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README.md

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+---
+language: ig
+language_name: Igbo
+language_family: atlantic_yoruba_igbo
+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-atlantic_yoruba_igbo
+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: 3.745
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8093
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Igbo - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Igbo** 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** | 3.236x | 3.24 | 0.3842% | 188,457 |
+| **16k** | 3.437x | 3.44 | 0.4081% | 177,404 |
+| **32k** | 3.614x | 3.62 | 0.4291% | 168,744 |
+| **64k** | 3.745x 🏆 | 3.75 | 0.4447% | 162,811 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Duli bu nwere ike izo aka na: Duli, Ardabil, Iran Duli, Hamadan, Iran Duli, Nepa...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)` | 41 |
+| 16k | `▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)` | 41 |
+| 32k | `▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)` | 41 |
+| 64k | `▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)` | 41 |
+
+**Sample 2:** `Purukotó (Purucotó) bụ asụsụ Cariban na-apụ n'anya . Kaufman debere ya na ngalab...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pu ru ko t ó ▁( pu ru co t ... (+30 more)` | 40 |
+| 16k | `▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+24 more)` | 34 |
+| 32k | `▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+22 more)` | 32 |
+| 64k | `▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+22 more)` | 32 |
+
+**Sample 3:** `Manombai (nke a dị ka Wokam) bụ otu n'ime Asụsụ Aru, nke ndị bi na Aru Islands, ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo ka ... (+24 more)` | 34 |
+| 16k | `▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)` | 33 |
+| 32k | `▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)` | 33 |
+| 64k | `▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)` | 33 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.745x compression
+- **Lowest UNK Rate:** 8k with 0.3842% 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 | 26,246 | 14.68 | 359,156 | 15.9% | 37.9% |
+| **2-gram** | Subword | 280 🏆 | 8.13 | 12,173 | 64.0% | 99.0% |
+| **3-gram** | Word | 161,068 | 17.30 | 916,288 | 6.8% | 18.8% |
+| **3-gram** | Subword | 2,183 | 11.09 | 87,468 | 30.4% | 71.2% |
+| **4-gram** | Word | 532,594 | 19.02 | 1,757,879 | 4.0% | 10.9% |
+| **4-gram** | Subword | 11,363 | 13.47 | 475,134 | 17.2% | 44.2% |
+| **5-gram** | Word | 559,672 | 19.09 | 1,291,016 | 3.5% | 8.9% |
+| **5-gram** | Subword | 42,173 | 15.36 | 1,479,265 | 10.6% | 30.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dị ka` | 140,163 |
+| 2 | `a na` | 112,277 |
+| 3 | `ọ bụ` | 105,148 |
+| 4 | `ya na` | 99,998 |
+| 5 | `site na` | 75,118 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ma ọ bụ` | 47,538 |
+| 2 | `dị ka onye` | 33,165 |
+| 3 | `dị iche iche` | 22,236 |
+| 4 | `ndi di ndụ` | 19,640 |
+| 5 | `na eme ihe` | 19,264 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mmadụ ndi di ndụ` | 17,108 |
+| 2 | `òtù mmadụ ndi di` | 17,101 |
+| 3 | `na eme ihe nkiri` | 13,842 |
+| 4 | `akụkọ ihe mere eme` | 12,735 |
+| 5 | `dị ka onye na` | 9,212 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `òtù mmadụ ndi di ndụ` | 17,099 |
+| 2 | `onye na eme ihe nkiri` | 6,973 |
+| 3 | `òtù pages with unreviewed translations` | 4,329 |
+| 4 | `e dere n ala ala` | 4,004 |
+| 5 | `ihe e dere n ala` | 3,927 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n` | 5,638,183 |
+| 2 | `a _` | 5,376,024 |
+| 3 | `e _` | 4,318,368 |
+| 4 | `n a` | 2,708,872 |
+| 5 | `_ a` | 2,215,860 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a` | 2,367,266 |
+| 2 | `n a _` | 1,687,800 |
+| 3 | `a _ n` | 1,387,006 |
