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README.md

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+---
+language: kcg
+language_name: Tyap
+language_family: atlantic_other
+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_other
+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: 4.834
+  - name: best_isotropy
+    type: isotropy
+    value: 0.3873
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Tyap - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tyap** 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** | 4.149x | 4.15 | 0.1551% | 192,111 |
+| **16k** | 4.452x | 4.46 | 0.1664% | 179,058 |
+| **32k** | 4.706x | 4.71 | 0.1760% | 169,365 |
+| **64k** | 4.834x 🏆 | 4.84 | 0.1807% | 164,911 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Atanii yet mam hwa kunin kyak avwou mun tsatsak ladi mang talata . Wikimedians Z...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁at ani i ▁yet ▁mam ▁hwa ▁ku nin ▁kyak ▁avwo ... (+11 more)` | 21 |
+| 16k | `▁atanii ▁yet ▁mam ▁hwa ▁ku nin ▁kyak ▁avwou ▁mun ▁tsatsak ... (+8 more)` | 18 |
+| 32k | `▁atanii ▁yet ▁mam ▁hwa ▁kunin ▁kyak ▁avwou ▁mun ▁tsatsak ▁ladi ... (+6 more)` | 16 |
+| 64k | `▁atanii ▁yet ▁mam ▁hwa ▁kunin ▁kyak ▁avwou ▁mun ▁tsatsak ▁ladi ... (+6 more)` | 16 |
+
+**Sample 2:** `Zong (á̱ ka ndyuut zwong a̱ni) yet jen nang a̱yin nswan a̱fa a̱khwot di̱ mi̱n ya...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)` | 29 |
+| 16k | `▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)` | 29 |
+| 32k | `▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)` | 29 |
+| 64k | `▁zong ▁( á̱ ▁ka ▁ndyuut ▁zwong ▁a̱ni ) ▁yet ▁jen ... (+18 more)` | 28 |
+
+**Sample 3:** `Ci̱ncai yet a̱cyuang ga̱swan ba̱ ya ka̱tako a̱ni. Ya̱fang`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁c i̱n c ai ▁yet ▁a̱cyuang ▁ga̱s wan ▁ba̱ ▁ya ... (+5 more)` | 15 |
+| 16k | `▁c i̱n c ai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tak ... (+4 more)` | 14 |
+| 32k | `▁ci̱ncai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tako ▁a̱ni . ▁ya̱fang` | 10 |
+| 64k | `▁ci̱ncai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tako ▁a̱ni . ▁ya̱fang` | 10 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.834x compression
+- **Lowest UNK Rate:** 8k with 0.1551% 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 | 2,665 | 11.38 | 5,715 | 23.5% | 60.8% |
+| **2-gram** | Subword | 265 🏆 | 8.05 | 1,919 | 66.6% | 99.3% |
+| **3-gram** | Word | 3,873 | 11.92 | 6,453 | 18.1% | 48.8% |
+| **3-gram** | Subword | 1,877 | 10.87 | 12,850 | 30.0% | 74.6% |
+| **4-gram** | Word | 6,271 | 12.61 | 8,735 | 12.5% | 36.0% |
+| **4-gram** | Subword | 8,185 | 13.00 | 52,350 | 17.1% | 47.5% |
+| **5-gram** | Word | 3,808 | 11.89 | 4,846 | 13.7% | 41.8% |
+| **5-gram** | Subword | 20,242 | 14.31 | 96,936 | 11.3% | 34.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nang á̱` | 1,002 |
+| 2 | `di̱ fam` | 924 |
+| 3 | `á̱ ku` | 675 |
+| 4 | `a̱ si̱` | 657 |
