wikilangs/lg

Ganda - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Ganda 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	3.693x	3.70	0.2870%	259,571
16k	4.077x	4.08	0.3168%	235,148
32k	4.439x	4.44	0.3449%	215,974
64k	4.749x 🏆	4.75	0.3690%	201,887

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Kigulu, ekibuga mu Kira Town mu Wakiso mu Yuganda.

Vocab	Tokens	Count
8k	▁ki gulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ... (+2 more)	12
16k	▁ki gulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ... (+2 more)	12
32k	▁kigulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ▁yuganda ... (+1 more)	11
64k	▁kigulu , ▁ekibuga ▁mu ▁kira ▁town ▁mu ▁wakiso ▁mu ▁yuganda ... (+1 more)	11

Sample 2: Kibuku nsi e disitulikit wa Yuganda. Obugazi: 490.2 km². Abantu: 181 700 mu Yuga...

Vocab	Tokens	Count
8k	▁kibu ku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ... (+21 more)	31
16k	▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)	30
32k	▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)	30
64k	▁kibuku ▁nsi ▁e ▁disitulikit ▁wa ▁yuganda . ▁obugazi : ▁ ... (+20 more)	30

Sample 3: thumbnail Flippy lwe e okuba mu naye nga Happy Tree Friends.

Vocab	Tokens	Count
8k	▁thumb na il ▁f li pp y ▁lwe ▁e ▁okuba ... (+12 more)	22
16k	▁thumb na il ▁f lipp y ▁lwe ▁e ▁okuba ▁mu ... (+8 more)	18
32k	▁thumb na il ▁f lipp y ▁lwe ▁e ▁okuba ▁mu ... (+7 more)	17
64k	▁thumbnail ▁flippy ▁lwe ▁e ▁okuba ▁mu ▁naye ▁nga ▁ha ppy ... (+3 more)	13

Key Findings

• Best Compression: 64k achieves 4.749x compression
• Lowest UNK Rate: 8k with 0.2870% 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	14,199	13.79	38,632	12.7%	34.6%
2-gram	Subword	219 🏆	7.77	2,147	71.2%	99.8%
3-gram	Word	26,700	14.70	56,796	9.4%	25.1%
3-gram	Subword	1,669	10.70	19,121	29.6%	78.5%
4-gram	Word	70,764	16.11	118,544	6.5%	15.1%
4-gram	Subword	8,452	13.05	97,188	14.1%	45.2%
5-gram	Word	61,726	15.91	94,350	7.0%	14.7%
5-gram	Subword	28,463	14.80	255,792	7.8%	27.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	okuva mu	5,167
2	mu uganda	4,435
3	y e	3,265
4	ya uganda	2,854
5	mu mwaka	2,411

3-grams (Word):

Rank	N-gram	Count
1	disitulikiti y e	1,864
2	mu mwaka gwa	1,837
3	mu disitulikiti y	1,235
4	okuva mu okutuuka	888
5	mu okutuuka mu	872

4-grams (Word):

Rank	N-gram	Count
1	mu disitulikiti y e	1,155
2	okuva mu okutuuka mu	831
3	mu ssaza ly e	811
4	united states of america	742
5	erisangibwa mu ssaza ly	735

5-grams (Word):

Rank	N-gram	Count
1	erisangibwa mu ssaza ly e	735
2	mu nsi ya united states	734
3	states of america g e	733
4	united states of america g	733
5	ya united states of america	733

2-grams (Subword):

Rank	N-gram	Count
1	a _	510,380
2	m u	198,216
3	u _	192,024
4	_ e	166,670
5	_ m	159,088

3-grams (Subword):

Rank	N-gram	Count
1	m u _	121,316
2	_ m u	114,905
3	o k u	71,249
4	w a _	70,500
5	a _ e	66,280

4-grams (Subword):

Rank	N-gram	Count
1	_ m u _	89,059
2	a _ m u	52,454
3	_ o k u	49,017
4	n g a _	45,054
5	b w a _	32,976

5-grams (Subword):

