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README.md

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+---
+language: ml
+language_name: Malayalam
+language_family: dravidian_south
+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-dravidian_south
+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: 5.366
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7956
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Malayalam - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Malayalam** 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** | 3.779x | 3.77 | 0.0796% | 1,899,588 |
+| **16k** | 4.330x | 4.33 | 0.0913% | 1,657,653 |
+| **32k** | 4.867x | 4.86 | 0.1026% | 1,474,841 |
+| **64k** | 5.366x 🏆 | 5.36 | 0.1131% | 1,337,826 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `സിറിയ പരസ്യമായി തുക്കുശിക്ഷ നടപ്പിലാക്കാറുണ്ട്. രണ്ട് ജൂതന്മാരെയും ഇസ്രായേൽ ചാരൻ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁സി റിയ ▁പരസ്യ മായി ▁തു ക്കു ശിക്ഷ ▁നടപ്പില ാക്ക ാറുണ്ട് ... (+29 more)` | 39 |
+| 16k | `▁സിറിയ ▁പരസ്യ മായി ▁തു ക്കു ശിക്ഷ ▁നടപ്പില ാക്ക ാറുണ്ട് . ... (+22 more)` | 32 |
+| 32k | `▁സിറിയ ▁പരസ്യമായി ▁തു ക്കു ശിക്ഷ ▁നടപ്പിലാക്ക ാറുണ്ട് . ▁രണ്ട് ▁ജൂത ... (+18 more)` | 28 |
+| 64k | `▁സിറിയ ▁പരസ്യമായി ▁തു ക്കു ശിക്ഷ ▁നടപ്പിലാക്ക ാറുണ്ട് . ▁രണ്ട് ▁ജൂത ... (+18 more)` | 28 |
+
+**Sample 2:** `ജെസ്നറിയേസീ കുടുംബത്തിലെ പൂച്ചെടികളുടെ ഒരു ഇനമാണ് അച്ചിമെനെസ് സെറ്റോന. ലാണ് എച്ച...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ജെ സ് ന റിയ േസീ ▁കുടുംബത്തിലെ ▁പൂ ച്ചെ ട ികളുടെ ... (+24 more)` | 34 |
+| 16k | `▁ജെ സ് ന റിയ േസീ ▁കുടുംബത്തിലെ ▁പൂ ച്ചെട ികളുടെ ▁ഒരു ... (+21 more)` | 31 |
+| 32k | `▁ജെ സ്ന റിയ േസീ ▁കുടുംബത്തിലെ ▁പൂച്ചെടികളുടെ ▁ഒരു ▁ഇനമാണ് ▁അ ച്ചി ... (+16 more)` | 26 |
+| 64k | `▁ജെ സ്ന റിയ േസീ ▁കുടുംബത്തിലെ ▁പൂച്ചെടികളുടെ ▁ഒരു ▁ഇനമാണ് ▁അ ച്ചി ... (+16 more)` | 26 |
+
+**Sample 3:** `ച���വന്ന സൂര്യകാന്തി അല്ലെങ്കിൽ മെക്സിക്കൻ സൂര്യകാന്തി (Tithonia rotundifolia) എന്...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ചുവന്ന ▁സൂര്യ കാ ന്തി ▁അല്ലെങ്കിൽ ▁മെക്സ ിക്കൻ ▁സൂര്യ കാ ന്തി ... (+15 more)` | 25 |
+| 16k | `▁ചുവന്ന ▁സൂര്യ കാ ന്തി ▁അല്ലെങ്കിൽ ▁മെക്സിക്കൻ ▁സൂര്യ കാ ന്തി ▁( ... (+13 more)` | 23 |
+| 32k | `▁ചുവന്ന ▁സൂര്യ കാന്തി ▁അല്ലെങ്കിൽ ▁മെക്സിക്കൻ ▁സൂര്യ കാന്തി ▁( t ith ... (+10 more)` | 20 |
+| 64k | `▁ചുവന്ന ▁സൂര്യകാന്തി ▁അല്ലെങ്കിൽ ▁മെക്സിക്കൻ ▁സൂര്യകാന്തി ▁( t ith onia ▁rotund ... (+7 more)` | 17 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.366x compression
+- **Lowest UNK Rate:** 8k with 0.0796% 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 | 139,290 | 17.09 | 379,181 | 5.1% | 14.6% |
