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+---
+language: pi
+language_name: Pali
+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: 2.300
+  - name: best_isotropy
+    type: isotropy
+    value: 0.0330
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Pali - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Pali** 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** | 2.300x 🏆 | 2.30 | 1.0840% | 94,738 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `वलहि एका सनातन ग्राम अत्थि, ईमा पतिठ्ठापना अंतो सोरठ पदेश।`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁वलहि ▁एका ▁सनातन ▁ग्राम ▁अत्थि , ▁ईमा ▁पतिठ्ठापना ▁अंतो ▁सोरठ ... (+2 more)` | 12 |
+
+**Sample 2:** `+दक्षिण क्यारोलिनाSouth Carolina 125px 125px 300px संदरिभ`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁+ दक्षिण ▁क्यारोलिना south ▁carolina ▁ 1 2 5 px ... (+11 more)` | 21 |
+
+**Sample 3:** `+वासिंगटन डि सिWashington, D.C. 125px 125px 300px वासिंगटन डि सि अभिञ्ञाणा`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁+ वासिंगटन ▁डि ▁सि washington , ▁d . c . ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 8k achieves 2.300x compression
+- **Lowest UNK Rate:** 8k with 1.0840% 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 | 266 🏆 | 8.05 | 416 | 54.4% | 100.0% |
+| **2-gram** | Subword | 827 | 9.69 | 2,901 | 42.1% | 88.9% |
+| **3-gram** | Word | 349 | 8.45 | 534 | 49.9% | 100.0% |
+| **3-gram** | Subword | 3,441 | 11.75 | 9,002 | 21.6% | 58.3% |
+| **4-gram** | Word | 1,582 | 10.63 | 1,950 | 21.7% | 63.4% |
+| **4-gram** | Subword | 8,498 | 13.05 | 20,231 | 15.6% | 40.0% |
+| **5-gram** | Word | 1,377 | 10.43 | 1,660 | 22.3% | 68.6% |
+| **5-gram** | Subword | 9,937 | 13.28 | 21,227 | 15.3% | 35.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `प्रकाश स्तंभ` | 223 |
+| 2 | `yā pana` | 189 |
+| 3 | `pana bhikkhunī` | 187 |
+| 4 | `टापू समूह` | 98 |
+| 5 | `sikkhā karaṇīyā` | 75 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yā pana bhikkhunī` | 187 |
+| 2 | `बालिआरिक टापू समूह` | 64 |
+| 3 | `प्रकाश स्तंभ 120px` | 62 |
+| 4 | `टापू समूह बालिआरिक` | 32 |
+| 5 | `समूह बालिआरिक टापू` | 32 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `बालिआरिक टापू समूह बालिआरिक` | 32 |
+| 2 | `टापू समूह बालिआरिक टापू` | 32 |
+| 3 | `समूह बालिआरिक टापू समूह` | 32 |
+| 4 | `frameless upright 0 2` | 29 |
+| 5 | `upright 0 2 link` | 25 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `बालिआरिक टापू समूह बालिआरिक टापू` | 32 |
+| 2 | `टापू समूह बालिआरिक टापू समूह` | 32 |
+| 3 | `frameless upright 0 2 link` | 25 |
+| 4 | `upright 0 2 link frameless` | 25 |
+| 5 | `0 2 link frameless upright` | 25 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a ṃ` | 1,530 |
+| 2 | `, _` | 1,307 |
+| 3 | `p a` | 1,306 |
+| 4 | `ṃ _` | 1,294 |
+| 5 | `ā _` | 1,256 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a ṃ _` | 1,183 |
+| 2 | `k k h` | 938 |
+| 3 | `i k k` | 900 |
+| 4 | `_ p a` | 621 |
