Upload all models and assets for ceb (latest)

README.md

@@ -33,734 +33,198 @@ dataset_info:
 metrics:
   - name: best_compression_ratio
     type: compression
-    value: 4.059
+    value: 4.164
   - name: best_isotropy
     type: isotropy
-    value: 0.7670
+    value: 0.8551
+  - name: best_alignment_r10
+    type: alignment
+    value: 0.5920
   - name: vocabulary_size
     type: vocab
-    value: 0
-generated: 2026-01-07
+    value: 208251
+generated: 2026-03-04
 ---
 
-# Cebuano - Wikilangs Models
-## Comprehensive Research Report & Full Ablation Study
+# Cebuano — Wikilangs Models
 
-This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Cebuano** Wikipedia data.
-We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **Cebuano** Wikipedia by [Wikilangs](https://wikilangs.org).
 
-## 📋 Repository Contents
+🌐 [Language Page](https://wikilangs.org/languages/ceb/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=ceb) · 📊 [Full Research Report](RESEARCH_REPORT.md)
 
-### Models & Assets
+## Language Samples
 
-- 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
+Example sentences drawn from the Cebuano Wikipedia corpus:
 
-![Performance Dashboard](visualizations/performance_dashboard.png)
+> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong mayor sa lalawigan sa Sugbo. Alkalde sa Lalawigan sa Sugbo Alkalde
 
-### Analysis and Evaluation
+> Ang sekswalidad puyde mopasabot sa: Sekswalidad sa tawo Sekswalidad sa tanom Sekswalidad (oryentasyon) Sekswalidad sa mananap
 
-- [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)
+> Katawhan ug Kultura Ekonomiya Heyograpiya Politikal Mga lungsod Dakbayan Mga dakbayan Pisikal Kaagi Mga sumpay sa gawas
 
----
-## 1. Tokenizer Evaluation
+> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong gobernador sa lalawigan sa Samar. Mga Gobernador Antonio Bolastig Milagrosa T. Tan Gobernador Gobernador sa Samar
 
-![Tokenizer Compression](visualizations/tokenizer_compression.png)
+> Kining maong panid gitagana alang sa lista sa mga tawo nga nahimong gobernador sa lalawigan sa Biliran. Mga Gobernador (gikan Wayne Jaro Rogelio J. Espina Gobernador Gobernador sa Biliran
 
-![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+## Quick Start
 
-![Tokenizer OOV](visualizations/tokenizer_oov.png)
+### Load the Tokenizer
 
-![Total Tokens](visualizations/tokenizer_total_tokens.png)
+```python
+import sentencepiece as spm
 
-### Results
+sp = spm.SentencePieceProcessor()
+sp.Load("ceb_tokenizer_32k.model")
 
-| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
-|------------|-------------|---------------|----------|--------------|
-| **8k** | 3.174x | 3.18 | 0.3878% | 267,679 |
-| **16k** | 3.550x | 3.55 | 0.4338% | 239,262 |
-| **32k** | 3.813x | 3.82 | 0.4660% | 222,758 |
-| **64k** | 4.059x 🏆 | 4.06 | 0.4960% | 209,290 |
+text = "Ang (MDCCL) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka tuig"
+tokens = sp.EncodeAsPieces(text)
+ids    = sp.EncodeAsIds(text)
 
-### Tokenization Examples
+print(tokens)  # subword pieces
+print(ids)     # integer ids
 
-Below are sample sentences tokenized with each vocabulary size:
+# Decode back
+print(sp.DecodeIds(ids))
+```
 
-**Sample 1:** `Kahenera sa mga kaka ang Cteniza. Ang Cteniza sakop sa kabanay nga Ctenizidae. A...`
+<details>
+<summary><b>Tokenization examples (click to expand)</b></summary>
+
+**Sample 1:** `Ang (MDCCL) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka tuig…`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁kahenera ▁sa ▁mga ▁kaka ▁ang ▁cten iza . ▁ang ▁cten ... (+23 more)` | 33 |
-| 16k | `▁kahenera ▁sa ▁mga ▁kaka ▁ang ▁cten iza . ▁ang ▁cten ... (+22 more)` | 32 |
-| 32k | `▁kahenera ▁sa ▁mga ▁kaka ▁ang ▁cten iza . ▁ang ▁cten ... (+21 more)` | 31 |
-| 64k | `▁kahenera ▁sa ▁mga ▁kaka ▁ang ▁cten iza . ▁ang ▁cten ... (+21 more)` | 31 |
+| 8k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+27 more)` | 37 |
+| 16k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+24 more)` | 34 |
+| 32k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa … (+22 more)` | 32 |
+| 64k | `▁ang ▁( md c cl ) ▁mao ▁ang ▁usa ▁ka … (+21 more)` | 31 |
 
-**Sample 2:** `Ang Jizō-saki ngalan niining mga mosunod: Heyograpiya Hapon Shakaga Hana, punta,...`
+**Sample 2:** `Vilnius - Ulohan, Lyetuwanya. lungsod ug dakbayan sa Uropa`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁ang ▁j iz ō - s aki ▁ngalan ▁ni in ... (+47 more)` | 57 |
-| 16k | `▁ang ▁j iz ō - s aki ▁ngalan ▁ni ining ... (+36 more)` | 46 |
-| 32k | `▁ang ▁j iz ō - s aki ▁ngalan ▁niining ▁mga ... (+32 more)` | 42 |
-| 64k | `▁ang ▁j iz ō - s aki ▁ngalan ▁niining ▁mga ... (+32 more)` | 42 |
+| 8k | `▁v il n ius ▁- ▁ulo han , ▁ly et … (+9 more)` | 19 |
+| 16k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an … (+7 more)` | 17 |
+| 32k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an … (+7 more)` | 17 |
+| 64k | `▁vil n ius ▁- ▁ulohan , ▁lyetuwanya . ▁lungsod ▁ug … (+3 more)` | 13 |
 
