wikilangs/hr

Croatian - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Croatian 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

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	3.541x	3.54	0.0441%	1,061,585
16k	3.929x	3.93	0.0489%	956,840
32k	4.292x	4.29	0.0534%	875,971
64k	4.592x 🏆	4.59	0.0572%	818,812

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: NGC je galaksija u zviježđu Vodenoj zmiji. Izvori Vanjske poveznice NGC

Vocab	Tokens	Count
8k	▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vode noj ▁z mi ji ... (+5 more)	15
16k	▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vode noj ▁z miji . ... (+4 more)	14
32k	▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vodenoj ▁zmiji . ▁izvori ▁vanjske ... (+2 more)	12
64k	▁ngc ▁je ▁galaksija ▁u ▁zviježđu ▁vodenoj ▁zmiji . ▁izvori ▁vanjske ... (+2 more)	12

Sample 2: Hrvatska: Kostadinovac (Križevci), gradsko naselje Križevaca Srbija: Kostadinova...

Vocab	Tokens	Count
8k	▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁grad ... (+24 more)	34
16k	▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁gradsko ... (+20 more)	30
32k	▁hrvatska : ▁kosta di novac ▁( križe vci ), ▁gradsko ... (+19 more)	29
64k	▁hrvatska : ▁kosta di novac ▁( križevci ), ▁gradsko ▁naselje ... (+17 more)	27

Sample 3: NGC 587 je galaksija u zviježđu Trokut. Izvori Vanjske poveznice NGC

Vocab	Tokens	Count
8k	▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁troku ... (+6 more)	16
16k	▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁troku ... (+6 more)	16
32k	▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁trokut ... (+5 more)	15
64k	▁ngc ▁ 5 8 7 ▁je ▁galaksija ▁u ▁zviježđu ▁trokut ... (+5 more)	15

Key Findings

• Best Compression: 64k achieves 4.592x compression
• Lowest UNK Rate: 8k with 0.0441% 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	267,023	18.03	1,536,962	6.2%	15.5%
2-gram	Subword	314 🏆	8.29	17,412	63.2%	99.0%
3-gram	Word	860,543	19.71	2,568,958	2.9%	8.5%
3-gram	Subword	3,101	11.60	146,611	21.1%	65.0%
4-gram	Word	2,007,494	20.94	4,346,865	2.5%	6.6%
4-gram	Subword	21,614	14.40	870,800	8.5%	30.3%
5-gram	Word	1,554,489	20.57	3,187,745	3.2%	7.7%
5-gram	Subword	106,845	16.71	3,145,742	3.9%	15.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	je u	105,341
2	vanjske poveznice	93,834
3	koji je	79,115
4	da je	76,085
5	bio je	64,808

3-grams (Word):

Rank	N-gram	Count
1	izvori vanjske poveznice	48,503
2	bosne i hercegovine	15,350
3	0 0 0	15,157
4	prema popisu stanovništva	14,804
5	popisu stanovništva iz	14,603

4-grams (Word):

Rank	N-gram	Count
1	prema popisu stanovništva iz	13,965
2	popisu stanovništva iz godine	9,055
3	0 0 0 0	7,718
4	stanovništvo prema popisu stanovništva	7,610
5	u bosni i hercegovini	7,346

5-grams (Word):

Rank	N-gram	Count
1	prema popisu stanovništva iz godine	8,505
2	stanovništvo prema popisu stanovništva iz	7,504
3	iz godine naselje je imalo	6,432
4	popisu stanovništva iz godine naselje	6,074
5	klub ut pob ner por	6,053

2-grams (Subword):

Rank	N-gram	Count
1	a _	11,772,034
2	e _	10,057,232
3	j e	9,032,733
4	i _	7,983,271
5	_ s	7,190,572

3-grams (Subword):

Rank	N-gram	Count
1	j e _	3,895,077
2	_ j e	2,710,825
3	_ p o	2,506,868
4	_ p r	2,383,257
5	_ n a	2,336,425

4-grams (Subword):