Rank	N-gram	Count
1	a _ m u _	43,937
2	_ n g a _	29,244
3	a _ o k u	23,484
4	g a n d a	23,102
5	u g a n d	22,563

Key Findings

• Best Perplexity: 2-gram (subword) with 219
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~28% 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	0.7920	1.731	5.69	115,787	20.8%
1	Subword	1.1968	2.292	11.50	360	0.0%
2	Word	0.2704	1.206	1.67	657,016	73.0%
2	Subword	1.2119	2.316	7.94	4,135	0.0%
3	Word	0.1023	1.073	1.18	1,092,887	89.8%
3	Subword	0.9620	1.948	4.72	32,782	3.8%
4	Word	0.0425 🏆	1.030	1.06	1,288,031	95.7%
4	Subword	0.6947	1.619	3.00	154,616	30.5%

Generated Text Samples (Word-based)

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

Context Size 1:

1. mu mutwe gwayo kwe yali mukiise mu mawanga g e kenya visa pour la masaka n
2. ku modern uganda olw amanyi mu kitundu ekisooka difiiri bw ebyoto ebisatu by ebijanjaalo biwerako er...
3. nga 7 223 530 mu mukundani eyatometa n asuulibwa eddalu lya uganda premier soccer league uganda

Context Size 2:

1. okuva mu bumannyirivu bwe ne famire ye jyavaamu baddamu ebigambo bye baba balowozaako h wewale okuka...
2. mu uganda judith babirye obuto bwe n okusoma kwe kakoma yazaalibwa mu buganda nga tewanabaawo kyefan...
3. y e makerere ayongerako nti yalina ekisanja kye ekyokubiri nga enkambi yamaje era essomero lino lwal...

Context Size 3:

1. disitulikiti y e buhweju olukalala lw abakyala abawandiisi mu uganda eka femrite era abadde muwandii...
2. mu mwaka gwa okutuuka mu yakomawo mu uganda mu n alondebwa okuba omusumba mu n awummula mu joseph
3. mu disitulikiti y e kayunga mu paalamenti ey omwenda mwalimu edward katumba wamala yazaalibwa ng enn...

Context Size 4:

1. mu disitulikiti y e ibanda siniya eyokuna yagimaliririza mu immaculate heart nyakibale secondary sch...
2. okuva mu okutuuka mu oluvannyuma yakola ng omukulu w essomero mu yalondebwa nga ssentebe w ekibiina ...
3. mu ssaza ly e texas mu nsi ya united states of america g e kentucky united states ebisangibwa mu

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _ddenndeti_e_ngo
2. a_kulizyokamu_ku
3. erokokin’o_ebake

Context Size 2:

1. a_n'ebyasootard,_
2. mu_neba_kiri_aba_
3. u_mmu_binovera_co

Context Size 3:

1. mu_kino_okwe_yatal
2. _mu_ugaziko_emisom
3. okutender,_mu_gulu

Context Size 4:

1. _mu_by'amateekera_e
2. a_mu_mabuvo_bwe_mu_
3. _okuva_mu_luguumiro

Key Findings

• Best Predictability: Context-4 (word) with 95.7% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (154,616 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	51,479
Total Tokens	1,503,057
Mean Frequency	29.20
Median Frequency	4
Frequency Std Dev	508.71

Most Common Words

Rank	Word	Frequency
1	mu	89,527
2	ku	30,386
3	nga	29,634
4	n	29,585
5	uganda	17,006
6	ne	15,431
7	era	14,238
8	y	12,890
9	e	10,819
10	ya	10,765

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	leku	2
2	agataalimu	2
3	gyebafuna	2
4	omuwuubi	2
5	baakyalira	2
6	eturude	2
7	bannanyinimu	2
8	obusoose	2
9	abanyanya	2
10	kalogo	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0794
R² (Goodness of Fit)	0.993590
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	36.5%
Top 1,000	63.8%
Top 5,000	82.3%
Top 10,000	88.9%

