README.md

---
language: tet
language_name: Tetum
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: 4.079
  - name: best_isotropy
    type: isotropy
    value: 0.2388
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-11
---

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

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tetum** 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** | 3.685x | 3.69 | 0.0920% | 220,741 |
| **16k** | 3.897x | 3.90 | 0.0973% | 208,698 |
| **32k** | 4.079x 🏆 | 4.08 | 0.1018% | 199,418 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `Paraná mak sai estadu iha Brazíl. Populasaun ema Ligasaun Ba Li'ur Governo do Es...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁par aná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ... (+18 more)` | 28 |
| 16k | `▁paraná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ▁ligasaun ... (+16 more)` | 26 |
| 32k | `▁paraná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ▁ligasaun ... (+16 more)` | 26 |

**Sample 2:** `Mekanika (Lian Latina mechanicus, husi Lian Yunani Mechanikos, ema ne'ebe espesi...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁mekanika ▁( lian ▁la tina ▁me ch an ic us ... (+24 more)` | 34 |
| 16k | `▁mekanika ▁( lian ▁latina ▁mechan ic us , ▁husi ▁lian ... (+18 more)` | 28 |
| 32k | `▁mekanika ▁( lian ▁latina ▁mechanicus , ▁husi ▁lian ▁yunani ▁mechanikos ... (+12 more)` | 22 |

**Sample 3:** `Inkscape hanesan Aplikasaun editor ba imajem ne'ebe ho kodigu nakloke iha lisens...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁in ks cape ▁hanesan ▁aplikasaun ▁ed itor ▁ba ▁imajem ▁ne ... (+15 more)` | 25 |
| 16k | `▁in ks cape ▁hanesan ▁aplikasaun ▁editor ▁ba ▁imajem ▁ne ' ... (+11 more)` | 21 |
| 32k | `▁inkscape ▁hanesan ▁aplikasaun ▁editor ▁ba ▁imajem ▁ne ' ebe ▁ho ... (+9 more)` | 19 |


### Key Findings

- **Best Compression:** 32k achieves 4.079x compression
- **Lowest UNK Rate:** 8k with 0.0920% 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 | 1,400 | 10.45 | 5,366 | 42.9% | 71.5% |
| **2-gram** | Subword | 284 🏆 | 8.15 | 1,827 | 67.5% | 99.3% |
| **3-gram** | Word | 1,275 | 10.32 | 6,153 | 49.9% | 70.9% |
| **3-gram** | Subword | 2,144 | 11.07 | 13,149 | 25.6% | 72.8% |
| **4-gram** | Word | 1,739 | 10.76 | 10,529 | 49.1% | 63.6% |
| **4-gram** | Subword | 8,921 | 13.12 | 53,309 | 14.4% | 45.0% |
| **5-gram** | Word | 1,049 | 10.03 | 7,279 | 55.7% | 71.0% |
| **5-gram** | Subword | 20,481 | 14.32 | 103,985 | 10.6% | 35.3% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ne e` | 2,579 |
| 2 | `ne ebé` | 2,254 |
| 3 | `iha tinan` | 1,036 |
| 4 | `timor leste` | 973 |
| 5 | `lorosa e` | 966 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `timór lorosa e` | 863 |
| 2 | `ba li ur` | 806 |
| 3 | `ligasaun ba li` | 803 |
| 4 | `timor leste nian` | 553 |
| 5 | `ne e iha` | 542 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `ligasaun ba li ur` | 803 |
| 2 | `iha timór lorosa e` | 486 |
| 3 | `da républica mit dem` | 440 |
| 4 | `républica mit dem diploma` | 440 |
| 5 | `jornal da républica mit` | 439 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `da républica mit dem diploma` | 440 |
| 2 | `jornal da républica mit dem` | 439 |
| 3 | `ida iha timór lorosa e` | 439 |
| 4 | `ur sensus fo fila fali` | 438 |
| 5 | `mit dem diploma ministerial n` | 438 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a _` | 55,311 |
| 2 | `a n` | 26,720 |
| 3 | `n _` | 25,228 |
| 4 | `_ n` | 24,195 |
| 5 | `e _` | 21,839 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n _` | 10,780 |
| 2 | `h a _` | 10,572 |
| 3 | `i h a` | 10,489 |
| 4 | `i a _` | 9,335 |
| 5 | `_ i h` | 9,184 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `i h a _` | 10,318 |
| 2 | `_ i h a` | 9,183 |
| 3 | `a u n _` | 6,940 |
| 4 | `_ n i a` | 6,849 |
| 5 | `s a u n` | 6,166 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ i h a _` | 9,098 |
| 2 | `s a u n _` | 5,780 |
| 3 | `_ s i r a` | 4,434 |
| 4 | `a s a u n` | 4,363 |
| 5 | `_ n i a n` | 3,879 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 284
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.8183 | 1.763 | 4.67 | 28,115 | 18.2% |
| **1** | Subword | 1.1849 | 2.274 | 9.58 | 386 | 0.0% |
| **2** | Word | 0.2239 | 1.168 | 1.48 | 130,951 | 77.6% |
| **2** | Subword | 1.0621 | 2.088 | 6.47 | 3,691 | 0.0% |
| **3** | Word | 0.0716 | 1.051 | 1.13 | 192,808 | 92.8% |
| **3** | Subword | 0.8599 | 1.815 | 3.88 | 23,852 | 14.0% |
| **4** | Word | 0.0258 🏆 | 1.018 | 1.04 | 216,360 | 97.4% |
| **4** | Subword | 0.5884 | 1.504 | 2.40 | 92,496 | 41.2% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `iha okos hosi ligasaun ba dook liu tan aplikasiaun simples konsistente no hetan ona kartaun natál`
2. `ne ebé afirma konkluzaun ka siénsia sira tenta seluk ne e haklakar an liu hanesan programa`
3. `no sosiál isabel de daroca td duxambé tanzánia td taxkent v de amor do escuta nian`

