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README.md

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+---
+language: gom
+language_name: Goan Konkani
+language_family: indoaryan_central
+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-indoaryan_central
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.001
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7594
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-09
+---
+
+# Goan Konkani - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Goan Konkani** 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** | 3.046x | 3.05 | 0.1017% | 1,326,874 |
+| **16k** | 3.432x | 3.43 | 0.1145% | 1,177,828 |
+| **32k** | 3.782x | 3.78 | 0.1262% | 1,068,751 |
+| **64k** | 4.001x 🏆 | 4.00 | 0.1335% | 1,010,214 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Muhammad Ali – American Boxer and civil rights campaigner Sondorbh Polleiat Muha...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mu ham m ad ▁ali ▁– ▁american ▁b ox er ... (+26 more)` | 36 |
+| 16k | `▁mu ham mad ▁ali ▁– ▁american ▁box er ▁and ▁c ... (+22 more)` | 32 |
+| 32k | `▁muhammad ▁ali ▁– ▁american ▁box er ▁and ▁civil ▁right s ... (+12 more)` | 22 |
+| 64k | `▁muhammad ▁ali ▁– ▁american ▁boxer ▁and ▁civil ▁rights ▁campaigner ▁sondorbh ... (+9 more)` | 19 |
+
+**Sample 2:** `Ddainn vo डाइण zaun asa ek nustem. thumb thumb Vaidneanik nanv: Scomberoides com...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁dd a inn ▁vo ▁ड ा इ ण ▁zaun ▁asa ... (+35 more)` | 45 |
+| 16k | `▁dd a inn ▁vo ▁ड ाइ ण ▁zaun ▁asa ▁ek ... (+32 more)` | 42 |
+| 32k | `▁dd a inn ▁vo ▁ड ाइ ण ▁zaun ▁asa ▁ek ... (+32 more)` | 42 |
+| 64k | `▁dd a inn ▁vo ▁डाइण ▁zaun ▁asa ▁ek ▁nustem . ... (+28 more)` | 38 |
+
+**Sample 3:** `Benazir Bhutto – – Prime Minister of Pakistan Sondorbh Polleiat Benazir_Bhutto P...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ben az ir ▁bh utt o ▁– ▁– ▁pr im ... (+21 more)` | 31 |
+| 16k | `▁ben az ir ▁bh utt o ▁– ▁– ▁prim e ... (+19 more)` | 29 |
+| 32k | `▁ben az ir ▁bh utto ▁– ▁– ▁prime ▁minister ▁of ... (+14 more)` | 24 |
+| 64k | `▁benazir ▁bh utto ▁– ▁– ▁prime ▁minister ▁of ▁pakistan ▁sondorbh ... (+10 more)` | 20 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.001x compression
+- **Lowest UNK Rate:** 8k with 0.1017% 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 | 5,111 | 12.32 | 27,660 | 26.5% | 53.2% |
+| **2-gram** | Subword | 1,903 🏆 | 10.89 | 38,505 | 35.0% | 75.7% |
+| **3-gram** | Word | 3,053 | 11.58 | 23,678 | 29.6% | 65.3% |
+| **3-gram** | Subword | 14,614 | 13.84 | 161,978 | 13.6% | 39.9% |
+| **4-gram** | Word | 7,146 | 12.80 | 58,546 | 20.9% | 54.3% |
+| **4-gram** | Subword | 60,268 | 15.88 | 513,861 | 9.1% | 23.5% |
+| **5-gram** | Word | 7,630 | 12.90 | 51,862 | 16.7% | 51.6% |