+| 5 | `ku yet` | 653 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `di̱ fam a̱tak` | 234 |
+| 2 | `nang á̱ ku` | 230 |
+| 3 | `di̱ fam a̱za` | 209 |
+| 4 | `nang á̱ ngyei` | 200 |
+| 5 | `ya̱fang a̱ka̱fwuop nta` | 196 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `zwat swak ma̱ng sweang` | 86 |
+| 2 | `kyiak neet ma̱ a̱lyia̱` | 82 |
+| 3 | `wiki bootcamp season 1` | 80 |
+| 4 | `di̱ fam a̱za hu` | 72 |
+| 5 | `di̱ fam a̱tyin hu` | 70 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `neet ma̱ a̱lyia̱ ba̱ng si̱` | 62 |
+| 2 | `á̱ lyen ma̱ng a̱lyoot a̱gwomna̱ti` | 62 |
+| 3 | `kyiak neet ma̱ a̱lyia̱ ba̱ng` | 59 |
+| 4 | `in tyap romanian and english` | 58 |
+| 5 | `together in tyap romanian and` | 58 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a̱` | 38,395 |
+| 2 | `n g` | 35,424 |
+| 3 | `a n` | 31,339 |
+| 4 | `t _` | 27,103 |
+| 5 | `a _` | 26,601 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 26,180 |
+| 2 | `a n g` | 17,304 |
+| 3 | `e t _` | 10,561 |
+| 4 | `_ m a̱` | 8,983 |
+| 5 | `a t _` | 7,766 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n g _` | 13,963 |
+| 2 | `y i a̱ _` | 6,492 |
+| 3 | `a̱ n g _` | 6,360 |
+| 4 | `_ m a̱ n` | 6,098 |
+| 5 | `m a̱ n g` | 5,713 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `m a̱ n g _` | 5,692 |
+| 2 | `_ m a̱ n g` | 5,676 |
+| 3 | `_ y e t _` | 4,648 |
+| 4 | `n a n g _` | 3,924 |
+| 5 | `b y i n _` | 3,628 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 265
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~34% 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 | 0.7557 | 1.688 | 4.71 | 28,147 | 24.4% |
+| **1** | Subword | 0.9793 | 1.972 | 6.49 | 911 | 2.1% |
+| **2** | Word | 0.2473 | 1.187 | 1.54 | 132,079 | 75.3% |
+| **2** | Subword | 0.8642 | 1.820 | 4.83 | 5,908 | 13.6% |
+| **3** | Word | 0.0833 | 1.059 | 1.13 | 202,426 | 91.7% |
+| **3** | Subword | 0.7719 | 1.708 | 3.49 | 28,551 | 22.8% |
+| **4** | Word | 0.0300 🏆 | 1.021 | 1.04 | 228,145 | 97.0% |
+| **4** | Subword | 0.5638 | 1.478 | 2.31 | 99,668 | 43.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ma̱ng a̱lyoot a̱liza̱nda mi̱ a̱bibyia̱ njen nang si̱tet ba̱yelsa shyia̱ cet a̱gwaza a̱nyiung di̱ fam...`
+2. `ku nihon shong kaswuo a̱ni nggu a̱tyoli sa̱mwila a̱cyia̱ shong mediterranean ba̱ nyia̱ a̱yaafim ku s...`
+3. `yet a̱tyulyuut ma̱ng a̱za jenshyung si̱tet ka̱duna a̱tak shong www stoa org dead keys in the`
+
+**Context Size 2:**
+
+1. `nang á̱ ku mbwuo lyulyoot a̱ni ni̱nia yet guadalajara monterrey puebla toluca tijuana ciudad juárez ...`
+2. `di̱ fam a̱byin jenshyung a̱siya a̱sa̱khwot nhu na a̱ni tamah si̱ ci a̱pyie ngu nang kham nsaai`
+3. `á̱ ku mi̱n a̱ khwuat a̱nietca̱tshot a̱niet khwo mba tai a̱ ku ngyei gini potuga a̱ni ma̱nang`
+
+**Context Size 3:**
+
+1. `di̱ fam a̱tak hu a̱za afrika si̱ myian a̱ja a̱wot di̱ fam a̱tak si̱tet ka̱duna naijeriya a̱ nyia̱`
+2. `nang á̱ ku byin nggu a̱tali̱gan a̱ga̱mi tshshekari was born in taligan magamia zangon kataf to paren...`