**Context Size 2:**

1. `ne e mós bele funsiona nu udar interiór nia kontinentál ho nuanse sira foho sira hotu sei`
2. `ne ebé mak marka prezensa iha sira nia komunikasaun ba malu bele mos aumenta e bele realiza`
3. `iha tinan total populasaun hamutuk área 97 37 km vinilale mak sai sidade kapitál seuta estremadura s...`

**Context Size 3:**

1. `timór lorosa e nian fatu lulik mak sai sidade inan ba giana populasaun 200 000 abit`
2. `ligasaun ba li ur sensus fo fila fali tetun pdf 8 6 mb referensia munisípiu timor leste nian`
3. `ba li ur iktiolojia`

**Context Size 4:**

1. `ligasaun ba li ur sensus fo fila fali tetun pdf 8 6 mb seeds of life suco information sheets`
2. `iha timór lorosa e suku ne e iha postu administrativu watucarbau munisípiu vikeke iha tinan total po...`
3. `républica mit dem diploma ministerial n 199 09 portugiesisch pdf 323 kb ligasaun ba li ur wikipédia ...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_a_n_la_bozatór_`
2. `ay_nan_simaraica`
3. `icçõe_bamo_a,_tg`

**Context Size 2:**

1. `a_psainfo_hos,_wi`
2. `anansusi_ca_anyea`
3. `n_semindo_lu_stro`

**Context Size 3:**

1. `an_niança_cola_fáb`
2. `ha_ami_lia_sendári`
3. `iha_progracts_lor=`

**Context Size 4:**

1. `iha_roma_mit_democr`
2. `_iha_moris_iha_kata`
3. `aun_su_entransa._f-`


### Key Findings

- **Best Predictability:** Context-4 (word) with 97.4% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (92,496 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 | 12,756 |
| Total Tokens | 256,639 |
| Mean Frequency | 20.12 |
| Median Frequency | 4 |
| Frequency Std Dev | 164.32 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | iha | 9,917 |
| 2 | ne | 5,971 |
| 3 | no | 5,164 |
| 4 | ba | 4,578 |
| 5 | sira | 4,433 |
| 6 | e | 4,309 |
| 7 | nian | 4,134 |
| 8 | nia | 3,341 |
| 9 | ho | 2,906 |
| 10 | ida | 2,823 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | injeta | 2 |
| 2 | injesaun | 2 |
| 3 | stiko | 2 |
| 4 | rezervatóriu | 2 |
| 5 | konfirmadu | 2 |
| 6 | profilaxe | 2 |
| 7 | 中华人民共和国国家卫生健康委员会 | 2 |
| 8 | uttar | 2 |
| 9 | pradesh | 2 |
| 10 | pántanu | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.1160 |
| R² (Goodness of Fit) | 0.992469 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 46.0% |
| Top 1,000 | 75.4% |
| Top 5,000 | 91.9% |
| Top 10,000 | 97.9% |