+| 4 | `e _ n` | 1,187,243 |
+| 5 | `_ n k` | 938,041 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a _` | 1,567,660 |
+| 2 | `_ n k e` | 743,366 |
+| 3 | `n k e _` | 735,578 |
+| 4 | `_ n a -` | 656,811 |
+| 5 | `a _ n a` | 579,489 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n k e _` | 722,504 |
+| 2 | `_ n d ị _` | 399,246 |
+| 3 | `_ i h e _` | 373,739 |
+| 4 | `_ n a - e` | 351,252 |
+| 5 | `a _ n a _` | 349,914 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 280
+- **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 | 0.8607 | 1.816 | 9.02 | 510,524 | 13.9% |
+| **1** | Subword | 1.0714 | 2.101 | 7.32 | 6,437 | 0.0% |
+| **2** | Word | 0.3599 | 1.283 | 2.38 | 4,598,546 | 64.0% |
+| **2** | Subword | 0.7215 | 1.649 | 4.70 | 47,137 | 27.9% |
+| **3** | Word | 0.1996 | 1.148 | 1.52 | 10,914,867 | 80.0% |
+| **3** | Subword | 0.6901 | 1.613 | 3.94 | 221,281 | 31.0% |
+| **4** | Word | 0.1054 🏆 | 1.076 | 1.21 | 16,623,256 | 89.5% |
+| **4** | Subword | 0.6621 | 1.582 | 3.28 | 871,504 | 33.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `na mmemme ahụ n ọtụtụ ndị dugara na abụọ nke 302 west sepik province nke a`
+2. `nke na kaduna kama nke ndị agha ebumnuche na ndị na otu a na ya olulu`
+3. `n ime ndị o kwuru na ahụ na eto ya niile na dholuo okpukpe n etiti`
+
+**Context Size 2:**
+
+1. `dị ka nke abụọ marathon nke etiopia onye otu bọọdụ na achọ ọfịs dabere na ike araromire`
+2. `a na enyo enyo ébé ọ bi na ya jide nche anwụ nke all progressives congress apc`
+3. `ọ bụ akụkụ nke machar colony akụkụ nke usoro nke na ezere ọkwa nna ya bụ 531`
+
+**Context Size 3:**
+
+1. `ma ọ bụ tin ore ihe ndị fọdụrụ na german army dina na nzuzo na eduga na nkwupụta`
+2. `dị ka onye edemede na onye na ezisa ozi ọma na ghana ebe ọ mmụta akwụkwọ na adịbeghị`
+3. `dị iche iche nke a ga enyocha n ihu nyocha nke chọpụtara ụzọ agha oke ala nke dara`
+
+**Context Size 4:**
+
+1. `òtù mmadụ ndi di ndụ òtù pages with unreviewed translations __lead_section__ áká_ịkẹngạ thumb ihe ej...`
+2. `na eme ihe nkiri kacha mma na ọrụ dị mkpa nke ala ala dị n ibéetiti ahụ áká_èkpè thumb`
+3. `akụkọ ihe mere eme na muizenberg cape town mbipụta abụ m na efe efe carapace doo wop girls of`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_i_natọ_nọ_ndona`
+2. `a_ngbụ_ondiy_ma_`
+3. `e_i_nnropana-e_ụ`
+
+**Context Size 2:**
+
+1. `_ng_porosii_nke_a`
+2. `a_ọdụ_na_ka_hasụ_`
+3. `e_12.2,_ndihe_ọzọ`
+
+**Context Size 3:**
+
+1. `_na-ụdị_nwunyere_o`
+2. `na_nke_na_gọzi_na_`
+3. `a_nke_umuagest_6_k`
+
+**Context Size 4:**
+
+1. `_na_baltham_taa_aː_`
+2. `_nke_12,_ndị_burugb`
+3. `nke_ọrụ_egypt_mara_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 89.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (871,504 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 | 220,608 |
+| Total Tokens | 24,129,478 |
+| Mean Frequency | 109.38 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 5866.90 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | na | 2,239,768 |
+| 2 | nke | 735,052 |
+| 3 | n | 615,909 |
+| 4 | ihe | 410,419 |
+| 5 | ndị | 405,283 |
+| 6 | ọ | 395,253 |
+| 7 | ya | 384,400 |
+| 8 | a | 339,042 |
+| 9 | dị | 325,019 |
+| 10 | onye | 319,693 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | agbalagbo | 2 |
+| 2 | akpalagu | 2 |
+| 3 | okwule | 2 |
+| 4 | otuogene | 2 |
+| 5 | ovili | 2 |
+| 6 | anyansi | 2 |
+| 7 | ifediorah | 2 |
+| 8 | chidalu | 2 |
+| 9 | okebo | 2 |
+| 10 | pdna | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2680 |
+| R² (Goodness of Fit) | 0.992771 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 50.1% |
+| Top 1,000 | 75.8% |
+| Top 5,000 | 88.4% |
+| Top 10,000 | 91.8% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9928 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 50.1% of corpus
