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README.md

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+---
+language: frp
+language_name: Arpitan
+language_family: romance_galloitalic
+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_galloitalic
+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: 4.432
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8533
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Arpitan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Arpitan** 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** | 3.752x | 3.76 | 0.1908% | 159,349 |
+| **16k** | 4.028x | 4.03 | 0.2048% | 148,425 |
+| **32k** | 4.260x | 4.27 | 0.2166% | 140,346 |
+| **64k** | 4.432x 🏆 | 4.44 | 0.2254% | 134,893 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `David Charvet (Liyon, 15 de mê est un actor francês d'origina arpetana. Charvet,...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁david ▁char vet ▁( liyon , ▁ 1 5 ▁de ... (+18 more)` | 28 |
+| 16k | `▁david ▁charvet ▁( liyon , ▁ 1 5 ▁de ▁mê ... (+15 more)` | 25 |
+| 32k | `▁david ▁charvet ▁( liyon , ▁ 1 5 ▁de ▁mê ... (+15 more)` | 25 |
+| 64k | `▁david ▁charvet ▁( liyon , ▁ 1 5 ▁de ▁mê ... (+15 more)` | 25 |
+
+**Sample 2:** `Cort-Mayor, tot-pariér Cort-Màyœr (Croméyeui en vâldoten), est na comena de la V...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁cort - mayor , ▁tot - pariér ▁cort - m ... (+28 more)` | 38 |
+| 16k | `▁cort - mayor , ▁tot - pariér ▁cort - m ... (+27 more)` | 37 |
+| 32k | `▁cort - mayor , ▁tot - pariér ▁cort - m ... (+26 more)` | 36 |
+| 64k | `▁cort - mayor , ▁tot - pariér ▁cort - màyœr ... (+21 more)` | 31 |
+
+**Sample 3:** `Antê est na comena de la Vâl d’Aoûta. de la Vâl d’Aoûta`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ant ê ▁est ▁na ▁comena ▁de ▁la ▁vâl ▁d ’ ... (+8 more)` | 18 |
+| 16k | `▁ant ê ▁est ▁na ▁comena ▁de ▁la ▁vâl ▁d ’ ... (+8 more)` | 18 |
+| 32k | `▁antê ▁est ▁na ▁comena ▁de ▁la ▁vâl ▁d ’ aoûta ... (+7 more)` | 17 |
+| 64k | `▁antê ▁est ▁na ▁comena ▁de ▁la ▁vâl ▁d ’ aoûta ... (+7 more)` | 17 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.432x compression
+- **Lowest UNK Rate:** 8k with 0.1908% 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 | 3,875 | 11.92 | 12,862 | 22.5% | 56.7% |
+| **2-gram** | Subword | 300 🏆 | 8.23 | 2,633 | 63.9% | 99.1% |
+| **3-gram** | Word | 7,576 | 12.89 | 21,319 | 15.1% | 45.6% |
+| **3-gram** | Subword | 2,356 | 11.20 | 19,570 | 26.8% | 69.6% |
+| **4-gram** | Word | 12,950 | 13.66 | 38,195 | 12.2% | 39.0% |
+| **4-gram** | Subword | 10,867 | 13.41 | 86,875 | 14.5% | 41.7% |
+| **5-gram** | Word | 10,775 | 13.40 | 31,168 | 12.8% | 41.5% |
+| **5-gram** | Subword | 28,788 | 14.81 | 185,811 | 9.5% | 30.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la` | 7,927 |
+| 2 | `de l` | 4,843 |
+| 3 | `en francês` | 2,035 |
+| 4 | `est un` | 1,537 |
+| 5 | `est na` | 1,506 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `notes et rèferences` | 921 |
+| 2 | `lims de defôr` | 887 |
+| 3 | `et rèferences notes` | 838 |
+| 4 | `que sè trôve` | 823 |
+| 5 | `du calendriér grègorien` | 787 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `notes et rèferences notes` | 838 |
+| 2 | `que sè trôve dens` | 676 |
+| 3 | `sè trôve dens lo` | 616 |
+| 4 | `règ ion ôvèrgne rôno` | 598 |
+| 5 | `trôve dens lo dèpartament` | 594 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `que sè trôve dens lo` | 610 |
+| 2 | `sè trôve dens lo dèpartament` | 594 |
+| 3 | `règ ion ôvèrgne rôno ârpes` | 583 |
+| 4 | `en règ ion ôvèrgne rôno` | 573 |
+| 5 | `trôve dens lo dèpartament de` | 541 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d` | 93,461 |
+| 2 | `e _` | 89,908 |
+| 3 | `s _` | 81,969 |
+| 4 | `a _` | 81,049 |
+| 5 | `_ l` | 70,807 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 53,565 |
