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+---
+language: io
+language_name: Ido
+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: 4.198
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7983
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Ido - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ido** 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** | 3.502x | 3.50 | 0.1398% | 1,043,846 |
+| **16k** | 3.779x | 3.78 | 0.1508% | 967,447 |
+| **32k** | 4.003x | 4.00 | 0.1597% | 913,354 |
+| **64k** | 4.198x 🏆 | 4.20 | 0.1675% | 870,791 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Surabaya esas urbo en Indonezia. Segun statistiki dil yaro ol havis habitanti. L...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁su ra ba ya ▁esas ▁urbo ▁en ▁indonezia . ▁segun ... (+22 more)` | 32 |
+| 16k | `▁sura ba ya ▁esas ▁urbo ▁en ▁indonezia . ▁segun ▁statistiki ... (+21 more)` | 31 |
+| 32k | `▁sura ba ya ▁esas ▁urbo ▁en ▁indonezia . ▁segun ▁statistiki ... (+21 more)` | 31 |
+| 64k | `▁sura baya ▁esas ▁urbo ▁en ▁indonezia . ▁segun ▁statistiki ▁dil ... (+20 more)` | 30 |
+
+**Sample 2:** `Alessandro Algardi (n. ye la 27ma di novembro til la 10ma di junio esis Italiana...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁al es sand ro ▁algar di ▁( n . ▁ye ... (+25 more)` | 35 |
+| 16k | `▁alessandro ▁algar di ▁( n . ▁ye ▁la ▁ 2 ... (+20 more)` | 30 |
+| 32k | `▁alessandro ▁algar di ▁( n . ▁ye ▁la ▁ 2 ... (+20 more)` | 30 |
+| 64k | `▁alessandro ▁algardi ▁( n . ▁ye ▁la ▁ 2 7 ... (+19 more)` | 29 |
+
+**Sample 3:** `127 aK <--> 125 aK / 2ma yarcento aK Eventi Naski Morti Demetrius 2ma, rejo di S...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 1 2 7 ▁ak ▁<--> ▁ 1 2 5 ... (+26 more)` | 36 |
+| 16k | `▁ 1 2 7 ▁ak ▁<--> ▁ 1 2 5 ... (+26 more)` | 36 |
+| 32k | `▁ 1 2 7 ▁ak ▁<--> ▁ 1 2 5 ... (+24 more)` | 34 |
+| 64k | `▁ 1 2 7 ▁ak ▁<--> ▁ 1 2 5 ... (+22 more)` | 32 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.198x compression
+- **Lowest UNK Rate:** 8k with 0.1398% 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 | 6,638 | 12.70 | 110,517 | 24.1% | 59.4% |
+| **2-gram** | Subword | 268 🏆 | 8.07 | 6,097 | 67.9% | 99.3% |
+| **3-gram** | Word | 11,261 | 13.46 | 195,686 | 21.4% | 52.5% |
+| **3-gram** | Subword | 1,922 | 10.91 | 41,403 | 28.8% | 75.7% |
+| **4-gram** | Word | 22,731 | 14.47 | 409,855 | 19.3% | 45.9% |
+| **4-gram** | Subword | 8,112 | 12.99 | 211,688 | 16.0% | 50.4% |
+| **5-gram** | Word | 26,396 | 14.69 | 378,626 | 19.1% | 42.8% |
+| **5-gram** | Subword | 22,092 | 14.43 | 608,475 | 11.3% | 38.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `la mezvalora` | 36,791 |
+| 2 | `en la` | 36,605 |
+| 3 | `de la` | 33,305 |
+| 4 | `o pluse` | 25,103 |
+| 5 | `yari o` | 24,921 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yari o pluse` | 24,876 |
+| 2 | `65 yari o` | 18,786 |
+| 3 | `min kam 18` | 18,694 |
+| 4 | `kam 18 yari` | 18,691 |
+| 5 | `la mezvalora revenuo` | 18,348 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `65 yari o pluse` | 18,782 |
+| 2 | `min kam 18 yari` | 18,690 |
+| 3 | `evante min kam 18` | 18,058 |
+| 4 | `evante 65 yari o` | 17,999 |
+| 5 | `la demografiala kontado di` | 13,448 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `evante min kam 18 yari` | 18,055 |
+| 2 | `evante 65 yari o pluse` | 17,995 |
+| 3 | `segun la demografiala kontado di` | 13,417 |
+| 4 | `vivis sub la povreso lineo` | 11,202 |
+| 5 | `esas plene lektebla en ido` | 11,081 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 1,248,693 |
+| 2 | `o _` | 1,110,924 |
+| 3 | `_ e` | 871,258 |
+| 4 | `_ d` | 779,897 |
+| 5 | `l a` | 719,367 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a _` | 510,264 |
+| 2 | `_ d i` | 407,611 |
+| 3 | `_ l a` | 400,860 |
+| 4 | `i s _` | 310,545 |
+| 5 | `_ e s` | 287,503 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ l a _` | 353,604 |
