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README.md

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+---
+language: oc
+language_name: Occitan
+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: 4.442
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7759
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Occitan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Occitan** 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** | 3.591x | 3.59 | 0.0580% | 1,039,006 |
+| **16k** | 3.939x | 3.94 | 0.0637% | 947,222 |
+| **32k** | 4.234x | 4.24 | 0.0684% | 881,100 |
+| **64k** | 4.442x 🏆 | 4.44 | 0.0718% | 839,942 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Lucas Reiner (n. es un actor e productor de cinèma american. american a Los Ange...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lu cas ▁rein er ▁( n . ▁es ▁un ▁actor ... (+11 more)` | 21 |
+| 16k | `▁lu cas ▁rein er ▁( n . ▁es ▁un ▁actor ... (+11 more)` | 21 |
+| 32k | `▁lucas ▁rein er ▁( n . ▁es ▁un ▁actor ▁e ... (+10 more)` | 20 |
+| 64k | `▁lucas ▁reiner ▁( n . ▁es ▁un ▁actor ▁e ▁productor ... (+9 more)` | 19 |
+
+**Sample 2:** `Altwis es un vilatjòt, e comuna soïssa, situat dins lo districte d'Hochdorf, e l...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁alt w is ▁es ▁un ▁vilat j òt , ▁e ... (+29 more)` | 39 |
+| 16k | `▁alt wis ▁es ▁un ▁vilat j òt , ▁e ▁comuna ... (+26 more)` | 36 |
+| 32k | `▁alt wis ▁es ▁un ▁vilatjòt , ▁e ▁comuna ▁soïssa , ... (+24 more)` | 34 |
+| 64k | `▁alt wis ▁es ▁un ▁vilatjòt , ▁e ▁comuna ▁soïssa , ... (+22 more)` | 32 |
+
+**Sample 3:** `Puebla de la Calzada es un municipi de la província espanhòla de Badajoz e de la...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pu eb la ▁de ▁la ▁cal z ada ▁es ▁un ... (+19 more)` | 29 |
+| 16k | `▁pu eb la ▁de ▁la ▁cal zada ▁es ▁un ▁municipi ... (+15 more)` | 25 |
+| 32k | `▁puebla ▁de ▁la ▁cal zada ▁es ▁un ▁municipi ▁de ▁la ... (+13 more)` | 23 |
+| 64k | `▁puebla ▁de ▁la ▁calzada ▁es ▁un ▁municipi ▁de ▁la ▁província ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.442x compression
+- **Lowest UNK Rate:** 8k with 0.0580% 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 | 40,858 | 15.32 | 382,833 | 16.0% | 32.3% |
+| **2-gram** | Subword | 256 🏆 | 8.00 | 9,724 | 69.1% | 99.1% |
+| **3-gram** | Word | 99,251 | 16.60 | 691,705 | 13.3% | 25.5% |
+| **3-gram** | Subword | 2,095 | 11.03 | 74,490 | 29.1% | 73.4% |
+| **4-gram** | Word | 144,878 | 17.14 | 1,152,073 | 14.3% | 26.4% |
+| **4-gram** | Subword | 11,826 | 13.53 | 411,965 | 14.7% | 42.1% |
+| **5-gram** | Word | 78,202 | 16.25 | 807,722 | 17.0% | 32.1% |
+| **5-gram** | Subword | 46,870 | 15.52 | 1,292,254 | 8.9% | 27.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la` | 213,501 |
+| 2 | `de l` | 104,259 |
+| 3 | `es una` | 53,804 |
+| 4 | `e la` | 52,175 |
+| 5 | `dins lo` | 51,918 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `es una comuna` | 35,541 |
+| 2 | `e monuments personalitats` | 35,308 |
+| 3 | `monuments personalitats ligadas` | 31,330 |
+| 4 | `e la region` | 31,001 |
+| 5 | `ligams extèrnes nòtas` | 30,871 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e monuments personalitats ligadas` | 31,329 |
+| 2 | `luòcs e monuments personalitats` | 29,121 |
+| 3 | `ligadas amb la comuna` | 28,401 |
+| 4 | `personalitats ligadas amb la` | 28,400 |
+| 5 | `monuments personalitats ligadas amb` | 27,977 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `personalitats ligadas amb la comuna` | 28,398 |
+| 2 | `e monuments personalitats ligadas amb` | 27,976 |
+| 3 | `monuments personalitats ligadas amb la` | 27,973 |
+| 4 | `luòcs e monuments personalitats ligadas` | 27,614 |
+| 5 | `demografia luòcs e monuments personalitats` | 27,263 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 3,234,853 |
+| 2 | `e _` | 3,172,126 |
+| 3 | `s _` | 3,063,418 |
+| 4 | `_ d` | 3,054,602 |
