Old English - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

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

---

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.107x	3.11	0.0859%	252,634
16k	3.441x	3.45	0.0951%	228,129
32k	3.763x	3.77	0.1040%	208,636
64k	4.012x 🏆	4.02	0.1109%	195,650

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Grēat Coldūn () is þorp in þæm East Þriding, se is Eoferƿicscire dǣl, on Englum....

Vocab	Tokens	Count
8k	▁grēat ▁c old ūn ▁() ▁is ▁þorp ▁in ▁þæm ▁east ... (+15 more)	25
16k	▁grēat ▁c old ūn ▁() ▁is ▁þorp ▁in ▁þæm ▁east ... (+15 more)	25
32k	▁grēat ▁cold ūn ▁() ▁is ▁þorp ▁in ▁þæm ▁east ▁þriding ... (+14 more)	24
64k	▁grēat ▁cold ūn ▁() ▁is ▁þorp ▁in ▁þæm ▁east ▁þriding ... (+14 more)	24

Sample 2: Lingua Franca Nova is gehugod sprǣc. Utweardlice bendas elefen.org gereord

Vocab	Tokens	Count
8k	▁l ing ua ▁franc a ▁nov a ▁is ▁geh ug ... (+11 more)	21
16k	▁l ing ua ▁franc a ▁nova ▁is ▁geh ug od ... (+10 more)	20
32k	▁ling ua ▁franca ▁nova ▁is ▁gehugod ▁sprǣc . ▁utweardlice ▁bendas ... (+5 more)	15
64k	▁lingua ▁franca ▁nova ▁is ▁gehugod ▁sprǣc . ▁utweardlice ▁bendas ▁ele ... (+4 more)	14

Sample 3: Andreas Iǣxcūn ƿæs se seofoða Foresittend þāra Geānlǣhtra Rīca, fram þǣm gēare ō...

Vocab	Tokens	Count
8k	▁andreas ▁i ǣ x c ūn ▁ƿæs ▁se ▁seof oða ... (+17 more)	27
16k	▁andreas ▁iǣx c ūn ▁ƿæs ▁se ▁seofoða ▁foresittend ▁þāra ▁geānlǣhtra ... (+14 more)	24
32k	▁andreas ▁iǣx c ūn ▁ƿæs ▁se ▁seofoða ▁foresittend ▁þāra ▁geānlǣhtra ... (+14 more)	24
64k	▁andreas ▁iǣxcūn ▁ƿæs ▁se ▁seofoða ▁foresittend ▁þāra ▁geānlǣhtra ▁rīca , ... (+12 more)	22

Key Findings

• Best Compression: 64k achieves 4.012x compression
• Lowest UNK Rate: 8k with 0.0859% 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	3,551	11.79	7,095	21.2%	53.1%
2-gram	Subword	365 🏆	8.51	3,006	61.0%	98.1%
3-gram	Word	3,411	11.74	6,128	21.1%	50.1%
3-gram	Subword	3,332	11.70	23,711	22.3%	62.8%
4-gram	Word	6,747	12.72	11,452	16.3%	36.7%
4-gram	Subword	18,651	14.19	105,677	10.6%	32.7%
5-gram	Word	4,718	12.20	8,067	18.6%	41.3%
5-gram	Subword	56,790	15.79	217,768	6.4%	20.2%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	on þǣm	784
2	in þǣm	762
3	in þæm	673
4	of the	645
5	se is	536

3-grams (Word):

Rank	N-gram	Count
1	td valign top	529
2	þæs geānedan cynerīces	312
3	is þorp in	311
4	on eoferwicscīre þæs	248
5	eoferwicscīre þæs geānedan	248

4-grams (Word):

Rank	N-gram	Count
1	on eoferwicscīre þæs geānedan	248
2	eoferwicscīre þæs geānedan cynerīces	248
3	is eoferƿicscire dǣl on	232
4	eoferƿicscire dǣl on englum	231
5	se is eoferƿicscire dǣl	229

5-grams (Word):