+| 5 | `_ b h` | 560 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i k k h` | 881 |
+| 2 | `_ b h i` | 455 |
+| 3 | `b h i k` | 453 |
+| 4 | `h i k k` | 453 |
+| 5 | `k k h u` | 452 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `b h i k k` | 453 |
+| 2 | `h i k k h` | 453 |
+| 3 | `_ b h i k` | 450 |
+| 4 | `i k k h u` | 449 |
+| 5 | `k k h u n` | 436 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (word) with 266
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~36% 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 | 0.4100 | 1.329 | 2.11 | 10,593 | 59.0% |
+| **1** | Subword | 0.9525 | 1.935 | 6.62 | 2,113 | 4.7% |
+| **2** | Word | 0.1078 | 1.078 | 1.17 | 22,259 | 89.2% |
+| **2** | Subword | 0.4969 | 1.411 | 2.61 | 13,978 | 50.3% |
+| **3** | Word | 0.0355 | 1.025 | 1.05 | 25,920 | 96.5% |
+| **3** | Subword | 0.3495 | 1.274 | 1.73 | 36,519 | 65.0% |
+| **4** | Word | 0.0185 🏆 | 1.013 | 1.03 | 27,208 | 98.1% |
+| **4** | Subword | 0.1994 | 1.148 | 1.34 | 63,113 | 80.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `है पूर्ण उदच्यते उत्पन्न होता है गले में किसी मनुष्य अथवा तकनीक है भ्रूण को पार`
+2. `के समान ही रह जाता है उसके नीचे क्रमश देवकी और स्वभाव और बहुतों के थे`
+3. `में लिखा गया है अंडा एक स्वतन्त्र एक कालाग्नि नामक एक एव बुद्धत्तं ति मञ्ञति सन्दब्भा`
+
+**Context Size 2:**
+
+1. `प्रकाश स्तंभ 120px आन्दलूसिया मालागा मारबिआ प्रकाश स्तंभ देल बाखो दे पोरतमान मूर्किया का कारतागेना ओ...`
+2. `yā pana bhikkhunī nānappakārakaṃ kayavikkayaṃ samāpajjeyya nissaggiyaṃ pācittiyaṃ aññacetāpana sikkh...`
+3. `pana bhikkhunī paripuṇṇavīsativassaṃ kumāribhūtaṃ dve vassāni chasu dhammesu sikkhitasikkhaṃ saṅghen...`
+
+**Context Size 3:**
+
+1. `yā pana bhikkhunī āsandiṃ vā pallaṅkaṃ vā paribhuñjeyya pācittiyaṃ suttakantanasikkhāpadaṃ 43 yā pan...`
+2. `बालिआरिक टापू समूह इबिसा और फोरमैनतेरा तागोमागो प्रकाश स्तंभ बालिआरिक टापू समूह मेनोरका सिउतादेया प्...`
+3. `प्रकाश स्तंभ 120px गालिसिया केप ओमे प्रकाश स्तंभ 120px बालिआरिक टापू समूह माखोरका केप गरोस प्रकाश स्...`
+
+**Context Size 4:**
+
+1. `समूह बालिआरिक टापू समूह इबिसा और फोरमैनतेरा पोएनसा प्रकाश स्तंभ बालिआरिक टापू समूह बालिआरिक टापू समू...`
+2. `टापू समूह बालिआरिक टापू समूह माखोरका पोरतो कोलोम प्रकाश स्तंभ बालिआरिक टापू समूह बालिआरिक टापू समूह ...`
+3. `बालिआरिक टापू समूह बालिआरिक टापू समूह माखोरका केप बलांक प्रकाश स्तंभ 120px बालिआरिक टापू समूह बालिआर...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_paṃ,_सिक्खामकूपनेत्तिभ_र`
+2. `aexy_स्तंभकी_o_sikki`
+3. `i._कृष्णवासमूहम्_suṇat`
+
+**Context Size 2:**
+
+1. `aṃ_–_"आभीर_एका_शुभदर्शी`
+2. `,_na_(कम्प्युटर_शून्य:_मिसि`
+3. `padaṃ_pāṇijabaṇīy`
+
+**Context Size 3:**
+
+1. `aṃ_bhikkhuniyo_bhi`
+2. `kkhuni_cells_theva`
+3. `ikkhā_evamerittikk`
+
+**Context Size 4:**
+
+1. `ikkhāpadaṃ_43._yā_p`
+2. `_bhikkhāpadaṃ_1._yā`
+3. `hikkhā_kareyya_‘‘ap`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (63,113 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 | 3,395 |
+| Total Tokens | 23,559 |