+3. `di̱ fam a̱za hu naat kyai a̱sa̱khwot caina a̱tak hu yet kyai a̱sa̱khwot ku shyia̱ di̱ ngaan fam`
+
+**Context Size 4:**
+
+1. `zwat swak ma̱ng sweang yet a̱tyukwai nfwuo á̱niet naijeriya wa a̱nyan wa yet byiek a̱kwak a̱son á̱gw...`
+2. `kyiak neet ma̱ a̱lyia̱ ba̱ng si̱ tat a̱ ku ba̱ng cucuk a̱gwomna̱ti jhyang di̱n jen ji̱ ku swak a̱ni`
+3. `di̱ fam a̱za hu a̱fganistan di̱ fam a̱tyin hu ka̱ ka̱u di̱ si̱sak nang lili a̱byin ka yet a̱ni`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_á̱_fabefandera_e`
+2. `anwu.,_hwunre,_m`
+3. `ngbamang_mi̱ta_á̱k`
+
+**Context Size 2:**
+
+1. `_a̱fangba̱_ny-fwuo_`
+2. `ng_hi_biya_bya_si̱`
+3. `ang_á̱ni._ya̱u_vin_`
+
+**Context Size 3:**
+
+1. `ng_a̱yaaethe_part_o`
+2. `angka̱i_a̱khai_ba_,_`
+3. `et_a̱lyen_shong_a̱ku`
+
+**Context Size 4:**
+
+1. `ang_gini_ka̱sitibin_`
+2. `yia̱_a̱yaapi̱rotidia._`
+3. `a̱ng_si̱_swak_mi̱_suso`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (99,668 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 | 11,223 |
+| Total Tokens | 236,752 |
+| Mean Frequency | 21.10 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 149.81 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ma̱ng | 5,701 |
+| 2 | ku | 5,107 |
+| 3 | yet | 4,705 |
+| 4 | si̱ | 3,684 |
+| 5 | a̱ni | 3,615 |
+| 6 | hu | 3,553 |
+| 7 | á̱ | 3,391 |
+| 8 | nang | 3,386 |
+| 9 | a̱ | 3,096 |
+| 10 | ka | 2,820 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tockus | 2 |
+| 2 | erythrorhynchus | 2 |
+| 3 | atu | 2 |
+| 4 | luwut | 2 |
+| 5 | akad | 2 |
+| 6 | أبو | 2 |
+| 7 | نواس | 2 |
+| 8 | nuwās | 2 |
+| 9 | a̱tyoka̱u | 2 |
+| 10 | basi̱li̱kata | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1596 |
+| R² (Goodness of Fit) | 0.992895 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.0% |
+| Top 1,000 | 78.4% |
+| Top 5,000 | 93.6% |
+| Top 10,000 | 99.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9929 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.0% of corpus
+- **Long Tail:** 1,223 words needed for remaining 1.0% 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 | 0.3873 🏆 | 0.4467 | N/A | N/A |
+| **mono_64d** | 64 | 0.0916 | 0.4260 | N/A | N/A |
+| **mono_128d** | 128 | 0.0123 | 0.4367 | N/A | N/A |
+| **aligned_32d** | 32 | 0.3873 | 0.4319 | 0.0240 | 0.1440 |
+| **aligned_64d** | 64 | 0.0916 | 0.4421 | 0.0200 | 0.1440 |
+| **aligned_128d** | 128 | 0.0123 | 0.4376 | 0.0160 | 0.1340 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.3873 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4368. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.4% 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 | **0.229** | 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 |
+|--------|----------|
+| `-a` | a̱tyuweang, a̱ka̱satyok, american |
+| `-n` | nia, ning, na̠ |
+| `-s` | sardi, songs, swot |
+| `-m` | ma̱m, ma̱li̱daviya, mabyin |
+| `-k` | kwaimam, kpantyin, kwom |
+| `-b` | bendel, bu, buzău |
+| `-t` | tyantung, ta̱lyi̱ri̱p, tunis |
+| `-ma` | ma̱m, ma̱li̱daviya, mabyin |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | nia, ania, ma̱li̱daviya |
