---
language: iu
language_name: Inuktitut
language_family: eskimoaleut
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-eskimoaleut
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: 3.905
  - name: best_isotropy
    type: isotropy
    value: 0.2183
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Inuktitut** 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** | 3.015x | 3.02 | 0.1769% | 75,744 |
| **16k** | 3.468x | 3.47 | 0.2035% | 65,854 |
| **32k** | 3.905x 🏆 | 3.91 | 0.2292% | 58,476 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `ᕙᐃᔅᐳᒃ ᒥᐊᓕᒐᐃᑦ ᓄᓇᖓᓐᓂ ᖃᕆᑕᐅᔭᒃᑯᑦ ᑐᑭᓯᒋᐊᕐᕕᒃ ᓴᖅᑭᑕᐅᓚᐅᖅᓯᒪᔪᖅ ᒫᒃ ᓵᑯᐴᒡᒧᑦ. ᕙᐃᔅᐳᒃ ᑐᓴᐅᒪᔭᐅᓂᖅᐹᖑᕗᖅ ...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ᕙᐃᔅᐳᒃ ▁ᒥᐊᓕᒐᐃᑦ ▁ᓄᓇᖓᓐᓂ ▁ᖃᕆᑕᐅᔭᒃᑯᑦ ▁ᑐᑭᓯᒋᐊ ᕐᕕᒃ ▁ᓴᖅᑭᑕᐅᓚᐅᖅᓯᒪᔪᖅ ▁ᒫᒃ ▁ᓵᑯ ᐴᒡ ... (+16 more)` | 26 |
| 16k | `▁ᕙᐃᔅᐳᒃ ▁ᒥᐊᓕᒐᐃᑦ ▁ᓄᓇᖓᓐᓂ ▁ᖃᕆᑕᐅᔭᒃᑯᑦ ▁ᑐᑭᓯᒋᐊ ᕐᕕᒃ ▁ᓴᖅᑭᑕᐅᓚᐅᖅᓯᒪᔪᖅ ▁ᒫᒃ ▁ᓵᑯᐴᒡᒧᑦ . ... (+10 more)` | 20 |
| 32k | `▁ᕙᐃᔅᐳᒃ ▁ᒥᐊᓕᒐᐃᑦ ▁ᓄᓇᖓᓐᓂ ▁ᖃᕆᑕᐅᔭᒃᑯᑦ ▁ᑐᑭᓯᒋᐊᕐᕕᒃ ▁ᓴᖅᑭᑕᐅᓚᐅᖅᓯᒪᔪᖅ ▁ᒫᒃ ▁ᓵᑯᐴᒡᒧᑦ . ▁ᕙᐃᔅᐳᒃ ... (+7 more)` | 17 |

**Sample 2:** `ᐅᓵᐃᐅ—[ᖃᓪᓗᓈᑎᑐᑦ—Ohio]— ) ᐃᑎᐊᔪᑦ ᐃᓗᐊᓂ. ᐅᓵᐃᐅ ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ. ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ-ᓄᓇᓖᑦ ᑰᕉᒻᐴᔅ «...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ᐅᓵᐃᐅ —[ ᖃᓪᓗᓈᑎᑐᑦ — ohio ]— ▁) ▁ᐃᑎᐊᔪᑦ ▁ᐃᓗᐊᓂ . ... (+27 more)` | 37 |
| 16k | `▁ᐅᓵᐃᐅ —[ ᖃᓪᓗᓈᑎᑐᑦ — ohio ]— ▁) ▁ᐃᑎᐊᔪᑦ ▁ᐃᓗᐊᓂ . ... (+22 more)` | 32 |
| 32k | `▁ᐅᓵᐃᐅ —[ ᖃᓪᓗᓈᑎᑐᑦ — ohio ]— ▁) ▁ᐃᑎᐊᔪᑦ ▁ᐃᓗᐊᓂ . ... (+22 more)` | 32 |