+| 2 | `d e _` | 42,218 |
+| 3 | `e s _` | 30,241 |
+| 4 | `l a _` | 24,855 |
+| 5 | `_ l a` | 20,309 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 40,630 |
+| 2 | `_ l a _` | 18,775 |
+| 3 | `d e _ l` | 16,081 |
+| 4 | `_ e t _` | 16,050 |
+| 5 | `_ d u _` | 12,274 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 15,995 |
+| 2 | `_ e s t _` | 8,935 |
+| 3 | `e _ l a _` | 8,731 |
+| 4 | `d e _ l a` | 7,987 |
+| 5 | `a _ d e _` | 7,692 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 300
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% 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 | 0.6833 | 1.606 | 3.88 | 60,657 | 31.7% |
+| **1** | Subword | 1.0805 | 2.115 | 8.29 | 778 | 0.0% |
+| **2** | Word | 0.2270 | 1.170 | 1.54 | 234,074 | 77.3% |
+| **2** | Subword | 0.9698 | 1.959 | 5.76 | 6,449 | 3.0% |
+| **3** | Word | 0.0984 | 1.071 | 1.18 | 358,473 | 90.2% |
+| **3** | Subword | 0.8264 | 1.773 | 3.96 | 37,109 | 17.4% |
+| **4** | Word | 0.0495 🏆 | 1.035 | 1.08 | 419,570 | 95.1% |
+| **4** | Subword | 0.6064 | 1.522 | 2.56 | 146,964 | 39.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de la ples èpatâs dens lo seto de les alemagnes ôtriche contre pendent sa m m`
+2. `la ferveur d or de l en règ ionalisto de l endrêt vocabulèro rèferences notes et`
+3. `et dictionnaire français liyon nèssences giuseppe mariano egaña universidad de vôd dês lo seto patoi...`
+
+**Context Size 2:**
+
+1. `de la rèpublica francêsa entre lo v continu et le r roulâ at étâ remplaciê per le`
+2. `de l alsace iwar werlen matthias grünert èd italica raetica gallica studia linguarum litterarum arti...`
+3. `en francês est na comena francêsa et arpetana de banye èthendiu per piérro duplê lo jouventua calço`
+
+**Context Size 3:**
+
+1. `notes et rèferences notes vocabulèro rèferences de l en de l en de tant qu en môrts roxelane`
+2. `et rèferences notes rèferences de la savouè francês de l isera les doux dèrriérs kilomètros ont uvèr...`
+3. `lims de defôr âjo de france`
+
+**Context Size 4:**
+
+1. `notes et rèferences notes rèferences de la savouè d avâl arpetan de sports d hivèrn du musê dôfenen ...`
+2. `que sè trôve dens lo dèpartament de la lêre en règ ion ôvèrgne rôno ârpes los habitents du velâjo`
+3. `sè trôve dens lo dèpartament de la lêre en règ ion borgogne franche comtât los habitents du velâjo s...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dir_dust_di,_t,`
+2. `et_ubolesatre_p.`
+3. `at._élyâyarçanâl`
+
+**Context Size 2:**
+
+1. `_des_de_de_39-64_`
+2. `e_nonqu’es_procal`
+3. `s_véls_devartiérs`
+
+**Context Size 3:**
+
+1. `_de_du_chârmetllar`
+2. `de_loirenciacionâr`
+3. `es_ont_de_la_vencr`
+
+**Context Size 4:**
+
+1. `_de_la_barmacopo_de`
+2. `_la_vela_des_vocabu`
+3. `de_la_bourk_»_adv_d`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (146,964 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 | 25,646 |
+| Total Tokens | 594,200 |
+| Mean Frequency | 23.17 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 373.02 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 41,301 |
+| 2 | la | 20,109 |
+| 3 | et | 16,321 |
+| 4 | en | 13,958 |
+| 5 | lo | 13,046 |
+| 6 | du | 12,396 |
+| 7 | l | 11,637 |
+| 8 | est | 9,993 |
+| 9 | d | 9,696 |
+| 10 | a | 6,854 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | gewesen | 2 |
+| 2 | müh | 2 |
+| 3 | professors | 2 |
+| 4 | seiant | 2 |
+| 5 | hoch | 2 |
+| 6 | sich | 2 |
+| 7 | too | 2 |
+| 8 | pereat | 2 |
+| 9 | pèreisset | 2 |
+| 10 | rêpond | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1126 |
+| R² (Goodness of Fit) | 0.996613 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.2% |
+| Top 1,000 | 74.5% |
+| Top 5,000 | 88.1% |
+| Top 10,000 | 93.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9966 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.2% of corpus
