README.md

---
language: gcr
language_name: Guianese Creole French
language_family: romance_creole
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-romance_creole
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: 4.196
  - name: best_isotropy
    type: isotropy
    value: 0.5597
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-04
---

# Guianese Creole French - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Guianese Creole French** 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** | 3.616x | 3.62 | 0.0142% | 231,792 |
| **16k** | 3.893x | 3.90 | 0.0153% | 215,302 |
| **32k** | 4.084x | 4.09 | 0.0161% | 205,238 |
| **64k** | 4.196x 🏆 | 4.20 | 0.0165% | 199,729 |

### Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

**Sample 1:** `sa roun lannen konmin ki ka koumansé oun jédi. An brèf Èvenman Fondasyon an Nésa...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁sa ▁roun ▁lannen ▁konmin ▁ki ▁ka ▁koumansé ▁oun ▁jédi . ... (+16 more)` | 26 |
| 16k | `▁sa ▁roun ▁lannen ▁konmin ▁ki ▁ka ▁koumansé ▁oun ▁jédi . ... (+16 more)` | 26 |
| 32k | `▁sa ▁roun ▁lannen ▁konmin ▁ki ▁ka ▁koumansé ▁oun ▁jédi . ... (+16 more)` | 26 |
| 64k | `▁sa ▁roun ▁lannen ▁konmin ▁ki ▁ka ▁koumansé ▁oun ▁jédi . ... (+16 more)` | 26 |

**Sample 2:** `Sa paj ka konserné lannen (MDCCCLIII an chif romen) di kalandriyé grégoryen. Évè...`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁sa ▁paj ▁ka ▁konserné ▁lannen ▁( mdccc li ii ▁an ... (+19 more)` | 29 |
| 16k | `▁sa ▁paj ▁ka ▁konserné ▁lannen ▁( mdcccli ii ▁an ▁chif ... (+18 more)` | 28 |
| 32k | `▁sa ▁paj ▁ka ▁konserné ▁lannen ▁( mdcccli ii ▁an ▁chif ... (+18 more)` | 28 |
| 64k | `▁sa ▁paj ▁ka ▁konserné ▁lannen ▁( mdcccliii ▁an ▁chif ▁romen ... (+17 more)` | 27 |

**Sample 3:** `Jwiyè sa sètyèm mwa di sé kalandriyé grégoryen é julyen.`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁jwiyè ▁sa ▁sèt yèm ▁mwa ▁di ▁sé ▁kalandriyé ▁grégoryen ▁é ... (+2 more)` | 12 |
| 16k | `▁jwiyè ▁sa ▁sèt yèm ▁mwa ▁di ▁sé ▁kalandriyé ▁grégoryen ▁é ... (+2 more)` | 12 |
| 32k | `▁jwiyè ▁sa ▁sètyèm ▁mwa ▁di ▁sé ▁kalandriyé ▁grégoryen ▁é ▁julyen ... (+1 more)` | 11 |
| 64k | `▁jwiyè ▁sa ▁sètyèm ▁mwa ▁di ▁sé ▁kalandriyé ▁grégoryen ▁é ▁julyen ... (+1 more)` | 11 |


### Key Findings

- **Best Compression:** 64k achieves 4.196x compression
- **Lowest UNK Rate:** 8k with 0.0142% 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 | 3,029 | 11.56 | 9,227 | 28.5% | 56.4% |
| **2-gram** | Subword | 255 🏆 | 7.99 | 1,903 | 67.3% | 99.5% |
| **3-gram** | Word | 4,466 | 12.12 | 10,898 | 22.8% | 46.8% |
| **3-gram** | Subword | 1,835 | 10.84 | 13,061 | 32.5% | 73.4% |
| **4-gram** | Word | 4,711 | 12.20 | 12,874 | 25.9% | 45.1% |
| **4-gram** | Subword | 8,432 | 13.04 | 56,722 | 17.5% | 45.2% |
| **5-gram** | Word | 2,063 | 11.01 | 6,395 | 34.0% | 58.7% |
| **5-gram** | Subword | 22,948 | 14.49 | 115,386 | 11.3% | 31.7% |