+| 5 | `_ l` | 2,264,276 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 2,037,812 |
+| 2 | `d e _` | 1,436,433 |
+| 3 | `_ l a` | 884,600 |
+| 4 | `l a _` | 867,260 |
+| 5 | `a s _` | 793,636 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 1,401,348 |
+| 2 | `_ l a _` | 689,470 |
+| 3 | `d e _ l` | 434,454 |
+| 4 | `i o n _` | 370,419 |
+| 5 | `a _ d e` | 370,367 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 432,360 |
+| 2 | `e _ l a _` | 285,044 |
+| 3 | `d e _ l a` | 266,977 |
+| 4 | `s _ d e _` | 254,104 |
+| 5 | `a _ d e _` | 253,709 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 256
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 0.9719 | 1.961 | 8.13 | 622,832 | 2.8% |
+| **1** | Subword | 0.8487 | 1.801 | 5.90 | 5,768 | 15.1% |
+| **2** | Word | 0.3692 | 1.292 | 2.14 | 5,057,510 | 63.1% |
+| **2** | Subword | 0.7604 | 1.694 | 4.98 | 33,998 | 24.0% |
+| **3** | Word | 0.1556 | 1.114 | 1.32 | 10,822,711 | 84.4% |
+| **3** | Subword | 0.7496 | 1.681 | 4.18 | 169,232 | 25.0% |
+| **4** | Word | 0.0609 🏆 | 1.043 | 1.10 | 14,277,493 | 93.9% |
+| **4** | Subword | 0.6863 | 1.609 | 3.33 | 707,794 | 31.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de comunas vesinas e solidaritat s auçant quitament d una comuna veire tanben ligams extèrnes nòtas`
+2. `la corona mas es l intoxicacion son concentradas de govèrn francés livre premier estudi meninosa d`
+3. `e posicion relativa istòria l entorn istòria revòlta del grand glise est attestée semble que depend`
+
+**Context Size 2:**
+
+1. `de la municipalitat qu es connectat e diferents ph es segon la definicion d un rai de`
+2. `de l arnm pòrta l anèl latin digitus annularis det de l industria unica de l union`
+3. `es una proprietat sus la luna esquèrra vinheta moïses trencant las taules de la nauta marna e`
+
+**Context Size 3:**
+
+1. `es una comuna francesa del departament de tarn e garona ligams extèrnes nòtas de gironda de la regio...`
+2. `e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas e referéncias de ...`
+3. `monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas de la nauta garona ...`
+
+**Context Size 4:**
+
+1. `e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas dels vòges`
+2. `luòcs e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas de normandi...`
+3. `ligadas amb la comuna véser tanben ligams extèrnes nòtas de normandia de la marga`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_daurenèrd'anaio`
+2. `art_deabesime,_p`
+3. `e_uzarive_se_ge_`
+
+**Context Size 2:**
+
+1. `a_doppsi_morlà_10`
+2. `e_menregièrnasist`
+3. `s_panar_mil_de_pl`
+
+**Context Size 3:**
+
+1. `_desfistòria_cap_a`
+2. `de_jacque_dismeniv`
+3. `_la_(∗)_régions_en`
+
+**Context Size 4:**
+
+1. `_de_la_grat_de_la_c`
+2. `_la_fibrairie_e_avi`
+3. `de_lieux_forcèt_l'a`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (707,794 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 | 298,767 |
+| Total Tokens | 19,561,503 |
+| Mean Frequency | 65.47 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 3567.99 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 1,412,762 |
+| 2 | la | 704,581 |
+| 3 | e | 516,061 |
+| 4 | d | 382,843 |
+| 5 | en | 367,851 |
+| 6 | lo | 364,128 |
+| 7 | l | 357,372 |
+| 8 | a | 301,072 |
+| 9 | es | 226,360 |
+| 10 | un | 196,170 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | shonkinita | 2 |
+| 2 | piròp | 2 |
+| 3 | lherzolita | 2 |
+| 4 | miéj | 2 |
+| 5 | mangiato | 2 |
+| 6 | ignaure | 2 |
+| 7 | langfors | 2 |
+| 8 | accouplés | 2 |
+| 9 | theodiscus | 2 |
+| 10 | nyamuragira | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0382 |
+| R² (Goodness of Fit) | 0.998226 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 45.3% |
+| Top 1,000 | 64.4% |
+| Top 5,000 | 78.5% |
+| Top 10,000 | 84.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9982 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 45.3% of corpus