+| 2 | `_ d i _` | 277,290 |
+| 3 | `o _ d i` | 199,478 |
+| 4 | `_ e n _` | 177,757 |
+| 5 | `e s i s` | 177,202 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e s i s _` | 168,704 |
+| 2 | `o _ d i _` | 160,395 |
+| 3 | `_ e s i s` | 149,207 |
+| 4 | `e s a s _` | 121,319 |
+| 5 | `_ e s a s` | 107,177 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 268
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~39% 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 | 0.9058 | 1.874 | 7.11 | 203,018 | 9.4% |
+| **1** | Subword | 0.8797 | 1.840 | 6.18 | 2,897 | 12.0% |
+| **2** | Word | 0.3104 | 1.240 | 1.87 | 1,423,283 | 69.0% |
+| **2** | Subword | 0.8077 | 1.750 | 4.92 | 17,895 | 19.2% |
+| **3** | Word | 0.1238 | 1.090 | 1.27 | 2,624,412 | 87.6% |
+| **3** | Subword | 0.7203 | 1.648 | 3.90 | 88,062 | 28.0% |
+| **4** | Word | 0.0636 🏆 | 1.045 | 1.13 | 3,294,506 | 93.6% |
+| **4** | Subword | 0.6809 | 1.603 | 3.13 | 342,746 | 31.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `la urbo amontis a polonia e polona linguo esas turkiana distrikto sieradz komono sideyo końskowola 6`
+2. `di iulius caesar vetero pos la demografiala kontado di qui rezidis en provinco białystok e to`
+3. `e kinadek e resursi nome illinois usa segun la mezvalora evo esis dum la 28ma di`
+
+**Context Size 2:**
+
+1. `la mezvalora revenuo po familio esis 3 01 personi la procento di habitanti segun evo esis 18`
+2. `en la montari serra do mar e zapolyarni referi distrikto yamal nenec e republiko komi denisovka vila...`
+3. `de la prezidanto di peru n józef cyrankiewicz chefministro di japonia n chadwick boseman usan aktoro...`
+
+**Context Size 3:**
+
+1. `yari o pluse esis 102 5 viri la mezvalora revenuo po familio esis 38 750 kontre 26 250`
+2. `65 yari o pluse qua vivis sole la mezvalora grandeso po hemanaro esis 2 80 personi e la`
+3. `min kam 18 yari 7 9 de 18 til 24 yari 27 9 de 25 til 44 yari`
+
+**Context Size 4:**
+
+1. `65 yari o pluse la mezvalora evo esis 29 yari po singla 100 mulieri esis 90 9 viri po`
+2. `min kam 18 yari 7 6 de 18 til 24 yari 30 7 de 25 til 44 yari 20`
+3. `evante min kam 18 yari en la domo 41 4 esis mariajita e habitis kune en 18 5 muliero`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_e_mbi_e_eri_vik`
+2. `adi,_mepha_lamia`
+3. `itam_adiestrista`
+
+**Context Size 2:**
+
+1. `a_sen_8_yaro_estr`
+2. `o_pozukto_(n._cia`
+3. `_esis_milietri._c`
+
+**Context Size 3:**
+
+1. `la_di_ventora_graf`
+2. `_dil_24_yarmin_kam`
+3. `_la_un_l'ado_e_la_`
+
+**Context Size 4:**
+
+1. `_la_denseso_portuo_`
+2. `_di_esis_hemanaro_o`
+3. `o_di_interko_di_rus`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (342,746 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 | 101,186 |
+| Total Tokens | 7,375,821 |
+| Mean Frequency | 72.89 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 2039.44 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | la | 358,980 |
+| 2 | di | 277,525 |
+| 3 | e | 204,731 |
+| 4 | en | 181,179 |
+| 5 | de | 158,269 |
+| 6 | esis | 149,214 |
+| 7 | esas | 107,376 |
+| 8 | yari | 80,594 |
+| 9 | 0 | 61,043 |
+| 10 | dil | 50,131 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | fekala | 2 |
+| 2 | 24h | 2 |
+| 3 | pisuisse | 2 |
+| 4 | gilliams | 2 |
+| 5 | stokely | 2 |
+| 6 | arĝentisto | 2 |
+| 7 | servisoj | 2 |
+| 8 | kandelingi | 2 |
+| 9 | aplicata | 2 |
+| 10 | tarcisius | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2179 |
+| R² (Goodness of Fit) | 0.996161 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 50.8% |
+| Top 1,000 | 78.9% |
+| Top 5,000 | 88.8% |
+| Top 10,000 | 92.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9962 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 50.8% of corpus
+- **Long Tail:** 91,186 words needed for remaining 7.6% 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 | 0.7983 | 0.3307 | N/A | N/A |
+| **mono_64d** | 64 | 0.7791 | 0.2594 | N/A | N/A |