### Top 5 N-grams by Size

**2-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a di` | 4,207 |
| 2 | `ki ka` | 2,586 |
| 3 | `ké référans` | 2,028 |
| 4 | `nòt ké` | 1,973 |
| 5 | `an di` | 1,857 |

**3-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nòt ké référans` | 1,972 |
| 2 | `ké référans lyen` | 972 |
| 3 | `référans wè osi` | 868 |
| 4 | `ké référans wè` | 867 |
| 5 | `référans lyen ègstèrn` | 799 |

**4-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nòt ké référans lyen` | 968 |
| 2 | `ké référans wè osi` | 867 |
| 3 | `nòt ké référans wè` | 853 |
| 4 | `ké référans lyen ègstèrn` | 799 |
| 5 | `lannen di kalandriyé julyen` | 520 |

**5-grams (Word):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `nòt ké référans wè osi` | 853 |
| 2 | `nòt ké référans lyen ègstèrn` | 795 |
| 3 | `lannen di kalandriyé julyen évènman` | 517 |
| 4 | `ka konsèrné lannen di kalandriyé` | 395 |
| 5 | `sa paj ka konsèrné lannen` | 393 |

**2-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `a n` | 67,633 |
| 2 | `n _` | 55,269 |
| 3 | `i _` | 51,326 |
| 4 | `_ k` | 47,854 |
| 5 | `_ d` | 45,738 |

**3-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ d i` | 30,724 |
| 2 | `d i _` | 27,985 |
| 3 | `a n _` | 26,039 |
| 4 | `_ k a` | 17,152 |
| 5 | `k a _` | 14,069 |

**4-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `_ d i _` | 27,442 |
| 2 | `_ k a _` | 13,407 |
| 3 | `o u n _` | 7,718 |
| 4 | `_ k i _` | 7,658 |
| 5 | `n _ d i` | 7,545 |

**5-grams (Subword):**

| Rank | N-gram | Count |
|------|--------|-------|
| 1 | `n _ d i _` | 7,037 |
| 2 | `a _ d i _` | 5,750 |
| 3 | `_ r o u n` | 5,587 |
| 4 | `r o u n _` | 5,388 |
| 5 | `_ d i _ l` | 4,647 |


### Key Findings

- **Best Perplexity:** 2-gram (subword) with 255
- **Entropy Trend:** Decreases with larger n-grams (more predictable)
- **Coverage:** Top-1000 patterns cover ~32% 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 | 0.8391 | 1.789 | 5.23 | 30,406 | 16.1% |
| **1** | Subword | 0.9390 | 1.917 | 6.16 | 905 | 6.1% |
| **2** | Word | 0.3036 | 1.234 | 1.73 | 158,744 | 69.6% |
| **2** | Subword | 0.8442 | 1.795 | 4.80 | 5,576 | 15.6% |
| **3** | Word | 0.1055 | 1.076 | 1.18 | 274,282 | 89.5% |
| **3** | Subword | 0.7809 | 1.718 | 3.67 | 26,754 | 21.9% |
| **4** | Word | 0.0330 🏆 | 1.023 | 1.05 | 322,219 | 96.7% |
| **4** | Subword | 0.5879 | 1.503 | 2.44 | 98,054 | 41.2% |

### Generated Text Samples (Word-based)

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

**Context Size 1:**

1. `di oryan 22 mars charles v ka enpozé à léchèl planétèr réchofman an chin i ka`
2. `a sa briga dérivé dé létazini di arabi saoudit oun dimanch évènman 26 janvyé trété sigré`
3. `ka kité antioche é ka dékouvri kouril taywann koré an di roun pis fika itilizé sé`

**Context Size 2:**

1. `a di koumin ké tout fòrm di transfè d énèrji mékanik kou pronmyé édikatò di lachin aprè`
2. `ki ka réponn à dé tanpératir ki ka enkli ou solidarité akordé sa varyab ka provini di`
3. `ké référans lyen ègstèrn en julián gil sou imdb es sit julián gil né 13 jen ka`