-**Sample 3:** `Ang (MCMLXXXIII) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka...`
+**Sample 3:** `Ang manunuwat usa ka tawo nga naay propesyon sa pagsulat.`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁ang ▁( m c m l xx x iii ) ... (+32 more)` | 42 |
-| 16k | `▁ang ▁( m c m l xx x iii ) ... (+28 more)` | 38 |
-| 32k | `▁ang ▁( m c m l xxx iii ) ▁mao ... (+24 more)` | 34 |
-| 64k | `▁ang ▁( mc m l xxx iii ) ▁mao ▁ang ... (+22 more)` | 32 |
-
-
-### Key Findings
-
-- **Best Compression:** 64k achieves 4.059x compression
-- **Lowest UNK Rate:** 8k with 0.3878% 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 | 3,171 | 11.63 | 3,446,236 | 37.4% | 76.3% |
-| **2-gram** | Subword | 218 🏆 | 7.77 | 33,604 | 70.8% | 99.5% |
-| **3-gram** | Word | 6,839 | 12.74 | 7,766,658 | 32.6% | 69.1% |
-| **3-gram** | Subword | 1,277 | 10.32 | 196,868 | 35.6% | 83.3% |
-| **4-gram** | Word | 13,177 | 13.69 | 16,952,568 | 31.0% | 62.8% |
-| **4-gram** | Subword | 3,898 | 11.93 | 1,019,139 | 22.5% | 67.3% |
-| **5-gram** | Word | 19,115 | 14.22 | 18,655,008 | 30.0% | 58.4% |
-| **5-gram** | Subword | 7,890 | 12.95 | 3,628,728 | 16.7% | 59.8% |
-
-### Top 5 N-grams by Size
-
-**2-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `sa nasod` | 7,048,649 |
-| 2 | `km sa` | 6,204,569 |
-| 3 | `palibot sa` | 5,653,512 |
-| 4 | `ang mga` | 5,645,464 |
-| 5 | `mga gi` | 5,576,920 |
-
-**3-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `mga gi basihan` | 5,576,915 |
-| 2 | `ang mga gi` | 5,576,913 |
-| 3 | `gi basihan niini` | 5,576,912 |
-| 4 | `geonames org cc` | 3,664,283 |
-| 5 | `org cc by` | 3,664,283 |
-
-**4-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `ang mga gi basihan` | 5,576,913 |
-| 2 | `mga gi basihan niini` | 5,576,912 |
-| 3 | `geonames org cc by` | 3,664,283 |
-| 4 | `org cc by post` | 3,664,270 |
-| 5 | `cc by post updated` | 3,664,269 |
-
-**5-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `ang mga gi basihan niini` | 5,576,912 |
-| 2 | `geonames org cc by post` | 3,664,270 |
-| 3 | `org cc by post updated` | 3,664,269 |
-| 4 | `cc by post updated database` | 3,664,234 |
-| 5 | `post updated database download sa` | 3,664,233 |
-
-**2-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `a _` | 176,572,408 |
-| 2 | `a n` | 170,636,786 |
-| 3 | `n g` | 127,660,424 |
-| 4 | `s a` | 126,044,028 |
-| 5 | `_ s` | 125,029,167 |
-
-**3-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ s a` | 104,157,280 |
-| 2 | `s a _` | 95,124,588 |
-| 3 | `a n g` | 80,898,551 |
-| 4 | `n g _` | 79,824,327 |
-| 5 | `_ a n` | 50,392,535 |
-
-**4-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ s a _` | 94,060,964 |
-| 2 | `a n g _` | 70,289,894 |
-| 3 | `_ a n g` | 46,728,827 |
-| 4 | `_ n g a` | 28,593,356 |
-| 5 | `n g a _` | 26,245,654 |
-
-**5-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ a n g _` | 46,539,851 |
-| 2 | `_ n g a _` | 26,090,887 |
-| 3 | `n _ s a _` | 24,592,104 |
-| 4 | `. _ a n g` | 21,317,144 |
-| 5 | `a n g _ k` | 20,331,305 |
-
-
-### Key Findings
-
-- **Best Perplexity:** 2-gram (subword) with 218
-- **Entropy Trend:** Decreases with larger n-grams (more predictable)
-- **Coverage:** Top-1000 patterns cover ~60% 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 | 1.4579 | 2.747 | 8.46 | 2,622,358 | 0.0% |
-| **1** | Subword | 1.5846 | 2.999 | 12.23 | 10,636 | 0.0% |
-| **2** | Word | 0.5081 | 1.422 | 2.51 | 21,964,306 | 49.2% |
-| **2** | Subword | 0.6448 | 1.564 | 3.57 | 129,845 | 35.5% |
-| **3** | Word | 0.2262 | 1.170 | 1.63 | 54,790,128 | 77.4% |
-| **3** | Subword | 0.6034 | 1.519 | 3.47 | 463,245 | 39.7% |
-| **4** | Word | 0.0992 🏆 | 1.071 | 1.32 | 89,104,487 | 90.1% |
-| **4** | Subword | 0.6107 | 1.527 | 3.20 | 1,608,648 | 38.9% |
-
-### Generated Text Samples (Word-based)
-
-Below are text samples generated from each word-based Markov chain model:
-
-**Context Size 1:**
-
-1. `sa lintjønnåsen bungtod mikkelhaugen ang poluostrov zuyeva sa amihanan sidlakan dagat kahaboga ang k...`
-2. `ang kinainitan nga matang nga sama niini turkey hill sa british columbia river ang kinahabogang dapi...`
-3. `nga sama niini villabuena del atlántico sur peru nga ugahon ang kinabasaan nga bulan hunyo sa`
-
-**Context Size 2:**
-
-1. `sa nasod ang klima bugnaw nga ugahon ang kasarangang giiniton c ang kasarangang pag ulan milimetro m...`
-2. `km sa amihanan kasadpan sa washington d c metros ibabaw sa dagat kahaboga ang nahimutangan sa mållok`
-3. `palibot sa desa caringin administratibo nga balangay ang kudumbuwa sa geonames org cc by post update...`
-
-**Context Size 3:**
-
-1. `mga gi basihan niini jessup guymer in austrobaileya 7 15 govaerts r ed for a full list of`
-2. `ang mga gi basihan niini kūh e tīr sa rehiyon palibot sa parksville knob hapit nalukop sa kaumahan`
-3. `gi basihan niini nhamiraze sa geonames org cc by post updated database download sa pahang suba sa ma...`
-
-**Context Size 4:**
-
-1. `ang mga gi basihan niini austdalen sa geonames org cc by post updated database download sa suba sa i...`
-2. `mga gi basihan niini cañada del mundo sa dominikanhong republika nahimutang ni sa sentro nga bahin s...`
-3. `geonames org cc by post updated database download sa bungtod sa northern estado sa sudan sa sudan ng...`
-
-
-### Generated Text Samples (Subword-based)
-
-Below are text samples generated from each subword-based Markov chain model:
-
-**Context Size 1:**
-
-1. `_nahinababaes_pi`
-2. `a_mga_nl._sangan`
-3. `nga_mibluagingal`
-
-**Context Size 2:**
-
-1. `a_amasmyctomihapr`
-2. `andsby)];_p.m._an`
-3. `ngaloado_nga_gel.`
-
-**Context Size 3:**
+| 8k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na … (+9 more)` | 19 |
+| 16k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na … (+8 more)` | 18 |
+| 32k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁naay … (+6 more)` | 16 |
+| 64k | `▁ang ▁man un uwat ▁usa ▁ka ▁tawo ▁nga ▁naay ▁propes … (+4 more)` | 14 |
 
-1. `_sa_hayop_sa_tro._`
-2. `sa_orrell_(cc-by)]`
-3. `ang_sourgoin_tom_n`
+</details>
 