**Sample 3:** `ᐊᐅᑦᓯᓇᖅᑐᖅ ᓱᓕᐊᖅ ᐊᓂᖅᐸᓈᖅᑑᔭᖅᑐᖅ ᐅᓚᐱᑉᐹ ᓴᐳᒻᒥᕚ ᑎᒥ. ᐅᑉᔭᒃᐳᖅ ᐊᓐᓄᕌᓂᒃ`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁ᐊᐅᑦᓯᓇᖅᑐᖅ ▁ᓱᓕᐊᖅ ▁ᐊᓂᖅᐸᓈᖅᑑᔭᖅᑐᖅ ▁ᐅᓚᐱ ᑉᐹ ▁ᓴᐳᒻᒥᕚ ▁ᑎᒥ . ▁ᐅᑉᔭᒃᐳᖅ ▁ᐊᓐᓄᕌᓂᒃ` | 10 |
| 16k | `▁ᐊᐅᑦᓯᓇᖅᑐᖅ ▁ᓱᓕᐊᖅ ▁ᐊᓂᖅᐸᓈᖅᑑᔭᖅᑐᖅ ▁ᐅᓚᐱᑉᐹ ▁ᓴᐳᒻᒥᕚ ▁ᑎᒥ . ▁ᐅᑉᔭᒃᐳᖅ ▁ᐊᓐᓄᕌᓂᒃ` | 9 |
| 32k | `▁ᐊᐅᑦᓯᓇᖅᑐᖅ ▁ᓱᓕᐊᖅ ▁ᐊᓂᖅᐸᓈᖅᑑᔭᖅᑐᖅ ▁ᐅᓚᐱᑉᐹ ▁ᓴᐳᒻᒥᕚ ▁ᑎᒥ . ▁ᐅᑉᔭᒃᐳᖅ ▁ᐊᓐᓄᕌᓂᒃ` | 9 |


### Key Findings

- **Best Compression:** 32k achieves 3.905x compression
- **Lowest UNK Rate:** 8k with 0.1769% 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 | 93 🏆 | 6.54 | 126 | 90.8% | 100.0% |
| **2-gram** | Subword | 962 | 9.91 | 3,039 | 37.0% | 87.0% |
| **3-gram** | Word | 130 | 7.03 | 174 | 73.9% | 100.0% |
| **3-gram** | Subword | 5,020 | 12.29 | 12,029 | 15.7% | 49.7% |
| **4-gram** | Word | 694 | 9.44 | 794 | 25.0% | 100.0% |
| **4-gram** | Subword | 14,093 | 13.78 | 28,526 | 8.8% | 30.5% |
| **5-gram** | Word | 607 | 9.25 | 676 | 24.5% | 100.0% |
| **5-gram** | Subword | 19,229 | 14.23 | 32,493 | 7.1% | 24.4% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `san marino` | 73 |
| 2 | `of the` | 55 |
| 3 | `ᖄᖓᒍᑦ ᖄᖓᒍᑦ` | 55 |
| 4 | `ᑭᒻᒧᑦ ᐅᖅᓯᖅ` | 47 |
| 5 | `ᑕᕆᐅᑉ ᐊᑭᐊᓂ` | 44 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ` | 51 |
| 2 | `ᑭᒻᒧᑦ ᐅᖅᓯᖅ www` | 30 |
| 3 | `ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ` | 22 |
| 4 | `ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ` | 22 |
| 5 | `ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ` | 22 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ` | 48 |
| 2 | `ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ` | 22 |
| 3 | `ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ` | 22 |
| 4 | `ᓄᓇᓖᑦ ᑭᒻᒧᑦ ᐅᖅᓯᖅ www` | 20 |
| 5 | `the grand and general` | 10 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ` | 45 |
| 2 | `ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ` | 22 |
| 3 | `the grand and general council` | 10 |
| 4 | `ᓄᓇ frameless upright 0 3` | 7 |
| 5 | `o canada we stand on` | 5 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ᑦ _` | 4,757 |
| 2 | `_ ᐊ` | 3,099 |
| 3 | `ᖅ _` | 2,694 |
| 4 | `_ ᐃ` | 2,386 |
| 5 | `, _` | 2,385 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ᐊ ᒻ ᒪ` | 851 |
| 2 | `_ ᐊ ᒻ` | 837 |
| 3 | `_ ᓄ ᓇ` | 816 |
| 4 | `ᓂ ᒃ _` | 784 |
| 5 | `ᑦ _ ᐊ` | 710 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ᐊ ᒻ ᒪ` | 833 |
| 2 | `ᐊ ᒻ ᒪ _` | 420 |
| 3 | `ᐊ ᒻ ᒪ ᓗ` | 407 |
| 4 | `ᖅ ᑐ ᖅ _` | 405 |
| 5 | `ᒻ ᒪ ᓗ _` | 385 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ ᐊ ᒻ ᒪ _` | 418 |
| 2 | `_ ᐊ ᒻ ᒪ ᓗ` | 400 |
| 3 | `ᐊ ᒻ ᒪ ᓗ _` | 385 |
| 4 | `_ t h e _` | 346 |
| 5 | `ᑦ _ ᐊ ᒻ ᒪ` | 218 |