+| **2-gram** | Subword | 4,333 🏆 | 12.08 | 159,693 | 25.9% | 60.4% |
+| **3-gram** | Word | 162,356 | 17.31 | 343,937 | 4.1% | 12.3% |
+| **3-gram** | Subword | 44,252 | 15.43 | 931,732 | 8.1% | 25.5% |
+| **4-gram** | Word | 421,212 | 18.68 | 683,653 | 2.4% | 7.3% |
+| **4-gram** | Subword | 274,830 | 18.07 | 3,800,547 | 3.9% | 12.9% |
+| **5-gram** | Word | 348,331 | 18.41 | 513,800 | 2.5% | 7.1% |
+| **5-gram** | Subword | 911,486 | 19.80 | 7,221,542 | 2.3% | 8.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `of the` | 18,339 |
+| 2 | `പുറത്തേക്കുള്ള കണ്ണികൾ` | 13,257 |
+| 3 | `അവലംബം പുറത്തേക്കുള്ള` | 8,470 |
+| 4 | `എന്ന പേരിൽ` | 7,743 |
+| 5 | `in the` | 7,446 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `അവലംബം പുറത്തേക്കുള്ള കണ്ണികൾ` | 8,392 |
+| 2 | `അവലംബം പുറം കണ്ണികൾ` | 3,573 |
+| 3 | `കെ ജെ യേശുദാസ്` | 3,343 |
+| 4 | `സി പി ഐ` | 3,263 |
+| 5 | `കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ്` | 2,878 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `സി പി ഐ എം` | 1,992 |
+| 2 | `കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ്` | 1,978 |
+| 3 | `കേരള സാഹിത്യ അക്കാദമി പുരസ്കാരം` | 1,461 |
+| 4 | `comments endemic to india` | 990 |
+| 5 | `സാഹിത്യ അക്കാദമി പുരസ്കാരം ലഭിച്ച` | 932 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ്` | 1,225 |
+| 2 | `സാഹിത്യ അക്കാദമി പുരസ്കാരം ലഭിച്ച കൃതികൾ` | 687 |
+| 3 | `archived from the original on` | 637 |
+| 4 | `കോൺഗ്രസ് ഐ യു ഡി എഫ്` | 589 |
+| 5 | `ലേബർ ലേബർ ലേബർ ലേബർ ലേബർ` | 588 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. _` | 1,783,270 |
+| 2 | `ൽ _` | 1,276,589 |
+| 3 | `_ അ` | 1,260,398 |
+| 4 | `, _` | 1,069,781 |
+| 5 | `ൻ _` | 661,867 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ന്നു . _` | 322,382 |
+| 2 | `_ അ വ` | 260,904 |
+| 3 | `ക ൾ _` | 257,512 |
+| 4 | `_ ഒ രു` | 245,468 |
+| 5 | `ഒ രു _` | 243,826 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ഒ രു _` | 235,393 |
+| 2 | `രു ന്നു . _` | 110,664 |
+| 3 | `_ എ ന്ന _` | 110,073 |
+| 4 | `t h e _` | 94,998 |
+| 5 | `_ t h e` | 94,093 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t h e _` | 87,370 |
+| 2 | `യി രു ന്നു . _` | 72,530 |
+| 3 | `_ അ വ ലം ബം` | 64,383 |
+| 4 | `അ വ ലം ബം _` | 54,405 |
+| 5 | `_ ഉ പ യോ ഗി` | 50,935 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 4,333
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~8% 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 | 0.6805 | 1.603 | 5.82 | 2,337,199 | 31.9% |
+| **1** | Subword | 0.9605 | 1.946 | 14.48 | 34,171 | 3.9% |
+| **2** | Word | 0.1758 | 1.130 | 1.40 | 13,589,609 | 82.4% |
