **Best Compression:** 64k achieves 4.962x compression
- **Lowest UNK Rate:** 8k with 0.1643% 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 | 7,224 | 12.82 | 18,432 | 17.6% | 40.2% |
| **2-gram** | Subword | 227 🏆 | 7.82 | 2,665 | 72.6% | 99.5% |
| **3-gram** | Word | 10,598 | 13.37 | 17,860 | 12.0% | 30.6% |
| **3-gram** | Subword | 1,902 | 10.89 | 18,913 | 28.7% | 75.5% |
| **4-gram** | Word | 17,687 | 14.11 | 21,653 | 5.2% | 18.7% |
| **4-gram** | Subword | 10,332 | 13.33 | 90,801 | 14.5% | 42.9% |
| **5-gram** | Word | 9,233 | 13.17 | 10,312 | 5.0% | 23.1% |
| **5-gram** | Subword | 32,680 | 15.00 | 217,159 | 9.9% | 28.5% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nopo nga` | 11,657 |
| 2 | `id suang` | 2,821 |
| 3 | `toi ko` | 1,861 |
| 4 | `ontok toun` | 1,828 |
| 5 | `nga iso` | 1,049 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nopo nga iso` | 951 |
| 2 | `diti nopo nga` | 935 |
| 3 | `id suang do` | 660 |
| 4 | `nopo nga songulun` | 600 |
| 5 | `nopo diti nga` | 439 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nopo nga iso mantad` | 117 |
| 2 | `nopo nga iso kawo` | 79 |
| 3 | `nopo nga songulun mimingkono` | 75 |
| 4 | `nopo nga kohompit no` | 71 |
| 5 | `nopo nga iso pogun` | 70 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `archived from the original on` | 42 |
| 2 | `toi ko lobi ointutunan sabaagi` | 34 |
| 3 | `koposion pogulu om pondidikan nosusu` | 25 |
| 4 | `toun uhu kono saluran tv` | 24 |
| 5 | `mw parser output reflist lower` | 24 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n` | 132,420 |
| 2 | `n _` | 100,917 |
| 3 | `o _` | 92,031 |
| 4 | `i _` | 88,621 |
| 5 | `o n` | 79,747 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n _` | 56,169 |
| 2 | `d o _` | 34,236 |
| 3 | `_ n o` | 33,345 |
| 4 | `_ d o` | 32,858 |
| 5 | `_ k o` | 28,766 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ d o _` | 30,800 |
| 2 | `_ i d _` | 22,452 |
| 3 | `_ o m _` | 19,951 |
| 4 | `_ n g a` | 17,310 |
| 5 | `n o p o` | 15,354 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ n g a _` | 14,567 |
| 2 | `_ n o p o` | 14,303 |
| 3 | `n o p o _` | 14,096 |
| 4 | `o n t o k` | 12,540 |
| 5 | `n t o k _` | 12,488 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 227
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~29% 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 | 0.8540 | 1.808 | 5.51 | 70,711 | 14.6% |
| **1** | Subword | 0.8991 | 1.865 | 5.16 | 1,986 | 10.1% |
| **2** | Word | 0.2712 | 1.207 | 1.62 | 388,589 | 72.9% |
| **2** | Subword | 0.6820 | 1.604 | 4.13 | 10,241 | 31.8% |
| **3** | Word | 0.0811 | 1.058 | 1.13 | 628,158 | 91.9% |
| **3** | Subword | 0.7746 | 1.711 | 3.85 | 42,293 | 22.5% |
| **4** | Word | 0.0237 🏆 | 1.017 | 1.03 | 709,279 | 97.6% |
| **4** | Subword | 0.6516 | 1.571 | 2.76 | 162,763 | 34.8% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `do tasu piipiro posis nopo nga bagas menteri malaysia toi ko 7 3w 7 808 güzelbahçe`
2. `id boros sweden maamaso timpu pogulu nosusu i nopo nga okito nogi i rajaa do amu`
3. `om papaharo sikul takawas id keningau diti nga kohompit om gisom pinoposiliu do dudumagang maritim m...`

