wikilangs/btm

Batak Mandailing - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Batak Mandailing 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	4.164x	4.17	0.0881%	216,736
16k	4.609x	4.61	0.0975%	195,810
32k	5.005x	5.01	0.1059%	180,321
64k	5.210x 🏆	5.22	0.1103%	173,224

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Kumpulan Setia ima sala sada huta na adong i kecamatan Huta Bargot, kabupaten Ma...

Vocab	Tokens	Count
8k	▁kumpulan ▁set ia ▁ima ▁sala ▁sada ▁huta ▁na ▁adong ▁i ... (+14 more)	24
16k	▁kumpulan ▁setia ▁ima ▁sala ▁sada ▁huta ▁na ▁adong ▁i ▁kecamatan ... (+13 more)	23
32k	▁kumpulan ▁setia ▁ima ▁sala ▁sada ▁huta ▁na ▁adong ▁i ▁kecamatan ... (+13 more)	23
64k	▁kumpulan ▁setia ▁ima ▁sala ▁sada ▁huta ▁na ▁adong ▁i ▁kecamatan ... (+13 more)	23

Sample 2: Muara Soma ima sala sada huta na ading i kecamatan Batang Natal, kabupaten Manda...

Vocab	Tokens	Count
8k	▁muara ▁so ma ▁ima ▁sala ▁sada ▁huta ▁na ▁ading ▁i ... (+14 more)	24
16k	▁muara ▁soma ▁ima ▁sala ▁sada ▁huta ▁na ▁ading ▁i ▁kecamatan ... (+13 more)	23
32k	▁muara ▁soma ▁ima ▁sala ▁sada ▁huta ▁na ▁ading ▁i ▁kecamatan ... (+13 more)	23
64k	▁muara ▁soma ▁ima ▁sala ▁sada ▁huta ▁na ▁ading ▁i ▁kecamatan ... (+13 more)	23

Sample 3: 24 Januari ima ari pa-24 i kalender Gregorian dohot 361 ari (sanga 362 ari i tao...

Vocab	Tokens	Count
8k	▁ 2 4 ▁januari ▁ima ▁ari ▁pa - 2 4 ... (+24 more)	34
16k	▁ 2 4 ▁januari ▁ima ▁ari ▁pa - 2 4 ... (+24 more)	34
32k	▁ 2 4 ▁januari ▁ima ▁ari ▁pa - 2 4 ... (+24 more)	34
64k	▁ 2 4 ▁januari ▁ima ▁ari ▁pa - 2 4 ... (+24 more)	34

Key Findings

• Best Compression: 64k achieves 5.210x compression
• Lowest UNK Rate: 8k with 0.0881% 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	2,149	11.07	3,846	24.9%	62.3%
2-gram	Subword	193 🏆	7.59	1,424	75.5%	99.7%
3-gram	Word	1,623	10.66	2,810	28.2%	64.8%
3-gram	Subword	1,481	10.53	9,326	32.5%	79.4%
4-gram	Word	1,998	10.96	3,539	27.5%	54.8%
4-gram	Subword	7,322	12.84	39,044	16.0%	47.2%
5-gram	Word	980	9.94	1,944	37.4%	71.2%
5-gram	Subword	20,669	14.34	80,096	9.7%	30.8%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	ima sada	626
2	on pe	512
3	na adong	416
4	sian on	373
5	i taon	359

3-grams (Word):

Rank	N-gram	Count
1	na adong i	265
2	kabupaten mandailing natal	178
3	i kalender gregorian	170
4	sumatera utara indonesia	160
5	ima ari pa	157

4-grams (Word):

Rank	N-gram	Count
1	provinsi sumatera utara indonesia	133
2	kabupaten mandailing natal provinsi	130
3	mandailing natal provinsi sumatera	129
4	natal provinsi sumatera utara	129
5	taon kabisat i kalender	126

5-grams (Word):

Rank	N-gram	Count
1	kabupaten mandailing natal provinsi sumatera	129
2	mandailing natal provinsi sumatera utara	129
3	natal provinsi sumatera utara indonesia	128
4	taon kabisat i kalender gregorian	126
5	huta na adong i kecamatan	112

2-grams (Subword):

Rank	N-gram	Count
1	a n	41,734
2	a _	37,272
3	n _	28,447
4	m a	25,826
5	i _	25,144

3-grams (Subword):

Rank	N-gram	Count
1	_ m a	15,579
2	a n _	13,475
3	_ n a	11,682
4	a n g	11,673
5	n a _	10,767

4-grams (Subword):

Rank	N-gram	Count
1	_ n a _	7,012
2	_ m a n	6,102
3	a _ m a	4,445
4	_ i m a	4,125
5	i m a _	4,121

5-grams (Subword):

Rank	N-gram	Count
1	_ i m a _	3,948
2	d o h o t	3,004
3	o h o t _	3,001
4	_ d o h o	2,997
5	_ d o t _	2,471

Key Findings

• Best Perplexity: 2-gram (subword) with 193
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~31% 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	0.8033	1.745	4.52	26,637	19.7%
1	Subword	0.8859	1.848	5.46	845	11.4%
2	Word	0.2155	1.161	1.41	119,766	78.4%
2	Subword	0.7876	1.726	4.38	4,613	21.2%
3	Word	0.0517	1.037	1.07	168,163	94.8%
3	Subword	0.7693	1.704	3.51	20,191	23.1%
4	Word	0.0122 🏆	1.008	1.02	179,311	98.8%
4	Subword	0.5814	1.496	2.41	70,850	41.9%