+- **Long Tail:** 15,646 words needed for remaining 6.7% 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 | 0.8533 | 0.3620 | N/A | N/A |
+| **mono_64d** | 64 | 0.7080 | 0.3125 | N/A | N/A |
+| **mono_128d** | 128 | 0.2790 | 0.2979 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8533 🏆 | 0.3573 | 0.0340 | 0.2060 |
+| **aligned_64d** | 64 | 0.7080 | 0.3022 | 0.0800 | 0.2980 |
+| **aligned_128d** | 128 | 0.2790 | 0.2962 | 0.1260 | 0.4020 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8533 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3213. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 12.6% 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 | **0.381** | 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 |
+|--------|----------|
+| `-co` | cornèlye, columbân, compto |
+| `-ch` | chouèséssont, chesalles, chasper |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | besièrs, mans, chesalles |
+| `-es` | chesalles, romanes, sassenajouèses |
+| `-on` | frutificacion, enstitucion, différenciation |
+| `-nt` | chouèséssont, variant, fassévont |
+| `-ns` | mans, dragons, pontesans |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ranc` | 1.61x | 40 contexts | franc, rancé, drance |
+| `cion` | 1.68x | 33 contexts | accion, nocion, nacion |
+| `etan` | 2.23x | 12 contexts | gaetano, arpetan, erpetan |
+| `anta` | 1.82x | 22 contexts | santa, antan, tanta |
+| `peta` | 2.23x | 11 contexts | petar, arpetan, erpetan |
+| `acio` | 1.82x | 20 contexts | nacion, lacion, stacion |
+| `avou` | 1.81x | 17 contexts | avoué, avouë, avouì |
+| `uiss` | 2.18x | 10 contexts | buisse, suisso, suisse |
+| `isto` | 1.53x | 26 contexts | visto, istos, cristo |
+| `iant` | 1.75x | 16 contexts | diant, aviant, étiant |
+| `rpet` | 2.23x | 8 contexts | arpetan, arpette, erpetan |
+| `omen` | 1.56x | 19 contexts | women, romen, comenê |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-co` | `-s` | 69 words | concèpcions, conches |
+| `-ch` | `-s` | 40 words | chexbres, chevâls |
+| `-co` | `-es` | 26 words | conches, comenes |
+| `-co` | `-on` | 22 words | comparèson, coalicion |
+| `-co` | `-nt` | 19 words | confondont, corent |
+| `-ch` | `-es` | 18 words | chexbres, chasèles |
+| `-co` | `-ns` | 13 words | concèpcions, cotens |
+| `-ch` | `-on` | 10 words | chambllon, chillon |
+| `-ch` | `-nt` | 5 words | chavonont, chantont |
+| `-ch` | `-ns` | 4 words | chens, chaneins |
+
+### 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 |
+|------|-----------------|------------|------|
+| trentines | **`trentin-es`** | 4.5 | `trentin` |
+| neuchâteloises | **`neuchâtelois-es`** | 4.5 | `neuchâtelois` |
+| reprèsentent | **`reprèsente-nt`** | 4.5 | `reprèsente` |
+| vôdouèses | **`vôdouès-es`** | 4.5 | `vôdouès` |
+| dèssèrtes | **`dèssèrt-es`** | 4.5 | `dèssèrt` |
+| grenoblouèses | **`grenoblouès-es`** | 4.5 | `grenoblouès` |
+| véselyinouèses | **`véselyinouès-es`** | 4.5 | `véselyinouès` |
+| appellent | **`appelle-nt`** | 4.5 | `appelle` |
+| charentes | **`ch-arent-es`** | 3.0 | `arent` |
+| conclusion | **`co-nclusi-on`** | 3.0 | `nclusi` |
+| comparèsons | **`co-mparèso-ns`** | 3.0 | `mparèso` |
+| siuventes | **`siuve-nt-es`** | 3.0 | `siuve` |
+| compèticions | **`co-mpèticio-ns`** | 3.0 | `mpèticio` |
+| châtenêècrivont | **`ch-âtenêècrivo-nt`** | 3.0 | `âtenêècrivo` |
+| communities | **`co-mmuniti-es`** | 3.0 | `mmuniti` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Arpitan 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 (4.43x) |
+| N-gram | **2-gram** | Lowest perplexity (300) |
+| Markov | **Context-4** | Highest predictability (95.1%) |
+| 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 14:50:14*

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