+| **5-gram** | Subword | 124,655 | 16.93 | 739,149 | 7.5% | 17.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `सगळ्यांत लागीं` | 12,985 |
+| 2 | `अंतराचेर आसा` | 11,720 |
+| 3 | `आसा गांवांत` | 10,615 |
+| 4 | `उपलब्ध ना` | 7,887 |
+| 5 | `आसा सगळ्यांत` | 6,281 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `आसा सगळ्यांत लागीं` | 6,260 |
+| 2 | `ना सगळ्यांत लागीं` | 6,129 |
+| 3 | `उपलब्ध ना सगळ्यांत` | 5,476 |
+| 4 | `परस चड अंतराचेर` | 5,262 |
+| 5 | `चड अंतराचेर आसा` | 5,261 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `उपलब्ध ना सगळ्यांत लागीं` | 5,455 |
+| 2 | `परस चड अंतराचेर आसा` | 5,261 |
+| 3 | `किलोमिटर परस चड अंतराचेर` | 5,083 |
+| 4 | `१० किलोमिटर परस चड` | 5,079 |
+| 5 | `अंतराचेर आसा सगळ्यांत लागीं` | 4,361 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `किलोमिटर परस चड अंतराचेर आसा` | 5,082 |
+| 2 | `१० किलोमिटर परस चड अंतराचेर` | 5,079 |
+| 3 | `५ ते १० किलोमिटराच्या अंतराचेर` | 3,486 |
+| 4 | `ते १० किलोमिटराच्या अंतराचेर आसा` | 3,485 |
+| 5 | `परस चड अंतराचेर आसा सगळ्यांत` | 2,545 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. _` | 148,478 |
+| 2 | `_ आ` | 121,490 |
+| 3 | `र _` | 93,882 |
+| 4 | `त _` | 93,586 |
+| 5 | `a n` | 88,705 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ आ सा` | 37,415 |
+| 2 | `_ आ नी` | 34,014 |
+| 3 | `आ नी _` | 32,755 |
+| 4 | `आ सा .` | 29,612 |
+| 5 | `सा . _` | 28,967 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ आ नी _` | 32,519 |
+| 2 | `_ आ सा .` | 29,600 |
+| 3 | `आ सा . _` | 28,938 |
+| 4 | `गां वां त _` | 16,050 |
+| 5 | `_ गां वां त` | 15,394 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ आ सा . _` | 28,926 |
+| 2 | `_ गां वां त _` | 15,269 |
+| 3 | `उ प ल ब्ध _` | 13,831 |
+| 4 | `_ उ प ल ब्ध` | 13,829 |
+| 5 | `स ग ळ्यां त _` | 13,782 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 1,903
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~18% 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 | 0.6729 | 1.594 | 4.16 | 282,021 | 32.7% |
+| **1** | Subword | 1.2577 | 2.391 | 16.60 | 7,296 | 0.0% |
+| **2** | Word | 0.1540 | 1.113 | 1.28 | 1,171,944 | 84.6% |
+| **2** | Subword | 0.6638 | 1.584 | 4.09 | 121,127 | 33.6% |
+| **3** | Word | 0.0305 | 1.021 | 1.04 | 1,503,180 | 97.0% |
+| **3** | Subword | 0.5013 | 1.416 | 2.73 | 495,687 | 49.9% |
+| **4** | Word | 0.0095 🏆 | 1.007 | 1.01 | 1,566,242 | 99.1% |
+| **4** | Subword | 0.3513 | 1.276 | 1.83 | 1,351,866 | 64.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `आनी ग्वाल्हेर इंदूर शारांत १ शासकीय भौशीक वा��पघर उपलब्ध ना सगळ्यांत लागीं चित्रपटगृह व्हिडिओ केंद्र ...`
+2. `आसा सगळ्यांत लागीं प्रसूति आनी जर विधिमंडळान केल्ल्या नळाच्या उदकाची नितळसाण गांवांत मंडी कायमचे बाज...`
+3. `गांवांत दूरध्वनी केंद्र ५ ०००ते ९ किलोमीटर अंतराचेर आसा बाजार ५ ते १० किलोमिटर परस चड`
+
+**Context Size 2:**
+
+1. `सगळ्यांत लागीं पॉलिटेक्निक verna ct १० किलोमिटर परस चड अंतराचेर आसा गावात उपपोस्ट ऑफिस उपलब्ध ना गां...`