+- **Long Tail:** 210,608 words needed for remaining 8.2% 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 | 0.8093 | 0.4233 | N/A | N/A |
+| **mono_64d** | 64 | 0.7925 | 0.3195 | N/A | N/A |
+| **mono_128d** | 128 | 0.7531 | 0.2578 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8093 🏆 | 0.4482 | 0.2740 | 0.7140 |
+| **aligned_64d** | 64 | 0.7925 | 0.3263 | 0.4540 | 0.8100 |
+| **aligned_128d** | 128 | 0.7531 | 0.2597 | 0.6140 | 0.8900 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8093 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3391. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 61.4% 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 | **-0.708** | 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` | agathon, aboudia, ankusha |
+| `-m` | mertsalov, millionaire, müttererholungsverein |
+| `-n` | naimdb, nasril, nwpl |
+| `-ma` | malitereihe, matsumoto, mackerdhuj |
+| `-s` | schnee, shabaka, shuaibiu |
+| `-b` | beloved, bourguiba, brunhild |
+| `-k` | kechie, kareem, kilolo |
+| `-e` | edekọrọ, eribake, edremoda |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | kechie, millionaire, ghọtahie |
+| `-a` | yulia, hekka, bourguiba |
+| `-s` | hypochlorous, pleiades, morcus |
+| `-n` | müttererholungsverein, fleischman, agathon |
+| `-i` | wabehi, hajjaji, adefarati |
+| `-r` | mountaineer, leaver, br |
+| `-o` | turbo, wamco, kilolo |
+| `-t` | chiat, rajput, zuidoost |
+
+### 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` | 2.41x | 79 contexts | ation, ratio, patio |
+| `fric` | 2.53x | 46 contexts | afric, frick, friche |
+| `nati` | 2.46x | 46 contexts | natij, inati, natie |
+| `epụt` | 2.22x | 64 contexts | kepụta, ndepụt, mepụta |
+| `alit` | 1.92x | 109 contexts | alita, alito, palit |
+| `kwad` | 2.39x | 40 contexts | kwadi, kwado, kwada |
+| `wany` | 1.95x | 71 contexts | wanyä, nwany, wanye |
+| `gbas` | 2.08x | 54 contexts | gbasa, egbas, ịgbasa |
+| `nwan` | 1.93x | 73 contexts | nwany, enwan, nwana |
+| `ụtar` | 2.04x | 56 contexts | ụtara, ụtarị, tụtara |
+| `ọpụt` | 1.94x | 68 contexts | ọpụta, kọpụta, họpụta |
+| `nwet` | 2.21x | 39 contexts | nweta, nwetụ, nwete |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-a` | 92 words | amazônia, arema |
+| `-m` | `-e` | 74 words | montefiore, mmachineke |
+| `-m` | `-s` | 70 words | marthinus, missionaries |
+| `-m` | `-a` | 69 words | mgbasasa, mëhneja |
+| `-a` | `-e` | 69 words | adae, adamorobe |
+| `-s` | `-s` | 66 words | schreiners, strives |
+| `-a` | `-s` | 62 words | antiperspirants, autonomous |
+| `-s` | `-e` | 55 words | stalemate, sute |
+| `-k` | `-a` | 53 words | kadina, katọkwara |
+| `-s` | `-a` | 51 words | spelaea, shadia |
+
+### 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 |
+|------|-----------------|------------|------|
+| avanzadoras | **`avanzador-a-s`** | 7.5 | `a` |
+| commutata | **`commu-ta-ta`** | 7.5 | `ta` |
+| starfruit | **`starfru-i-t`** | 7.5 | `i` |
+| johnsonmain | **`johnsonm-a-in`** | 7.5 | `a` |
+| maniapoto | **`maniapo-t-o`** | 7.5 | `t` |
+| hollywoodland | **`hollywoodl-an-d`** | 7.5 | `an` |
+| camptoceras | **`camptoce-ra-s`** | 7.5 | `ra` |
+| expressway | **`express-wa-y`** | 7.5 | `wa` |
+| minnijean | **`minnij-e-an`** | 7.5 | `e` |
+| multiflora | **`multifl-o-ra`** | 7.5 | `o` |
+| christened | **`christe-n-ed`** | 7.5 | `n` |
+| westfälisch | **`westfälis-c-h`** | 7.5 | `c` |
+| caballero | **`ca-baller-o`** | 6.0 | `baller` |
+| personnel | **`person-ne-l`** | 6.0 | `person` |
+| ameringer | **`ameri-ng-er`** | 6.0 | `ameri` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Igbo 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 (3.75x) |
+| N-gram | **2-gram** | Lowest perplexity (280) |
+| Markov | **Context-4** | Highest predictability (89.5%) |
+| 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 05:45:06*

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