+| `-n` | american, a̱yangka̱nan, rénmín |
+| `-ng` | ga̱swúong, a̱tyuweang, tyantung |
+| `-t` | lilyuut, felt, list |
+| `-g` | ga̱swúong, a̱tyuweang, tyantung |
+| `-i` | yhui, a̱yaazoni, a̱vwui |
+| `-s` | prayers, songs, français |
+| `-e` | fare, harare, senate |
+
+### 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 |
+|------|----------|------------------|----------|
+| `yang` | 1.38x | 56 contexts | gyang, lyang, jyang |
+| `wang` | 1.62x | 25 contexts | gwang, nwang, swang |
+| `eang` | 1.59x | 26 contexts | keang, weang, teang |
+| `tion` | 1.88x | 13 contexts | action, nation, notion |
+| `wuan` | 1.50x | 23 contexts | swuan, fwuan, vwuan |
+| `yiak` | 1.67x | 16 contexts | tyiak, kyiak, byiak |
+| `yiat` | 1.56x | 18 contexts | tyiat, lyiat, kyiat |
+| `wuon` | 1.51x | 19 contexts | fwuon, vwuon, bwuon |
+| `hyan` | 1.69x | 11 contexts | nhyan, ghyang, hihyan |
+| `nshy` | 1.33x | 14 contexts | nshye, nshya, nshyie |
+| `kean` | 1.50x | 9 contexts | keang, keana, a̱kean |
+| `nyiu` | 1.48x | 9 contexts | a̱nyiu, nyiung, anyiuk |
+
+### 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` | `-g` | 194 words | anbang, a̱tyubwuanng |
+| `-a` | `-ng` | 193 words | anbang, a̱tyubwuanng |
+| `-a` | `-t` | 166 words | a̱gwut, a̱tat |
+| `-a` | `-i` | 144 words | a̱ta̱nii, agwii |
+| `-a` | `-a` | 137 words | alata, a̱jiya |
+| `-a` | `-n` | 131 words | afwun, a̱zabyin |
+| `-a` | `-k` | 104 words | acucuk, akanok |
+| `-a` | `-an` | 53 words | ashan, american |
+| `-c` | `-s` | 41 words | collins, caucasus |
+| `-k` | `-a` | 41 words | kola, ki̱risi̱ta |
+
+### 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 |
+|------|-----------------|------------|------|
+| marketing | **`market-i-ng`** | 7.5 | `i` |
+| kuzangmam | **`kuzang-m-am`** | 7.5 | `m` |
+| a̱ka̱safang | **`a̱ka̱saf-a-ng`** | 7.5 | `a` |
+| kyangtutu | **`kyangtu-t-u`** | 7.5 | `t` |
+| ka̱zaktan | **`ka̱zak-t-an`** | 7.5 | `t` |
+| á̱nietnzop | **`á̱nietnz-o-p`** | 7.5 | `o` |
+| christians | **`christi-an-s`** | 7.5 | `an` |
+| atakjenshyung | **`at-ak-jenshyung`** | 7.5 | `jenshyung` |
+| nvwuomaat | **`nvwuom-a-at`** | 7.5 | `a` |
+| institution | **`institut-i-on`** | 7.5 | `i` |
+| a̱tyulyiai | **`a̱tyuly-i-ai`** | 7.5 | `i` |
+| nggwoneam | **`nggwon-e-am`** | 7.5 | `e` |
+| a̱nyanyan | **`a̱nyan-ya-n`** | 6.0 | `a̱nyan` |
+| africaines | **`africa-in-es`** | 6.0 | `africa` |
+| a̱kwokwak | **`a̱kwok-wa-k`** | 6.0 | `a̱kwok` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tyap 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 (4.83x) |
+| N-gram | **2-gram** | Lowest perplexity (265) |
+| Markov | **Context-4** | Highest predictability (97.0%) |
+| 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 07:27:32*

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models/vocabulary/kcg_vocabulary_metadata.json

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+{
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+}