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README.md

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+---
+language: ms
+language_name: Malay
+language_family: austronesian_malay
+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_malay
+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: 5.467
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7590
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Malay - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Malay** 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** | 4.396x | 4.40 | 0.0792% | 1,649,775 |
+| **16k** | 4.887x | 4.89 | 0.0880% | 1,484,029 |
+| **32k** | 5.238x | 5.24 | 0.0943% | 1,384,577 |
+| **64k** | 5.467x 🏆 | 5.47 | 0.0985% | 1,326,382 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Patavan merupakan sebuah kawasan yang terletak di Iran. Rujukan di Kaunti Sowme'...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+10 more)` | 20 |
+| 16k | `▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+9 more)` | 19 |
+| 32k | `▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+8 more)` | 18 |
+| 64k | `▁pata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran . ... (+7 more)` | 17 |
+
+**Sample 2:** `Elikesik, Alanya merupakan sebuah kawasan yang terletak di Turki. Lihat juga Dae...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁e lik es ik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ... (+13 more)` | 23 |
+| 16k | `▁e lik es ik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ... (+13 more)` | 23 |
+| 32k | `▁e lik esik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ▁yang ... (+12 more)` | 22 |
+| 64k | `▁e lik esik , ▁alanya ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ... (+11 more)` | 21 |
+
+**Sample 3:** `Mélagues ialah komun di jabatan di Aveyron selatan Perancis. Lihat juga Komun di...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁m é lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁av ... (+17 more)` | 27 |
+| 16k | `▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)` | 21 |
+| 32k | `▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)` | 21 |
+| 64k | `▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.467x compression
+- **Lowest UNK Rate:** 8k with 0.0792% 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 | 154,800 | 17.24 | 1,398,741 | 9.5% | 21.6% |
+| **2-gram** | Subword | 230 🏆 | 7.85 | 34,956 | 72.4% | 99.3% |
+| **3-gram** | Word | 325,387 | 18.31 | 2,569,328 | 10.9% | 21.5% |
+| **3-gram** | Subword | 1,970 | 10.94 | 177,272 | 30.0% | 74.6% |
+| **4-gram** | Word | 377,074 | 18.52 | 3,858,678 | 13.0% | 24.9% |
+| **4-gram** | Subword | 11,507 | 13.49 | 899,517 | 15.2% | 43.5% |
+| **5-gram** | Word | 161,998 | 17.31 | 2,522,183 | 16.9% | 31.6% |
+| **5-gram** | Subword | 45,772 | 15.48 | 2,934,104 | 9.2% | 28.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pada tahun` | 182,854 |
+| 2 | `merupakan sebuah` | 180,992 |
+| 3 | `terletak di` | 177,681 |
+| 4 | `yang terletak` | 164,205 |
+| 5 | `pautan luar` | 142,071 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yang terletak di` | 145,449 |
+| 2 | `rujukan pautan luar` | 85,486 |
+| 3 | `komun di jabatan` | 62,009 |
+| 4 | `ini juga merupakan` | 48,368 |
+| 5 | `merupakan sebuah kawasan` | 47,805 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kayu yang telah ditebang` | 47,171 |
+| 2 | `pada batang kayu hidup` | 47,170 |
+| 3 | `kayu dan dapat menyebabkan` | 47,170 |
+| 4 | `hidup atau kayu yang` | 47,158 |
+| 5 | `atau kayu yang telah` | 47,158 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pada batang kayu hidup atau` | 47,158 |
+| 2 | `kayu hidup atau kayu yang` | 47,158 |
+| 3 | `hidup atau kayu yang telah` | 47,158 |
+| 4 | `atau kayu yang telah ditebang` | 47,158 |
+| 5 | `batang kayu hidup atau kayu` | 47,158 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n` | 20,347,605 |
+| 2 | `n _` | 12,967,523 |
+| 3 | `a _` | 9,650,615 |
+| 4 | `n g` | 8,591,886 |
