Slovenian - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Slovenian 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	3.326x	3.33	0.6705%	1,060,027
16k	3.677x	3.68	0.7414%	958,786
32k	4.017x	4.02	0.8100%	877,496
64k	4.308x 🏆	4.31	0.8686%	818,369

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Tinja je lahko: Tinja Donja (Bosna in Hercegovina) Tinja Gornja (Bosna in Herceg...

Vocab	Tokens	Count
8k	▁tin ja ▁je ▁lahko : ▁tin ja ▁do nja ▁( ... (+27 more)	37
16k	▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+22 more)	32
32k	▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+21 more)	31
64k	▁tin ja ▁je ▁lahko : ▁tin ja ▁donja ▁( bosna ... (+21 more)	31

Sample 2: Škrabčeva ulica je lahko naziv več ulic: Škrabčeva ulica, Ljubljana Škrabčeva ul...

Vocab	Tokens	Count
8k	▁š kra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁u ... (+17 more)	27
16k	▁š kra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic ... (+16 more)	26
32k	▁škra b čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic : ... (+13 more)	23
64k	▁škrab čeva ▁ulica ▁je ▁lahko ▁naziv ▁več ▁ulic : ▁škrab ... (+10 more)	20

Sample 3: Kreševo je lahko: Kreševo, Bosna in Hercegovina Kreševo, Hrvaška glej tudi Kruše...

Vocab	Tokens	Count
8k	▁kre še vo ▁je ▁lahko : ▁kre še vo , ... (+13 more)	23
16k	▁kre še vo ▁je ▁lahko : ▁kre še vo , ... (+13 more)	23
32k	▁kre ševo ▁je ▁lahko : ▁kre ševo , ▁bosna ▁in ... (+9 more)	19
64k	▁kre ševo ▁je ▁lahko : ▁kre ševo , ▁bosna ▁in ... (+9 more)	19

Key Findings

• Best Compression: 64k achieves 4.308x compression
• Lowest UNK Rate: 8k with 0.6705% 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	230,065	17.81	1,580,681	8.6%	18.2%
2-gram	Subword	307 🏆	8.26	19,173	64.1%	99.0%
3-gram	Word	628,655	19.26	2,417,119	4.1%	11.2%
3-gram	Subword	3,012	11.56	154,341	21.4%	65.5%
4-gram	Word	1,262,619	20.27	3,613,803	3.4%	8.8%
4-gram	Subword	20,514	14.32	898,859	9.3%	30.9%
5-gram	Word	764,287	19.54	2,274,837	4.9%	11.6%
5-gram	Subword	99,057	16.60	3,187,233	4.7%	16.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	je bil	242,649
2	se je	241,005
3	ki je	174,450
4	je bila	164,565
5	ki so	110,725

3-grams (Word):

Rank	N-gram	Count
1	glej tudi seznam	56,792
2	pr n št	34,003
3	ki ga je	25,570
4	sklici zunanje povezave	21,261
5	opombe glej tudi	21,007

4-grams (Word):

Rank	N-gram	Count
1	glej tudi seznam naselij	20,688
2	in opombe glej tudi	20,455
3	opombe glej tudi seznam	19,924
4	viri in opombe glej	16,449
5	ki upravno spada pod	15,090

5-grams (Word):

Rank	N-gram	Count
1	in opombe glej tudi seznam	19,642
2	opombe glej tudi seznam naselij	16,702
3	viri in opombe glej tudi	16,449
4	glej tudi seznam naselij v	10,861
5	glej tudi seznam naselij na	9,825

2-grams (Subword):

Rank	N-gram	Count
1	a _	11,379,031
2	e _	9,711,848
3	i _	7,963,628
4	_ p	7,248,467
5	_ s	7,019,681

3-grams (Subword):

Rank	N-gram	Count
1	j e _	4,141,312
2	_ j e	2,970,998
3	_ p o	2,883,740
4	_ p r	2,720,662
5	_ n a	2,547,716