+2. `अंतराचेर आसा सगळ्यांत लागीं क्षयरोग उपचार केंद्र १० किलोमिटर परस चड अंतराचेर आसा गांवांत कृषी उत्पन्...`
+3. `आसा गांवांत शुद्धीकरण केल्लें नळाचें उदक पुरवण ना गांवांत न्हाणीघर सोडून सार्वजनिक स्वच्छता घर उपलब्...`
+
+**Context Size 3:**
+
+1. `आसा सगळ्यांत लागीं अनौपचारिक प्रशिक्षणकेंद्र valpoi ५ किलोमिटरा परस कमी अंतराचेर आसा गांवांत खाजगी क...`
+2. `ना सगळ्यांत लागीं कृषी उत्पन्न बाजार समिती उपलब्ध ना सगळ्यांत लागीं सहकारी सावकारी पेडी आसा संदर्भ ग...`
+3. `उपलब्ध ना सगळ्यांत लागीं शेतकी कर्ज संस्था १० किलोमिटर परस चड अंतराचेर आसा सगळ्यांत लागीं पॉलिटेक्नि...`
+
+**Context Size 4:**
+
+1. `उपलब्ध ना सगळ्यांत लागीं इंटरनेट सुवीधा १० किलोमिटर परस चड अंतराचेर आसा सगळ्यांत लागीं पॉलिटेक्निक c...`
+2. `परस चड अंतराचेर आसा वैजकी सुविधा अशासकीय गांवांत १ बाह्यरुग्ण वैजकी सुविधा आसा पिवपाचे उदक गांवांत श...`
+3. `किलोमिटर परस चड अंतराचेर आसा सगळ्यांत लागीं पर्यायी वैजकी रुग्णालय १० किलोमिटर परस चड अंतराचेर आसा ग...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_su_bdeasatsondi`
+2. `addannchvokonant`
+3. `o_varleden_कुर_ಸೋಡುಂ`
+
+**Context Size 2:**
+
+1. `._स्वतंत्र_समाजवाडाय_मुरली.`
+2. `_आसून_गेलो._नामनाथ_वळखुं`
+3. `र_ऑफीस_चवथ्या_खरें_लग्नाबद`
+
+**Context Size 3:**
+
+1. `_आसा._गांवांत_अशे_आसा._३६`
+2. `_आनीक_आपली_आसा._सगळ्यांत_`
+3. `आनी_कार्यात्मक_आनी_पाली_वा_गर`
+
+**Context Size 4:**
+
+1. `_आनी_निळी_रंगाची_उत्तं_गीत._ना`
+2. `_आसा._आरोग्य_उपकेंद्र_५_ते_१`
+3. `आसा._गांवांतल्या_देशांत_बरोवपाक_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,351,866 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 | 104,377 |
+| Total Tokens | 1,826,394 |
+| Mean Frequency | 17.50 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 218.96 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | आनी | 32,869 |
+| 2 | आसा | 32,307 |
+| 3 | गांवांत | 16,033 |
+| 4 | ह्या | 13,866 |
+| 5 | उपलब्ध | 13,831 |
+| 6 | सगळ्यांत | 13,779 |
+| 7 | ani | 13,657 |
+| 8 | ना | 13,460 |
+| 9 | लागीं | 13,438 |
+| 10 | अंतराचेर | 11,895 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | नवादिया | 2 |
+| 2 | पर्वतरांगेच्या | 2 |
+| 3 | शिमी | 2 |
+| 4 | शिमेसावन | 2 |
+| 5 | रेंजाच्या | 2 |
+| 6 | बफर | 2 |
+| 7 | फॅन्टा | 2 |
+| 8 | जलविज्ञानीक | 2 |
+| 9 | grandis | 2 |
+| 10 | बुडलेले | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9897 |
+| R² (Goodness of Fit) | 0.993258 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 24.3% |
+| Top 1,000 | 49.3% |
+| Top 5,000 | 69.2% |
+| Top 10,000 | 77.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9933 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 24.3% of corpus
+- **Long Tail:** 94,377 words needed for remaining 22.9% 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 | 0.7594 | 0.3761 | N/A | N/A |
+| **mono_64d** | 64 | 0.7357 | 0.3105 | N/A | N/A |
+| **mono_128d** | 128 | 0.6506 | 0.2584 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7594 🏆 | 0.3713 | 0.0100 | 0.1300 |
