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+---
+language: kge
+language_name: Komering
+language_family: austronesian_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-austronesian_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: 3.778
+  - name: best_isotropy
+    type: isotropy
+    value: 0.4759
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Komering - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Komering** 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** | 3.083x | 3.09 | 1.8482% | 160,535 |
+| **16k** | 3.454x | 3.46 | 2.0700% | 143,330 |
+| **32k** | 3.778x 🏆 | 3.78 | 2.2644% | 131,028 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ण atawa Ṇa joda da salah osay huruf say uwat dilom Aksara Diwanagori.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ ण ▁atawa ▁ṇ a ▁joda ▁da ▁salah ▁osay ▁huruf ... (+6 more)` | 16 |
+| 16k | `▁ ण ▁atawa ▁ṇa ▁joda ▁da ▁salah ▁osay ▁huruf ▁say ... (+5 more)` | 15 |
+| 32k | `▁ण ▁atawa ▁ṇa ▁joda ▁da ▁salah ▁osay ▁huruf ▁say ▁uwat ... (+4 more)` | 14 |
+
+**Sample 2:** `Jolma Inuit Greenland joda da salah osay kalompok etnis say ngaman di Greenland,...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁jolma ▁in uit ▁greenland ▁joda ▁da ▁salah ▁osay ▁kalompok ▁etnis ... (+11 more)` | 21 |
+| 16k | `▁jolma ▁inuit ▁greenland ▁joda ▁da ▁salah ▁osay ▁kalompok ▁etnis ▁say ... (+10 more)` | 20 |
+| 32k | `▁jolma ▁inuit ▁greenland ▁joda ▁da ▁salah ▁osay ▁kalompok ▁etnis ▁say ... (+10 more)` | 20 |
+
+**Sample 3:** `350 atawa Tolu ratus lima puluh joda da bilangan asli diantara 349 rik 351.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 3 5 0 ▁atawa ▁tolu ▁ratus ▁lima ▁puluh ▁joda ... (+14 more)` | 24 |
+| 16k | `▁ 3 5 0 ▁atawa ▁tolu ▁ratus ▁lima ▁puluh ▁joda ... (+14 more)` | 24 |
+| 32k | `▁ 3 5 0 ▁atawa ▁tolu ▁ratus ▁lima ▁puluh ▁joda ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 3.778x compression
+- **Lowest UNK Rate:** 8k with 1.8482% 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 | 1,114 | 10.12 | 3,446 | 38.1% | 72.7% |
+| **2-gram** | Subword | 307 🏆 | 8.26 | 3,285 | 68.9% | 96.9% |
+| **3-gram** | Word | 2,200 | 11.10 | 6,072 | 31.1% | 57.0% |
+| **3-gram** | Subword | 2,122 | 11.05 | 14,216 | 30.7% | 72.1% |
+| **4-gram** | Word | 6,974 | 12.77 | 15,206 | 19.9% | 36.4% |
+| **4-gram** | Subword | 8,682 | 13.08 | 50,196 | 18.6% | 46.5% |
+| **5-gram** | Word | 7,727 | 12.92 | 14,198 | 17.3% | 33.4% |
+| **5-gram** | Subword | 16,325 | 13.99 | 74,544 | 15.7% | 37.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `joda da` | 3,206 |
+| 2 | `salah osay` | 1,901 |
+| 3 | `da salah` | 1,879 |
+| 4 | `say uwat` | 827 |
+| 5 | `di nagara` | 713 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `joda da salah` | 1,874 |
+| 2 | `da salah osay` | 1,847 |
+| 3 | `salah osay huruf` | 517 |
+| 4 | `say uwat dilom` | 482 |
+| 5 | `osay huruf say` | 451 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `joda da salah osay` | 1,842 |
+| 2 | `da salah osay huruf` | 516 |
+| 3 | `salah osay huruf say` | 451 |
+| 4 | `huruf say uwat dilom` | 442 |
+| 5 | `osay huruf say uwat` | 439 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `joda da salah osay huruf` | 514 |
+| 2 | `da salah osay huruf say` | 451 |
+| 3 | `osay huruf say uwat dilom` | 439 |
+| 4 | `salah osay huruf say uwat` | 439 |
+| 5 | `joda da salah osay basa` | 258 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 26,179 |
+| 2 | `s a` | 16,199 |
+| 3 | `a n` | 15,647 |
+| 4 | `_ s` | 12,853 |
+| 5 | `_ d` | 11,531 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ s a` | 8,334 |
+| 2 | `a y _` | 7,563 |
+| 3 | `d a _` | 7,016 |
+| 4 | `_ d i` | 6,107 |
+| 5 | `s a y` | 6,094 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s a y _` | 6,012 |
+| 2 | `_ s a y` | 3,997 |
+| 3 | `a _ s a` | 3,635 |
+| 4 | `a _ d a` | 3,356 |
+| 5 | `d a _ d` | 3,303 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ s a y _` | 3,977 |
+| 2 | `d a _ d a` | 3,221 |
+| 3 | `j o d a _` | 3,221 |
+| 4 | `_ j o d a` | 3,220 |