-**Context Size 4:**
+### Load Word Embeddings
 
-1. `_sa_nasod,_km_sa_[_`
-2. `ang_patag_tuig._kin`
-3. `_ang_kinabarat_aaku`
+```python
+from gensim.models import KeyedVectors
 
+# Aligned embeddings (cross-lingual, mapped to English vector space)
+wv = KeyedVectors.load("ceb_embeddings_128d_aligned.kv")
 
-### Key Findings
-
-- **Best Predictability:** Context-4 (word) with 90.1% predictability
-- **Branching Factor:** Decreases with context size (more deterministic)
-- **Memory Trade-off:** Larger contexts require more storage (1,608,648 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 | 2,197,636 |
-| Total Tokens | 770,818,249 |
-| Mean Frequency | 350.75 |
-| Median Frequency | 6 |
-| Frequency Std Dev | 78759.96 |
-
-### Most Common Words
-
-| Rank | Word | Frequency |
-|------|------|-----------|
-| 1 | sa | 95,123,802 |
-| 2 | ang | 48,189,862 |
-| 3 | nga | 26,091,942 |
-| 4 | ug | 11,614,833 |
-| 5 | mga | 11,196,843 |
-| 6 | c | 9,761,410 |
-| 7 | ni | 8,490,669 |
-| 8 | niini | 7,626,074 |
-| 9 | palibot | 7,306,530 |
-| 10 | nasod | 7,071,533 |
-
-### Least Common Words (from vocabulary)
-
-| Rank | Word | Frequency |
-|------|------|-----------|
-| 1 | kaliforńijo | 2 |
-| 2 | kaliforniya | 2 |
-| 3 | کیلیفورنیا | 2 |
-| 4 | couzzens | 2 |
-| 5 | hellgrammite | 2 |
-| 6 | powena | 2 |
-| 7 | californië | 2 |
-| 8 | mcgarva | 2 |
-| 9 | fightertown | 2 |
-| 10 | ferril | 2 |
-
-### Zipf's Law Analysis
-
-| Metric | Value |
-|--------|-------|
-| Zipf Coefficient | 1.4288 |
-| R² (Goodness of Fit) | 0.993579 |
-| Adherence Quality | **excellent** |
-
-### Coverage Analysis
-
-| Top N Words | Coverage |
-|-------------|----------|
-| Top 100 | 63.2% |
-| Top 1,000 | 88.4% |
-| Top 5,000 | 93.1% |
-| Top 10,000 | 94.4% |
-
-### Key Findings
-
-- **Zipf Compliance:** R²=0.9936 indicates excellent adherence to Zipf's law
-- **High Frequency Dominance:** Top 100 words cover 63.2% of corpus
-- **Long Tail:** 2,187,636 words needed for remaining 5.6% 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 | 0.7670 🏆 | 0.3194 | N/A | N/A |
-| **mono_64d** | 64 | 0.7432 | 0.2748 | N/A | N/A |
-| **mono_128d** | 128 | 0.6660 | 0.2423 | N/A | N/A |
-| **aligned_32d** | 32 | 0.7670 | 0.3286 | 0.1020 | 0.4400 |
-| **aligned_64d** | 64 | 0.7432 | 0.2716 | 0.2480 | 0.6140 |
-| **aligned_128d** | 128 | 0.6660 | 0.2452 | 0.3300 | 0.7240 |
+similar = wv.most_similar("word", topn=5)
+for word, score in similar:
+    print(f"  {word}: {score:.3f}")
+```
 
-### Key Findings
+### Load N-gram Model
 
-- **Best Isotropy:** mono_32d with 0.7670 (more uniform distribution)
-- **Semantic Density:** Average pairwise similarity of 0.2803. Lower values indicate better semantic separation.
-- **Alignment Quality:** Aligned models achieve up to 33.0% R@1 in cross-lingual retrieval.
-- **Recommendation:** 128d aligned for best cross-lingual performance
+```python
+import pyarrow.parquet as pq
 
----
-## 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 | **-0.024** | 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 |
-|--------|----------|
-| `-ma` | mazanderanica, magnesita, magnhildmyra |
-
-#### Productive Suffixes
-| Suffix | Examples |
-|--------|----------|
-| `-a` | susumwa, pucanaylla, mazanderanica |
-| `-s` | heteraxinoides, gastroglottis, supersentiens |
-| `-en` | sveinebakken, elgemyrdalen, føytongjen |
-| `-is` | gastroglottis, nooksackensis, naraiensis |
-| `-us` | pseudogymnostreptus, rearedpiaractus, supremus |
-| `-ia` | omphalomia, eugomontia, leucospilaria |
-| `-la` | pucanaylla, diltilla, bulbulla |
-| `-na` | thunbergiana, jajina, coolarrikinna |
-
-### 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 |
-|------|----------|------------------|----------|
-| `lson` | 2.69x | 160 contexts | olson, alson, elson |
-| `ahim` | 2.83x | 95 contexts | kahim, rahim, tahim |
-| `eona` | 2.74x | 87 contexts | teona, meona, leona |
-| `ngto` | 2.54x | 108 contexts | hangto, singto, langto |
-| `ugna` | 2.37x | 146 contexts | yugna, pugna, ugnat |
-| `ogue` | 2.44x | 115 contexts | bogue, logue, gogue |
-| `etro` | 2.08x | 203 contexts | netro, uetro, etrou |
-| `ands` | 2.06x | 206 contexts | sands, wands, pands |
-| `abaw` | 2.19x | 74 contexts | mabaw, labaw, tabaw |
-| `ecie` | 2.61x | 34 contexts | decie, pecies, specie |
-| `ated` | 2.52x | 37 contexts | dated, rated, hated |
-| `atag` | 1.65x | 256 contexts | atagn, datag, atago |
-
-### 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 |
-|--------|--------|-----------|----------|
-| `-ma` | `-a` | 56 words | matarrala, mahmudiya |
-| `-ma` | `-s` | 25 words | macrostrobilus, macroconus |
-| `-ma` | `-na` | 13 words | magiana, manvoumouna |
-| `-ma` | `-us` | 9 words | macrostrobilus, macroconus |
-| `-ma` | `-la` | 8 words | matarrala, macunolla |
-| `-ma` | `-is` | 7 words | mallecensis, marizópolis |
-| `-ma` | `-ia` | 4 words | maligia, mariahuslia |
-| `-ma` | `-ra` | 3 words | mautotara, macrochiera |
-| `-ma` | `-en` | 3 words | maben, maureen |
-| `-ma` | `-es` | 2 words | macroscelides, mashes |
-
-### 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 |
-|------|-----------------|------------|------|
-| whittieriana | **`whittier-ia-na`** | 6.0 | `whittier` |
-| darwiniana | **`darwin-ia-na`** | 6.0 | `darwin` |
-| huicumera | **`huicume-ra`** | 4.5 | `huicume` |
-| javorkana | **`javorka-na`** | 4.5 | `javorka` |
-| olavsbekken | **`olavsbekk-en`** | 4.5 | `olavsbekk` |
-| campelles | **`campell-es`** | 4.5 | `campell` |
-| apolinaria | **`apolinar-ia`** | 4.5 | `apolinar` |
-| steyskalia | **`steyskal-ia`** | 4.5 | `steyskal` |
-| liniholmen | **`liniholm-en`** | 4.5 | `liniholm` |
-| finngrunden | **`finngrund-en`** | 4.5 | `finngrund` |
-| maaprobahan | **`ma-aprobahan`** | 4.5 | `aprobahan` |
-| macrostylospora | **`ma-crostylospo-ra`** | 3.0 | `crostylospo` |
-| saharolana | **`saharo-la-na`** | 3.0 | `saharo` |
-| maxwellensis | **`ma-xwellens-is`** | 3.0 | `xwellens` |
-| mappianthus | **`ma-ppianth-us`** | 3.0 | `ppianth` |
-
-### 6.6 Linguistic Interpretation
-
-> **Automated Insight:**
-The language Cebuano shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+df = pq.read_table("ceb_3gram_word.parquet").to_pandas()
+print(df.head())
+```
 
