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+---
+language: tdd
+language_name: Tai Nüa
+language_family: taikadai_other
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-taikadai_other
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.452
+  - name: best_isotropy
+    type: isotropy
+    value: 0.1576
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tai Nüa - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tai Nüa** 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** | 3.452x 🏆 | 3.45 | 0.8999% | 134,463 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ธ ᥕᥧᥱ ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ ᥗᥭᥰ ᥘᥢᥳ ᥙ​​​ᥥᥢ ᥗᥤᥳ ᥔᥩᥒᥴ ᥔᥤᥙᥴ ᥔᥤᥱ，ᥑᥙᥳ ᥐᥢᥲ ᥘᥒᥴ ท ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ธ ▁ᥕᥧᥱ ▁ᥘᥬᥰ ▁ᥖᥨᥝ ▁ᥘᥤᥐ ▁ᥗᥭᥰ ▁ᥘᥢᥳ ▁ᥙ ▁ᥥᥢ ▁ᥗᥤᥳ ... (+14 more)` | 24 |
+
+**Sample 2:** `ᥜᥭᥰ ᥛᥭᥲ ᥘᥩᥭ, ᥟᥤᥱ ᥑᥣᥴ ᥑᥩᥭ ᥖᥨᥝᥰ ᥜᥧᥢᥰ, ᥜᥭᥰ ᥛᥭᥲ ᥔᥨᥢᥴ, ᥟᥤᥱ ᥑᥣᥴ ᥑᥧᥢᥴ ᥖᥒᥲ ᥐᥨᥢᥲ, ᥜᥭᥰ ᥛᥭᥲ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ᥜᥭᥰ ▁ᥛᥭᥲ ▁ᥘᥩᥭ , ▁ᥟᥤᥱ ▁ᥑᥣᥴ ▁ᥑᥩᥭ ▁ᥖᥨᥝᥰ ▁ᥜᥧᥢᥰ , ... (+20 more)` | 30 |
+
+**Sample 3:** `ᥔᥩᥒᥴ ᥐᥝ ᥖᥒᥰ ᥛᥬᥰ ᥙᥦᥒᥰ ᥐᥢ ᥘᥩᥰ ᥙᥭᥱ ᥘᥣ ᥘᥪᥛᥰ ( ᥞᥦᥴ ) ᥘᥣᥲ ᥘᥒᥴ ᥐᥝᥱ ( ᥞᥫ ᥞᥫᥭᥰ ) , ᥙᥫᥢ ᥝᥣ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ᥔᥩᥒᥴ ▁ᥐᥝ ▁ᥖᥒᥰ ▁ᥛᥬᥰ ▁ᥙᥦᥒᥰ ▁ᥐᥢ ▁ᥘᥩᥰ ▁ᥙᥭᥱ ▁ᥘᥣ ▁ᥘᥪᥛᥰ ... (+31 more)` | 41 |
+
+
+### Key Findings
+
+- **Best Compression:** 8k achieves 3.452x compression
+- **Lowest UNK Rate:** 8k with 0.8999% 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 | 2,092 | 11.03 | 3,132 | 19.7% | 64.7% |
+| **2-gram** | Subword | 254 🏆 | 7.99 | 1,907 | 67.9% | 98.3% |
+| **3-gram** | Word | 2,477 | 11.27 | 3,460 | 19.4% | 56.6% |
+| **3-gram** | Subword | 1,269 | 10.31 | 7,339 | 34.2% | 83.4% |
+| **4-gram** | Word | 4,559 | 12.15 | 6,235 | 15.9% | 38.4% |
+| **4-gram** | Subword | 5,182 | 12.34 | 21,769 | 16.8% | 52.9% |
+| **5-gram** | Word | 3,124 | 11.61 | 4,326 | 19.6% | 43.7% |
+| **5-gram** | Subword | 13,546 | 13.73 | 39,860 | 11.4% | 34.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥓᥦᥲ ᥝᥥᥒᥰ` | 339 |
+| 2 | `ᥘᥢᥳ ᥕᥝᥳ` | 218 |
+| 3 | `ᥘᥭᥳ ᥙᥥᥢ` | 213 |
+| 4 | `ᥟᥣ ᥛᥥᥝᥰ` | 160 |
+| 5 | `ᥖᥨᥝ ᥘᥤᥐ` | 148 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥕᥧᥱ ᥘᥬᥰ ᥖᥨᥝ` | 76 |