### Key Findings

- **Best Perplexity:** 2-gram (word) with 93
- **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 | 0.3388 | 1.265 | 1.76 | 15,002 | 66.1% |
| **1** | Subword | 1.4995 | 2.827 | 13.51 | 541 | 0.0% |
| **2** | Word | 0.0479 | 1.034 | 1.07 | 26,047 | 95.2% |
| **2** | Subword | 0.9813 | 1.974 | 4.39 | 7,301 | 1.9% |
| **3** | Word | 0.0129 | 1.009 | 1.02 | 27,517 | 98.7% |
| **3** | Subword | 0.5441 | 1.458 | 2.22 | 31,981 | 45.6% |
| **4** | Word | 0.0049 🏆 | 1.003 | 1.01 | 27,602 | 99.5% |
| **4** | Subword | 0.3121 | 1.242 | 1.55 | 70,999 | 68.8% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `ᐊᒻᒪ ᐱᕈᖅᓯᐊᖅ ᑭᒃᑯᑦ ᐅᐊᑎᒌᓯᕆᒥᒻᒧᑦ ᓴᐃᓇᒃᑭᐅᔪᖅ ᐊᑎᖃᕐᒥᑕᐅᓂᖏᓐᓂᒃ ᐊᓂᔨᖃᕆᔪᑦ ᐅᓂᖅᑕᖃᕐᑕᐅᔪᑦ ᐅᑎᓇᐅᖃᑎᒌᑦ ᐱᒻᒥᕐᒥᐅᑕᓗᑉ ᑭᒻᒧᑦ ᐅᖅᓯᖅ www...`
2. `ᐊᒻᒪᓗ ᐊᐅᓚᓃᑦ ᐱᓕᕆᖃᑎᒌᖃᑦᑕᖅᑐᑦ ᐋᖅᑭᐅᒪᑎᑦᑎᓂᐊᕐᓗᓂ ᐊᖏᕐᕋᒥᒃ ᐅᓗᕆᐊᓇᙱᑦᑐᒃᑯᑦ ᓲᕐᓗ ᕕᑐᕆᑯ ᐃᓇᓗᒃᑲ ᐃᒡᓗᓐᓂ ᓄᓇᖃᖅᐳᑦ ᐸᑏᑎ ᐃᓕᓚᐅᖅᑕᕋ ᐊᐅᓚ...`
3. `the roman republic the sammarinese fascist government declared war on their passports citation neede...`

**Context Size 2:**

1. `san marino appealed to pope boniface viii against the contribution demands by the legate papal gover...`
2. `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᒥᑭᔫᖕᓂᒃ ᑐᐊᑎᐊᓂᒃ ᐊᒻᒪ ᐃᓛᓐᓂᒃᑯᑦ ᓴᓇᔭᐅᕙᒃᖢᑎᒃ ᒑᑲᒧᓕᒧᑦ ᓵᓪᓴᒧᑦ ᓂᐅᓐᔅᒧᑦ ᐊᒻᒪ ᓯᓚᓐᑐᒧᑦ ᑯᕆᓐᑐ ᒪᑉᐱ...`
3. `of the european union it is the fifth smallest country in europe after vatican city and state`