----
-## 7. Summary & Recommendations
+## Models Overview
 
 ![Performance Dashboard](visualizations/performance_dashboard.png)
 
-### Production Recommendations
-
-| Component | Recommended | Rationale |
-|-----------|-------------|-----------|
-| Tokenizer | **64k BPE** | Best compression (4.06x) |
-| N-gram | **2-gram** | Lowest perplexity (218) |
-| Markov | **Context-4** | Highest predictability (90.1%) |
-| 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 |
+| Category | Assets |
+|----------|--------|
+| Tokenizers | BPE at 8k, 16k, 32k, 64k vocab sizes |
+| N-gram models | 2 / 3 / 4 / 5-gram (word & subword) |
+| Markov chains | Context 1–5 (word & subword) |
+| Embeddings | 32d, 64d, 128d — mono & aligned |
+| Vocabulary | Full frequency list + Zipf analysis |
+| Statistics | Corpus & model statistics JSON |
+
+## Metrics Summary
+
+| Component | Model | Key Metric | Value |
+|-----------|-------|------------|-------|
+| Tokenizer | 8k BPE | Compression | 3.20x |
+| Tokenizer | 16k BPE | Compression | 3.59x |
+| Tokenizer | 32k BPE | Compression | 3.89x |
+| Tokenizer | 64k BPE | Compression | 4.16x 🏆 |
+| N-gram | 2-gram (subword) | Perplexity | 244 🏆 |
+| N-gram | 2-gram (word) | Perplexity | 1,490 |
+| N-gram | 3-gram (subword) | Perplexity | 1,343 |
+| N-gram | 3-gram (word) | Perplexity | 2,538 |
+| N-gram | 4-gram (subword) | Perplexity | 3,750 |
+| N-gram | 4-gram (word) | Perplexity | 4,059 |
+| N-gram | 5-gram (subword) | Perplexity | 6,751 |
+| N-gram | 5-gram (word) | Perplexity | 5,049 |
+| Markov | ctx-1 (subword) | Predictability | 13.0% |
+| Markov | ctx-1 (word) | Predictability | 0.0% |
+| Markov | ctx-2 (subword) | Predictability | 32.8% |
+| Markov | ctx-2 (word) | Predictability | 66.0% |
+| Markov | ctx-3 (subword) | Predictability | 28.5% |
+| Markov | ctx-3 (word) | Predictability | 83.0% |
+| Markov | ctx-4 (subword) | Predictability | 31.1% |
+| Markov | ctx-4 (word) | Predictability | 94.4% 🏆 |
+| Vocabulary | full | Size | 208,251 |
+| Vocabulary | full | Zipf R² | 0.9938 |
+| Embeddings | mono_32d | Isotropy | 0.8551 |
+| Embeddings | mono_64d | Isotropy | 0.8254 |
+| Embeddings | mono_128d | Isotropy | 0.7631 |
+| Embeddings | aligned_32d | Isotropy | 0.8551 🏆 |
+| Embeddings | aligned_64d | Isotropy | 0.8254 |
+| Embeddings | aligned_128d | Isotropy | 0.7631 |
+| Alignment | aligned_32d | R@1 / R@5 / R@10 | 5.8% / 18.8% / 31.4% |
+| Alignment | aligned_64d | R@1 / R@5 / R@10 | 11.2% / 32.6% / 46.4% |
+| Alignment | aligned_128d | R@1 / R@5 / R@10 | 23.8% / 47.0% / 59.2% 🏆 |
+
+📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**
 
 ---
-## 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.
+## About
 
-### Project
+Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.
 
-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)
+A project by **[Wikilangs](https://wikilangs.org)** · 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},
+  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}
+  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)
+- 🌐 [wikilangs.org](https://wikilangs.org)
+- 🌍 [Language page](https://wikilangs.org/languages/ceb/)
+- 🎮 [Playground](https://wikilangs.org/playground/?lang=ceb)
+- 🤗 [HuggingFace models](https://huggingface.co/wikilangs)
+- 📊 [wikipedia-monthly dataset](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 [Omar Kamali](https://huggingface.co/omarkamali)
 - 🤝 Sponsor: [Featherless AI](https://featherless.ai)
----
-*Generated by Wikilangs Models Pipeline*
 
-*Report Date: 2026-01-07 20:10:38*
+**License:** MIT — free for academic and commercial use.
+
+---
+*Generated by Wikilangs Pipeline · 2026-03-04 08:49:55*