+| **2** | Subword | 0.7010 | 1.626 | 5.25 | 494,724 | 29.9% |
+| **3** | Word | 0.0434 | 1.031 | 1.07 | 18,962,615 | 95.7% |
+| **3** | Subword | 0.5422 | 1.456 | 3.38 | 2,596,737 | 45.8% |
+| **4** | Word | 0.0148 🏆 | 1.010 | 1.02 | 20,273,957 | 98.5% |
+| **4** | Subword | 0.4401 | 1.357 | 2.35 | 8,776,422 | 56.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ഒരു ദുരന്തനാടകമായ മാക്ബെത്തിലെ ലേഡി ബേർഡ് എന്നിവയെ ലയിപ്പിച്ച് ജിയോസ്റ്റാർ ഇന്ത്യ കോസ്റ്റാറിക്ക യു ഡ...`
+2. `ഈ പ്രദേശത്തിന്റെ സാമൂഹിക സമത്വം എന്ന നിലയിലാണ് പ്രശസ്തൻ ജൊനാഥൻ വാൻ ഐക്കിന്റെ ചിത്രമാണ് sify archived...`
+3. `എന്ന ഗാനത്തിന് ഹിന്ദി ടെലിവിഷനിലും അഭിനയം കൂടാതെ ഭസ്മലേപനം തപോവേഷം യോഗ്യമല്ലാത്ത വിഭവങ്ങൾ ഉപയോഗിക്കു...`
+
+**Context Size 2:**
+
+1. `of the vietnam war protests vietnam war in history set for tuesday observer reporter retrieved decem...`
+2. `പുറത്തേക്കുള്ള കണ്ണികൾ സർവ്വകലാശാലക്കു കീഴിലുള്ള കലാലയങ്ങൾ പുറേത്തേക്കുള്ള കണ്ണികൾ ഗവൺെമെന്റ് കാേളേജ...`
+3. `അവലംബം പുറത്തേക്കുള്ള കണ്ണികൾ bananas org മുസെല്ല ലാസിയോകാർപമ്യൂസെല്ല ലാസിയോകാർപ മരങ്ങൾ സസ്യങ്ങൾ ബംഗ...`
+
+**Context Size 3:**
+
+1. `അവലംബം പുറത്തേക്കുള്ള കണ്ണികൾ strasbourg the most european french city official french website in en...`
+2. `അവലംബം പുറം കണ്ണികൾ അമേരിക്കയിലെ സസ്യജാലം സസ്യജാലം സസ്യജാലം സസ്യജാലം സസ്യജാലം അധിനിവേശസസ്യങ്ങൾ`
+3. `കെ ജെ യേശുദാസ് കൈതപ്രം ദാമോദരൻ നമ്പൂതിരി തങ്കനിലാ കെ എസ് ചിത്ര അണിയറ പ്രവർത്തകർ ഛായാഗ്രഹണം ആനന്ദക്കു...`
+
+**Context Size 4:**
+
+1. `സി പി ഐ എം ആക്കാവിള സതീക്ക് എൻ ഡി എ എം കെ രാഘവൻ കോൺഗ്രസ് ഐ യു ഡി എഫ് ഫിലിപ്പോസ് തോമസ്`
+2. `കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് കൺസർവേറ്റീവ് ലേബർhull south west ലിബറൽ ലിബറൽ ലിബറൽ ലിബറൽ ലിബറ...`
+3. `കേരള സാഹിത്യ അക്കാദമി പുരസ്കാരം ഈ കൃതിക്ക് ലഭിച്ചിട്ടുണ്ട് ഈ പുസ്തകത്തിൽ നോവലിന്റെയോ കഥപറച്ചിലിന്റെയ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_234_ഭാവരിൽ_കളജില്ല._`
+2. `._കുറൽ_._നാപുറൻസിന്റായിക്കു`
+3. `കൾഡ്_ചലയുള്ളയ്ക്ക്_ഗുണങ്ങളെ_ൽ`
+
+**Context Size 2:**
+
+1. `._തരണത്തിലേർപ്പെട്ടില്ല._അക്കിലിനു`
+2. `ൽ_ബസിയിൽ_നിക്കോളാസ്_പ്രകൃതിയുടെ`
+3. `_അവനോടെ_പൗരന്മാർ_മന്നേയൻ_റി`
+
+**Context Size 3:**
+
+1. `ന്നു._അതിർത്തിയോ_കൃഷ്ണൻ_:_♂).`
+2. `_അവതരിപ്പിക്കുന്നതുവരെ_പോരാട്ടത്തിനു_`
+3. `കൾ_ഡാർട്ട്_ഗാലറി_ഡൈക്ലോഫെനാക്_ഉ`
+
+**Context Size 4:**
+
+1. `_ഒരു_മെമ്മറിയും_താലൂക്കുകൾ,_മകൾ`
+2. `രുന്നു._ചൈനീസ്_ലിപി:_মোহাম্মদ_সাহা`
+3. `_എന്ന_പട്ടണം._ഒന്നാം_ലോകമഹായുദ്ധസ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (8,776,422 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 | 801,863 |
+| Total Tokens | 20,480,775 |
+| Mean Frequency | 25.54 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 511.27 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ഒരു | 243,510 |