**Context Size 2:**

1. `nopo nga okito id posorili do kuil kuil bongunan bongunan winonsoi o kinoyonon diti galeri sukuon pa...`
2. `id suang pambalajalan loolobi id gana do sains sosial om ekonomi mogigion do pulau bali winonsoi o`
3. `toi ko bandar raya santiago gurun atacama ii gersang id utara chile nopo nga kosoruan ointutunan sab...`

**Context Size 3:**

1. `nopo nga iso kakadayan komponen kalas ko 5 id kointayadan do 50 tondu yahudi di bobos boroson id`
2. `diti nopo nga kiwaa totos okuri nopo nga kirati do tudan udan talasu om i bobos poinwagu nopo`
3. `id suang do watas tenom om id siriba kotoinaan do upis watas keningau di laid abaabayan dii nopo`

**Context Size 4:**

1. `nopo nga iso mantad tolu puruan tinimungan slav kosilahon ii kakal po do pharo ii suai nopo nga monu...`
2. `nopo nga iso kawo boros dayak i popohompit do duo dialek daro om matu dialek mantad boros austronesi...`
3. `nopo nga songulun mimingkono di abantung kopio maya piipiro film miagal ko x men apocalypse om nogi ...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_2_,_suhyl_palal`
2. `aheacasomomoid_p`
3. `ombaaiayosiesili`

**Context Size 2:**

1. `an_gan_ka_kopoko_`
2. `n_mek_koudions_gr`
3. `o_dukul_bihaguluh`

**Context Size 3:**

1. `an_abaagu_di_aut"_`
2. `do_sukuon_debutang`
3. `_nokobol_kopo_ngam`

**Context Size 4:**

1. `_do_ponuan_chillage`
2. `_id_sabaagi_gisom_n`
3. `_om_institud_5.11-3`


### Key Findings

- **Best Predictability:** Context-4 (word) with 97.6% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (162,763 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 | 30,571 |
| Total Tokens | 714,971 |
| Mean Frequency | 23.39 |
| Median Frequency | 4 |
| Frequency Std Dev | 322.81 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | do | 30,939 |
| 2 | id | 22,604 |
| 3 | om | 20,001 |
| 4 | nga | 15,882 |
| 5 | nopo | 14,210 |
| 6 | di | 13,677 |
| 7 | i | 9,637 |
| 8 | mantad | 7,460 |
| 9 | ontok | 6,784 |
| 10 | sabaagi | 5,793 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | nın | 2 |
| 2 | tarihçesi | 2 |
| 3 | paü | 2 |
| 4 | eğitim | 2 |
| 5 | dergisi | 2 |
| 6 | sayı | 2 |
| 7 | mongumang | 2 |
| 8 | mikattiwang | 2 |
| 9 | sisimbarpulou | 2 |
| 10 | koz | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.0496 |
| R² (Goodness of Fit) | 0.994075 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 41.6% |
| Top 1,000 | 66.1% |
| Top 5,000 | 84.5% |
| Top 10,000 | 91.2% |

### Key Findings

- **Zipf Compliance:** R²=0.9941 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 41.6% of corpus
- **Long Tail:** 20,571 words needed for remaining 8.8% 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 | 0.8679 🏆 | 0.3272 | N/A | N/A |
| **mono_64d** | 64 | 0.7620 | 0.2632 | N/A | N/A |
| **mono_128d** | 128 | 0.3462 | 0.2417 | N/A | N/A |
| **aligned_32d** | 32 | 0.8679 | 0.3226 | 0.0560 | 0.2820 |
| **aligned_64d** | 64 | 0.7620 | 0.2720 | 0.1060 | 0.3860 |
| **aligned_128d** | 128 | 0.3462 | 0.2427 | 0.2020 | 0.5260 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.8679 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.2782. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 20.2% 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 | **-0.189** | 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 |
|--------|----------|
| `-po` | poinkilong, pointounda, poninong |
| `-ko` | kopogonuan, kontinjen, kokomoi |
| `-mo` | monongkuyaan, mongingit, mohd |
| `-mi` | mind, millennium, minsingumbal |
| `-ma` | maru, many, matter |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-n` | louson, sukun, monongkuyaan |
| `-an` | monongkuyaan, kopogonuan, keahlian |
| `-on` | louson, southampton, unsubon |
| `-ng` | poinkilong, skateboarding, dropping |