RESEARCH_REPORT.md

@@ -0,0 +1,686 @@
+# Cebuano — Full Ablation Study & Research Report
+
+Detailed evaluation of all model variants trained on **Cebuano** Wikipedia data by [Wikilangs](https://wikilangs.org).
+
+👈 [Back to README](README.md)
+
+## 📋 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** | 3.198x | 3.20 | 0.4957% | 265,676 |
+| **16k** | 3.587x | 3.59 | 0.5559% | 236,895 |
+| **32k** | 3.895x | 3.90 | 0.6036% | 218,173 |
+| **64k** | 4.164x 🏆 | 4.17 | 0.6455% | 204,032 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ang (MDCCL) mao ang usa ka tuig sa kalendaryong Gregoryano. Ang maoy usa ka tuig...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa ... (+27 more)` | 37 |
+| 16k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa ... (+24 more)` | 34 |
+| 32k | `▁ang ▁( m d c cl ) ▁mao ▁ang ▁usa ... (+22 more)` | 32 |
+| 64k | `▁ang ▁( md c cl ) ▁mao ▁ang ▁usa ▁ka ... (+21 more)` | 31 |
+
+**Sample 2:** `Vilnius - Ulohan, Lyetuwanya. lungsod ug dakbayan sa Uropa`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁v il n ius ▁- ▁ulo han , ▁ly et ... (+9 more)` | 19 |
+| 16k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an ... (+7 more)` | 17 |
+| 32k | `▁vil n ius ▁- ▁ulohan , ▁ly et uw an ... (+7 more)` | 17 |
+| 64k | `▁vil n ius ▁- ▁ulohan , ▁lyetuwanya . ▁lungsod ▁ug ... (+3 more)` | 13 |
+
+**Sample 3:** `Ang manunuwat usa ka tawo nga naay propesyon sa pagsulat.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na ... (+9 more)` | 19 |
+| 16k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁na ... (+8 more)` | 18 |
+| 32k | `▁ang ▁man un u wat ▁usa ▁ka ▁tawo ▁nga ▁naay ... (+6 more)` | 16 |
+| 64k | `▁ang ▁man un uwat ▁usa ▁ka ▁tawo ▁nga ▁naay ▁propes ... (+4 more)` | 14 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.164x compression
+- **Lowest UNK Rate:** 8k with 0.4957% 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 | 1,490 | 10.54 | 185,133 | 57.1% | 77.3% |
+| **2-gram** | Subword | 244 🏆 | 7.93 | 4,031 | 67.3% | 99.8% |
+| **3-gram** | Word | 2,538 | 11.31 | 375,720 | 52.5% | 71.1% |
+| **3-gram** | Subword | 1,343 | 10.39 | 30,833 | 30.7% | 83.1% |
+| **4-gram** | Word | 4,059 | 11.99 | 640,004 | 49.1% | 65.5% |
+| **4-gram** | Subword | 3,750 | 11.87 | 184,896 | 19.6% | 67.9% |
+| **5-gram** | Word | 5,049 | 12.30 | 714,886 | 47.5% | 62.8% |
+| **5-gram** | Subword | 6,751 | 12.72 | 629,698 | 15.6% | 62.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nga matang` | 332,031 |
+| 2 | `ang mga` | 257,884 |
+| 3 | `sakop sa` | 255,886 |
+| 4 | `catalogue of` | 255,734 |
+| 5 | `mga gi` | 255,465 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ang mga gi` | 255,464 |
+| 2 | `mga gi basihan` | 255,464 |
+| 3 | `gi basihan niini` | 255,464 |
+| 4 | `catalogue of life` | 247,130 |
+| 5 | `sakop sa kahenera` | 225,289 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mga gi basihan niini` | 255,464 |
+| 2 | `ang mga gi basihan` | 255,464 |
+| 3 | `sakop sa kahenera nga` | 225,289 |
+| 4 | `una ning gihulagway ni` | 221,595 |
+| 5 | `leiden the netherlands issn` | 218,326 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ang mga gi basihan niini` | 255,464 |
+| 2 | `annual checklist roskov y ower` | 218,326 |
+| 3 | `of life annual checklist roskov` | 218,326 |
+| 4 | `y ower g orrell t` | 218,326 |
+| 5 | `roskov y ower g orrell` | 218,326 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 5,047,183 |
+| 2 | `, _` | 4,781,955 |
+| 3 | `a n` | 4,335,344 |
+| 4 | `_ n` | 4,282,281 |
+| 5 | `n g` | 3,457,212 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `. , _` | 2,839,080 |
+| 2 | `_ s a` | 2,121,271 |
+| 3 | `n g _` | 2,068,103 |
+| 4 | `_ n i` | 1,666,672 |
+| 5 | `a n g` | 1,567,452 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n g _` | 1,504,868 |
+| 2 | `_ s a _` | 1,342,701 |
+| 3 | `_ n g a` | 1,178,364 |
+| 4 | `n g a _` | 1,167,784 |
+| 5 | `_ a n g` | 872,500 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n g a _` | 1,165,749 |
+| 2 | `_ a n g _` | 865,643 |
+| 3 | `n _ s a _` | 599,691 |
+| 4 | `t a n g _` | 499,600 |
+| 5 | `s p e c i` | 496,776 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 244
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~63% 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 | 1.1540 | 2.225 | 5.53 | 285,670 | 0.0% |
+| **1** | Subword | 0.8701 | 1.828 | 5.60 | 2,205 | 13.0% |
+| **2** | Word | 0.3400 | 1.266 | 1.77 | 1,571,794 | 66.0% |
+| **2** | Subword | 0.6716 | 1.593 | 4.58 | 12,300 | 32.8% |
+| **3** | Word | 0.1703 | 1.125 | 1.39 | 2,770,828 | 83.0% |
+| **3** | Subword | 0.7154 | 1.642 | 4.50 | 56,330 | 28.5% |
+| **4** | Word | 0.0559 🏆 | 1.040 | 1.22 | 3,842,457 | 94.4% |
+| **4** | Subword | 0.6886 | 1.612 | 3.49 | 253,091 | 31.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `sa turkeya aserbaiyan iran and speciation the world spider catalog version in species naturalis leid...`
+2. `nga sama niini nga onychogomphus maculivertex sakop sa java pulo sa mont saint franchy usa ka`
+3. `ang mga gi basihan niini gordon d bailly n kirk p m bourgoin t custodian nicolson`
+
+**Context Size 2:**
+
+1. `nga matang nga sama niini ang mga gi basihan niini pycnobase bamber r n lea and j`
+2. `ang mga gi basihan niini boyko c b taiti s schotte m wilson g d f d`
+3. `sakop sa kahenera nga episinus ug kabanay nga sisoridae giklaseklase sa iucn ang kaliwatan sa manana...`
+
+**Context Size 3:**
+
+1. `mga gi basihan niini millard n a h monograph on the hydroida dredged by h m s challenger`
+2. `ang mga gi basihan niini jeekel c a w nomenclator generum et familiarum diplopodorum a list of the`
+3. `catalogue of life annual checklist roskov y ower g orrell t nicolson d bailly n kirk p m`
+
+**Context Size 4:**
+
+1. `ang mga gi basihan niini bock p gordon d worms bryozoa world list of bryozoa version in species itis`