+| Mean Frequency | 6.94 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 20.62 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | है | 395 |
+| 2 | के | 395 |
+| 3 | में | 356 |
+| 4 | vā | 314 |
+| 5 | से | 276 |
+| 6 | और | 265 |
+| 7 | हैं | 261 |
+| 8 | bhikkhunī | 254 |
+| 9 | प्रकाश | 229 |
+| 10 | स्तंभ | 224 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | नामान्तरण | 2 |
+| 2 | मिलाबट | 2 |
+| 3 | घटनाओं | 2 |
+| 4 | वेदिक | 2 |
+| 5 | शबदस्स | 2 |
+| 6 | दरुश | 2 |
+| 7 | गरिंथात | 2 |
+| 8 | अनूसार | 2 |
+| 9 | करविन | 2 |
+| 10 | हिंदू | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.8293 |
+| R² (Goodness of Fit) | 0.980447 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 37.8% |
+| Top 1,000 | 75.0% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9804 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 37.8% of corpus
+- **Long Tail:** -6,605 words needed for remaining 100.0% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.0330 🏆 | 0.5234 | N/A | N/A |
+| **mono_64d** | 64 | 0.0047 | 0.5510 | N/A | N/A |
+| **mono_128d** | 128 | 0.0008 | 0.5621 | N/A | N/A |
+| **aligned_32d** | 32 | 0.0330 | 0.5288 | 0.0240 | 0.1377 |
+| **aligned_64d** | 64 | 0.0047 | 0.5520 | 0.0240 | 0.1257 |
+| **aligned_128d** | 128 | 0.0008 | 0.5541 | 0.0180 | 0.1437 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.0330 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.5452. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 2.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.910** | 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 |
+|--------|----------|
+| `-स` | सच्चानीति, स्टेम, संवाद |
+| `-प` | प्रवृति, पाताल, प्रभुने |
+| `-sa` | saṅghikaṃ, samayā, sambhuñjeyya |
+| `-pa` | paṭiggahetabbaṃ, paṭisevato, pakkameyya |
+| `-पर` | परिपूर्णतम, परायण, परवर्ती |
+| `-an` | announcement, anniversary, and |
+| `-vi` | via, vikappaṃ, vinassā |
+| `-मह` | महावग्गो, महाविराट्के, महेश्वर |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-aṃ` | saṅghikaṃ, dhammaṃ, nālaṃ |
+| `-ṃ` | saṅghikaṃ, dhammaṃ, nālaṃ |
+| `-a` | wikimania, acchindāpeyya, uddhareyya |
+| `-ya` | acchindāpeyya, uddhareyya, pakkameyya |
+| `-ā` | vuccamānā, āpannā, cetāpetvā |
+| `-na` | saññācikena, saṅghikena, dhammena |
+| `-yo` | bhikkhuniyo, māyyāyo, ayyāyo |
+| `-yā` | samayā, dubbalyā, karaṇīyā |
+
+### 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 |
+|------|----------|------------------|----------|
+| `eyya` | 1.78x | 8 contexts | seyyaṃ, cāveyya, kareyya |
+| `ikkh` | 1.65x | 6 contexts | sikkhā, sikkhaṃ, bhikkhu |
+| `kkhu` | 1.78x | 5 contexts | bhikkhu, bhikkhuṃ, bhikkhunī |
+| `añña` | 1.76x | 3 contexts | aññaṃ, aññatra, anaññaṃ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-pa` | `-a` | 20 words | pakkameyya, paggaṇheyya |
+| `-sa` | `-a` | 19 words | sambhuñjeyya, saññācikena |
+| `-sa` | `-ṃ` | 15 words | saṅghikaṃ, saṅghādisesaṃ |
+| `-pa` | `-ṃ` | 15 words | paṭiggahetabbaṃ, paraṃ |
+| `-pa` | `-ya` | 14 words | pakkameyya, paggaṇheyya |