Rank	N-gram	Count
1	_ j e _	2,225,392
2	_ n a _	884,954
3	_ s e _	864,331
4	_ p r o	684,557
5	_ k o j	681,175

5-grams (Subword):

Rank	N-gram	Count
1	a _ j e _	584,793
2	o _ j e _	536,381
3	_ g o d i	464,832
4	g o d i n	453,046
5	o d i n e	358,859

Key Findings

• Best Perplexity: 2-gram (subword) with 314
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~16% 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	1.0357	2.050	12.27	1,815,273	0.0%
1	Subword	1.2283	2.343	8.11	7,670	0.0%
2	Word	0.3287	1.256	2.06	22,242,688	67.1%
2	Subword	0.7670	1.702	5.14	62,088	23.3%
3	Word	0.1208	1.087	1.25	45,802,650	87.9%
3	Subword	0.8038	1.746	4.62	318,839	19.6%
4	Word	0.0449 🏆	1.032	1.07	57,168,259	95.5%
4	Subword	0.7427	1.673	3.77	1,471,918	25.7%

Generated Text Samples (Word-based)

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

Context Size 1:

1. je jedini gol bod1 orijent expressu od do polufinala nastupila je manji zbog toga dragocjena u
2. u 56 km kvadratnih kilometara je postao vodeći u dundu maroju armandu kemičara i bečki i
3. i izraz malo energije na njihovo je također povezivanje svakoga naroda onaj za istraživanje je minog...

Context Size 2:

1. je u sabirni logor za zarobljene španjolske muškarce i žene koji su bez uspjeha robert lowie je
2. vanjske poveznice hrvatske kazališne manifestacije u hrvatskoj reformsko krilo koje se smatra normal...
3. koji je osvojio pojedinačnu medalju na austrian openu u osmini završnice osam i protjerivan sedam pu...

Context Size 3:

1. izvori vanjske poveznice hartmut frommert revidirani novi opći katalog eng izvangalaktička baza poda...
2. 0 0 0 0 0 4 1 kvalifikacije za afrički kup nacija 08 17 21 lipnja abuja national
3. bosne i hercegovine postao je slobodno područje izabran je za izvanrednog profesora na harvardu te v...

Context Size 4:

1. prema popisu stanovništva iz godine rajčići su imali 4 stanovnika vanjske poveznice o blažević dolu ...
2. popisu stanovništva iz godine naselje je imalo 0 stanovnikapopis stanovništva www dzs hr te 25 obite...
3. 0 0 0 0 0 hispanoamerikanci 4 0 9 12 1 4 ukupno 844 861 vrela vanjske poveznice u

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _poskovopr._vi_d
2. av_jeni_staog_1.
3. ire_zbe._n_pledo

Context Size 2:

1. a_prednog_reba_me
2. e_urisamom_kakvu.
3. jedina_jensih_fij

Context Size 3:

1. je_udružen_uglavno
2. _je_je_meki_držana
3. _postavu_i_murski_

Context Size 4:

1. _je_i_„bijedložili_
2. _na_bio_je_breedler
3. _se_tada_satenu_dat

Key Findings

• Best Predictability: Context-4 (word) with 95.5% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,471,918 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	865,837
Total Tokens	68,760,487
Mean Frequency	79.42
Median Frequency	4
Frequency Std Dev	4611.66

Most Common Words

Rank	Word	Frequency
1	je	2,245,537
2	u	2,108,487
3	i	2,058,490
4	na	897,376
5	se	873,737
6	su	661,725
7	za	564,276
8	od	535,634
9	s	445,590
10	a	436,542

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	uerpmann	2
2	cociancicha	2
3	fornasari	2
4	federighi	2
5	ulanoff	2
6	svelteov	2
7	ractive	2
8	jsdoc	2
9	vercel	2
10	onsubmit	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.9105
R² (Goodness of Fit)	0.998328
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	29.2%
Top 1,000	47.5%
Top 5,000	64.0%
Top 10,000	71.5%

