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README.md

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+---
+language: war
+language_name: Waray
+language_family: austronesian_philippine_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-austronesian_philippine_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: 3.934
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8470
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Waray - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Waray** 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** | 3.050x | 3.05 | 0.5424% | 356,557 |
+| **16k** | 3.360x | 3.36 | 0.5975% | 323,705 |
+| **32k** | 3.653x | 3.66 | 0.6496% | 297,739 |
+| **64k** | 3.934x 🏆 | 3.94 | 0.6996% | 276,458 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `An Condado han Philadelphia in uska condado ha estado han Estados Unidos nga Pen...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+9 more)` | 19 |
+| 16k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+6 more)` | 16 |
+| 32k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+5 more)` | 15 |
+| 64k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+5 more)` | 15 |
+
+**Sample 2:** `An Condado han Atchison in uska condado ha estado han Estados Unidos nga Missour...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 |
+| 16k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 |
+| 32k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 |
+| 64k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 |
+
+**Sample 3:** `An Briatexte amo in usa ka komyun ha departamento han Tarn ngan ha rehiyon han M...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+21 more)` | 31 |
+| 16k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 |
+| 32k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 |
+| 64k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.934x compression
+- **Lowest UNK Rate:** 8k with 0.5424% 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 | 6,771 | 12.73 | 587,286 | 39.6% | 56.6% |
+| **2-gram** | Subword | 247 🏆 | 7.95 | 5,747 | 68.3% | 99.7% |
+| **3-gram** | Word | 17,032 | 14.06 | 1,208,224 | 31.7% | 47.8% |
+| **3-gram** | Subword | 1,598 | 10.64 | 47,440 | 34.4% | 76.9% |
+| **4-gram** | Word | 50,245 | 15.62 | 3,168,479 | 23.5% | 38.5% |
+| **4-gram** | Subword | 5,867 | 12.52 | 301,900 | 26.2% | 55.3% |
+| **5-gram** | Word | 68,533 | 16.06 | 2,392,175 | 19.2% | 34.2% |
+| **5-gram** | Subword | 14,203 | 13.79 | 1,156,846 | 23.4% | 47.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nahilalakip ha` | 1,196,502 |
+| 2 | `in nahilalakip` | 1,196,444 |
+| 3 | `in uska` | 1,161,092 |
+| 4 | `mga kasarigan` | 1,140,811 |
+| 5 | `familia nga` | 1,136,619 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `in nahilalakip ha` | 1,196,442 |
+| 2 | `ha genus nga` | 1,059,395 |
+| 3 | `nahilalakip ha genus` | 1,059,391 |
+| 4 | `uska species han` | 1,059,159 |
+| 5 | `in uska species` | 1,059,158 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nahilalakip ha genus nga` | 1,059,391 |
+| 2 | `in nahilalakip ha genus` | 1,059,349 |
+| 3 | `in uska species han` | 1,059,153 |
+| 4 | `hini subspecies nga nakalista` | 1,022,924 |
+| 5 | `waray hini subspecies nga` | 1,022,924 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `in nahilalakip ha genus nga` | 1,059,349 |
+| 2 | `waray hini subspecies nga nakalista` | 1,022,924 |
+| 3 | `hini subspecies nga nakalista mga` | 1,022,834 |
+| 4 | `subspecies nga nakalista mga kasarigan` | 1,022,831 |
+| 5 | `in uska species han magnoliopsida` | 202,211 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 15,944,152 |
+| 2 | `n _` | 13,013,735 |
+| 3 | `a n` | 11,288,391 |
+| 4 | `_ n` | 11,062,347 |
+| 5 | `g a` | 9,641,404 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 7,717,416 |
+| 2 | `n g a` | 6,387,666 |
+| 3 | `_ n g` | 6,209,737 |
+| 4 | `g a _` | 6,146,693 |
+| 5 | `_ h a` | 4,483,798 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n g a` | 6,206,771 |
+| 2 | `n g a _` | 4,713,126 |
+| 3 | `g a n _` | 2,648,745 |