Rank	N-gram	Count
1	on eoferwicscīre þæs geānedan cynerīces	248
2	is eoferƿicscire dǣl on englum	231
3	se is eoferƿicscire dǣl on	229
4	þriding se is eoferƿicscire dǣl	224
5	east þriding se is eoferƿicscire	170

2-grams (Subword):

Rank	N-gram	Count
1	e _	68,542
2	a n	60,904
3	n _	55,318
4	s _	47,837
5	n d	40,759

3-grams (Subword):

Rank	N-gram	Count
1	a n d	24,396
2	n d _	20,668
3	a n _	16,952
4	_ a n	16,629
5	o n _	16,182

4-grams (Subword):

Rank	N-gram	Count
1	a n d _	16,673
2	_ a n d	14,847
3	_ o n _	10,364
4	_ i s _	10,180
5	_ i n _	9,895

5-grams (Subword):

Rank	N-gram	Count
1	_ a n d _	14,216
2	_ t h e _	3,853
3	_ þ ǣ m _	3,654
4	_ þ æ s _	3,541
5	_ h i s _	3,480

Key Findings

• Best Perplexity: 2-gram (subword) with 365
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~20% 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	0.6200	1.537	3.57	86,918	38.0%
1	Subword	0.8434	1.794	6.43	1,235	15.7%
2	Word	0.1550	1.113	1.30	307,624	84.5%
2	Subword	0.9640	1.951	5.90	7,944	3.6%
3	Word	0.0385	1.027	1.05	397,324	96.2%
3	Subword	0.8649	1.821	4.02	46,823	13.5%
4	Word	0.0127 🏆	1.009	1.02	415,064	98.7%
4	Subword	0.6219	1.539	2.55	188,154	37.8%

Generated Text Samples (Word-based)

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

Context Size 1:

1. and bedældede hine in þǣm geānedum rīcum þā protest sang rocc and sīþe hrēðcyninges hām to
2. on francum in þæm miclum burgum and his ƿæter hit hê hê willgesweostor shes laid back
3. is unesco æfter déaðe drepe þrōƿade heorosƿeng heardn ond sēo hēafodmearc iesuitisces rǣses it was f...

Context Size 2:

1. on þǣm fylle þǣm þe nāhwæþer ne þā ġeānedan land sculon ne ǣniġ land sceal ætfōn oþþe
2. in þǣm indiscum lande uttar pradesh þæt land þæt ƿæs corēan independence activist politicians and jo...
3. in þæm east þriding se is eoferƿicscire dǣl on englum hit hæfþ 11 351 būendas on eoferwicscīre

Context Size 3:

1. td valign top ualentinianus ii td valign top td to 297 td valign top co emperor with honorius
2. is þorp in soria on castile and leóne in spēonlande and þorpas on sorie
3. eoferwicscīre þæs geānedan cynerīces and hēafodman þæs behealdenda hēapes siþðan mǣdmōnaþ he is gebē...

Context Size 4:

1. on eoferwicscīre þæs geānedan cynerīces
2. is eoferƿicscire dǣl on englalande on eoferwicscīre þæs geānedan cynerīces
3. eoferƿicscire dǣl on englum mid grēatum hǣþfelda ġesċieppaþ hie þone burgsċipe of hǣþfelda on eoferw...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _htofunes_anōre_
2. e_c_weaþǣfyn_sca
3. n_þeal_wun_berie

Context Size 2:

1. e_of_fi_94oðbe_tw
2. an_thoseadand_īeg
3. n_nīƿ_mesprytt,_þ

Context Size 3:

1. and_und_ofher_mā_s
2. nd_titutede_him._h
3. an_asscran_betwa_ǣ

Context Size 4:

1. and_belalan_(mother
2. _and_ġecosta_tƿiste
3. _on_þā_habbað_nofgo

Key Findings

• Best Predictability: Context-4 (word) with 98.7% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (188,154 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	31,186
Total Tokens	403,003
Mean Frequency	12.92
Median Frequency	3
Frequency Std Dev	156.70

Most Common Words

Rank	Word	Frequency
1	and	14,299
2	on	10,683
3	is	10,302
4	in	10,147
5	of	6,062
6	se	4,316
7	the	3,973
8	þǣm	3,669
9	þæs	3,610
10	his	3,501