+- **Long Tail:** 288,767 words needed for remaining 16.0% 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 | 0.7759 | 0.3605 | N/A | N/A |
+| **mono_64d** | 64 | 0.7311 | 0.2808 | N/A | N/A |
+| **mono_128d** | 128 | 0.7021 | 0.2184 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7759 🏆 | 0.3741 | 0.2480 | 0.6180 |
+| **aligned_64d** | 64 | 0.7311 | 0.2733 | 0.3600 | 0.7300 |
+| **aligned_128d** | 128 | 0.7021 | 0.2172 | 0.5080 | 0.8180 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7759 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2874. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 50.8% 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 | **0.170** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | arrecebèva, auroish, apròhe |
+| `-s` | sfrf, suris, saëns |
+| `-ma` | manqueront, mahlkirch, maçacans |
+| `-c` | colomberiis, chaohusaurus, campanhard |
+| `-b` | bièle, brixey, bartl |
+| `-m` | mcgowan, manqueront, mahlkirch |
+| `-p` | pennante, pousser, pisuerga |
+| `-ca` | campanhard, casalabriva, castelpers |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | suris, kohs, colomberiis |
+| `-a` | goja, fonologica, arrecebèva |
+| `-e` | pennante, bièle, podiosalicone |
+| `-t` | manqueront, projèct, convertissent |
+| `-n` | mcgowan, esteron, réligion |
+| `-as` | termonuclearas, taças, refractàrias |
+| `-es` | ecoulettes, vongnes, neuffontaines |
+| `-on` | esteron, réligion, diferencièron |
+
+### 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 |
+|------|----------|------------------|----------|
+| `itat` | 2.00x | 167 contexts | pitat, gitat, itata |
+| `acio` | 2.06x | 128 contexts | acion, bacio, racion |
+| `ogra` | 1.83x | 133 contexts | dogra, logran, lograr |
+| `raci` | 1.80x | 136 contexts | racim, oraci, braci |
+| `tats` | 2.06x | 67 contexts | stats, états, etats |
+| `ntre` | 1.86x | 105 contexts | antre, entre, intre |
+| `énci` | 2.13x | 49 contexts | éncia, réncia, siéncia |
+| `icio` | 1.84x | 83 contexts | licio, vicios, bricio |
+| `stra` | 1.35x | 282 contexts | stray, strat, strad |
+| `lita` | 1.67x | 94 contexts | litas, elita, clita |
+| `anbe` | 2.53x | 19 contexts | anben, tanbe, tanben |
+| `tanb` | 2.49x | 19 contexts | tanbn, tanbe, tanban |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-s` | 210 words | conreats, cippus |
+| `-a` | `-s` | 171 words | annexis, annuentes |
+| `-p` | `-s` | 168 words | palays, prébois |
+| `-s` | `-s` | 126 words | senˈtises, sevas |
+| `-c` | `-a` | 119 words | casalta, conoguda |
+| `-a` | `-a` | 101 words | abjura, abominabla |
+| `-b` | `-s` | 94 words | brindas, barangays |
+| `-c` | `-e` | 94 words | colloverge, coroe |
+| `-p` | `-a` | 91 words | partidària, plantada |
+| `-m` | `-s` | 80 words | meus, majusculas |
+
+### 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 |
+|------|-----------------|------------|------|
+| velhiment | **`velhi-me-nt`** | 7.5 | `me` |
+| hermaphroditism | **`hermaphroditi-s-m`** | 7.5 | `s` |
+| tuscaloosa | **`tuscaloo-s-a`** | 7.5 | `s` |
+| drepanocitòsi | **`drepanocitò-s-i`** | 7.5 | `s` |
+| sarrasiet | **`sarrasi-e-t`** | 7.5 | `e` |
+| acomplisca | **`acompli-s-ca`** | 7.5 | `s` |
+| daissarem | **`daissar-e-m`** | 7.5 | `e` |
+| condusent | **`condus-e-nt`** | 7.5 | `e` |
+| étroussat | **`étrous-s-at`** | 7.5 | `s` |
+| garrwanas | **`garrw-an-as`** | 7.5 | `an` |
+| prehistoria | **`p-re-historia`** | 7.5 | `historia` |
+| cerevisiae | **`cerevisi-a-e`** | 7.5 | `a` |
+| billinghurst | **`billinghur-s-t`** | 7.5 | `s` |
+| europeans | **`europe-an-s`** | 7.5 | `an` |
+| cherquesses | **`cherques-s-es`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Occitan shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.44x) |
+| N-gram | **2-gram** | Lowest perplexity (256) |
+| Markov | **Context-4** | Highest predictability (93.9%) |
+| 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 18:02:02*

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