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Tongan - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Tongan Wikipedia data. We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.

📋 Repository Contents

Models & Assets

Tokenizers (8k, 16k, 32k, 64k)
N-gram models (2, 3, 4, 5-gram)
Markov chains (context of 1, 2, 3, 4 and 5)
Subword N-gram and Markov chains
Embeddings in various sizes and dimensions (aligned and unaligned)
Language Vocabulary
Language Statistics

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.249x	3.26	0.0193%	181,040
16k	3.400x	3.41	0.0202%	173,006
32k	3.497x 🏆	3.50	0.0208%	168,209

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: *E.W. Gifford, Tongan myths and tales, Bernice Pauahi Bishop museum bulletin 8,

Vocab	Tokens	Count
8k	`▁* e . w . ▁gifford , ▁tongan ▁myths ▁and ... (+10 more)`	20
16k	`▁* e . w . ▁gifford , ▁tongan ▁myths ▁and ... (+10 more)`	20
32k	`▁* e . w . ▁gifford , ▁tongan ▁myths ▁and ... (+10 more)`	20

Sample 2: Ko Pulukalia ko ha fonua ia ʻi ʻEulope. ʻi ʻEulope

Vocab	Tokens	Count
8k	`▁ko ▁puluka lia ▁ko ▁ha ▁fonua ▁ia ▁ʻ i ▁ʻ ... (+6 more)`	16
16k	`▁ko ▁pulukalia ▁ko ▁ha ▁fonua ▁ia ▁ʻ i ▁ʻ eulope ... (+5 more)`	15
32k	`▁ko ▁pulukalia ▁ko ▁ha ▁fonua ▁ia ▁ʻ i ▁ʻ eulope ... (+5 more)`	15

Sample 3: *E. Wood-Ellem, Queen Sālote of Tonga, Auckland university press,

Vocab	Tokens	Count
8k	`▁* e . ▁wood - ellem , ▁queen ▁sālote ▁of ... (+6 more)`	16
16k	`▁* e . ▁wood - ellem , ▁queen ▁sālote ▁of ... (+6 more)`	16
32k	`▁* e . ▁wood - ellem , ▁queen ▁sālote ▁of ... (+6 more)`	16

Key Findings

Best Compression: 32k achieves 3.497x compression
Lowest UNK Rate: 8k with 0.0193% 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

Results

N-gram	Variant	Perplexity	Entropy	Unique N-grams	Top-100 Coverage	Top-1000 Coverage
2-gram	Word	948	9.89	3,349	42.1%	79.1%
2-gram	Subword	208 🏆	7.70	1,246	74.3%	99.8%
3-gram	Word	2,018	10.98	5,257	30.2%	67.0%
3-gram	Subword	1,378	10.43	7,944	35.9%	79.6%
4-gram	Word	2,947	11.53	8,403	28.6%	57.9%
4-gram	Subword	5,492	12.42	31,079	20.2%	53.5%
5-gram	Word	1,994	10.96	5,965	34.1%	63.8%
5-gram	Subword	12,282	13.58	57,462	14.5%	40.7%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	`ko e`	4,790
2	`ʻi he`	2,011
3	`ʻo e`	1,405
4	`ʻa e`	1,335
5	`mo e`	1,134

3-grams (Word):

Rank	N-gram	Count
1	`ʻi he ngaahi`	431
2	`ko e fuʻu`	383
3	`e fuʻu ʻakau`	375
4	`hingoa ʻi he`	341
5	`he ngaahi lea`	336

4-grams (Word):

Rank	N-gram	Count
1	`ko e fuʻu ʻakau`	365
2	`ʻi he ngaahi lea`	335
3	`he ngaahi lea kehe`	331
4	`hingoa ʻi he ngaahi`	328
5	`vaʻa fekumi ngoue vainī`	309

5-grams (Word):

Rank	N-gram	Count
1	`ʻi he ngaahi lea kehe`	331
2	`hingoa ʻi he ngaahi lea`	328
3	`hokohoko ngaahi ʻakau vaʻa fekumi`	309
4	`ngaahi ʻakau vaʻa fekumi ngoue`	309
5	`ʻakau vaʻa fekumi ngoue vainī`	309

2-grams (Subword):

Rank	N-gram	Count
1	`e _`	28,242
2	`a _`	24,953
3	`i _`	22,182
4	`o _`	19,549
5	`_ ʻ`	19,509

3-grams (Subword):