**Context Size 3:**

1. `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᒃᑲᓐᓂᖅ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ`
2. `ᑭᒻᒧᑦ ᐅᖅᓯᖅ www sd gov`
3. `ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ ᐲᕐ ᖃᓪᓗᓈᑎᑐᑦ pierre ᓄᓇᓖᑦ ᑭᒻᒧᑦ ᐅᖅᓯᖅ www ok gov`

**Context Size 4:**

1. `ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ ᖄᖓᒍᑦ`
2. `ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ ᐴᕐᑦᓛᓐᑦ ᖃᓪᓗᓈᑎᑐᑦ portland ᓄᓇᓖᑦ ᑭᒻᒧᑦ ᐅᖅᓯᖅ www nv gov`
3. `ᐃᓄᖁᑎ ᐊᒥᐊᓕᑲ ᐊᐅᓚᑦᑎᔩᑦ ᓯᕗᓕᖅᑎᖓᑦ ᓄᓇᓖᑦ ᓂᐅ ᐆᕐᓖᓐᔅ ᖃᓪᓗᓈᑎᑐᑦ new orleans ᓄᓇᓖᑦ ᑭᒻᒧᑦ ᐅᖅᓯᖅ www idaho gov`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_ᕕᒃ_ᒐᔪᑎᓪᓗᑕᑭᓯᐊᒻᒪ_`
2. `ᖅᑐᒃ_ontunixiteco`
3. `ᑦᑕ)_ᑲ,_ᖄᕐᒥᓱᐊᕈᑎᐊᒻ`