+| 5 | `e r` | 8,563,362 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 10,591,409 |
+| 2 | `a n g` | 4,452,343 |
+| 3 | `n g _` | 3,798,949 |
+| 4 | `_ m e` | 3,721,748 |
+| 5 | `_ d a` | 3,505,831 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `k a n _` | 2,911,587 |
+| 2 | `a n g _` | 2,824,382 |
+| 3 | `_ m e n` | 1,732,841 |
+| 4 | `d a n _` | 1,723,759 |
+| 5 | `_ d a n` | 1,689,159 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d a n _` | 1,659,387 |
+| 2 | `y a n g _` | 1,558,610 |
+| 3 | `_ y a n g` | 1,536,001 |
+| 4 | `p a d a _` | 1,174,037 |
+| 5 | `n g a n _` | 1,075,874 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 230
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.8903 | 1.854 | 12.05 | 1,355,695 | 11.0% |
+| **1** | Subword | 1.3613 | 2.569 | 10.50 | 18,334 | 0.0% |
+| **2** | Word | 0.4107 | 1.329 | 2.43 | 16,287,373 | 58.9% |
+| **2** | Subword | 0.5296 | 1.444 | 3.13 | 192,441 | 47.0% |
+| **3** | Word | 0.1577 | 1.116 | 1.33 | 39,552,428 | 84.2% |
+| **3** | Subword | 0.5062 | 1.420 | 3.06 | 602,656 | 49.4% |
+| **4** | Word | 0.0565 🏆 | 1.040 | 1.09 | 52,397,468 | 94.4% |
+| **4** | Subword | 0.5777 | 1.492 | 3.09 | 1,842,490 | 42.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `dan berukuran kecil jepun pautan luar pesisir permaisuri imperial jepun dia juga berkhidmat di pulau...`
+2. `di sini pada perlawanan 213 kampung rujukan pautan luar the kebar district south park hyo song`
+3. `yang paling muda bahagian barat daya perancis lihat juga komun di rantau ini mempunyai ketahanan sel...`
+
+**Context Size 2:**
+
+1. `pada tahun album lagu tema dua taman negara namadgi nil desperandum dan rock im park kwang jae`
+2. `merupakan sebuah sistem perakaman bunyi sehingga syarikat yang sepatutnya dia kemudian diganti naman...`
+3. `terletak di daerah schmalkalden meiningen thüringen jerman perbandaran di león senarai kawasan perba...`
+
+**Context Size 3:**
+
+1. `yang terletak di wilayah hajdú bihar di timur hungary di hungary perbandaran di hungary hu kazár`
+2. `rujukan pautan luar national portal of india bagli`
+3. `komun di jabatan oise di utara perancis lihat juga komun di jabatan cantal di selatan tengah austral...`
+
+**Context Size 4:**
+
+1. `kayu yang telah ditebang rujukan the world of jewel beetles bellamy c l 25 aug kumbang`
+2. `kayu dan dapat menyebabkan kerosakan pada batang kayu hidup atau kayu yang telah ditebang rujukan ti...`
+3. `pada batang kayu hidup atau kayu yang telah ditebang rujukan the world of jewel beetles bellamy c l ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `an_il_mekuardan_`
+2. `_rum_bun_an_pawa`
+3. `nyarekomert_ris,`
+
+**Context Size 2:**
+
+1. `ang_turuparri_ton`
+2. `n_merun,_85*_esik`
+3. `a_bahur,_"udperja`
+
+**Context Size 3:**
+
+1. `an_perkan_diberkan`
+2. `ang_dia_/_bum_man_`
+3. `ng_timusi_mina_ada`
+
+**Context Size 4:**
+
+1. `kan_syed_muar_roorg`
+2. `ang_ditebangsa._mel`
+3. `_mengen,_arya_acara`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,842,490 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 | 605,972 |
+| Total Tokens | 70,999,280 |
+| Mean Frequency | 117.17 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4627.23 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | dan | 1,667,877 |
+| 2 | di | 1,592,069 |
+| 3 | yang | 1,542,694 |
+| 4 | pada | 780,935 |
+| 5 | dalam | 669,363 |
+| 6 | dengan | 531,319 |
+| 7 | ini | 520,575 |
+| 8 | untuk | 476,200 |
+| 9 | sebagai | 428,345 |
+| 10 | dari | 375,471 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | 石塘社 | 2 |
+| 2 | 燕尾脊 | 2 |
+| 3 | ekapadashirshasana | 2 |
+| 4 | danjae | 2 |
+| 5 | sinminhoe | 2 |
+| 6 | 국가 | 2 |
+| 7 | kukka | 2 |
+| 8 | muhurtam | 2 |
+| 9 | sasayan | 2 |
+| 10 | norr | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1100 |
+| R² (Goodness of Fit) | 0.989051 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 30.1% |
+| Top 1,000 | 59.6% |
+| Top 5,000 | 78.8% |
+| Top 10,000 | 85.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9891 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 30.1% of corpus