### 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 |
|------|----------|------------------|----------|
| `anga` | 1.64x | 146 contexts | ganga, tanga, manga |
| `ngan` | 1.88x | 34 contexts | songan, jangan, dengan |
| `oros` | 2.02x | 26 contexts | boros, oroso, doros |
| `anta` | 1.48x | 88 contexts | banta, manta, antad |
| `boro` | 2.19x | 19 contexts | boros, oboros, borough |
| `ongu` | 1.63x | 50 contexts | tongue, tongus, mongua |
| `impu` | 1.96x | 24 contexts | limpu, timpu, limput |
| `mont` | 1.81x | 26 contexts | monto, montk, monte |
| `ampa` | 1.48x | 47 contexts | campa, gampa, rampa |
| `uang` | 1.59x | 33 contexts | huang, duang, ruang |
| `ogun` | 1.79x | 21 contexts | oguno, pogun, koguno |
| `mpai` | 1.95x | 13 contexts | ampai, rumpai, mimpai |

### 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 |
|--------|--------|-----------|----------|
| `-ko` | `-n` | 164 words | kolintuhunan, koyomutan |
| `-po` | `-n` | 148 words | poimpohon, porundangan |
| `-ko` | `-an` | 121 words | kolintuhunan, koyomutan |
| `-po` | `-an` | 109 words | porundangan, pomutulan |
| `-po` | `-on` | 39 words | poimpohon, potingkodon |
| `-ko` | `-on` | 37 words | kohinoon, kosogubon |
| `-mi` | `-ng` | 29 words | minanamong, minongisonong |
| `-mi` | `-n` | 23 words | million, miimpohon |
| `-mo` | `-ng` | 22 words | momoguring, moyang |
| `-po` | `-ng` | 16 words | poring, poning |

### 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 |
|------|-----------------|------------|------|
| kopomolobusan | **`ko-po-mo-lobus-an`** | 9.0 | `lobus` |
| popokobong | **`po-po-ko-bong`** | 7.5 | `bong` |
| pomokritik | **`po-mo-kritik`** | 6.0 | `kritik` |
| popobibas | **`po-po-bibas`** | 6.0 | `bibas` |
| momooboros | **`mo-mo-oboros`** | 6.0 | `oboros` |
| mamagakom | **`ma-ma-gakom`** | 6.0 | `gakom` |
| pomodolinan | **`po-mo-dolin-an`** | 4.5 | `dolin` |
| koingkuri | **`ko-ingkuri`** | 4.5 | `ingkuri` |
| tungkusan | **`tungkus-an`** | 4.5 | `tungkus` |
| pengurusan | **`pengurus-an`** | 4.5 | `pengurus` |
| kopogisuusuayan | **`ko-po-gisuusuay-an`** | 4.5 | `gisuusuay` |
| pesisiran | **`pesisir-an`** | 4.5 | `pesisir` |
| kopomoogian | **`ko-po-mo-ogian`** | 4.5 | `ogian` |
| pomudagangan | **`po-mudaga-ng-an`** | 4.5 | `mudaga` |
| pomobodilan | **`po-mo-bodil-an`** | 4.5 | `bodil` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Central Dusun 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 (4.96x) |
| N-gram | **2-gram** | Lowest perplexity (227) |
| Markov | **Context-4** | Highest predictability (97.6%) |
| 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: 2026-01-04 02:42:58*