**Context Size 2:**

1. `ᑦ_(ᐱᓚᕈ,_ᑎᑎᓪᓗᓕᖃᖅᐳᑦ`
2. `_ᐊᐅᐸᐃᒡ_ᐊᖕᓇᖅ_ᑭᓕᐊᑉ_`
3. `ᖅ_ᑕᐃᑲᓂᐸ,_ᓄᓇᓖᑦ_ᐃᒡᓗ`

**Context Size 3:**

1. `ᐊᒻᒪᓗ_ᕿᓚᒃ._ᓴᓂᑭᓗᐊᕐᒥ.`
2. `_ᐊᒻᒪ_ᐃᓗᐊᓃᑐᓂ._ᐃᓚᖃᖅᑐ`
3. `_ᓄᓇᖃᐃᓐᓇᕆᐊᓚᐅᖅᐳᖅ_ᐊᕋᕕ`

**Context Size 4:**

1. `_ᐊᒻᒪ_ᑎᓴᒪᓂᒃ_ᓄᓇᒥᐅᑕᐅᕗᑦ`
2. `ᐊᒻᒪ_ᑕᑯᑦᑎᐊᔪᐃᓐᓇᕐᒥᒃ_ᐱᖃ`
3. `ᐊᒻᒪᓗ_ᖁᕕᐊᓱᖕᓂᖅ")ᐃᙱᐅᓯᖓ`


### Key Findings

- **Best Predictability:** Context-4 (word) with 99.5% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (70,999 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 | 3,802 |
| Total Tokens | 18,925 |
| Mean Frequency | 4.98 |
| Median Frequency | 2 |
| Frequency Std Dev | 13.99 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ᐊᒻᒪ | 424 |
| 2 | ᐊᒻᒪᓗ | 392 |
| 3 | the | 353 |
| 4 | of | 210 |
| 5 | ᐃᓄᐃᑦ | 139 |
| 6 | and | 131 |
| 7 | ᐅᕝᕙᓘᓐᓃᑦ | 114 |
| 8 | in | 106 |
| 9 | ᖃᓪᓗᓈᑎᑐᑦ | 104 |
| 10 | to | 98 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | ᑕᑯᔭᒐᖃᕐᕕᐅᔪᑦ | 2 |
| 2 | ᒥᐅᓯᐅ | 2 |
| 3 | ᓴᒃᑯᑐᖃᕐᓄᑦ | 2 |
| 4 | ᓴᕕᕋᔭᓄᑦ | 2 |
| 5 | ᐊᒥᐊᖅᑕᐅᓯᒪᔪᑦ | 2 |
| 6 | ᑭᐊᕋᒥ | 2 |
| 7 | ᔨᐊᓇᕆ | 2 |
| 8 | ᓴᓇᓐᖑᐊᒐᐃᑦ | 2 |
| 9 | ᓅᑉᐸᓪᓕᐊᔪᓄᑦ | 2 |
| 10 | ᓄᓇᒥᐅᑕᓕᕆᓂᕐᒧᑦ | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 0.6869 |
| R² (Goodness of Fit) | 0.969855 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 30.3% |
| Top 1,000 | 65.3% |
| Top 5,000 | 0.0% |
| Top 10,000 | 0.0% |

### Key Findings

- **Zipf Compliance:** R²=0.9699 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 30.3% of corpus
- **Long Tail:** -6,198 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 | 0.2183 | 0.4714 | N/A | N/A |
| **mono_64d** | 64 | 0.0445 | 0.4570 | N/A | N/A |
| **mono_128d** | 128 | 0.0046 | 0.4821 | N/A | N/A |
| **aligned_32d** | 32 | 0.2183 🏆 | 0.4659 | 0.0189 | 0.1384 |
| **aligned_64d** | 64 | 0.0445 | 0.4550 | 0.0314 | 0.1384 |
| **aligned_128d** | 128 | 0.0046 | 0.4794 | 0.0503 | 0.1509 |

### Key Findings

- **Best Isotropy:** aligned_32d with 0.2183 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.4685. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 5.0% 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 | **3.097** | 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 |
|--------|----------|
| `-ᐊ` | ᐊᑐᖅᑕᐅᓯᒪᔪᖅ, ᐊᔅᑦᕌᓕᐊ, ᐊᓂᒍᖅᑎᓪᓗᒋᑦ |
| `-ᐃ` | ᐃᓅᖃᑎᒌᑦ, ᐃᖃᑦᑐᖅ, ᐃᓱ |
| `-ᐅ` | ᐅᓪᓗᓂᒃ, ᐅᓛᓴᒥ, ᐅᑭᐅᖃᓕᖅᑎᓪᓗᒋᑦ |
| `-ᐅᖃ` | ᐅᖃᓕᒫᒐᓄᑦ, ᐅᖃᐅᓯᒃᓴᓂᖏᑦ, ᐅᖃᐅᓯᕐᖓᐅᑎᖃᕐᒪᑎᑕ |
| `-ᓄᓇ` | ᓄᓇᕕᐅᑉ, ᓄᓇᖁᑎᖓᓂᒃ, ᓄᓇᙳᐊᖓ |
| `-ᑕᐃ` | ᑕᐃᒫᑦᓴᐃᓐᓇᖅ, ᑕᐃᒃᓱᒪᓂ, ᑕᐃᒃᑯᓇᓂ |
| `-ᐃᓄ` | ᐃᓄᒃ, ᐃᓄᒋᐊᓛᖑᓪᓗᓂ, ᐃᓄᖕᓂᒃ |
| `-co` | coca, corporate, country |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-ᑦ` | ᖃᓚᒪᓐᖏᑑᓗᑎᓘᓐᓃᑦ, ᐃᓅᖃᑎᒌᑦ, ᐱᓕᕆᑦᑎᐊᕐᓂᖏᓐᓄᑦ |
| `-ᖅ` | ᐃᖃᑦᑐᖅ, ᐊᑐᖅᑕᐅᓯᒪᔪᖅ, ᓯᐅᕋᖅ |
| `-ᒃ` | ᓯᕗᓪᓕᖅᐹᒃ, ᐊᑕᐅᓯᕐᒥᒃ, ᐅᓪᓗᓂᒃ |
| `-ᓂᒃ` | ᐅᓪᓗᓂᒃ, ᒥᓕᐊᓐᓂᒃ, ᓂᐊᖁᕐᓂᒃ |
| `-ᑐᖅ` | ᐃᖃᑦᑐᖅ, ᓯᐅᕋᐅᔮᖅᑐᖅ, ᐃᓅᓕᖅᑐᖅ |
| `-ᓄᑦ` | ᐱᓕᕆᑦᑎᐊᕐᓂᖏᓐᓄᑦ, ᑭᖑᓪᓕᖅᐹᖅᓯᐅᑎᓄᑦ, ᐊᑕᐅᓯᐅᖃᑎᒌᓄᑦ |
| `-ᓂ` | ᓯᓚᑖᓂ, ᐃᓚᐅᙱᖦᖢᓂ, ᖃᓂᒋᔭᖓᓂ |
| `-t` | aallatqiit, pitquhiinit, anngutikhaqanngittagaangat |