4-grams (Subword):

Rank	N-gram	Count
1	_ j e _	2,761,955
2	_ i n _	2,026,152
3	_ n a _	1,018,082
4	_ p r e	991,101
5	e g a _	882,400

5-grams (Subword):

Rank	N-gram	Count
1	a _ j e _	682,064
2	, _ k i _	656,040
3	_ l e t a	563,205
4	_ j e _ b	529,211
5	j e _ b i	504,001

Key Findings

• Best Perplexity: 2-gram (subword) with 307
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~17% 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	1.0363	2.051	12.35	1,898,880	0.0%
1	Subword	0.7016	1.626	5.31	18,900	29.8%
2	Word	0.3404	1.266	2.08	23,427,544	66.0%
2	Subword	0.5506	1.465	3.88	100,242	44.9%
3	Word	0.1238	1.090	1.26	48,734,695	87.6%
3	Subword	0.7077	1.633	4.16	388,929	29.2%
4	Word	0.0464 🏆	1.033	1.08	61,326,075	95.4%
4	Subword	0.7050	1.630	3.60	1,617,081	29.5%

Generated Text Samples (Word-based)

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

Context Size 1:

1. je leta drugi strani gore budizma na sinaju pa mohor bogataj obrobje lokovške planote ima v
2. in nkoma kmalu zatem poslala s katero ima toplo hrano kristersson kandiderar till holger in long
3. v kombinaciji piracetama ni usmerjen v očetovi smrti ljubljane upravno spada pod konradom auffenstei...

Context Size 2:

1. je bil dirkalnik konkurenčen in ickx vztrajala da cape financira svoje zadeve anonymous 24 januar go...
2. se je letalo prehitelo ekspresni vlak za florido glej tudi seznam francoskih skladateljev g rafael g...
3. ki je proučeval razmerja med svojo razgradnjo produktov oksalna kislina je zelo jezen ker je imelo z...

Context Size 3:

1. glej tudi seznam naselij v črni gori ki upravno spada pod občino čoka slednja pa je del šumadijskega
2. pr n št či leta 357 pr n št posvetili cesarju avgustu sklici viri viri ljudstva
3. ki ga je po podatkih statističnega urada republike slovenije na pobudo prekmurskega društva general ...

Context Size 4:

1. glej tudi seznam naselij v srbiji raškega upravnega okraja
2. in opombe glej tudi čini slovenske vojske čini ustanovljeni leta čini ukinjeni leta
3. opombe glej tudi seznam naselij v srbiji zlatiborskega upravnega okraja

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _dedna_bhe_ka._(
2. ahodnizart;_go_p
3. eštske'ijem_z_zo

Context Size 2:

1. a_gle_nači_zereva
2. e_vršče,_cepubrek
3. i_pozem_ske_merib

Context Size 3:

1. je_hočenisoke_dogs
2. _je_tren._podatki_
3. _potekajo_zgodklon

Context Size 4:

1. _je_po_znan_pedagog
2. _in_renesisteinhart
3. _na_polkovniških_od

Key Findings

• Best Predictability: Context-4 (word) with 95.4% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,617,081 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	897,122
Total Tokens	72,710,721
Mean Frequency	81.05
Median Frequency	4
Frequency Std Dev	4937.29

Most Common Words

Rank	Word	Frequency
1	je	2,786,159
2	in	2,040,383
3	v	2,029,005
4	na	1,031,510
5	so	822,699
6	se	719,502
7	ki	679,959
8	za	672,987
9	leta	518,963
10	z	469,417

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	landesgericht	2
2	súkup	2
3	rozec	2
4	malkolma	2
5	sumate	2
6	chamlanga	2
7	presmučala	2
8	luzejem	2
9	edrika	2
10	sigefertovo	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.8973
R² (Goodness of Fit)	0.998517
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	30.5%
Top 1,000	47.9%
Top 5,000	64.2%
Top 10,000	71.7%