+2. `mga gi basihan niini frank norman ramus erica a complete guide to scientific and common names of rep...`
+3. `sakop sa kahenera nga rhyacodrilis ug kabanay nga almidae walay nalista nga matang nga sama niini an...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_pemahi_tit._he.`
+2. `al_sopol_i_ong_h`
+3. `n_chewal:_ahydsa`
+
+**Context Size 2:**
+
+1. `a_ni._&_ficoce_ws`
+2. `,_e.,_ta_decologu`
+3. `anal_c.,_dus_&_it`
+
+**Context Size 3:**
+
+1. `.,_data_nuzelatta_`
+2. `_sa_hason_fromallo`
+3. `ng_mga_tural_check`
+
+**Context Size 4:**
+
+1. `ang_kadagatang_kaba`
+2. `_sa_hulagway_ni_wil`
+3. `_nga_matang_hayop_n`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (253,091 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 | 208,251 |
+| Total Tokens | 32,410,695 |
+| Mean Frequency | 155.63 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 6860.23 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | sa | 1,466,791 |
+| 2 | nga | 1,165,822 |
+| 3 | ang | 906,355 |
+| 4 | of | 522,496 |
+| 5 | t | 521,002 |
+| 6 | species | 490,688 |
+| 7 | e | 486,096 |
+| 8 | niini | 478,412 |
+| 9 | ni | 451,703 |
+| 10 | the | 433,952 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | parvanalis | 2 |
+| 2 | micronemus | 2 |
+| 3 | distolothrix | 2 |
+| 4 | dolicholophia | 2 |
+| 5 | brachypopterus | 2 |
+| 6 | moolenburghae | 2 |
+| 7 | debauwi | 2 |
+| 8 | buffei | 2 |
+| 9 | longibarbis | 2 |
+| 10 | durinii | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2679 |
+| R² (Goodness of Fit) | 0.993803 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 72.0% |
+| Top 1,000 | 87.3% |
+| Top 5,000 | 93.0% |
+| Top 10,000 | 94.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9938 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 72.0% of corpus
+- **Long Tail:** 198,251 words needed for remaining 5.1% 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 | 0.8551 | 0.3308 | N/A | N/A |
+| **mono_64d** | 64 | 0.8254 | 0.2774 | N/A | N/A |
+| **mono_128d** | 128 | 0.7631 | 0.2408 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8551 🏆 | 0.3257 | 0.0580 | 0.3140 |
+| **aligned_64d** | 64 | 0.8254 | 0.2774 | 0.1120 | 0.4640 |
+| **aligned_128d** | 128 | 0.7631 | 0.2443 | 0.2380 | 0.5920 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8551 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2827. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 23.8% 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 | **-0.003** | 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` | amotus, aethes, appolinard |
+| `-ma` | macrura, maigné, magpapatik |
+| `-s` | stieren, solasteridae, spermophilopsis |
+| `-b` | bahit, baod, berchtold |
+| `-p` | pseudocollinus, pseudoannulata, pseudocompressa |
+| `-m` | macrura, moscu, maigné |
+| `-pa` | panomya, pagkaayo, pagkapagka |
+| `-ca` | carteroniella, caudaornata, catmon |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | amotus, turdinus, pseudocollinus |
+| `-a` | elucubata, macrura, coccopoma |
+| `-us` | amotus, turdinus, pseudocollinus |
+| `-is` | dactylis, yambaensis, tenuis |
+| `-e` | hyèvre, ogyridione, raspailiidae |
+| `-ae` | raspailiidae, solasteridae, mitwabae |
+| `-i` | heurni, gaskelli, ogdeni |
+| `-es` | corneilles, récoltes, fragilipes |
+
+### 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 |
+|------|----------|------------------|----------|
+| `aban` | 2.82x | 102 contexts | abang, gaban, daban |
+| `icol` | 2.34x | 196 contexts | nicol, bicol, vicola |
+| `lson` | 2.80x | 38 contexts | olson, nelson, bulson |
+| `kaba` | 2.65x | 43 contexts | kabay, kabat, kabag |
+| `ihan` | 2.85x | 27 contexts | gihan, atihan, dihang |
+| `rell` | 1.89x | 103 contexts | torell, trelly, crella |
+| `orre` | 2.07x | 56 contexts | yorre, orret, orres |
+| `ener` | 1.96x | 61 contexts | enero, tener, eener |
+| `atal` | 1.89x | 56 contexts | datal, batal, natal |
+| `sako` | 2.86x | 12 contexts | sakop, masako, masakop |
+| `akop` | 2.88x | 10 contexts | sakop, panakop, sinakop |
+| `nera` | 1.77x | 41 contexts | minera, cinera, ponera |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-s` | 290 words | pteroctopus, purpurescens |
+| `-a` | `-s` | 246 words | apopkensis, albicaudatus |
+| `-p` | `-a` | 218 words | pontoparta, paiwa |
+| `-s` | `-s` | 207 words | suctotegeus, stavropoulos |
+| `-c` | `-s` | 207 words | camelopardalis, conjugalis |
+| `-p` | `-us` | 155 words | pteroctopus, piliocolobus |
+| `-s` | `-a` | 153 words | siqueira, sexmacula |
+| `-a` | `-a` | 151 words | alaria, arafoera |
+| `-t` | `-s` | 134 words | thyroidus, trapelus |
+| `-b` | `-s` | 132 words | billings, bourdeilles |
+
+### 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 |
+|------|-----------------|------------|------|
+| bruneitarsis | **`bruneitar-s-is`** | 7.5 | `s` |
+| validentata | **`valident-a-ta`** | 7.5 | `a` |
+| pretoriaensis | **`pretoriaen-s-is`** | 7.5 | `s` |
+| geograpsus | **`geograp-s-us`** | 7.5 | `s` |
+| gimatangmatang | **`gimatangmat-a-ng`** | 7.5 | `a` |
+| labropsis | **`labrop-s-is`** | 7.5 | `s` |
+| chihuahuaensis | **`chihuahuaen-s-is`** | 7.5 | `s` |
+| chevannay | **`chevann-a-y`** | 7.5 | `a` |
+| ovosetosa | **`ovoseto-s-a`** | 7.5 | `s` |
+| leporosum | **`leporo-s-um`** | 7.5 | `s` |
+| schistosum | **`schisto-s-um`** | 7.5 | `s` |
+| antromysis | **`antromy-s-is`** | 7.5 | `s` |
+| chalonnes | **`chalon-n-es`** | 7.5 | `n` |
+| strongyloxea | **`strongylox-e-a`** | 7.5 | `e` |
+| paragaveae | **`paragav-e-ae`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Cebuano 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 (4.16x) |
+| N-gram | **2-gram** | Lowest perplexity (244) |
+| Markov | **Context-4** | Highest predictability (94.4%) |
+| 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 |
+
+---
+👈 [Back to README](README.md)
+
+*Generated by Wikilangs Pipeline · 2026-03-04 08:50:39*