Rank	N-gram	Count
1	`_ e _`	9,332
2	`n g a`	8,789
3	`k o _`	7,921
4	`h e _`	7,566
5	`o _ e`	7,505

4-grams (Subword):

Rank	N-gram	Count
1	`o _ e _`	7,450
2	`_ k o _`	5,828
3	`k o _ e`	4,823
4	`_ h e _`	4,523
5	`_ ʻ i _`	3,803

5-grams (Subword):

Rank	N-gram	Count
1	`k o _ e _`	4,799
2	`_ k o _ e`	4,059
3	`i _ h e _`	3,520
4	`_ f a k a`	3,147
5	`ʻ o k u _`	2,999

Key Findings

Best Perplexity: 2-gram (subword) with 208
Entropy Trend: Decreases with larger n-grams (more predictable)
Coverage: Top-1000 patterns cover ~41% of corpus
Recommendation: 4-gram or 5-gram for best predictive performance

3. Markov Chain Evaluation

Results

Context	Variant	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	Word	0.7580	1.691	4.11	15,572	24.2%
1	Subword	0.8923	1.856	6.74	432	10.8%
2	Word	0.2407	1.182	1.56	63,563	75.9%
2	Subword	0.9268	1.901	5.18	2,905	7.3%
3	Word	0.1088	1.078	1.20	98,178	89.1%
3	Subword	0.7964	1.737	3.51	15,009	20.4%
4	Word	0.0474 🏆	1.033	1.07	116,582	95.3%
4	Subword	0.5667	1.481	2.28	52,648	43.3%

Generated Text Samples (Word-based)

Below are text samples generated from each word-based Markov chain model:

Context Size 1:

e limufonua ko e ʻakau kālava maʻa e ongo foha ʻo tonga land acts v l
ko e vahe hihifo ʻo e meʻa kelekele mo e matalaʻiʻakau kula kuusi ko e lotu
he ngaahi lau ʻoku mamaha ʻa ha alaufuli pea ʻoku ui foki ko e pī pea

Context Size 2:

ko e motuʻa feituʻu ʻi he talatupuʻa ʻo ʻahoʻeitu ʻi he taimi ni koeʻuhi ʻenau hiki ki
ʻi he ngaahi lea kehe ʻara lea fakakuki kalabuci damu lea fakafisi pūrau lea fakatahisi hutu he
ʻo e lea heliaki ko e matapā ʻeni tokua naʻe ʻomi ki tongá ni ʻi tongá ni

Context Size 3:

ʻi he ngaahi lea kehe tōmāti lea fakakuki lea fakatahisi ʻōhiʻa ma ka nahele lea fakahauaiʻi s pimpi...
ko e fuʻu ʻakau lahi ia ʻoku tupu ofi ki he haʻamonga ʻa maui pupunga fetuʻu ko e
e fuʻu ʻakau siʻi ia mei he ʻatamai ʻo māmani ʻa ia ʻoku hoko ai ʻa e ngaahi

Context Size 4:

ko e fuʻu ʻakau lahi ia mo e ngaahi ngeʻesi ʻelilivao ʻoku ui ko e nati foki ʻoku kulokula
ʻi he ngaahi lea kehe mākeke lea fakahauaiʻi masitati kapisi lea fakahaʻamoa kāpati lea fakakuki pīn...
he ngaahi lea kehe toua lea fakaniuē malina lea fakahaʻamoa rōpiāni piāni lea fakakuki tataku hokoho...

Generated Text Samples (Subword-based)

Below are text samples generated from each subword-based Markov chain model:

Context Size 1:

_nofo_pue,_kae_b
au,_hi_ngaʻau_i_
ita_hi_ctowo_ʻot

Context Size 2:

e_ressia_motonga_
a_kā_ku_meʻa,_por
i_loquene,_va_‘e_

Context Size 3:

_e_pea._ko_e_ngaah
ngaahi_aʻu._kolo_ʻ
ko_hono_puʻangua_s

Context Size 4:

o_e_hine_taha_micro
_ko_e_tuituitaha_ko
ko_e_taha_kā_ko_e_h

Key Findings

Best Predictability: Context-4 (word) with 95.3% predictability
Branching Factor: Decreases with context size (more deterministic)
Memory Trade-off: Larger contexts require more storage (52,648 contexts)
Recommendation: Context-3 or Context-4 for text generation

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	6,787
Total Tokens	149,227
Mean Frequency	21.99
Median Frequency	3
Frequency Std Dev	193.13