### Key Findings

- **Zipf Compliance:** R²=0.9925 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 46.0% of corpus
- **Long Tail:** 2,756 words needed for remaining 2.1% 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 | 0.2388 | 0.4660 | N/A | N/A |
| **mono_64d** | 64 | 0.0465 | 0.4453 | N/A | N/A |
| **mono_128d** | 128 | 0.0060 | 0.4698 | N/A | N/A |
| **aligned_32d** | 32 | 0.2388 🏆 | 0.4494 | 0.0280 | 0.1680 |
| **aligned_64d** | 64 | 0.0465 | 0.4460 | 0.0280 | 0.1920 |
| **aligned_128d** | 128 | 0.0060 | 0.4501 | 0.0340 | 0.2000 |

### Key Findings

- **Best Isotropy:** aligned_32d with 0.2388 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.4544. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 3.4% 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 | **1.010** | 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 |
|--------|----------|
| `-a` | agosto, asosia, acumau |
| `-s` | sigla, sleep, simples |
| `-m` | manulai, metan, markadór |
| `-k` | knananuk, konvite, krioulu |
| `-ma` | manulai, markadór, mamuk |
| `-b` | berliu, bazeada, belém |
| `-p` | polimentadu, penalidade, pandang |
| `-l` | leburema, livru, lollipop |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-a` | bazeada, ispánia, leburema |
| `-u` | polimentadu, berliu, impulsu |
| `-n` | metan, gestaun, union |
| `-e` | penalidade, opole, konvite |
| `-s` | simples, sukumatias, prepirenéus |
| `-un` | gestaun, turkomenistaun, kirgizistaun |
| `-o` | agosto, bailoro, pelo |
| `-ia` | ispánia, sekundária, podlakia |

### 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 |
|------|----------|------------------|----------|
| `asau` | 1.75x | 24 contexts | sasau, asaun, rasaun |
| `ente` | 1.68x | 26 contexts | enter, sente, gente |
| `ment` | 1.65x | 22 contexts | mental, mentál, aumentu |
| `aran` | 1.59x | 23 contexts | naran, laran, maran |
| `entu` | 1.78x | 15 contexts | eventu, bentuk, century |
| `isau` | 1.66x | 15 contexts | bisau, misaun, lisaun |
| `orma` | 1.50x | 16 contexts | forma, norma, formas |
| `idad` | 1.68x | 10 contexts | idade, cidade, sidade |
| `nist` | 1.47x | 10 contexts | ministro, amnistia, ministry |
| `ensi` | 1.40x | 11 contexts | ensinu, ensino, ensina |
| `stra` | 1.36x | 11 contexts | stray, strange, estraga |
| `istr` | 1.38x | 10 contexts | distritu, ministro, ministry |

### 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 |
|--------|--------|-----------|----------|
| `-a` | `-a` | 102 words | alexandria, américa |
| `-k` | `-a` | 96 words | kassa, kompana |
| `-p` | `-a` | 96 words | póvoa, portuguesa |
| `-k` | `-u` | 87 words | kompañeiru, kriadu |
| `-m` | `-a` | 83 words | manega, medisina |
| `-s` | `-a` | 75 words | sosa, sida |
| `-k` | `-n` | 69 words | kukun, kedan |
| `-a` | `-u` | 67 words | asesu, adversáriu |
| `-s` | `-o` | 64 words | sukucarlito, são |
| `-p` | `-n` | 59 words | pokémon, prizaun |

### 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 |
|------|-----------------|------------|------|
| listening | **`listen-i-ng`** | 7.5 | `i` |
| konstituinte | **`konstitui-n-te`** | 7.5 | `n` |
| bahalarauain | **`bahalarau-a-in`** | 7.5 | `a` |
| haturalan | **`hatur-al-an`** | 7.5 | `al` |
| tradusaun | **`tradus-a-un`** | 7.5 | `a` |
| maubaralissa | **`maubaralis-s-a`** | 7.5 | `s` |
| administrasaun | **`administra-sa-un`** | 7.5 | `sa` |
| honorável | **`honoráv-e-l`** | 7.5 | `e` |
| deskrisaun | **`deskris-a-un`** | 7.5 | `a` |
| sobrevivente | **`sobrevive-n-te`** | 7.5 | `n` |
| computing | **`comput-i-ng`** | 7.5 | `i` |
| calataiud | **`calatai-u-d`** | 7.5 | `u` |
| dokumentasuan | **`dokumentas-u-an`** | 7.5 | `u` |
| evolusaun | **`evolus-a-un`** | 7.5 | `a` |
| prehistory | **`p-re-history`** | 6.0 | `history` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Tetum 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 (4.08x) |
| N-gram | **2-gram** | Lowest perplexity (284) |
| Markov | **Context-4** | Highest predictability (97.4%) |
| 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: 2026-01-11 00:39:26*