models/embeddings/aligned/ceb_128d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3eb8cd36ef12787b52a4668ec8d932c15ae2506ba3d10269c67067feb027418f
-size 2541152375
+oid sha256:8f5302a1d541d62e832452300f0b993cc5d8b4e162c893d214a4471a9969ecd5
+size 1138230096

models/embeddings/aligned/ceb_128d.projection.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ea5b20fdaac8234f50ae2c532a86b279e3aff39e4d73f285b1e0a70647b71f91
+oid sha256:82e90144b729d2438104ea319cc51f9ce6e67842d80ba8a7a40f33a961da14e9
 size 65664

models/embeddings/aligned/ceb_128d_metadata.json

@@ -3,6 +3,6 @@
   "dimension": 128,
   "version": "aligned",
   "hub_language": "en",
-  "seed_vocab_size": 152433,
-  "vocab_size": 1454790
+  "seed_vocab_size": 25620,
+  "vocab_size": 109514
 }

models/embeddings/aligned/ceb_32d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eafa8758d8b2648080dbb701fc676f2b6825d9fc3b9caa5a358d6a42f7c68238
-size 655873655
+oid sha256:23b77adefa3dd1d53bdbd3a9aae8d3323324cda0bd92453942d35b7f8e22673e
+size 286123344

models/embeddings/aligned/ceb_32d.projection.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:94bb2c9dcb6b024b0a37da5310b8164eaccd4a8d06893cf45a28f70e9ed38b1d
+oid sha256:f4bddb5514e0bf08687825d17b4ce5a6c2927cd7efe80755336501e3503f0a7c
 size 4224

models/embeddings/aligned/ceb_32d_metadata.json

@@ -3,6 +3,6 @@
   "dimension": 32,
   "version": "aligned",
   "hub_language": "en",
-  "seed_vocab_size": 152433,
-  "vocab_size": 1454790
+  "seed_vocab_size": 25620,
+  "vocab_size": 109514
 }

models/embeddings/aligned/ceb_64d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a051326edada076b29c9b2bc4a1c13e1b92c0e582e710fcf349f23daf828fb19
-size 1284299895
+oid sha256:62551e10a38d11cf18a8464bfc535ec388ffd8961d56bdd8dfcf6c26bce4ee28
+size 570158928

models/embeddings/aligned/ceb_64d.projection.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5c352c1836ef6cb3518cfb8cf48cd40d6d99ca83e50dedbf9849d9518e0efe65
+oid sha256:076228f74776162de5fce0455b925f33e3d5e5cf5395694a1292a01eb9b07c60
 size 16512

models/embeddings/aligned/ceb_64d_metadata.json

@@ -3,6 +3,6 @@
   "dimension": 64,
   "version": "aligned",
   "hub_language": "en",
-  "seed_vocab_size": 152433,
-  "vocab_size": 1454790
+  "seed_vocab_size": 25620,
+  "vocab_size": 109514
 }

models/embeddings/monolingual/ceb_128d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3eb8cd36ef12787b52a4668ec8d932c15ae2506ba3d10269c67067feb027418f
-size 2541152375
+oid sha256:8f5302a1d541d62e832452300f0b993cc5d8b4e162c893d214a4471a9969ecd5
+size 1138230096

models/embeddings/monolingual/ceb_128d_metadata.json

@@ -9,7 +9,8 @@
     "negative": 5,
     "epochs": 5,
     "encoding_method": "rope",
-    "dim": 128
+    "dim": 128,
+    "threads": 20
   },
-  "vocab_size": 1454790
+  "vocab_size": 109514
 }

models/embeddings/monolingual/ceb_32d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eafa8758d8b2648080dbb701fc676f2b6825d9fc3b9caa5a358d6a42f7c68238
-size 655873655
+oid sha256:23b77adefa3dd1d53bdbd3a9aae8d3323324cda0bd92453942d35b7f8e22673e
+size 286123344

models/embeddings/monolingual/ceb_32d_metadata.json

@@ -9,7 +9,8 @@
     "negative": 5,
     "epochs": 5,
     "encoding_method": "rope",
-    "dim": 32
+    "dim": 32,
+    "threads": 20
   },
-  "vocab_size": 1454790
+  "vocab_size": 109514
 }

models/embeddings/monolingual/ceb_64d.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a051326edada076b29c9b2bc4a1c13e1b92c0e582e710fcf349f23daf828fb19
-size 1284299895
+oid sha256:62551e10a38d11cf18a8464bfc535ec388ffd8961d56bdd8dfcf6c26bce4ee28
+size 570158928

models/embeddings/monolingual/ceb_64d_metadata.json

@@ -9,7 +9,8 @@
     "negative": 5,
     "epochs": 5,
     "encoding_method": "rope",
-    "dim": 64
+    "dim": 64,
+    "threads": 20
   },
-  "vocab_size": 1454790
+  "vocab_size": 109514
 }

models/subword_markov/ceb_markov_ctx1_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4f768ccf3c1b17ee6c3155af7196c8d3225ee1b5b17f295b763044d932178548
-size 843230
+oid sha256:417205c9014561f72f84c76b62d2112de933b0bf78bd8bffd6ce31ad9ac85c57
+size 104832

models/subword_markov/ceb_markov_ctx1_subword_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 1,
   "variant": "subword",
   "language": "ceb",
-  "unique_contexts": 10636,
-  "total_transitions": 4741280452
+  "unique_contexts": 2205,
+  "total_transitions": 205985910
 }

models/subword_markov/ceb_markov_ctx2_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:740d6da8735c5fe348a7d02eb110099031a0c7520aa307d58faedb1d948abea4
-size 4133523
+oid sha256:b63d43963ed1ecb0ae734609c3b43735df9f64a65b7ffe0f7120bf765137a4a9
+size 566849

models/subword_markov/ceb_markov_ctx2_subword_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 2,
   "variant": "subword",
   "language": "ceb",
-  "unique_contexts": 129845,
-  "total_transitions": 4735165236
+  "unique_contexts": 12300,
+  "total_transitions": 205685910
 }

models/subword_markov/ceb_markov_ctx3_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7280cd84fb22bd825ecd5918fd659b4ee44d894dd426f7e4863f41140c3b9065
-size 15714134
+oid sha256:b8210d4209c9d53eafaa49f275959900b56a0c2471d3ef03135b6f1de55f85e3
+size 2452378

models/subword_markov/ceb_markov_ctx3_subword_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 3,
   "variant": "subword",
   "language": "ceb",
-  "unique_contexts": 463245,
-  "total_transitions": 4729050020
+  "unique_contexts": 56330,
+  "total_transitions": 205385910
 }

models/subword_markov/ceb_markov_ctx4_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c541cfbd127565d46008005e601455b973d3ebc5d5be3c000d9b4177c0faf931
-size 50278691
+oid sha256:3e4f92c1369e01ee0859ee0a9cad993490fcf7c61b57a826a41d176a0ab3884c
+size 8303017

models/subword_markov/ceb_markov_ctx4_subword_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 4,
   "variant": "subword",
   "language": "ceb",
-  "unique_contexts": 1608648,
-  "total_transitions": 4722934804
+  "unique_contexts": 253091,
+  "total_transitions": 205085910
 }

models/subword_ngram/ceb_2gram_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:75effbc8c3c560b65d736c9461a76644273c13e5a1bdf333accf36f01b482fd9
-size 472677
+oid sha256:de7ca288dce41fe2351b4062e0cf08f0de9ba3d521fa8ec3b7eb827eabf69f21
+size 59472