Key Findings

• Zipf Compliance: R²=0.9983 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 29.2% of corpus
• Long Tail: 855,837 words needed for remaining 28.5% 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	0.7990	0.3752	N/A	N/A
mono_64d	64	0.7419	0.2943	N/A	N/A
mono_128d	128	0.6113	0.2735	N/A	N/A
aligned_32d	32	0.7990 🏆	0.3713	0.2440	0.6400
aligned_64d	64	0.7419	0.2911	0.4700	0.8320
aligned_128d	128	0.6113	0.2771	0.6240	0.8980

Key Findings

• Best Isotropy: aligned_32d with 0.7990 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3137. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 62.4% 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	-0.514	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
-s	saccharina, stažem, sieversia
-a	appleton, aromatika, antipatros
-ma	macv, mahajangu, manfredonija
-m	mešetari, midp, megasten
-k	konfederacije, kumarom, karlovačku
-p	prostalih, portulani, panopticum
-b	breviarium, bandašica, botticellija
-t	terpenoide, tamnocrvenkast, teregova

Productive Suffixes

Suffix	Examples
-a	saccharina, sieversia, premašenima
-e	konfederacije, terpenoide, elaboracije
-i	portulani, vori, mešetari
-m	stažem, panopticum, breviarium
-u	nahalu, ikonostasu, karlovačku
-om	kumarom, samarom, kokom
-s	servas, winos, clupeoides
-o	dezorijentirano, dsno, papio

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
anov	1.67x	1068 contexts	anove, hanov, banov
cije	2.00x	238 contexts	cijel, cijev, cijem
acij	1.85x	273 contexts	lacij, acije, racij
ijel	1.69x	293 contexts	cijel, ijele, dijel
ansk	1.35x	1078 contexts	ansko, anski, dansk
ljen	1.42x	618 contexts	kljen, pljen, ljeni
avlj	1.51x	394 contexts	javlja, vavlje, lavlji
elik	1.71x	176 contexts	melik, jelik, çelik
ijsk	1.36x	538 contexts	hijska, bijsku, kijski
egov	1.60x	208 contexts	negov, begov, egove
novn	1.84x	95 contexts	onovno, pnovno, ponovno
telj	1.66x	146 contexts	atelj, artelj, stelje

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	-a	202 words	prekorava, petruša
-s	-a	178 words	suverenizma, sritna
-p	-e	114 words	produbljavanje, perenense
-k	-a	106 words	kanatima, koruška
-p	-i	97 words	protoni, poigravati
-a	-a	93 words	almanusa, alžirka
-s	-i	88 words	svesokolski, saeculi
-d	-a	88 words	disonancija, denzimetrija
-b	-a	85 words	barista, bhattija
-p	-m	85 words	perfectum, punicum

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
auchenipteridae	auchenipterid-a-e	7.5	a
neprikazane	neprikaz-a-ne	7.5	a
arunkumar	arunkum-a-r	7.5	a
intervjuua	intervju-u-a	7.5	u
domeciidae	domeciid-a-e	7.5	a
ventricosus	ventrico-s-us	7.5	s
codiaceae	codiace-a-e	7.5	a
anastasiju	anastas-i-ju	7.5	i
sistemsko	sistem-s-ko	7.5	s
pattalophyllia	pattalophyll-i-a	7.5	i
studenske	studen-s-ke	7.5	s
modernizirani	modernizir-a-ni	7.5	a
filtrirani	filtrir-a-ni	7.5	a
postavljane	postavlj-a-ne	7.5	a
coriariaceae	coriariace-a-e	7.5	a

6.6 Linguistic Interpretation

Automated Insight: The language Croatian 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 (4.59x)
N-gram	2-gram	Lowest perplexity (314)
Markov	Context-4	Highest predictability (95.5%)
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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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:

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

License

MIT License - Free for academic and commercial use.

Links

• 🌐 Website: wikilangs.org
• 🤗 Models: huggingface.co/wikilangs
• 📊 Data: wikipedia-monthly
• 👤 Author: Omar Kamali
• 🤝 Sponsor: Featherless AI

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Generated by Wikilangs Models Pipeline

Report Date: 2026-01-10 10:10:35