Key Findings

• Zipf Compliance: R²=0.9985 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 30.5% of corpus
• Long Tail: 887,122 words needed for remaining 28.3% 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	0.7907	0.3497	N/A	N/A
mono_64d	64	0.7532	0.2937	N/A	N/A
mono_128d	128	0.6831	0.2333	N/A	N/A
aligned_32d	32	0.7907 🏆	0.3571	0.3140	0.6660
aligned_64d	64	0.7532	0.2862	0.5440	0.8920
aligned_128d	128	0.6831	0.2351	0.6300	0.8880

Key Findings

• Best Isotropy: aligned_32d with 0.7907 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2925. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 63.0% 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	-0.314	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	samoporjavitev, severvzhodni, svilne
-a	argonne, aleutskega, aiya
-ma	mathur, mashel, maerua
-m	mowbed, migracijah, melodramatične
-k	kickl, kaškega, koknese
-p	picramniales, ptujskega, picinus
-b	brelich, berruguete, bandai
-t	tragedov, teus, travmatsko

Productive Suffixes

Suffix	Examples
-a	zimunya, ptujskega, churchosrednja
-i	zaposliti, gallicani, rurikoviči
-e	berruguete, melodramatične, svilne
-o	porfiriato, travmatsko, fajdo
-m	levičarskem, jožefom, utesnjenem
-ga	ptujskega, igmanskega, petrarkovega
-s	picramniales, chlorotis, teus
-ih	znotrajjetrnih, boucherjevih, pozitivističnih

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
skeg	2.01x	178 contexts	skega, jskega, muskeg
enje	1.57x	357 contexts	genje, enjeu, ženje
ovez	2.40x	45 contexts	povez, poveza, povezi
orab	1.90x	106 contexts	korab, vorab, porab
ični	1.47x	356 contexts	lični, vični, nični
ijsk	1.34x	565 contexts	bijsk, bijsku, krijska
ranj	1.42x	366 contexts	vranj, ranji, kranj
tičn	1.43x	329 contexts	tičnik, stični, atične
ržav	2.01x	54 contexts	držav, državy, državi
acij	1.37x	302 contexts	lacij, acija, tacij
nske	1.34x	343 contexts	ønske, unske, sanske
avlj	1.33x	320 contexts	javlja, lavlje, kavlja

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	171 words	pumapunkuja, pahmutova
-s	-a	133 words	soteška, suvalščizna
-p	-i	120 words	prostorninskimi, prisegami
-p	-e	100 words	pionirke, priimkie
-k	-a	97 words	kolegialnega, khandrika
-s	-i	90 words	sampi, stavkati
-a	-a	89 words	alkalna, alkibiadesa
-p	-o	84 words	postkolonialno, plješivico
-b	-a	83 words	brahmsa, benna
-s	-e	76 words	stradanje, seksualizirane

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
desetstrane	desetstr-a-ne	7.5	a
globalizirano	globalizir-a-no	7.5	a
craigallian	craigalli-a-n	7.5	a
šahrastani	šahrast-a-ni	7.5	a
sušilnicah	sušilnic-a-h	7.5	a
sirakuzah	sirakuz-a-h	7.5	a
grdoselski	grdosel-s-ki	7.5	s
spremenljivkama	spremenljivka-m-a	7.5	m
krogličar	kroglič-a-r	7.5	a
skristaliziral	skristalizir-a-l	7.5	a
pritajeno	pritaj-e-no	7.5	e
izhodiščnega	izhodišč-ne-ga	7.5	ne
lastovsko	lastov-s-ko	7.5	s
orbeliani	orbeli-a-ni	7.5	a
igralkodallas	igralkodall-a-s	7.5	a

6.6 Linguistic Interpretation

Automated Insight: The language Slovenian 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 (4.31x)
N-gram	2-gram	Lowest perplexity (307)
Markov	Context-4	Highest predictability (95.4%)
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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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: 2026-01-17 08:23:12