+| 2 | `ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ` | 75 |
+| 3 | `ᥙ ᥥᥢ ᥗᥤᥳ` | 75 |
+| 4 | `ᥘᥢᥳ ᥙ ᥥᥢ` | 74 |
+| 5 | `ᥟᥣ ᥛᥥᥝᥰ ᥖᥭᥰ` | 71 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥕᥧᥱ ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ` | 75 |
+| 2 | `ᥘᥢᥳ ᥙ ᥥᥢ ᥗᥤᥳ` | 74 |
+| 3 | `ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ` | 68 |
+| 4 | `size 5em line height` | 49 |
+| 5 | `5em line height 1` | 49 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `1 2em vertical align super` | 49 |
+| 2 | `height 1 2em vertical align` | 49 |
+| 3 | `5em line height 1 2em` | 49 |
+| 4 | `size 5em line height 1` | 49 |
+| 5 | `span style font size 5em` | 49 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥰ _` | 21,662 |
+| 2 | `_ ᥘ` | 16,383 |
+| 3 | `ᥱ _` | 15,128 |
+| 4 | `ᥴ _` | 11,888 |
+| 5 | `ᥳ _` | 9,912 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥒ ᥰ _` | 5,607 |
+| 2 | `ᥰ _ ᥘ` | 3,794 |
+| 3 | `ᥢ ᥰ _` | 3,733 |
+| 4 | `_ ᥘ ᥣ` | 2,627 |
+| 5 | `_ ᥘ ᥭ` | 2,613 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥫ ᥒ ᥰ _` | 1,836 |
+| 2 | `ᥛ ᥫ ᥒ ᥰ` | 1,646 |
+| 3 | `_ ᥙ ᥥ ᥢ` | 1,542 |
+| 4 | `_ ᥛ ᥫ ᥒ` | 1,528 |
+| 5 | `_ ᥕ ᥝ ᥳ` | 1,457 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ᥛ ᥫ ᥒ ᥰ _` | 1,522 |
+| 2 | `_ ᥛ ᥫ ᥒ ᥰ` | 1,516 |
+| 3 | `_ ᥙ ᥥ ᥢ _` | 1,376 |
+| 4 | `_ ᥘ ᥢ ᥳ _` | 1,156 |
+| 5 | `_ ᥘ ᥭ ᥳ _` | 1,114 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 254
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~34% 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 | 1.0436 | 2.061 | 6.61 | 7,646 | 0.0% |
+| **1** | Subword | 0.8264 | 1.773 | 4.89 | 1,510 | 17.4% |
+| **2** | Word | 0.2992 | 1.230 | 1.58 | 50,365 | 70.1% |
+| **2** | Subword | 0.6143 | 1.531 | 3.08 | 7,320 | 38.6% |
+| **3** | Word | 0.0928 | 1.066 | 1.13 | 79,152 | 90.7% |
+| **3** | Subword | 0.4606 | 1.376 | 2.22 | 22,469 | 53.9% |
+| **4** | Word | 0.0344 🏆 | 1.024 | 1.04 | 89,118 | 96.6% |
+| **4** | Subword | 0.4009 | 1.320 | 1.94 | 49,645 | 59.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ᥛᥫᥒᥰ ᥛᥣᥰ ᥟᥛᥱ ᥞᥐᥳ ᥟᥥᥐᥱ ᥑᥨᥝ ᥐᥧᥰ ᥙᥩᥐᥳ ᥟᥫᥒᥱ ᥑᥣᥲ ᥖᥥᥴ ᥘᥭᥲ ᥟᥝ ᥘᥤᥐ ᥗᥭᥰ ᥔᥣ`