+| 4 | `_ i n _` | 2,593,266 |
+| 5 | `s p e c` | 2,524,428 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n g a _` | 4,702,772 |
+| 2 | `s p e c i` | 2,510,965 |
+| 3 | `p e c i e` | 2,471,786 |
+| 4 | `e c i e s` | 2,467,835 |
+| 5 | `c i e s _` | 2,396,660 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 247
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~48% 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 | 0.9524 | 1.935 | 6.58 | 763,014 | 4.8% |
+| **1** | Subword | 0.8828 | 1.844 | 5.77 | 3,095 | 11.7% |
+| **2** | Word | 0.3392 | 1.265 | 1.87 | 4,989,422 | 66.1% |
+| **2** | Subword | 0.6967 | 1.621 | 4.99 | 17,724 | 30.3% |
+| **3** | Word | 0.2062 | 1.154 | 1.43 | 9,222,656 | 79.4% |
+| **3** | Subword | 0.7365 | 1.666 | 4.85 | 88,254 | 26.3% |
+| **4** | Word | 0.1336 🏆 | 1.097 | 1.25 | 13,040,247 | 86.6% |
+| **4** | Subword | 0.7108 | 1.637 | 3.87 | 427,478 | 28.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `nga nakalista mga sumpay ha familia nga ginhulagway ni léon dufour hadton an notocelis ngan familia`
+2. `in nahilalakip ha genus nga nakalista mga kasarigan website zoonomen zoological nomenclature and ter...`
+3. `an leptomastix tanasijtshuki in uska species han bryozoa world checklist of the global lepidoptera n...`
+
+**Context Size 2:**
+
+1. `nahilalakip ha genus nga tonnoiriella ngan familia nga terebellidae waray hini subspecies nga nakali...`
+2. `in nahilalakip ha genus nga clitoria ngan familia nga rubiaceae waray hini subspecies nga nakalista ...`
+3. `in uska species han diptera nga ginhulagway ni henri ernest baillon ngan ginhatag han pagkayana nga ...`
+
+**Context Size 3:**
+
+1. `in nahilalakip ha genus nga buxus ngan familia nga cheliferidae waray hini subspecies nga nakalista ...`
+2. `ha genus nga exorista ngan familia nga tetranychidae waray hini subspecies nga nakalista mga kasarig...`
+3. `nahilalakip ha genus nga miostauropus ngan familia nga rutelidae waray hini subspecies nga nakalista...`
+
+**Context Size 4:**
+
+1. `nahilalakip ha genus nga euprosopia ngan familia nga platystomatidae mabibilngan ini ha sri lanka wa...`
+2. `in nahilalakip ha genus nga haploops ngan familia nga ampeliscidae waray hini subspecies nga nakalis...`
+3. `in uska species han liliopsida nga ginhulagway ni john hutchinson ngan john mcewan dalziel an bertie...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_doni_n_his_wano`
+2. `abs_ithonce_ndul`
+3. `ngspysogakid_and`
+
+**Context Size 2:**
+
+1. `a_ini_wcspectic_t`
+2. `n_in_390._ara).,_`
+3. `anterk_nganthopte`
+
+**Context Size 3:**
+
+1. `an_pindros_the_e._`
+2. `nga_cera_nga_syste`
+3. `_nga_sipalmeestrit`
+
+**Context Size 4:**
+
+1. `_nga_syahan_diaphen`
+2. `nga_ginhulagway_ni_`
+3. `gan_familia_nga_nak`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 86.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (427,478 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 | 548,741 |
+| Total Tokens | 79,294,042 |
+| Mean Frequency | 144.50 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 11269.34 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | nga | 4,703,546 |
+| 2 | in | 2,921,835 |
+| 3 | an | 2,589,624 |
+| 4 | ha | 1,893,436 |
+| 5 | han | 1,625,103 |
+| 6 | ngan | 1,415,641 |
+| 7 | mga | 1,401,419 |
+| 8 | species | 1,374,922 |
+| 9 | of | 1,347,178 |
+| 10 | nahilalakip | 1,196,509 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | sermersooq | 2 |
+| 2 | kulusuk | 2 |
+| 3 | sporcle | 2 |
+| 4 | loocnon | 2 |
+| 5 | estadyum | 2 |
+| 6 | katalyst | 2 |
+| 7 | haluk | 2 |
+| 8 | bilginer | 2 |
+| 9 | squibb | 2 |
+| 10 | paca | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1284 |
+| R² (Goodness of Fit) | 0.998865 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 58.7% |
+| Top 1,000 | 76.4% |
+| Top 5,000 | 85.5% |
+| Top 10,000 | 89.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9989 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 58.7% of corpus
+- **Long Tail:** 538,741 words needed for remaining 10.9% 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 | 0.8470 | 0.3349 | N/A | N/A |