**Context Size 3:**

1. `nòt ké référans lyen ègstèrn en julián gil sou imdb es sit julián gil`
2. `ké référans lyen ègstèrn wè osi`
3. `ké référans wè osi bibliografi artik konèks istwè di listwè natirèl mizé ya dé lar dékoratif mizé ya`

**Context Size 4:**

1. `nòt ké référans lyen ègstèrn en julián gil sou imdb es sit julián gil`
2. `nòt ké référans wè osi di lagwiyann`
3. `di kalandriyé julyen évènman 9 janvyé gérard di bourgogn fitir nicolas ii ka divini lévèk a difloren...`


### Generated Text Samples (Subword-based)

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

**Context Size 1:**

1. `_é-ashakagra_ano`
2. `ashi_di_dini_pan`
3. `n.)_g_-an_bav_dé`

**Context Size 2:**

1. `an_d'l_azyen_mat_`
2. `n_tanséyonm_:_lis`
3. `i_sa_di_lòt_ki_ké`

**Context Size 3:**

1. `_di_mochellonngleb`
2. `di_roun_lagrik_é_k`
3. `an_kataï_atlannèt_`

**Context Size 4:**

1. `_di_jan_»,_oun_mili`
2. `_ka_di_nò)_xie_syèk`
3. `oun_véyé_térès_ki_e`


### Key Findings

- **Best Predictability:** Context-4 (word) with 96.7% predictability
- **Branching Factor:** Decreases with context size (more deterministic)
- **Memory Trade-off:** Larger contexts require more storage (98,054 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 | 13,710 |
| Total Tokens | 351,658 |
| Mean Frequency | 25.65 |
| Median Frequency | 4 |
| Frequency Std Dev | 349.69 |

### Most Common Words

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | di | 27,630 |
| 2 | a | 14,106 |
| 3 | ka | 13,462 |
| 4 | an | 11,986 |
| 5 | sa | 7,807 |
| 6 | ki | 7,763 |
| 7 | ké | 6,554 |
| 8 | roun | 5,399 |
| 9 | dé | 5,226 |
| 10 | é | 5,096 |

### Least Common Words (from vocabulary)

| Rank | Word | Frequency |
|------|------|-----------|
| 1 | chemin | 2 |
| 2 | bassières | 2 |
| 3 | présidan | 2 |
| 4 | penal | 2 |
| 5 | siprèm | 2 |
| 6 | kasasyon | 2 |
| 7 | lanné | 2 |
| 8 | tala | 2 |
| 9 | una | 2 |
| 10 | feltrinelli | 2 |

### Zipf's Law Analysis

| Metric | Value |
|--------|-------|
| Zipf Coefficient | 1.1491 |
| R² (Goodness of Fit) | 0.988430 |
| Adherence Quality | **excellent** |

### Coverage Analysis

| Top N Words | Coverage |
|-------------|----------|
| Top 100 | 53.1% |
| Top 1,000 | 77.3% |
| Top 5,000 | 92.9% |
| Top 10,000 | 97.9% |

### Key Findings

- **Zipf Compliance:** R²=0.9884 indicates excellent adherence to Zipf's law
- **High Frequency Dominance:** Top 100 words cover 53.1% of corpus
- **Long Tail:** 3,710 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 | 0.5597 🏆 | 0.4098 | N/A | N/A |
| **mono_64d** | 64 | 0.4951 | 0.3631 | N/A | N/A |
| **mono_128d** | 128 | 0.0393 | 0.4011 | N/A | N/A |
| **aligned_32d** | 32 | 0.5597 | 0.4129 | 0.0280 | 0.1820 |
| **aligned_64d** | 64 | 0.4951 | 0.3678 | 0.0120 | 0.1240 |
| **aligned_128d** | 128 | 0.0393 | 0.3973 | 0.0480 | 0.2300 |

### Key Findings

- **Best Isotropy:** mono_32d with 0.5597 (more uniform distribution)
- **Semantic Density:** Average pairwise similarity of 0.3920. Lower values indicate better semantic separation.
- **Alignment Quality:** Aligned models achieve up to 4.8% 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 | **5.000** | High morphological productivity | Reliable analysis |
| Idiomaticity Gap | **0.850** | 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 |
|--------|----------|
| `-ko` | konplété, kontajyéz, komèstib |
| `-la` | lar, lagèr, lafyèv |
| `-pr` | prénon, proche, provizwè |