### 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 |
|------|----------|------------------|----------|
| `ᕗᓪᓕᖅ` | 1.82x | 6 contexts | ᓯᕗᓪᓕᖅ, ᓯᕗᓪᓕᖅᐹᒃ, ᓯᕗᓪᓕᖅᐹᖅ |
| `ᓯᕗᓪᓕ` | 1.82x | 5 contexts | ᓯᕗᓪᓕᖅ, ᓯᕗᓪᓕᕐᒥ, ᓯᕗᓪᓕᖅᐹᒃ |
| `ᖅᓯᒪᔪ` | 1.50x | 6 contexts | ᓇᐃᓈᖅᓯᒪᔪᖅ, ᑎᑎᕋᖅᓯᒪᔪᖅ, ᑎᑎᕋᖅᓯᒪᔪᒥ |
| `ᓯᒪᔪᖅ` | 1.72x | 4 contexts | ᐃᓚᓯᒪᔪᖅ, ᓴᓇᓯᒪᔪᖅ, ᓴᖅᑭᓯᒪᔪᖅ |
| `ᖑᓪᓗᓂ` | 1.89x | 3 contexts | ᒥᑭᓛᖑᓪᓗᓂ, ᐊᖏᓛᖑᓪᓗᓂ, ᐊᖏᓛᖑᓪᓗᓂᓗ |

### 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 |
|--------|--------|-----------|----------|
| `-ᐊ` | `-ᑦ` | 61 words | ᐊᓂᒍᖅᑎᓪᓗᒋᑦ, ᐊᐅᓚᑦᑎᐊᕈᓐᓃᖅᑐᑦ |
| `-ᐃ` | `-ᖅ` | 47 words | ᐃᖃᑦᑐᖅ, ᐃᓅᓕᖅᑐᖅ |
| `-ᐃ` | `-ᑦ` | 46 words | ᐃᓅᖃᑎᒌᑦ, ᐃᓯᒐᐃᑦ |
| `-ᐅ` | `-ᑦ` | 41 words | ᐅᑭᐅᖃᓕᖅᑎᓪᓗᒋᑦ, ᐅᖃᓕᒫᒐᓄᑦ |
| `-ᐊ` | `-ᖅ` | 37 words | ᐊᑐᖅᑕᐅᓯᒪᔪᖅ, ᐊᖏᓛᖑᔪᖅ |
| `-ᐃ` | `-ᒃ` | 33 words | ᐃᓄᒃ, ᐃᓕᓐᓂᐊᕈᑎᒥᒃ |
| `-ᐊ` | `-ᒃ` | 24 words | ᐊᑕᐅᓯᕐᒥᒃ, ᐊᑯᓕᕕᒃ |
| `-ᐃ` | `-ᓂᒃ` | 19 words | ᐃᓄᖕᓂᒃ, ᐃᕐᕋᕕᖏᓐᓂᒃ |
| `-ᐅ` | `-ᖅ` | 19 words | ᐅᐱᕐᖓᖅ, ᐅᖃᐅᓯᖅ |
| `-ᐊ` | `-ᓂ` | 17 words | ᐊᑐᖅᑕᐅᓪᓗᓂ, ᐊᖏᔪᒻᒪᕆᐊᓘᓪᓗᓂ |