+- **Long Tail:** 595,972 words needed for remaining 14.8% 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 | 0.7590 🏆 | 0.3685 | N/A | N/A |
+| **mono_64d** | 64 | 0.7509 | 0.3058 | N/A | N/A |
+| **mono_128d** | 128 | 0.6593 | 0.3026 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7590 | 0.3657 | 0.4180 | 0.7660 |
+| **aligned_64d** | 64 | 0.7509 | 0.3027 | 0.6580 | 0.9240 |
+| **aligned_128d** | 128 | 0.6593 | 0.3113 | 0.7120 | 0.9240 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7590 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3261. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 71.2% 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 | **0.114** | 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` | shapie, skalstad, smps |
+| `-a` | a602, altomare, ablan |
+| `-ma` | maunoury, mascheix, marzian |
+| `-k` | kimbirila, krasnopolye, khairune |
+| `-m` | metropolitans, mpumnosemerahtimbalan, mesoamerican |
+| `-p` | patronnya, piscatori, pagalungan |
+| `-b` | badoglio, bougaâ, baturuyuk |
+| `-t` | tabuk, thomalla, terlelap |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | rundengan, nyongbyon, ablan |
+| `-a` | thomalla, gombaknya, patronnya |
+| `-s` | metropolitans, smps, cynops |
+| `-an` | rundengan, ablan, pagalungan |
+| `-e` | altomare, shapie, stadsgemeente |
+| `-i` | piscatori, pipaltari, molinari |
+| `-ya` | gombaknya, patronnya, prosidurnya |
+| `-r` | toner, headmaster, wijsmuller |
+
+### 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 |
+|------|----------|------------------|----------|
+| `angk` | 2.06x | 551 contexts | angku, angky, angka |
+| `ngka` | 2.08x | 409 contexts | ingka, engka, ongka |
+| `ingg` | 2.12x | 247 contexts | ningg, tingg, ingga |
+| `ukan` | 2.12x | 227 contexts | gukan, pukan, bukan |
+| `embe` | 1.99x | 165 contexts | membe, bembe, embed |
+| `rkan` | 2.25x | 90 contexts | erkan, orkan, arkan |
+| `memb` | 2.34x | 73 contexts | membe, memba, member |
+| `enja` | 1.99x | 152 contexts | penja, senja, kenja |
+| `meny` | 2.36x | 63 contexts | ameny, menya, menye |
+| `ebag` | 2.54x | 42 contexts | sebag, kebagu, sebagi |
+| `mber` | 1.76x | 217 contexts | imber, amber, umber |
+| `ndar` | 1.71x | 238 contexts | ndara, indar, undar |
+
+### 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` | 126 words | perkaitan, penyelerasan |
+| `-k` | `-n` | 108 words | kalinin, kaputihan |
+| `-p` | `-an` | 100 words | perkaitan, penyelerasan |
+| `-p` | `-a` | 100 words | polyandra, pencuba |
+| `-s` | `-a` | 90 words | sayura, sametha |
+| `-m` | `-n` | 87 words | mininggalkan, mazan |
+| `-k` | `-an` | 80 words | kaputihan, kiniéran |
+| `-s` | `-n` | 78 words | suthan, satarazwan |
+| `-s` | `-s` | 78 words | sponsors, suvalmas |
+| `-a` | `-a` | 66 words | ammochloa, avaa |
+
+### 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 |
+|------|-----------------|------------|------|
+| qinghuang | **`qinghu-a-ng`** | 7.5 | `a` |
+| tordehumos | **`tordehum-o-s`** | 7.5 | `o` |
+| pulangnya | **`pulang-n-ya`** | 7.5 | `n` |
+| dialirkan | **`dialir-k-an`** | 7.5 | `k` |
+| penggertak | **`pengger-ta-k`** | 7.5 | `ta` |
+| christiansted | **`christians-t-ed`** | 7.5 | `t` |
+| epikurean | **`epikur-e-an`** | 7.5 | `e` |
+| unitedpathum | **`unitedpat-h-um`** | 7.5 | `h` |
+| chandrasena | **`chandrase-n-a`** | 7.5 | `n` |
+| panggungnya | **`panggung-n-ya`** | 7.5 | `n` |
+| sanskritnya | **`sanskrit-n-ya`** | 7.5 | `n` |
+| bibliografinya | **`bibliografi-n-ya`** | 7.5 | `n` |
+| kondiadou | **`kondiad-o-u`** | 7.5 | `o` |
+| azmatkhan | **`azmatk-h-an`** | 7.5 | `h` |
+| karakteristiknya | **`karakteristik-n-ya`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Malay 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 (5.47x) |
+| N-gram | **2-gram** | Lowest perplexity (230) |
+| Markov | **Context-4** | Highest predictability (94.4%) |
+| 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 19:01:46*

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