models/subword_ngram/ceb_2gram_subword_metadata.json

@@ -2,6 +2,6 @@
   "n": 2,
   "variant": "subword",
   "language": "ceb",
-  "unique_ngrams": 33604,
-  "total_ngrams": 4741280452
+  "unique_ngrams": 4031,
+  "total_ngrams": 205985910
 }

models/subword_ngram/ceb_3gram_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bf35e60b92e9a30a6bddca322cbb5e9b26d7208c3b985403b5ad20e71d81c489
-size 2514513
+oid sha256:69a6c36d31632f45ed367042d1b7af476afb7588a46d17fbeb25ac5ed153b8f6
+size 431287

models/subword_ngram/ceb_3gram_subword_metadata.json

@@ -2,6 +2,6 @@
   "n": 3,
   "variant": "subword",
   "language": "ceb",
-  "unique_ngrams": 196868,
-  "total_ngrams": 4735165236
+  "unique_ngrams": 30833,
+  "total_ngrams": 205685910
 }

models/subword_ngram/ceb_4gram_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c8bed27ef2e0fd850c1d5afdae26a0c1d582caa13740b70d686a3c4b6572dfe6
-size 12745233
+oid sha256:7382f70fb756466be829ab6673f7e428e9d14ccbfb1778fc3e71d74190a7e126
+size 2199050

models/subword_ngram/ceb_4gram_subword_metadata.json

@@ -2,6 +2,6 @@
   "n": 4,
   "variant": "subword",
   "language": "ceb",
-  "unique_ngrams": 1019139,
-  "total_ngrams": 4729050020
+  "unique_ngrams": 184896,
+  "total_ngrams": 205385910
 }

models/subword_ngram/ceb_5gram_subword.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ded5fa72b1333db15722dc0eadc2671d488350d645d3763e7c475a650a835795
-size 44204990
+oid sha256:d9a1e6d68e5ff6bbc150d183517661652c513ad6dfd9851dbd5f47d413d64adb
+size 7448778

models/subword_ngram/ceb_5gram_subword_metadata.json

@@ -2,6 +2,6 @@
   "n": 5,
   "variant": "subword",
   "language": "ceb",
-  "unique_ngrams": 3628728,
-  "total_ngrams": 4722934804
+  "unique_ngrams": 629698,
+  "total_ngrams": 205085910
 }

models/tokenizer/ceb_tokenizer_16k.model

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d0524fd2b40acc6f5b34cb83b12b24872b8c64abbd29e4a958043cd2c376fce7
-size 510736
+oid sha256:47d8ed69810c7f0dc5095603d89fee3c7cbcc0ccdaa1515cb03cd440b3798cbb
+size 514642

models/tokenizer/ceb_tokenizer_32k.model

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:388d9039e50faf9eeb7281c832737aadf1e832bbe627c92b606b3ed690ea18f8
-size 798937
+oid sha256:bb14fab709a83541842c1c49bc3c969fa29d2ae8f17fad5850d76e12c989913d
+size 804931

models/tokenizer/ceb_tokenizer_64k.model

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0eaa647544e23d6b15bfee9173d40a3962bae64e5745da112bfba70081de1b77
-size 1394915
+oid sha256:393d48aba8597deedd04c537c087801339d27d2960e4d926f6193b53153d31e2
+size 1409733

models/tokenizer/ceb_tokenizer_8k.model

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:074f697a2540d914dbc182aa0ad66124f9535c265b7cfad75e78a6a21b055930
-size 373160
+oid sha256:5a33de50ec527f1cec19b369e6c20fd73f09fc4cb3802a7b266e29a35ffb521b
+size 374313

models/vocabulary/ceb_vocabulary.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4393f136f1e1677ad29b26e914ac0063db786da099d2088540d60a7c8d0a9015
-size 30923898
+oid sha256:5a63d67e3e6b60f76c47f015642b53b97eeed351841f39e26aa45adce7fa75dc
+size 3301786

models/vocabulary/ceb_vocabulary_metadata.json

@@ -1,17 +1,17 @@
 {
   "language": "ceb",
-  "vocabulary_size": 2197636,
+  "vocabulary_size": 208251,
   "variant": "full",
   "statistics": {
-    "type_token_ratio": 0.0034010600533183766,
+    "type_token_ratio": 0.00879715028031059,
     "coverage": {
-      "top_100": 0.6313898005092059,
-      "top_1000": 0.8839513376148225,
-      "top_5000": 0.9301546296408513,
-      "top_10000": 0.9430208060874313
+      "top_100": 0.7179463478609249,
+      "top_1000": 0.8706587686722903,
+      "top_5000": 0.927508710226541,
+      "top_10000": 0.9471302669199029
     },
-    "hapax_count": 425410,
-    "hapax_ratio": 0.1621816773323838,
-    "total_documents": 6115216
+    "hapax_count": 77553,
+    "hapax_ratio": 0.2713502960070538,
+    "total_documents": 300000
   }
 }

models/word_markov/ceb_markov_ctx1_word.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8d44f8a21899c99ba775bbcd5e8e5e784ac724f54d572a7815600b0965968b7e
-size 175693160
+oid sha256:2be8ddfd01b6ff55edafbee1bf428ad5f7f31eeaa2d15da78b4960e248072ce6
+size 15131685

models/word_markov/ceb_markov_ctx1_word_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 1,
   "variant": "word",
   "language": "ceb",
-  "unique_contexts": 2622358,
-  "total_transitions": 765128443
+  "unique_contexts": 285670,
+  "total_transitions": 32188248
 }

models/word_markov/ceb_markov_ctx2_word.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5967ac4958767576a34d1fec05136d6b8ecc2206dab7bb66d700c74150976f95
-size 502255980
+oid sha256:fd6a6f27f5588f0fefca0a0c2994b22cf1a3de0c3b3845c3959fcb177eaecca4
+size 30898564

models/word_markov/ceb_markov_ctx2_word_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 2,
   "variant": "word",
   "language": "ceb",
-  "unique_contexts": 21964306,
-  "total_transitions": 759013227
+  "unique_contexts": 1571794,
+  "total_transitions": 31888248
 }

models/word_markov/ceb_markov_ctx3_word.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:59d6750f62c81fc93014aa3a7e2903459e0c63ede1f5a6e861221be385b09152
-size 924339417
+oid sha256:5a21899f9e0db05b10991f7d5eb992e7e1a3791f748d9d6994a2cdce155927c8
+size 47112000

models/word_markov/ceb_markov_ctx3_word_metadata.json

@@ -2,6 +2,6 @@
   "context_size": 3,
   "variant": "word",
   "language": "ceb",
-  "unique_contexts": 54790128,
-  "total_transitions": 752898011
+  "unique_contexts": 2770828,
+  "total_transitions": 31588248
 }