Key Findings

• Zipf Compliance: R²=0.9936 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 36.5% of corpus
• Long Tail: 41,479 words needed for remaining 11.1% 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	0.8731	0.3015	N/A	N/A
mono_64d	64	0.8603	0.2214	N/A	N/A
mono_128d	128	0.6513	0.2031	N/A	N/A
aligned_32d	32	0.8731 🏆	0.2999	0.0840	0.3380
aligned_64d	64	0.8603	0.2236	0.0980	0.3860
aligned_128d	128	0.6513	0.2073	0.1800	0.5160

Key Findings

• Best Isotropy: aligned_32d with 0.8731 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2428. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 18.0% 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	-0.474	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	atuzibwa, abafirosoofa, amazze
-e	ekifuula, enschede, ebisonjola
-ba	banabyamizannyo, bazanye, babonabona
-b	banabyamizannyo, bazanye, braun
-m	musambi, miracle, margret
-k	kyaleetera, kikungiri, kipande
-ka	kabwegyere, kaweefube, kagoma
-o	okwefuula, omugate, okulinnyisibwa

Productive Suffixes

Suffix	Examples
-a	atuzibwa, lwawangula, okwefuula
-wa	atuzibwa, okulinnyisibwa, obubwa
-o	banabyamizannyo, luweero, ssonko
-e	enschede, omugate, kipande
-ra	kyaleetera, yakyogera, luddirira
-i	musambi, kikungiri, ppulaani
-u	ekizungu, ntenjeru, gyawulwamu
-za	awezezza, byanjigiriza, kulowooza

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
teek	2.29x	104 contexts	teeka, ateeka, eteeka
tion	2.59x	28 contexts	action, motion, cation
wang	1.81x	117 contexts	wangu, wangi, lwang
gand	2.28x	38 contexts	ganda, ugand, nganda
anny	1.99x	62 contexts	danny, zannya, zannyi
atio	2.45x	26 contexts	ratio, cation, nation
embe	2.09x	37 contexts	ember, dembe, ddembe
ugan	1.97x	46 contexts	ugand, uganda, ugandas
erez	2.01x	30 contexts	perez, tereza, wereza
omuk	1.90x	34 contexts	omuko, omuka, omukka
okus	1.82x	37 contexts	okusa, okussa, okusiba
okuk	1.97x	26 contexts	okuka, okukka, okukuu

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
-o	-a	720 words	okukiikirirwa, okumusaba
-e	-a	516 words	eyonooneddwa, entakyuuka
-k	-a	384 words	kuvuganya, kubula
-a	-a	376 words	atea, akomererwa
-e	-o	230 words	ebipapajjo, ekigattikakibabiro
-ba	-a	197 words	bamerika, balumbagana
-b	-a	185 words	bamerika, balumbagana
-o	-o	147 words	ogwomukwano, okuzaawo
-e	-wa	138 words	eyonooneddwa, egizannyirwa
-o	-u	116 words	ogusibukamu, ogirimu

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
baamatwale	baamat-wa-le	7.5	wa
ebikwatibwako	ebikwatib-wa-ko	7.5	wa
okuyisaawo	okuyisa-a-wo	7.5	a
ekisingamu	ekising-a-mu	7.5	a
obusingirayo	obusingir-a-yo	7.5	a
kumuyamba	ku-mu-yamba	7.5	yamba
kwatuukibwako	kwatuukib-wa-ko	7.5	wa
mukungaaniramu	mukungaanir-a-mu	7.5	a
ekitangaala	ekitanga-a-la	7.5	a
batandikawo	batandik-a-wo	7.5	a
bannyonyola	bannyony-o-la	7.5	o
obunakuwavu	obunaku-wa-vu	7.5	wa
akaateekebwawo	akaateekeb-wa-wo	7.5	wa
okwetuusaako	okwetuusa-a-ko	7.5	a
ekwatibwako	ekwatib-wa-ko	7.5	wa

6.6 Linguistic Interpretation

Automated Insight: The language Ganda 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 (4.75x)
N-gram	2-gram	Lowest perplexity (219)
Markov	Context-4	Highest predictability (95.7%)
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 10:45:52