+| **mono_64d** | 64 | 0.8029 | 0.3103 | N/A | N/A |
+| **mono_128d** | 128 | 0.7779 | 0.2306 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8470 🏆 | 0.3453 | 0.0380 | 0.2940 |
+| **aligned_64d** | 64 | 0.8029 | 0.3113 | 0.1440 | 0.4780 |
+| **aligned_128d** | 128 | 0.7779 | 0.2362 | 0.2780 | 0.6400 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8470 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2947. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 27.8% 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** | 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 |
+|--------|----------|
+| `-a` | ameziane, alucao, astroma |
+| `-ma` | majerorum, maracasi, marsa |
+| `-s` | sustansiya, szijji, subtriflora |
+| `-pa` | parle, pasiphimus, paradontophora |
+| `-ca` | callites, callochiton, cabanisii |
+| `-p` | parle, psdamyc, polystylata |
+| `-ba` | basilewskyanus, bathyprion, balangigan |
+| `-b` | biselinifera, basilewskyanus, bicolora |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | basilewskyanus, bomareoides, pasiphimus |
+| `-a` | sustansiya, wolynia, biselinifera |
+| `-us` | basilewskyanus, pasiphimus, hesperotychus |
+| `-is` | niasensis, moniliventris, admotalis |
+| `-e` | ocellate, ameziane, parle |
+| `-um` | majerorum, curvinervium, tapinelytrum |
+| `-i` | kuehhasii, minjujuŭi, maracasi |
+| `-es` | bomareoides, callites, pedes |
+
+### 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 |
+|------|----------|------------------|----------|
+| `kasa` | 2.52x | 45 contexts | akasa, kasaï, kasal |
+| `ilal` | 2.61x | 36 contexts | silal, bilal, kilal |
+| `asar` | 2.10x | 62 contexts | casar, asara, pasar |
+| `opte` | 1.62x | 202 contexts | opter, scoptes, eoptera |
+| `ulag` | 2.87x | 14 contexts | bulag, dulag, nabulag |
+| `ahil` | 2.32x | 27 contexts | dahil, tahil, kahili |
+| `akal` | 2.15x | 34 contexts | zakal, yakal, bakal |
+| `akip` | 3.06x | 11 contexts | lakip, nakipa, kalakip |
+| `agwa` | 2.64x | 16 contexts | dagway, magway, nagwara |
+| `fami` | 2.07x | 24 contexts | famiy, famil, famiie |
+| `peci` | 1.77x | 36 contexts | specis, specia, pecina |
+| `subs` | 2.90x | 8 contexts | subst, subsp, subsaga |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-s` | 303 words | antoninus, albocingulatus |
+| `-p` | `-s` | 258 words | plethobasus, platypygus |
+| `-a` | `-a` | 251 words | artopenna, anahita |
+| `-p` | `-a` | 250 words | pindica, paradoxophyla |
+| `-s` | `-a` | 225 words | spodopa, synpsylla |
+| `-s` | `-s` | 220 words | simaethis, spartiformis |
+| `-m` | `-s` | 147 words | machetis, mesozonalis |
+| `-a` | `-us` | 135 words | antoninus, albocingulatus |
+| `-b` | `-s` | 111 words | beombawigulensis, boridiensis |
+| `-m` | `-a` | 109 words | missiona, mauritacantha |
+
+### 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 |
+|------|-----------------|------------|------|
+| yungchangense | **`yungchangen-s-e`** | 7.5 | `s` |
+| betinensis | **`betinen-s-is`** | 7.5 | `s` |
+| hundungensis | **`hundungen-s-is`** | 7.5 | `s` |
+| yoneyamai | **`yoneyam-a-i`** | 7.5 | `a` |
+| austrocolumbiana | **`austrocolumbi-a-na`** | 7.5 | `a` |
+| glycereen | **`glycer-e-en`** | 7.5 | `e` |
+| agesilaus | **`agesi-la-us`** | 7.5 | `la` |
+| discoelongata | **`discoelong-a-ta`** | 7.5 | `a` |
+| desordenata | **`desorden-a-ta`** | 7.5 | `a` |
+| sexpectinata | **`sexpectin-a-ta`** | 7.5 | `a` |
+| pluvigena | **`pluvig-e-na`** | 7.5 | `e` |
+| chondrichthyans | **`chondrichthy-a-ns`** | 7.5 | `a` |
+| ecorticata | **`ecortic-a-ta`** | 7.5 | `a` |
+| binuangan | **`binuang-a-n`** | 7.5 | `a` |
+| dallasiana | **`dallasi-a-na`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Waray 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 (3.93x) |
+| N-gram | **2-gram** | Lowest perplexity (247) |
+| Markov | **Context-4** | Highest predictability (86.6%) |
+| 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 05:52:21*

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