+| **mono_128d** | 128 | 0.7299 | 0.2100 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7983 🏆 | 0.3377 | 0.1260 | 0.5080 |
+| **aligned_64d** | 64 | 0.7791 | 0.2656 | 0.2460 | 0.6360 |
+| **aligned_128d** | 128 | 0.7299 | 0.2168 | 0.2800 | 0.6480 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7983 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2700. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 28.0% 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 | **0.105** | 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 |
+|--------|----------|
+| `-s` | stolbova, skripta, sɔsˈnufka |
+| `-a` | arnhim, avioni, adlard |
+| `-k` | klozado, kano, kalm |
+| `-ma` | makedonian, maher, macbride |
+| `-b` | bret, beggars, bombard |
+| `-m` | mineirão, mobilizita, millán |
+| `-p` | pontono, probez, pirat |
+| `-t` | turkian, templego, très |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | mobilizita, neseparebla, stolbova |
+| `-o` | editero, pontono, mineirão |
+| `-i` | cieli, enskriburi, slobodskoi |
+| `-s` | ramis, beggars, efstratios |
+| `-e` | opolskie, macbride, impe |
+| `-n` | millán, turkian, makedonian |
+| `-ta` | mobilizita, skripta, dicinta |
+| `-ra` | letra, teklinowopropra, mieczkipropra |
+
+### 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 |
+|------|----------|------------------|----------|
+| `vant` | 1.93x | 47 contexts | vanto, avant, levant |
+| `olon` | 1.90x | 45 contexts | solon, polon, rolon |
+| `trik` | 1.97x | 36 contexts | triki, striki, striko |
+| `abit` | 2.17x | 23 contexts | habiti, habito, abitov |
+| `istr` | 1.76x | 49 contexts | istra, istro, istros |
+| `kont` | 1.74x | 48 contexts | kontr, konto, konti |
+| `metr` | 1.85x | 32 contexts | metro, metri, metra |
+| `itan` | 1.46x | 82 contexts | eitan, titan, titano |
+| `rovi` | 1.77x | 34 contexts | rovin, trovis, provis |
+| `habi` | 2.02x | 18 contexts | habis, habib, dhabi |
+| `ovin` | 1.84x | 23 contexts | rovin, lovin, bovino |
+| `omet` | 1.76x | 26 contexts | comet, domett, dometo |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-a` | 143 words | senoia, senforma |
+| `-k` | `-a` | 138 words | kalorizita, kruˈlɛfska |
+| `-k` | `-o` | 127 words | kaloro, kreinto |
+| `-p` | `-o` | 121 words | pleanto, poniardago |
+| `-p` | `-a` | 119 words | prishtina, progresema |
+| `-a` | `-o` | 113 words | anulo, arbusto |
+| `-a` | `-a` | 101 words | andréa, australa |
+| `-s` | `-o` | 88 words | sanatorio, sproso |
+| `-d` | `-a` | 82 words | dekesisesma, dalayna |
+| `-p` | `-s` | 76 words | pezas, pleasures |
+
+### 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 |
+|------|-----------------|------------|------|
+| davisboro | **`davisb-o-ro`** | 7.5 | `o` |
+| personaro | **`person-a-ro`** | 7.5 | `a` |
+| kompozado | **`kompoz-a-do`** | 7.5 | `a` |
+| dinastiala | **`dinasti-a-la`** | 7.5 | `a` |
+| militaral | **`milit-ar-al`** | 7.5 | `ar` |
+| billboard | **`billbo-ar-d`** | 7.5 | `ar` |
+| singulara | **`singu-la-ra`** | 7.5 | `la` |
+| senmariajita | **`se-n-mariajita`** | 7.5 | `mariajita` |
+| exercesis | **`exerce-s-is`** | 7.5 | `s` |
+| grafikala | **`grafi-ka-la`** | 7.5 | `ka` |
+| provincial | **`provinc-i-al`** | 7.5 | `i` |
+| companheiro | **`companhe-i-ro`** | 7.5 | `i` |
+| landskrona | **`landskr-o-na`** | 7.5 | `o` |
+| konskriptis | **`ko-n-skriptis`** | 7.5 | `skriptis` |
+| chanjesis | **`chanje-s-is`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ido 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 (4.20x) |
+| N-gram | **2-gram** | Lowest perplexity (268) |
+| Markov | **Context-4** | Highest predictability (93.6%) |
+| 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 04:52:07*
diff --git a/io_morph_tokenizer.json b/io_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..7fa97343d1e930c543b6dacb29810c6870249616
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