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README.md

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+---
+language: ie
+language_name: Interlingue
+language_family: constructed_auxlang
+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-constructed_auxlang
+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: 4.092
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8056
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Interlingue - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Interlingue** 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** | 3.608x | 3.61 | 0.0821% | 148,512 |
+| **16k** | 3.803x | 3.81 | 0.0866% | 140,899 |
+| **32k** | 3.974x | 3.98 | 0.0905% | 134,848 |
+| **64k** | 4.092x 🏆 | 4.10 | 0.0932% | 130,939 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Heliconia es un village locat in Antioquia, Columbia. It have un population de h...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁he lic onia ▁es ▁un ▁village ▁locat ▁in ▁antioquia , ... (+9 more)` | 19 |
+| 16k | `▁helic onia ▁es ▁un ▁village ▁locat ▁in ▁antioquia , ▁columbia ... (+8 more)` | 18 |
+| 32k | `▁helic onia ▁es ▁un ▁village ▁locat ▁in ▁antioquia , ▁columbia ... (+8 more)` | 18 |
+| 64k | `▁heliconia ▁es ▁un ▁village ▁locat ▁in ▁antioquia , ▁columbia . ... (+7 more)` | 17 |
+
+**Sample 2:** `Herramélluri es un municipie situat in li comunité autonom de La Rioja, Hispania...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁her ram é ll uri ▁es ▁un ▁municipie ▁situat ▁in ... (+19 more)` | 29 |
+| 16k | `▁her ram é ll uri ▁es ▁un ▁municipie ▁situat ▁in ... (+19 more)` | 29 |
+| 32k | `▁her ram é ll uri ▁es ▁un ▁municipie ▁situat ▁in ... (+19 more)` | 29 |
+| 64k | `▁her ram é ll uri ▁es ▁un ▁municipie ▁situat ▁in ... (+19 more)` | 29 |
+
+**Sample 3:** `Extremaduran es un lingue romanic parlat in li comunité autonom hispan de Extrem...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁extrem ad ur an ▁es ▁un ▁lingue ▁romanic ▁parlat ▁in ... (+10 more)` | 20 |
+| 16k | `▁extremad ur an ▁es ▁un ▁lingue ▁romanic ▁parlat ▁in ▁li ... (+8 more)` | 18 |
+| 32k | `▁extremad uran ▁es ▁un ▁lingue ▁romanic ▁parlat ▁in ▁li ▁comunité ... (+6 more)` | 16 |
+| 64k | `▁extremaduran ▁es ▁un ▁lingue ▁romanic ▁parlat ▁in ▁li ▁comunité ▁autonom ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.092x compression
+- **Lowest UNK Rate:** 8k with 0.0821% 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 | 2,631 | 11.36 | 21,646 | 36.0% | 64.2% |
+| **2-gram** | Subword | 241 🏆 | 7.91 | 3,184 | 71.3% | 99.2% |
+| **3-gram** | Word | 4,146 | 12.02 | 34,445 | 32.6% | 58.5% |
+| **3-gram** | Subword | 1,702 | 10.73 | 22,847 | 31.9% | 77.0% |
+| **4-gram** | Word | 6,878 | 12.75 | 62,031 | 30.3% | 52.0% |
+| **4-gram** | Subword | 7,188 | 12.81 | 108,171 | 20.2% | 51.9% |
+| **5-gram** | Word | 5,337 | 12.38 | 50,418 | 33.2% | 55.0% |
+| **5-gram** | Subword | 18,128 | 14.15 | 240,674 | 15.2% | 41.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `in li` | 30,772 |
+| 2 | `es un` | 12,459 |
+| 3 | `provincia de` | 11,763 |
+| 4 | `situat in` | 8,192 |
+| 5 | `have un` | 7,384 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `situat in li` | 7,870 |
+| 2 | `it have un` | 6,504 |
+| 3 | `un population de` | 6,452 |
+| 4 | `have un population` | 6,414 |
+| 5 | `in li comunité` | 6,340 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `have un population de` | 6,414 |
+| 2 | `it have un population` | 6,405 |
+| 3 | `hispania it have un` | 6,047 |
+| 4 | `in li comunité autonom` | 5,959 |
+| 5 | `li comunité autonom de` | 5,958 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `it have un population de` | 6,405 |
+| 2 | `hispania it have un population` | 6,047 |
+| 3 | `in li comunité autonom de` | 5,958 |
+| 4 | `situat in li provincia de` | 5,691 |
+| 5 | `un municipie situat in li` | 5,426 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 215,243 |
+| 2 | `d e` | 150,699 |
+| 3 | `_ d` | 138,972 |
+| 4 | `n _` | 138,753 |