### 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 |
|------|-----------------|------------|------|
| ᐋᖅᑭᒃᓯᒪᓂᖓᓄᑦ | **`ᐋᖅᑭᒃᓯᒪᓂᖓ-ᓄᑦ`** | 4.5 | `ᐋᖅᑭᒃᓯᒪᓂᖓ` |
| presented | **`present-ed`** | 4.5 | `present` |
| uniformed | **`uniform-ed`** | 4.5 | `uniform` |
| ᓄᓇᓕᐸᐅᔭᖓᓄᑦ | **`ᓄᓇᓕᐸᐅᔭᖓ-ᓄᑦ`** | 4.5 | `ᓄᓇᓕᐸᐅᔭᖓ` |
| ᑖᒃᓰᔭᐃᔭᕈᑎᑦ | **`ᑖᒃᓰᔭᐃᔭᕈᑎ-ᑦ`** | 4.5 | `ᑖᒃᓰᔭᐃᔭᕈᑎ` |
| ᑖᒃᓰᔭᐃᔭᕈᑎᓄᑦ | **`ᑖᒃᓰᔭᐃᔭᕈᑎ-ᓄᑦ`** | 4.5 | `ᑖᒃᓰᔭᐃᔭᕈᑎ` |
| ᑖᒃᓰᔭᐃᔭᕈᑎᓂᒃ | **`ᑖᒃᓰᔭᐃᔭᕈᑎ-ᓂᒃ`** | 4.5 | `ᑖᒃᓰᔭᐃᔭᕈᑎ` |
| ᒫᓐᑎᕕᐅᓪᑐᒧᑦ | **`ᒫᓐᑎᕕᐅᓪᑐ-ᒧᑦ`** | 4.5 | `ᒫᓐᑎᕕᐅᓪᑐ` |
| ᐊᕕᑦᑐᖅᓯᒪᔪᓂᑦ | **`ᐊᕕᑦᑐᖅᓯᒪᔪᓂ-ᑦ`** | 4.5 | `ᐊᕕᑦᑐᖅᓯᒪᔪᓂ` |
| ᐃᓕᓐᓂᐊᕈᑎᒥᒃ | **`ᐃᓕᓐᓂᐊᕈᑎ-ᒥᒃ`** | 4.5 | `ᐃᓕᓐᓂᐊᕈᑎ` |
| ᐃᓕᓐᓂᐊᖅᑎᓂᒃ | **`ᐃᓕᓐᓂᐊᖅᑎ-ᓂᒃ`** | 4.5 | `ᐃᓕᓐᓂᐊᖅᑎ` |
| ᐋᖅᑭᒃᓯᒪᓂᖓᓂᒃ | **`ᐋᖅᑭᒃᓯᒪᓂᖓ-ᓂᒃ`** | 4.5 | `ᐋᖅᑭᒃᓯᒪᓂᖓ` |
| ᐃᓚᒋᔭᐅᓕᖅᑐᖅ | **`ᐃᓚᒋᔭᐅᓕ-ᖅ-ᑐᖅ`** | 3.0 | `ᐃᓚᒋᔭᐅᓕ` |
| ᐊᒥᐊᖅᑕᐅᓯᒪᔪᑦ | **`ᐊ-ᒥᐊᖅᑕᐅᓯᒪᔪ-ᑦ`** | 3.0 | `ᒥᐊᖅᑕᐅᓯᒪᔪ` |
| ᐃᓄᑐᐃᓐᓇᕐᓂᒃ | **`ᐃᓄ-ᑐᐃᓐᓇᕐ-ᓂᒃ`** | 3.0 | `ᑐᐃᓐᓇᕐ` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Inuktitut 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 | **32k BPE** | Best compression (3.91x) |
| N-gram | **2-gram** | Lowest perplexity (93) |
| Markov | **Context-4** | Highest predictability (99.5%) |
| 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 04:55:45*