Most Common Words

Rank	Word	Frequency
1	e	9,737
2	ko	7,170
3	he	4,551
4	ʻi	3,854
5	ʻo	3,196
6	ʻoku	2,983
7	ia	2,271
8	ngaahi	2,189
9	ʻa	2,030
10	mo	1,983

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	fohi	2
2	tekau	2
3	fakapā	2
4	fahaʻi	2
5	mutumutu	2
6	koká	2
7	mahoaʻá	2
8	kaí	2
9	lauʻolungá	2
10	folahi	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1311
R² (Goodness of Fit)	0.992429
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	56.6%
Top 1,000	83.9%
Top 5,000	97.6%
Top 10,000	0.0%

Key Findings

Zipf Compliance: R²=0.9924 indicates excellent adherence to Zipf's law
High Frequency Dominance: Top 100 words cover 56.6% of corpus
Long Tail: -3,213 words needed for remaining 100.0% coverage

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	0.1197 🏆	0.5003	N/A	N/A
mono_64d	64	0.0193	0.5134	N/A	N/A
mono_128d	128	0.0028	0.4946	N/A	N/A
aligned_32d	32	0.1197	0.4991	0.0100	0.0900
aligned_64d	64	0.0193	0.5081	0.0140	0.0980
aligned_128d	128	0.0028	0.5043	0.0140	0.0920

Key Findings

Best Isotropy: mono_32d with 0.1197 (more uniform distribution)
Semantic Density: Average pairwise similarity of 0.5033. Lower values indicate better semantic separation.
Alignment Quality: Aligned models achieve up to 1.4% R@1 in cross-lingual retrieval.
Recommendation: 128d aligned for best cross-lingual performance

6. Morphological Analysis (Experimental)

This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.

6.1 Productivity & Complexity

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	0.569	High formulaic/idiomatic content	-

6.2 Affix Inventory (Productive Units)

These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.

Productive Prefixes

Prefix	Examples
`-t`	translation, totokamaka, tooi
`-s`	seen, state, surely
`-m`	mole, maʻamaʻa, menemene
`-p`	parazoa, puna, palm
`-ma`	maʻamaʻa, mamata, masipā
`-f`	foo, fimbristylis, floribunda
`-l`	lafalafa, laufale, longomapu
`-k`	kākā, kelenatā, kakamika

Productive Suffixes

Suffix	Examples
`-a`	ʻekea, floribunda, maʻamaʻa
`-i`	hui, tooi, tuki
`-e`	mole, because, menemene
`-u`	tatafu, longomapu, tāupoʻou
`-s`	fimbristylis, berenices, occidentalis
`-o`	foo, epikopo, sio
`-ia`	pilitania, ʻaositelēlia, terminalia
`-ga`	moʻunga, hengehenga, taunga

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
`akat`	1.59x	25 contexts	kakato, fakatu, fakatū
`onga`	1.41x	25 contexts	konga, tonga, nonga
`ngat`	1.64x	15 contexts	ngata, ngatú, ngatu
`ʻang`	1.66x	12 contexts	ʻanga, paʻanga, hūʻanga
`kata`	1.53x	15 contexts	katafa, fakatau, akataha
`kala`	1.38x	19 contexts	kalae, kalasi, kakala
`hing`	1.53x	13 contexts	thing, hinga, ahinga
`ʻaka`	1.71x	8 contexts	ʻakau, ʻakaú, ʻakana
`akah`	1.56x	10 contexts	fakahū, fakaha, fakahā
`tata`	1.49x	11 contexts	tatau, tatafu, tatala
`akal`	1.48x	11 contexts	kakala, fakalao, fakalau
`ahin`	1.48x	10 contexts	vahine, ahinga, mahino

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
`-f`	`-a`	152 words	floribunda, fukofuka
`-t`	`-a`	143 words	totokamaka, taunga
`-f`	`-i`	123 words	fakafefiofi, falevai
`-m`	`-a`	122 words	maʻamaʻa, moʻunga
`-ʻ`	`-a`	77 words	ʻekea, ʻalava
`-t`	`-u`	74 words	tatafu, tāupoʻou
`-t`	`-i`	70 words	tooi, tuki
`-p`	`-a`	66 words	parazoa, puna
`-l`	`-a`	64 words	lafalafa, laveʻimoa
`-k`	`-a`	52 words	kakamika, kulukona