+| 5 | `l i` | 117,810 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 121,852 |
+| 2 | `_ l i` | 86,236 |
+| 3 | `l i _` | 81,835 |
+| 4 | `d e _` | 81,137 |
+| 5 | `_ i n` | 65,595 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l i _` | 79,051 |
+| 2 | `_ d e _` | 73,928 |
+| 3 | `_ i n _` | 48,622 |
+| 4 | `n _ l i` | 34,069 |
+| 5 | `_ d e l` | 32,612 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _ l i _` | 33,170 |
+| 2 | `_ d e l _` | 32,443 |
+| 3 | `_ i n _ l` | 31,429 |
+| 4 | `i n _ l i` | 31,004 |
+| 5 | `a t i o n` | 18,523 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 241
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~41% 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 | 0.7892 | 1.728 | 4.90 | 74,694 | 21.1% |
+| **1** | Subword | 1.0068 | 2.009 | 7.54 | 1,050 | 0.0% |
+| **2** | Word | 0.2700 | 1.206 | 1.67 | 364,659 | 73.0% |
+| **2** | Subword | 0.9485 | 1.930 | 5.61 | 7,906 | 5.2% |
+| **3** | Word | 0.1159 | 1.084 | 1.23 | 604,985 | 88.4% |
+| **3** | Subword | 0.8121 | 1.756 | 4.04 | 44,321 | 18.8% |
+| **4** | Word | 0.0587 🏆 | 1.042 | 1.11 | 737,025 | 94.1% |
+| **4** | Subword | 0.6591 | 1.579 | 2.74 | 178,838 | 34.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `li sud ossetia con li comunité autonom de marie agnes sapper comensat interessar les accessibil in`
+2. `de wta championships tournament mvp award katharina stark watzinger demissionat li 8 im de bremen 1`
+3. `in li max grand cité esset presidente del sale lago inari es nha trang li sobranie`
+
+**Context Size 2:**
+
+1. `in li nord de germania li subdistrict have 131 662 habitantes e un area de 124 quadrat`
+2. `es un actor de dania por li electiones parlamentari ye li 30 im de julí in dallas`
+3. `provincia de valladolid in li marte ella fundat li partise del economic e political cariera ivan bra...`
+
+**Context Size 3:**
+
+1. `situat in li sud de germania in li parlament del quinesim republica consiste ex du singul discipline...`
+2. `it have un population de habitantes location e geografie historie del provincia de salamanca in li c...`
+3. `un population de habitantes del provincia de segovia in li comunité autonom de andalusia hispania it...`
+
+**Context Size 4:**
+
+1. `have un population de habitantes location e geografie historie del provincia de málaga todos zurdos`
+2. `it have un population de habitantes location e geografie historie del provincia de teruel liste de m...`
+3. `hispania it have un population de inhabitantes de la rioja`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_"gria_a_tre_de_`
+2. `enuancipisse_und`
+3. `iatkmopopovipxte`
+
+**Context Size 2:**
+
+1. `e_popul,_hectonal`
+2. `del_revivego,_il_`
+3. `_de_un_popubeia_s`
+
+**Context Size 3:**
+
+1. `_del_e_partise_neč`
+2. `_li_ciuda_un_heimn`
+3. `li_artipp_li_antes`
+
+**Context Size 4:**
+
+1. `_li_comunité_autono`
+2. `_de_saxonia,_nomía_`
+3. `_in_li_cupremie_li_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (178,838 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 | 33,220 |
+| Total Tokens | 1,149,726 |
+| Mean Frequency | 34.61 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 774.72 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | li | 80,769 |
+| 2 | de | 74,091 |
+| 3 | in | 49,037 |
+| 4 | del | 32,477 |
+| 5 | e | 32,108 |
+| 6 | un | 31,151 |
+| 7 | es | 28,327 |
+| 8 | provincia | 12,234 |
+| 9 | it | 11,607 |
+| 10 | have | 11,493 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ollscoil | 2 |
+| 2 | gur | 2 |
+| 3 | idirnáisiúnta | 2 |
+| 4 | iberoamericana | 2 |
+| 5 | caribican | 2 |
+| 6 | philipsburg | 2 |
+| 7 | marten | 2 |
+| 8 | eurohandball | 2 |
+| 9 | neckarsulm | 2 |
+| 10 | hohm | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0974 |
+| R² (Goodness of Fit) | 0.997325 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 54.3% |
+| Top 1,000 | 77.4% |
+| Top 5,000 | 88.7% |
+| Top 10,000 | 93.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9973 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 54.3% of corpus