+| 2 | ഈ | 162,409 |
+| 3 | എന്ന | 110,774 |
+| 4 | the | 94,058 |
+| 5 | of | 88,580 |
+| 6 | അവലംബം | 64,447 |
+| 7 | ഇത് | 55,817 |
+| 8 | നിന്ന് | 50,269 |
+| 9 | അദ്ദേഹം | 49,641 |
+| 10 | and | 49,492 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | areekode | 2 |
+| 2 | ഭാഗമാകുവാനും | 2 |
+| 3 | ഗായകനെയും | 2 |
+| 4 | ഗഫൂറിനെ | 2 |
+| 5 | സ്‌കൂളിനായി | 2 |
+| 6 | തെല്ലിപ്പളൈ | 2 |
+| 7 | എരവല്ലൻ | 2 |
+| 8 | കൈകാടി | 2 |
+| 9 | പട്ടപ്പു | 2 |
+| 10 | ടെനോം | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8771 |
+| R² (Goodness of Fit) | 0.995192 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 14.6% |
+| Top 1,000 | 34.1% |
+| Top 5,000 | 52.2% |
+| Top 10,000 | 60.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9952 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 14.6% of corpus
+- **Long Tail:** 791,863 words needed for remaining 39.4% 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 | 0.7956 | 0.3438 | N/A | N/A |
+| **mono_64d** | 64 | 0.7180 | 0.2843 | N/A | N/A |
+| **mono_128d** | 128 | 0.6063 | 0.2328 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7956 🏆 | 0.3488 | 0.0920 | 0.4000 |
+| **aligned_64d** | 64 | 0.7180 | 0.2864 | 0.1940 | 0.5680 |
+| **aligned_128d** | 128 | 0.6063 | 0.2384 | 0.2720 | 0.6480 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7956 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2891. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 27.2% 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 |
+|--------|----------|
+| `-ക` | കൽപി, കുടുംബത്തിൽ‌, കൊയ്തിരുന്നു |
+| `-മ` | മോപ്പസാങ്ങ്, മുറിയിലേയ്ക്കു, മെലിന |
+| `-സ` | സമഗ്രസംഭാവനാ, സ്വാധീനിച്ചിരുന്നത്, സർവ്വകലാശാല |
+| `-അ` | അറിയപ്പെടുന്നതായുണ്ട്, അഥാനം, അൽഗൊരിതത്തെ |
+| `-പ` | പ്രസാദാത്മകതയും, പാർലമെന്റിലേയ്ക്ക്, പ്രവിശ്യാഭരണാധികാരികൾ |
+| `-വ` | വ്യാകരണതലമെന്നും, വോൾഗാനദിയുടെ, വെട്ടിപ്പരുക്കേൽ‌പ്പിച്ചതിൽ |
+| `-ന` | നിരുത്സാഹപ്പെടുത്തുന്നതിനായി, നാകമൂറക്ക്, നസ്ർ |
+| `-ബ` | ബക്ത്, ബന്ധിപ്പിക്കണം, ബാൽഡ്‌വിൻ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ൽ` | ദിവ്യശക്തിയിൽ, വെട്ടിപ്പരുക്കേൽ‌പ്പിച്ചതിൽ, വ്യത്യാസപ്പെടുന്നുവെങ്കിൽ |
+| `-ൾ` | പ്രവിശ്യാഭരണാധികാരികൾ, മോണോഗ്രാഫുകൾ, മനുഷ്യാവശിഷ്ടങ്ങൾ |
+| `-യ` | വിടർത്തിയ, ഊർജസ്വലവുമായ, വ്യവസായഗ്രൂപ്പുകളിലൊന്നായ |
+| `-ള` | തിരക്കുള്ള, വീട്ടാവശ്യത്തിനുള്ള, പുഞ്ചിരികണ്ണുള്ള |
+| `-ൻ` | റെസിൻ, ഗ്രുൻ, ബാൽഡ്‌വിൻ |
+| `-കൾ` | പ്രവിശ്യാഭരണാധികാരികൾ, മോണോഗ്രാഫുകൾ, ആദിമപ്രപഞ്ചമാതൃകകൾ |
+| `-ന` | മെലിന, ഉപയോഗിക്കുകയെന്ന, ഭരിച്ചുപോന്ന |
+| `-ർ` | എതിർത്തവർ, നസ്ർ, വിഷർ |
+
+### 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` | 3.14x | 71 contexts | ratio, jatio, ratios |