#### Productive Suffixes
| Suffix | Examples |
|--------|----------|
| `-n` | enskripsyon, gradjan, dann |
| `-on` | enskripsyon, sirpopilasyon, nègmaron |
| `-an` | gradjan, aménajman, khorasan |
| `-yon` | enskripsyon, sirpopilasyon, lédikasyon |
| `-syon` | enskripsyon, sirpopilasyon, lédikasyon |
| `-en` | osyéannyen, éropéyen, rèstren |
| `-man` | aménajman, dégajman, pannanman |
| `-ik` | jéyografik, yonik, adriyatik |

### 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 |
|------|----------|------------------|----------|
| `asyo` | 1.69x | 20 contexts | pasyon, kasyon, nasyon |
| `éran` | 1.66x | 19 contexts | koéran, adéran, opérann |
| `isyo` | 1.57x | 22 contexts | misyon, sisyon, fisyon |
| `arti` | 1.45x | 21 contexts | artis, artik, parti |
| `nman` | 1.33x | 22 contexts | ronman, anmann, manman |
| `lann` | 1.30x | 23 contexts | lanné, glann, lanng |
| `konp` | 1.52x | 12 contexts | konpa, konpri, konpak |
| `nnan` | 1.42x | 14 contexts | annan, yunnan, pannan |
| `inis` | 1.35x | 16 contexts | tinis, minis, inisyé |
| `féra` | 1.66x | 9 contexts | diféran, diférans, préférab |
| `anna` | 1.31x | 17 contexts | annam, annan, kanna |
| `kons` | 1.36x | 15 contexts | konsa, konsou, konsèp |

### 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` | 88 words | kominéman, konténan |
| `-pr` | `-n` | 57 words | prentan, profon |
| `-ko` | `-on` | 42 words | kouminikasyon, konsantrasyon |
| `-ko` | `-yon` | 41 words | kouminikasyon, konsantrasyon |
| `-ko` | `-syon` | 37 words | kouminikasyon, konsantrasyon |
| `-la` | `-n` | 32 words | lannimasyon, lajan |
| `-pr` | `-on` | 30 words | profon, prronmilgasyon |
| `-ko` | `-an` | 27 words | kominéman, konténan |
| `-pr` | `-yon` | 27 words | prronmilgasyon, protègsyon |
| `-pr` | `-syon` | 27 words | prronmilgasyon, protègsyon |

### 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 |
|------|-----------------|------------|------|
| pannanman | **`pann-an-man`** | 6.0 | `pann` |
| jéyografikman | **`jéyograf-ik-man`** | 6.0 | `jéyograf` |
| réglémanté | **`réglé-man-té`** | 6.0 | `réglé` |
| èstrenmman | **`èstrenm-man`** | 4.5 | `èstrenm` |
| konstriksyon | **`ko-nstr-ik-syon`** | 4.5 | `nstr` |
| parsyèlman | **`parsyèl-man`** | 4.5 | `parsyèl` |
| gwiyannan | **`gwiyann-an`** | 4.5 | `gwiyann` |
| sèrtennman | **`sèrtenn-man`** | 4.5 | `sèrtenn` |
| paralèlman | **`paralèl-man`** | 4.5 | `paralèl` |
| disansyon | **`disan-syon`** | 4.5 | `disan` |
| étrwatman | **`étrwat-man`** | 4.5 | `étrwat` |
| lagwadloup | **`la-gwadloup`** | 4.5 | `gwadloup` |
| difisilman | **`difisil-man`** | 4.5 | `difisil` |
| nouvèlman | **`nouvèl-man`** | 4.5 | `nouvèl` |
| tanporèrman | **`tanporèr-man`** | 4.5 | `tanporèr` |

### 6.6 Linguistic Interpretation

> **Automated Insight:**
The language Guianese Creole French 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 | **64k BPE** | Best compression (4.20x) |
| N-gram | **2-gram** | Lowest perplexity (255) |
| Markov | **Context-4** | Highest predictability (96.7%) |
| 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 15:07:18*