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
ʻafilikani	`ʻafilik-a-ni`	7.5	`a`
fetuʻutaki	`fetuʻut-a-ki`	7.5	`a`
talafoʻou	`ta-la-foʻou`	7.5	`foʻou`
fakataimi	`fa-ka-taimi`	7.5	`taimi`
fehokotaki	`fehokot-a-ki`	7.5	`a`
polotonga	`po-lo-tonga`	7.5	`tonga`
sterninae	`sternin-a-e`	7.5	`a`
christian	`christi-a-n`	7.5	`a`
lauraceae	`laurace-a-e`	7.5	`a`
siulolovao	`siulolov-a-o`	7.5	`a`
vavalangi	`va-va-langi`	7.5	`langi`
grossulariaceae	`grossulariace-a-e`	7.5	`a`
fakakakai	`fakakak-a-i`	7.5	`a`
matafanga	`ma-ta-fanga`	7.5	`fanga`
afuhaʻapai	`afuhaʻap-a-i`	7.5	`a`

6.6 Linguistic Interpretation

Automated Insight: The language Tongan shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

Note on Idiomaticity: The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	32k BPE	Best compression (3.50x)
N-gram	2-gram	Lowest perplexity (208)
Markov	Context-4	Highest predictability (95.3%)
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

Compare within model families: Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
Consider trade-offs: Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
Context matters: Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
Corpus influence: All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
Language-specific patterns: Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.

Visualizations Index

Visualization	Description
Tokenizer Compression	Compression ratios by vocabulary size
Tokenizer Fertility	Average token length by vocabulary
Tokenizer OOV	Unknown token rates
Tokenizer Total Tokens	Total tokens by vocabulary
N-gram Perplexity	Perplexity by n-gram size
N-gram Entropy	Entropy by n-gram size
N-gram Coverage	Top pattern coverage
N-gram Unique	Unique n-gram counts
Markov Entropy	Entropy by context size
Markov Branching	Branching factor by context
Markov Contexts	Unique context counts
Zipf's Law	Frequency-rank distribution with fit
Vocab Frequency	Word frequency distribution
Top 20 Words	Most frequent words
Vocab Coverage	Cumulative coverage curve
Embedding Isotropy	Vector space uniformity
Embedding Norms	Vector magnitude distribution
Embedding Similarity	Word similarity heatmap
Nearest Neighbors	Similar words for key terms
t-SNE Words	2D word embedding visualization
t-SNE Sentences	2D sentence embedding visualization
Position Encoding	Encoding method comparison
Model Sizes	Storage requirements
Performance Dashboard	Comprehensive performance overview

About This Project

Data Source

Models trained on wikipedia-monthly - a monthly snapshot of Wikipedia articles across 300+ languages.

Project

A project by Wikilangs - Open-source NLP models for every Wikipedia language.

Maintainer

Omar Kamali - Omneity Labs

Citation

If you use these models in your research, please cite:

bibtex

@misc{wikilangs2025,
  author = {Kamali, Omar},
  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
  year = {2025},
  doi = {10.5281/zenodo.18073153},
  publisher = {Zenodo},
  url = {https://huggingface.co/wikilangs}
  institution = {Omneity Labs}
}

License

MIT License - Free for academic and commercial use.

wikilangs/to

Tongan - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

📋 Repository Contents

Models & Assets

Analysis and Evaluation

1. Tokenizer Evaluation

Results

Tokenization Examples

Key Findings

2. N-gram Model Evaluation

Results

Top 5 N-grams by Size

Key Findings

3. Markov Chain Evaluation

Results

Generated Text Samples (Word-based)

Generated Text Samples (Subword-based)

Key Findings

4. Vocabulary Analysis

Statistics

Most Common Words

Least Common Words (from vocabulary)

Zipf's Law Analysis

Coverage Analysis

Key Findings

5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

5.2 Model Comparison

Key Findings

6. Morphological Analysis (Experimental)

6.1 Productivity & Complexity

6.2 Affix Inventory (Productive Units)

Productive Prefixes

Productive Suffixes

6.3 Bound Stems (Lexical Roots)

6.4 Affix Compatibility (Co-occurrence)

6.5 Recursive Morpheme Segmentation

6.6 Linguistic Interpretation

7. Summary & Recommendations

Production Recommendations

Appendix: Metrics Glossary & Interpretation Guide

Tokenizer Metrics

N-gram Model Metrics

Markov Chain Metrics

Vocabulary & Zipf's Law Metrics

Word Embedding Metrics

General Interpretation Guidelines

Visualizations Index

About This Project

Data Source

Project

Maintainer

Citation

License

Links

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