+| `tion` | 3.05x | 76 contexts | tiong, action, nation |
+| `ment` | 3.00x | 81 contexts | ament, mentz, mentv |
+| `nter` | 2.90x | 79 contexts | inter, unter, enter |
+| `stor` | 3.04x | 62 contexts | storm, stora, stork |
+| `isto` | 3.32x | 40 contexts | histo, cristo, aristo |
+| `ture` | 2.88x | 57 contexts | turek, future, suture |
+| `mber` | 2.89x | 56 contexts | ember, amber, imber |
+| `nati` | 3.23x | 36 contexts | nation, donati, naties |
+| `ctio` | 3.04x | 39 contexts | action, sectio, lectio |
+| `iver` | 2.86x | 48 contexts | river, liver, siver |
+| `ersi` | 2.82x | 44 contexts | persia, mersin, bersih |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ക` | `-ൽ` | 76 words | കാരണത്താൽ, കോട്ടമുറിക്കൽ |
+| `-പ` | `-ൽ` | 58 words | പാരവശ്യത്തിൽ, പ്രഫഷനൽ |
+| `-അ` | `-ൽ` | 36 words | അ‍ഞ്ചൽ, അക്ബർനാമയിൽ |
+| `-ക` | `-ൾ` | 33 words | കാസ്റ്റിംഗുകൾ, കുടുംബവഴക്കുകൾ |
+| `-ക` | `-ൻ` | 33 words | കാളിയൻ, കോടീശ്വരൻ |
+| `-പ` | `-യ` | 33 words | പ്രായ, പ്രോഗ്രാമായ |
+| `-സ` | `-ൽ` | 32 words | സൈപ്രസ്സിൽ, സെപ്റ്റംബരിൽ |
+| `-ക` | `-ള` | 32 words | ക്ഷേത്രകടവിലുള്ള, കാഴ്ചപ്പാടിലുള്ള |
+| `-വ` | `-ൽ` | 31 words | വിധേയരാകുന്നതിൽ, വെൽഡനാഗ്രത്തിൽ |
+| `-വ` | `-ള` | 28 words | വംശങ്ങളോടുള്ള, വഞ്ചിമുട്ടംപിള്ള |
+
+### 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 |
+|------|-----------------|------------|------|
+| theophilus | **`theophil-us`** | 4.5 | `theophil` |
+| ഉന്നതപദവികൾ | **`ഉന്നതപദവി-കൾ`** | 4.5 | `ഉന്നതപദവി` |
+| മലയാളലിപികൾ | **`മലയാളലിപി-കൾ`** | 4.5 | `മലയാളലിപി` |
+| നക്ഷത്രമാണിത് | **`ന-ക-്ഷത്രമാണിത്`** | 4.5 | `്ഷത്രമാണിത്` |
+| ഡക്കാന്റെ | **`ഡ-ക-്കാന്റെ`** | 4.5 | `്കാന്റെ` |
+| അമരക്കാരും | **`അ-മരക്കാരും`** | 4.5 | `മരക്കാരും` |
+| അറസ്റ്റിൽ | **`അ-റസ്റ്റിൽ`** | 4.5 | `റസ്റ്റിൽ` |
+| അശ്രദ്ധയും | **`അ-ശ്രദ്ധയും`** | 4.5 | `ശ്രദ്ധയും` |
+| നിംബാർക്കൻ | **`നിംബാർക്ക-ൻ`** | 4.5 | `നിംബാർക്ക` |
+| michigans | **`michigan-s`** | 4.5 | `michigan` |
+| schimperi | **`schimper-i`** | 4.5 | `schimper` |
+| പാടിത്തുടങ്ങിയ | **`പാടിത്തുടങ്ങി-യ`** | 4.5 | `പാടിത്തുടങ്ങി` |
+| orientales | **`oriental-es`** | 4.5 | `oriental` |
+| കൊത്തുപണികൾ | **`കൊത്തുപണി-കൾ`** | 4.5 | `കൊത്തുപണി` |
+| ശബ്ദംകേട്ട് | **`ശ-ബ-്ദംകേട്ട്`** | 3.0 | `്ദംകേട്ട്` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Malayalam 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 (5.37x) |
+| N-gram | **2-gram** | Lowest perplexity (4,333) |
+| Markov | **Context-4** | Highest predictability (98.5%) |
+| 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 16:23:14*

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