+| 5 | `a _ d a _` | 3,216 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 307
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~37% 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 | 0.5728 | 1.487 | 3.11 | 22,053 | 42.7% |
+| **1** | Subword | 0.4326 | 1.350 | 3.48 | 5,154 | 56.7% |
+| **2** | Word | 0.1743 | 1.128 | 1.34 | 67,279 | 82.6% |
+| **2** | Subword | 0.4377 | 1.354 | 2.63 | 17,949 | 56.2% |
+| **3** | Word | 0.0679 | 1.048 | 1.12 | 88,369 | 93.2% |
+| **3** | Subword | 0.4188 | 1.337 | 2.19 | 47,199 | 58.1% |
+| **4** | Word | 0.0336 🏆 | 1.024 | 1.06 | 96,472 | 96.6% |
+| **4** | Subword | 0.3340 | 1.261 | 1.72 | 103,240 | 66.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `say jak apuy aktif lagi tipakay dilom pira ngabungkal huruf á ە ـە یو жj j`
+2. `da salah osay huruf jopang samantara huruf say tarakhir algar partamo say uwat dilom bahasa tajik`
+3. `di batusangkar joda da salah osay provinsi jawa atau 00f rgb 255 255 255 255 255`
+
+**Context Size 2:**
+
+1. `joda da sambahyang juma at joda da salah osay kabupaten say uwat dilom alfabet sirilik huruf ҙ`
+2. `salah osay basa sey tipakay di nagara cina say nuturko bahasa malta`
+3. `da salah osay basa say digunako jolma seediq di nagara bagiyan andhra pradesh di nagara ghana tiyan`
+
+**Context Size 3:**
+
+1. `joda da salah osay huruf bujenis aksara semisilabis say tipakay untuk nulisko pira ngabungkal basa d...`
+2. `da salah osay kalompok etnis say ngaman di nagara myanmar rik bangladesh tiyan ja makay bahasa jopan...`
+3. `salah osay huruf say uwat dilom alfabet latin huruf ɣ balak ɣ ronik`
+
+**Context Size 4:**
+
+1. `joda da salah osay kabupaten di provinsi jawa timur say ibukutana di tuban`
+2. `da salah osay huruf say uwat dilom aksara diwanagori`
+3. `salah osay huruf say uwat dilom alfabet latin huruf ŷ balak ŷ ronik`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_loda_ـ_talan:_s`
+2. `anuripangunasaha`
+3. `iat._hang_dath_d`
+
+**Context Size 2:**
+
+1. `a_ngkassoriyang,_`
+2. `sal_turuf_buruju_`
+3. `an_voki_bagah_asa`
+
+**Context Size 3:**
+
+1. `_salah_sa_qiangya_`
+2. `ay_kuta_say_kuta_p`
+3. `da_sona_kim_perú_c`
+
+**Context Size 4:**
+
+1. `say_basa_kikai_rik_`
+2. `_say_ngama_uzbek:_s`
+3. `a_salah_osay_ditutu`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (103,240 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 | 10,204 |
+| Total Tokens | 135,845 |
+| Mean Frequency | 13.31 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 94.14 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | say | 3,995 |
+| 2 | da | 3,328 |
+| 3 | di | 3,241 |
+| 4 | joda | 3,221 |
+| 5 | rik | 2,433 |
+| 6 | huruf | 2,283 |
+| 7 | osay | 1,955 |
+| 8 | salah | 1,942 |
+| 9 | basa | 1,659 |
+| 10 | nagara | 1,381 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | vlaicu | 2 |
+| 2 | luca | 2 |
+| 3 | transnistria | 2 |
+| 4 | ubakna | 2 |
+| 5 | kampanye | 2 |
+| 6 | renamed | 2 |
+| 7 | eschate | 2 |
+| 8 | maharaja | 2 |
+| 9 | sawai | 2 |
+| 10 | leenvertaling | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0145 |
+| R² (Goodness of Fit) | 0.985126 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 43.9% |
+| Top 1,000 | 73.1% |
+| Top 5,000 | 91.9% |
+| Top 10,000 | 99.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9851 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 43.9% of corpus
+- **Long Tail:** 204 words needed for remaining 0.3% 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 | 0.4759 🏆 | 0.4316 | N/A | N/A |
+| **mono_64d** | 64 | 0.1580 | 0.4265 | N/A | N/A |
+| **mono_128d** | 128 | 0.0253 | 0.4281 | N/A | N/A |
+| **aligned_32d** | 32 | 0.4759 | 0.4371 | 0.0100 | 0.1340 |
+| **aligned_64d** | 64 | 0.1580 | 0.4395 | 0.0340 | 0.1880 |
+| **aligned_128d** | 128 | 0.0253 | 0.4352 | 0.0600 | 0.2700 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.4759 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4330. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.0% 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 | **-0.104** | 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` | sanjar, serbia, sipa |