+| `-sa` | `-aṃ` | 13 words | saṅghikaṃ, saṅghādisesaṃ |
+| `-pa` | `-aṃ` | 12 words | paṭiggahetabbaṃ, paraṃ |
+| `-sa` | `-ā` | 10 words | samayā, saṅghādisesā |
+| `-vi` | `-a` | 8 words | via, vivekaññeva |
+| `-sa` | `-ya` | 7 words | sambhuñjeyya, saṃvaṇṇeyya |
+
+### 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 |
+|------|-----------------|------------|------|
+| communities | **`communi-ti-es`** | 3.0 | `communi` |
+| ukkhittakāya | **`ukkhittak-ā-ya`** | 3.0 | `ukkhittak` |
+| bhaginīnaṃ | **`bhaginīn-aṃ`** | 1.5 | `bhaginīn` |
+| vūpasamāya | **`vūpasamā-ya`** | 1.5 | `vūpasamā` |
+| ubbhatasmiṃ | **`ubbhatasmi-ṃ`** | 1.5 | `ubbhatasmi` |
+| sahadhammena | **`sa-hadhammena`** | 1.5 | `hadhammena` |
+| sattarasa | **`sattaras-a`** | 1.5 | `sattaras` |
+| pañcakkhattuṃ | **`pañcakkhattu-ṃ`** | 1.5 | `pañcakkhattu` |
+| dvattikkhattuṃ | **`dvattikkhattu-ṃ`** | 1.5 | `dvattikkhattu` |
+| susaṃvutā | **`susaṃvut-ā`** | 1.5 | `susaṃvut` |
+| सीहनादवग्गो | **`स-ीहनादवग्गो`** | 1.5 | `ीहनादवग्गो` |
+| sannidhikārakaṃ | **`sannidhikārak-aṃ`** | 1.5 | `sannidhikārak` |
+| pattavaggo | **`pa-ttavaggo`** | 1.5 | `ttavaggo` |
+| desessāmīti | **`desessāmī-ti`** | 1.5 | `desessāmī` |
+| सुत्तपिटक | **`स-ुत्तपिटक`** | 1.5 | `ुत्तपिटक` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Pali 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 | **8k BPE** | Best compression (2.30x) |
+| N-gram | **2-gram** | Lowest perplexity (266) |
+| Markov | **Context-4** | Highest predictability (98.1%) |
+| 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: 2026-01-10 17:45:44*
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diff --git a/models/vocabulary/pi_vocabulary.parquet b/models/vocabulary/pi_vocabulary.parquet
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+version https://git-lfs.github.com/spec/v1
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diff --git a/models/vocabulary/pi_vocabulary_metadata.json b/models/vocabulary/pi_vocabulary_metadata.json
new file mode 100644
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--- /dev/null
+++ b/models/vocabulary/pi_vocabulary_metadata.json
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+{
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+  "vocabulary_size": 3395,
+  "variant": "full",
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+  }
+}
\ No newline at end of file
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+version https://git-lfs.github.com/spec/v1
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diff --git a/models/word_markov/pi_markov_ctx1_word_metadata.json b/models/word_markov/pi_markov_ctx1_word_metadata.json
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+{
+  "context_size": 1,
+  "variant": "word",
+  "language": "pi",
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+  "total_transitions": 30694
+}
\ No newline at end of file
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