+2. `ᥙᥥᥢ ᥛᥫᥒᥰ ᥐᥢ ᥖᥪᥰ ᥟᥤᥴ ᥓᥤᥴ ᥛᥣᥭᥱ ᥘᥝᥲ ᥙᥝᥱ ᥝᥭᥳ ᥐᥣᥢᥲ ᥑᥛᥰ ᥘᥭᥴ ᥘᥦᥳ ᥟᥛᥱ ᥛᥤᥰ`
+3. `ᥕᥝᥳ ᥘᥧᥐᥳ ᥖᥤ ᥘᥤᥐ ᥖᥬ university press standard dialect one hears on girl guy star fruit`
+
+**Context Size 2:**
+
+1. `ᥓᥦᥲ ᥝᥥᥒᥰ ᥛᥫᥒᥰ ᥛᥩᥐᥱ ᥓᥦᥲ ᥝᥥᥒᥰ ᥙᥨᥝᥱ ᥖ ᥗᥩᥒᥱ ᥓᥦᥲ ᥝᥥᥒᥰ ᥭᥩᥒᥱ ᥖᥨᥒᥰ ᥓᥦᥲ ᥝᥥᥒᥰ ᥟᥢᥰ ᥖᥣᥢᥱ`
+2. `ᥘᥭᥳ ᥙᥥᥢ ᥖᥨᥝ ᥓᥣᥙ ᥙᥩᥒ ᥛᥥ ᥓᥣᥙᥛᥥ ᥓᥣᥙ 15 ᥖᥨᥝ ᥔᥥᥴ ᥙᥨᥝᥰ ᥙᥭ ᥟᥩᥐᥱ ᥑᥨᥢᥴ ᥖᥣᥒᥰ ᥟᥢ`
+3. `ᥘᥢᥳ ᥕᥝᥳ ᥓᥩᥖᥱ ᥞᥩᥖ ᥙᥣᥭ ᥙᥫᥒ ᥔᥣᥛᥴ ᥐᥢᥱ ᥖᥨᥝᥰ ᥘᥥᥐ ᥘᥧᥛᥱ ᥗᥝᥲ ᥔᥣᥛᥴ ᥐᥣᥙ ᥑᥩᥒᥴ ᥟᥣ ᥛᥥᥝᥰ`
+
+**Context Size 3:**
+
+1. `ᥕᥧᥱ ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ ᥗᥭᥰ ᥘᥢᥳ ᥙ ᥥᥢ ᥗᥤᥳ ᥔᥣᥛᥴ ᥔᥤᥙᥴ ᥐᥝᥲ ᥑᥙᥳ ᥐᥢᥲ ᥘᥒᥴ ว ᥖᥒᥰ ᥕᥧᥱ`
+2. `ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ ᥘᥣ ᥖᥤᥒ paraipa ᥖᥣ ᥢᥦ`
+3. `ᥙ ᥥᥢ ᥗᥤᥳ ᥔᥤᥱ ᥔᥤᥙᥴ ᥔᥣᥛᥴ ᥑᥙᥳ ᥐᥢᥲ ᥘᥒᥴ ต ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ ถ`
+
+**Context Size 4:**
+
+1. `ᥕᥧᥱ ᥘᥬᥰ ᥖᥨᥝ ᥘᥤᥐ ᥗᥭᥰ ᥘᥢᥳ ᥙ ᥥᥢ ᥗᥤᥳ ᥔᥤᥙᥴ ᥔᥣᥛᥴ ᥑᥙᥳ ᥐᥢᥲ ᥘᥒᥴ ภ ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ`
+2. `ᥘᥢᥳ ᥙ ᥥᥢ ᥗᥤᥳ ᥔᥤᥙᥴ ᥔᥤᥱ ᥑᥙᥳ ᥐᥢᥲ ᥘᥒᥴ m ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ ต`
+3. `ᥖᥒᥰ ᥕᥧᥱ ᥙᥣᥲ ᥘᥣᥲ ซ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_.._ᥘᥣᥒᥲ_ᥘᥤᥲ_ᥑᥣᥒ`
+2. `ᥰ._ᥙᥫᥢᥨᥢᥴ_ᥑᥣᥛᥲ_ᥙ`
+3. `ᥣᥭᥲ_ᥘᥫᥴ_ᥖᥴᥤᥢᥰ_ᥘᥣ`
+
+**Context Size 2:**
+
+1. `ᥰ_ᥐᥛᥰ_ᥖᥫᥒᥰ_ᥞᥣᥛᥲ_ᥟ`
+2. `_ᥘᥤᥐ:_ᥐᥩᥲ_ᥛᥫᥒᥰ_”ᥐ`
+3. `ᥱ_ᥔᥝᥰ_(33_ᥟᥣᥛᥱ_(1`
+
+**Context Size 3:**
+
+1. `ᥒᥰ_ᥛᥭᥳ_ᥙᥥᥢ_ᥔᥥᥴ_ᥙᥩᥐ`
+2. `ᥰ_ᥘᥢᥳ_ᥑᥪᥢᥲ_ᥛᥣᥐᥱ_ᥛᥣ`
+3. `ᥢᥰ_ᥚᥣᥐ_ᥖᥥᥰ_ᥗᥭᥴ_ᥐᥩᥢ`
+
+**Context Size 4:**
+
+1. `ᥫᥒᥰ_ᥛᥣᥱ_ᥘᥤᥳ_ᥞᥣᥱ_ᥕᥧᥱ`
+2. `ᥛᥫᥒᥰ_ᥖᥣᥲ_ᥘᥛᥳ_ᥘᥫᥴᥓᥬᥲ`
+3. `_ᥙᥥᥢ_ᥘᥧᥳ_ᥕᥬᥱ_ᥔᥩᥖᥱ_ᥖ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (49,645 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 | 3,667 |
+| Total Tokens | 98,753 |
+| Mean Frequency | 26.93 |
+| Median Frequency | 5 |
+| Frequency Std Dev | 91.04 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ᥛᥫᥒᥰ | 1,556 |
+| 2 | ᥕᥝᥳ | 1,469 |
+| 3 | ᥙᥥᥢ | 1,422 |
+| 4 | ᥘᥢᥳ | 1,266 |
+| 5 | ᥘᥭᥳ | 1,185 |
+| 6 | ᥟᥢ | 1,100 |
+| 7 | ᥕᥧᥱ | 1,054 |
+| 8 | ᥛᥤᥰ | 1,019 |
+| 9 | ᥖᥤ | 988 |
+| 10 | ᥔᥥᥴ | 982 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mid | 2 |
+| 2 | class | 2 |
+| 3 | wikitable | 2 |
+| 4 | hul | 2 |
+| 5 | um | 2 |
+| 6 | ō | 2 |
+| 7 | ꞵ̡ | 2 |
+| 8 | paraipa | 2 |
+| 9 | ꞔ | 2 |