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	minga	2
2	blæcfugolond	2
3	ƿīleacstede	2
4	cōcsċīre	2
5	winnebagsċīre	2
6	ælfrēdingtūn	2
7	irfung	2
8	larēodo	2
9	grœndā	2
10	dǣlungs	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.9344
R² (Goodness of Fit)	0.998034
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	38.0%
Top 1,000	59.6%
Top 5,000	77.9%
Top 10,000	86.2%

Key Findings

• Zipf Compliance: R²=0.9980 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 38.0% of corpus
• Long Tail: 21,186 words needed for remaining 13.8% 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	0.7896	0.3585	N/A	N/A
mono_64d	64	0.4746	0.3175	N/A	N/A
mono_128d	128	0.1353	0.3004	N/A	N/A
aligned_32d	32	0.7896 🏆	0.3555	0.0300	0.2480
aligned_64d	64	0.4746	0.3090	0.0860	0.3400
aligned_128d	128	0.1353	0.3041	0.1280	0.4020

Key Findings

• Best Isotropy: aligned_32d with 0.7896 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3242. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 12.8% 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	1.044	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
-ge	geondrīcisce, gebold, gemyndgung

Productive Suffixes

Suffix	Examples
-e	ārwurðnysse, cǣġe, farende
-s	celebrations, villages, annivs
-es	villages, ides, missiles
-an	þēodacynewīsan, hāligan, europiscan
-um	dorsætum, maniȝum, elpendum
-de	farende, ungeƿilde, bestandende
-en	ƿriten, eċġen, hyrneġen
-on	edmonton, huffington, aragon

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
enne	2.04x	48 contexts	fenne, etenne, cenneþ
mani	2.03x	43 contexts	amani, maniȝ, maniġ
wear	1.91x	43 contexts	wearð, wearg, weard
ster	1.67x	59 contexts	sister, ēaster, faster
unge	1.77x	46 contexts	tunge, tunges, jungen
tion	2.19x	19 contexts	motion, nation, action
inga	1.72x	34 contexts	þinga, minga, ðinga
ning	1.64x	35 contexts	mining, cining, cyning
aste	1.69x	27 contexts	taste, easte, ēaste
ynin	2.21x	11 contexts	cynin, cyning, cyninȝ
afod	1.82x	18 contexts	hēafod, heafod, ƿafode
nisc	1.49x	27 contexts	rūnisc, denisc, dēnisc

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
-ge	-e	79 words	geƿorhte, geƿǣre
-ge	-en	35 words	getimbroden, geferræden
-ge	-de	35 words	geanede, gehiersomode
-ge	-s	29 words	genus, geardas
-ge	-an	20 words	gegildan, gemæccan
-ge	-um	20 words	gerādum, germanicum
-ge	-es	17 words	geofones, geānlǣhtes
-ge	-on	9 words	gestaðoledon, gestrēon

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
gehƿilcum	ge-hƿilc-um	6.0	hƿilc
gefeahten	ge-feaht-en	6.0	feaht
underbyrigum	underbyrig-um	4.5	underbyrig
geþoftscipe	ge-þoftscipe	4.5	þoftscipe
sanghordes	sanghord-es	4.5	sanghord
gesweoster	ge-sweoster	4.5	sweoster
russlandes	russland-es	4.5	russland
þēodisclandes	þēodiscland-es	4.5	þēodiscland
gestrēonum	ge-strē-on-um	4.5	strē
drȳġelandes	drȳġeland-es	4.5	drȳġeland
drēamhordes	drēamhord-es	4.5	drēamhord
andweardum	andweard-um	4.5	andweard
engliscan	englisc-an	4.5	englisc
stǣrlican	stǣrlic-an	4.5	stǣrlic
bedæleden	bedæled-en	4.5	bedæled

6.6 Linguistic Interpretation

Automated Insight: The language Old English 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	64k BPE	Best compression (4.01x)
N-gram	2-gram	Lowest perplexity (365)
Markov	Context-4	Highest predictability (98.7%)
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

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

---

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

---

Generated by Wikilangs Models Pipeline

Report Date: 2026-01-03 16:22:13