+| `-k` | kʼani, korcing, kapulauan |
+| `-t` | tokoh, tujia, tsʰ |
+| `-b` | baloo, buvariasi, bangli |
+| `-a` | animation, atlanta, about |
+| `-m` | map, moksha, malik |
+| `-p` | pir, protestan, phapphā |
+| `-ka` | kapulauan, kalumpukartikulasi, kaum |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | animation, protestan, amazon |
+| `-a` | isina, atlanta, moksha |
+| `-an` | protestan, kapulauan, sabduwan |
+| `-i` | kʼani, asingipabunyi, buvariasi |
+| `-o` | baloo, hulontalo, euro |
+| `-ng` | halimawong, korcing, badung |
+| `-g` | halimawong, korcing, badung |
+| `-na` | isina, saunina, buguna |
+
+### 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 |
+|------|----------|------------------|----------|
+| `amba` | 1.74x | 31 contexts | hamba, tambah, sambal |
+| `anda` | 1.61x | 39 contexts | tanda, danda, banda |
+| `atan` | 1.88x | 20 contexts | katan, datang, ikatan |
+| `ngga` | 1.67x | 29 contexts | unggas, dangga, unggak |
+| `anta` | 1.85x | 18 contexts | ganta, antar, manta |
+| `angg` | 1.72x | 20 contexts | anggur, dangga, manggo |
+| `angk` | 1.67x | 22 contexts | angka, nangka, bangka |
+| `aran` | 1.70x | 19 contexts | jarang, barani, ataran |
+| `anga` | 1.68x | 19 contexts | nanga, manga, sanga |
+| `ngan` | 1.75x | 16 contexts | nganak, pangan, mongan |
+| `tara` | 1.79x | 12 contexts | utara, ataran, satara |
+| `tang` | 1.77x | 12 contexts | datang, tangut, batang |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-n` | 92 words | pembaptisan, pangombangan |
+| `-k` | `-n` | 92 words | kagiatan, kakayoan |
+| `-k` | `-an` | 82 words | kagiatan, kakayoan |
+| `-p` | `-an` | 81 words | pembaptisan, pangombangan |
+| `-t` | `-o` | 77 words | tihaluko, timanfaatko |
+| `-s` | `-a` | 48 words | sirkasia, sinhala |
+| `-b` | `-n` | 46 words | bualiran, butopatan |
+| `-p` | `-a` | 45 words | posisina, persiya |
+| `-m` | `-a` | 42 words | maha, moldova |
+| `-s` | `-n` | 40 words | shan, swan |
+
+### 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 |
+|------|-----------------|------------|------|
+| mazanderani | **`mazander-an-i`** | 7.5 | `an` |
+| anggotana | **`anggot-an-a`** | 7.5 | `an` |
+| kamungkinan | **`kamungki-n-an`** | 7.5 | `n` |
+| padananna | **`padan-an-na`** | 7.5 | `an` |
+| pakngapuluh | **`pa-k-ngapuluh`** | 7.5 | `ngapuluh` |
+| vientiane | **`vienti-an-e`** | 7.5 | `an` |
+| kahikanna | **`kahik-an-na`** | 7.5 | `an` |
+| panggunaan | **`panggu-na-an`** | 7.5 | `na` |
+| batanghari | **`batangh-a-ri`** | 7.5 | `a` |
+| rangkaian | **`rangka-i-an`** | 7.5 | `i` |
+| sinngguway | **`s-in-ngguway`** | 7.5 | `ngguway` |
+| louisiana | **`louisi-an-a`** | 7.5 | `an` |
+| pamarintahanna | **`pamarintah-an-na`** | 7.5 | `an` |
+| panghubung | **`pa-ng-hubung`** | 7.5 | `hubung` |
+| homorganik | **`homorg-an-ik`** | 7.5 | `an` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Komering 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 | **32k BPE** | Best compression (3.78x) |
+| N-gram | **2-gram** | Lowest perplexity (307) |
+| Markov | **Context-4** | Highest predictability (96.6%) |
+| 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 07:36:21*
diff --git a/kge_morph_tokenizer.json b/kge_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..ce4ba4021de4110e76263d36d19f4221183c438e
--- /dev/null
+++ b/kge_morph_tokenizer.json
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+version https://git-lfs.github.com/spec/v1
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diff --git a/models/vocabulary/kge_vocabulary_metadata.json b/models/vocabulary/kge_vocabulary_metadata.json
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--- /dev/null
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+{
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diff --git a/models/word_markov/kge_markov_ctx2_word.parquet b/models/word_markov/kge_markov_ctx2_word.parquet
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