+| 10 | ᥙ̬ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.3118 |
+| R² (Goodness of Fit) | 0.963118 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 45.6% |
+| Top 1,000 | 87.7% |
+| Top 5,000 | 0.0% |
+| Top 10,000 | 0.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9631 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 45.6% of corpus
+- **Long Tail:** -6,333 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 | 0.1576 🏆 | 0.6123 | N/A | N/A |
+| **mono_64d** | 64 | 0.0349 | 0.6483 | N/A | N/A |
+| **mono_128d** | 128 | 0.0075 | 0.6492 | N/A | N/A |
+| **aligned_32d** | 32 | 0.1576 | 0.6164 | 0.0210 | 0.2308 |
+| **aligned_64d** | 64 | 0.0349 | 0.6418 | 0.0350 | 0.3287 |
+| **aligned_128d** | 128 | 0.0075 | 0.6540 | 0.0420 | 0.3357 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.1576 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.6370. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.2% 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 | **0.284** | 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 |
+|--------|----------|
+| `-ᥟ` | ᥟᥩᥛᥳ, ᥟᥪᥢᥲ, ᥟᥣᥭ |
+| `-ᥘ` | ᥘᥧᥛᥱ, ᥘᥤᥐ, ᥘᥨᥛᥴ |
+| `-ᥖ` | ᥖᥭᥰ, ᥖᥪᥰ, ᥖᥨᥒᥲ |
+| `-ᥔ` | ᥔᥤᥳ, ᥔᥦᥢᥰ, ᥔᥢᥱ |
+| `-ᥐ` | ᥐᥣᥢᥴ, ᥐᥤ, ᥐᥤᥖᥳ |
+| `-ᥛ` | ᥛျᥩᥒᥰ, ᥛᥦᥢᥰ, ᥛᥥᥐᥳ |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+
+### 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.
+
+*No significant bound stems detected.*
+
+
+### 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 |
+|------|-----------------|------------|------|
+| ᥟᥧᥐɐɞ̞ᥟᥧᥐäɒ̈ | **`ᥟ-ᥧᥐɐɞ̞ᥟᥧᥐäɒ̈`** | 1.5 | `ᥧᥐɐɞ̞ᥟᥧᥐäɒ̈` |
+| ᥟᥢᥴᥝᥣᥲᥢᥢᥳ | **`ᥟ-ᥢᥴᥝᥣᥲᥢᥢᥳ`** | 1.5 | `ᥢᥴᥝᥣᥲᥢᥢᥳ` |
+| ᥟᥥᥒᥰᥐᥘᥥᥖᥲᥢᥭᥳ | **`ᥟ-ᥥᥒᥰᥐᥘᥥᥖᥲᥢᥭᥳ`** | 1.5 | `ᥥᥒᥰᥐᥘᥥᥖᥲᥢᥭᥳ` |
+| ᥟᥢᥴᥕᥧᥱᥔᥝᥰ | **`ᥟ-ᥢᥴᥕᥧᥱᥔᥝᥰ`** | 1.5 | `ᥢᥴᥕᥧᥱᥔᥝᥰ` |
+| ᥟᥛᥱᥞᥩᥖᥲᥛᥣᥐᥱᥚᥨᥝᥱ | **`ᥟ-ᥛᥱᥞᥩᥖᥲᥛᥣᥐᥱᥚᥨᥝᥱ`** | 1.5 | `ᥛᥱᥞᥩᥖᥲᥛᥣᥐᥱᥚᥨᥝᥱ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tai Nüa 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 (3.45x) |
+| N-gram | **2-gram** | Lowest perplexity (254) |
+| Markov | **Context-4** | Highest predictability (96.6%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-11 00:31:50*
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