+| **aligned_64d** | 64 | 0.7357 | 0.3144 | 0.0200 | 0.1480 |
+| **aligned_128d** | 128 | 0.6506 | 0.2631 | 0.0340 | 0.1920 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7594 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3157. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 3.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **1.887** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-या` | बारश्या, ब्रिटनांतल्या, आशिललया |
+| `-्या` | बारश्या, ब्रिटनांतल्या, तपश्र्चर्या |
+| `-चो` | ब्रिटिशांचो, लिंबाचो, पीठाचो |
+| `-ें` | सायबीणीचें, सुरवातीचें, खोडयें |
+
+### 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 |
+|------|----------|------------------|----------|
+| `anch` | 2.38x | 228 contexts | nanch, panch, anchi |
+| `antl` | 2.59x | 78 contexts | hantle, tantle, hantli |
+| `rant` | 2.59x | 74 contexts | grant, prant, xarant |
+| `iche` | 2.46x | 86 contexts | aiche, hiche, liche |
+| `nche` | 2.44x | 86 contexts | xanche, tanche, hanche |
+| `tach` | 2.30x | 94 contexts | tache, tacho, tachi |
+| `rach` | 2.28x | 97 contexts | prachy, sirach, porach |
+| `honn` | 2.65x | 47 contexts | mhonn, dhonn, ghonn |
+| `orta` | 2.48x | 61 contexts | vorta, sorta, corta |
+| `aran` | 2.44x | 56 contexts | daran, faran, xaran |
+| `eant` | 2.52x | 44 contexts | leant, goeant, ujeant |
+| `eche` | 2.49x | 46 contexts | teche, veche, techem |
+
+### 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.
+
+*No significant affix co-occurrences detected.*
+
+
+### 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 |
+|------|-----------------|------------|------|
+| दादल्यांचो | **`दादल्यां-चो`** | 4.5 | `दादल्यां` |
+| झुजाऱ्यांचो | **`झुजाऱ्यां-चो`** | 4.5 | `झुजाऱ्यां` |
+| शांरांतल्या | **`शांरांतल-्या`** | 1.5 | `शांरांतल` |
+| देवविद्या | **`देवविद-्या`** | 1.5 | `देवविद` |
+| शेतांतलें | **`शेतांतल-ें`** | 1.5 | `शेतांतल` |
+| चयापचयांतल्या | **`चयापचयांतल-्या`** | 1.5 | `चयापचयांतल` |
+| बेकारीच्या | **`बेकारीच-्या`** | 1.5 | `बेकारीच` |
+| रूजायच्या | **`रूजायच-्या`** | 1.5 | `रूजायच` |
+| प्रशासनाचें | **`प्रशासनाच-ें`** | 1.5 | `प्रशासनाच` |
+| न्युयॉर्कांतल्या | **`न्युयॉर्कांतल-्या`** | 1.5 | `न्युयॉर्कांतल` |
+| तोबिताचें | **`तोबिताच-ें`** | 1.5 | `तोबिताच` |
+| दक्षिणेकडचो | **`दक्षिणेकड-चो`** | 1.5 | `दक्षिणेकड` |
+| राज्यसत्तेच्या | **`राज्यसत्तेच-्या`** | 1.5 | `राज्यसत्तेच` |
+| फुडारिल्ल्या | **`फुडारिल्ल-्या`** | 1.5 | `फुडारिल्ल` |
+| मोनजातीचें | **`मोनजातीच-ें`** | 1.5 | `मोनजातीच` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Goan Konkani shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.00x) |
+| N-gram | **2-gram** | Lowest perplexity (1,903) |
+| Markov | **Context-4** | Highest predictability (99.1%) |
+| 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: 2026-01-09 23:51:36*

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