+- **Long Tail:** 23,220 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 | 0.8056 | 0.3250 | N/A | N/A |
+| **mono_64d** | 64 | 0.6386 | 0.2829 | N/A | N/A |
+| **mono_128d** | 128 | 0.2078 | 0.2618 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8056 🏆 | 0.3257 | 0.0960 | 0.3840 |
+| **aligned_64d** | 64 | 0.6386 | 0.2764 | 0.1440 | 0.4740 |
+| **aligned_128d** | 128 | 0.2078 | 0.2627 | 0.1760 | 0.5200 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8056 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2891. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 17.6% 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 | **0.341** | 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 |
+|--------|----------|
+| `-s` | stéphane, selk, summarium |
+| `-a` | attaccat, ambiciosi, aguiló |
+| `-b` | believe, biddle, baqir |
+| `-c` | commercial, chief, cs |
+| `-m` | marbode, messages, mataró |
+| `-p` | punat, psichic, politiques |
+| `-ma` | marbode, mataró, mahesh |
+| `-d` | delmonte, dvořák, dunărea |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | klaas, fields, rames |
+| `-e` | believe, stéphane, órbite |
+| `-n` | eisleben, surprisantmen, precision |
+| `-a` | radiologia, nirvana, española |
+| `-es` | rames, messages, politiques |
+| `-t` | attaccat, punat, influent |
+| `-on` | precision, persecution, répartition |
+| `-r` | sauber, slender, gostivar |
+
+### 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 |
+|------|----------|------------------|----------|
+| `atio` | 1.73x | 46 contexts | nation, cation, oratio |
+| `tion` | 1.66x | 50 contexts | nation, notion, cation |
+| `ntes` | 1.73x | 26 contexts | antes, entes, fontes |
+| `lati` | 1.84x | 20 contexts | latif, latin, colati |
+| `muni` | 1.69x | 24 contexts | munich, almunia, comunica |
+| `onom` | 1.82x | 16 contexts | econom, autonom, astronom |
+| `omun` | 1.93x | 12 contexts | comun, comuna, comune |
+| `sset` | 1.91x | 12 contexts | esset, musset, essset |
+| `inci` | 1.90x | 12 contexts | vinci, finci, coincide |
+| `opul` | 1.78x | 14 contexts | popul, populo, popules |
+| `itan` | 1.44x | 24 contexts | titan, dritan, britan |
+| `rovi` | 1.54x | 19 contexts | šarović, provide, provinz |
+
+### 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` | 121 words | capillas, contextus |
+| `-c` | `-a` | 87 words | casarabonela, catharina |
+| `-p` | `-s` | 82 words | programmas, politicos |
+| `-s` | `-s` | 77 words | skvernelis, solanas |
+| `-c` | `-e` | 75 words | cive, cove |
+| `-c` | `-t` | 74 words | cultivat, consacrat |
+| `-s` | `-e` | 73 words | sylvie, seattle |
+| `-m` | `-e` | 71 words | matilde, maggie |
+| `-m` | `-s` | 67 words | maroons, mills |
+| `-s` | `-n` | 65 words | schatten, substitution |
+
+### 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 |
+|------|-----------------|------------|------|
+| guadalcanal | **`guadalc-an-al`** | 7.5 | `an` |
+| villasila | **`villas-i-la`** | 7.5 | `i` |
+| deschanel | **`deschan-e-l`** | 7.5 | `e` |
+| edmondson | **`edmond-s-on`** | 7.5 | `s` |
+| centennie | **`centen-n-ie`** | 7.5 | `n` |
+| navarcles | **`navarc-l-es`** | 7.5 | `l` |
+| publicmen | **`public-m-en`** | 7.5 | `m` |
+| hallesches | **`halles-ch-es`** | 7.5 | `ch` |
+| achternbusch | **`achternbu-s-ch`** | 7.5 | `s` |
+| kircheisen | **`kirchei-s-en`** | 7.5 | `s` |
+| guvernamant | **`guvernam-a-nt`** | 7.5 | `a` |
+| chuquisaca | **`chuquis-a-ca`** | 7.5 | `a` |
+| tillerson | **`tiller-s-on`** | 7.5 | `s` |
+| balineses | **`ba-lines-es`** | 6.0 | `lines` |
+| irlandesi | **`irland-es-i`** | 6.0 | `irland` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Interlingue 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 (4.09x) |
+| N-gram | **2-gram** | Lowest perplexity (241) |
+| Markov | **Context-4** | Highest predictability (94.1%) |
+| 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: 2026-01-10 03:57:27*

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