RESEARCH_REPORT.md

Cebuano — Full Ablation Study & Research Report

Detailed evaluation of all model variants trained on Cebuano Wikipedia data by Wikilangs.

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📋 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

Analysis and Evaluation

• 1. Tokenizer Evaluation
• 2. N-gram Model Evaluation
• 3. Markov Chain Evaluation
• 4. Vocabulary Analysis
• 5. Word Embeddings Evaluation
• 6. Morphological Analysis (Experimental)
• 7. Summary & Recommendations
• Metrics Glossary
• Visualizations Index

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1. Tokenizer Evaluation

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

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2. N-gram Model Evaluation

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

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3. Markov Chain Evaluation

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

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4. Vocabulary Analysis

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

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5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

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

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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.

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7. Summary & Recommendations

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

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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

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Generated by Wikilangs Pipeline · 2026-03-04 08:50:39