Generated Text Samples (Word-based)

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

Context Size 1:

1. i kota di kotu isa rupana kahanggi namar sisolkot ni eme ni awak dot mamakena pala
2. na mandung manjadi aliran eksistensialisme sartre ima al qur an sm 180 an sm 70 an
3. ima sada provinsi sumatera utara aek sasataon rodang momo tarida do anggina si baroar dibaon na

Context Size 2:

1. ima sada sunni mazhab hanafi vasilij vladimirovič bartold art by barbara brend p 130 tai ulama na
2. on pe mandung dewasa pakean nai gunaon pakean adat belitong tai i instrospeksi eksperimental sudena ...
3. na adong juo alak sunni dot 10 huruf ngolu vokal sapetona hangeul adongdope 3 konsonannai dot 1

Context Size 3:

1. na adong i ruang woktu i sakitar lubang nalomlom adong parmukoan na i dokon horizon peristiwa objek ...
2. kabupaten mandailing natal provinsi sumatera utara indonesia i botung adong luak parmayaman na deges...
3. ima ari pa 103 ari pa 104 i taon kabisat i kalender gregorian dohot 363 ari sanga 364

Context Size 4:

1. kabupaten mandailing natal provinsi sumatera utara indonesia sumberna
2. natal provinsi sumatera utara indonesia pula sian on panyabungan tu kecamatan on
3. mandailing natal provinsi sumatera utara indonesia sumberna

Generated Text Samples (Subword-based)

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

Context Size 1:

1. alan_a_rian_ruse
2. _ana_ontuon._tan
3. nang_akeon_asapa

Context Size 2:

1. an_niviusi,_hamel
2. a_ida_lak_nai_jun
3. n_sentat_dokon_ng

Context Size 3:

1. _mambaen_dohot_par
2. an_ibad_oktu_piga_
3. _nagoda_marcoundur

Context Size 4:

1. _na_ibaen_herito_la
2. _manjadi_i_ruar_tu_
3. a_marisi.dw:_menek_

Key Findings

• Best Predictability: Context-4 (word) with 98.8% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (70,850 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	11,148
Total Tokens	176,428
Mean Frequency	15.83
Median Frequency	4
Frequency Std Dev	130.57

Most Common Words

Rank	Word	Frequency
1	i	7,229
2	na	7,125
3	on	3,997
4	ima	3,996
5	dohot	2,990
6	ni	2,685
7	dot	2,484
8	sada	1,834
9	tu	1,711
10	ma	1,485

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	lil	2
2	imah	2
3	nasida	2
4	sunusi	2
5	nunga	2
6	majmu	2
7	fatawa	2
8	fiqhi	2
9	panjalakian	2
10	martoba	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0705
R² (Goodness of Fit)	0.989075
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	41.8%
Top 1,000	71.1%
Top 5,000	91.4%
Top 10,000	98.7%

Key Findings

• Zipf Compliance: R²=0.9891 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 41.8% of corpus
• Long Tail: 1,148 words needed for remaining 1.3% 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	0.4518 🏆	0.4274	N/A	N/A
mono_64d	64	0.1211	0.4252	N/A	N/A
mono_128d	128	0.0249	0.4089	N/A	N/A
aligned_32d	32	0.4518	0.4145	0.0140	0.1240
aligned_64d	64	0.1211	0.4363	0.0200	0.1760
aligned_128d	128	0.0249	0.4097	0.0540	0.2300

Key Findings

• Best Isotropy: mono_32d with 0.4518 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.4203. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 5.4% R@1 in cross-lingual retrieval.
• Recommendation: 128d aligned for best cross-lingual performance

---

6. Morphological Analysis (Experimental)

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

6.1 Productivity & Complexity

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	1.311	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
-ma	marmasak, mamuloi, maligina
-pa	paderi, parkumpulan, pangajaran
-man	manakik, manyorang, mangajari
-mar	marmasak, marwujud, mariner
-sa	samananjung, sati, sakral
-ta	tarpusat, takar, tajikistan

Productive Suffixes

Suffix	Examples
-n	tubagasan, ringkasan, disusun
-a	nikola, studia, katua
-an	tubagasan, ringkasan, parkumpulan
-ng	samananjung, pedagang, kacang
-on	bandingkon, dibandingkon, pelestarion
-na	maligina, umurna, ajayaanna
-ang	pedagang, kacang, sumbayang

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
anga	1.46x	77 contexts	nanga, angan, sanga
angk	1.47x	58 contexts	angko, angke, angka
anda	1.43x	54 contexts	ganda, tanda, banda
mang	1.59x	31 contexts	mango, amang, lomang
amba	1.49x	39 contexts	hamba, tamba, sambal
ngan	1.40x	43 contexts	angan, lengan, sangan
dang	1.40x	42 contexts	udang, ndang, dangka
aran	1.35x	48 contexts	arana, arang, saran
angg	1.32x	39 contexts	anggi, anggo, nangge
anja	1.36x	34 contexts	hanja, banjar, anjadi
ngga	1.37x	30 contexts	hingga, rongga, mangga
ting	1.34x	32 contexts	tingo, uting, tingon

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
-pa	-n	307 words	panjalakan, pambaenan
-pa	-an	271 words	panjalakan, pambaenan
-ma	-n	241 words	mangombangkon, maximilian
-ma	-on	157 words	mangombangkon, manyesuaion
-ma	-a	98 words	maringana, manurutnia
-ma	-ng	69 words	malang, marancang
-ma	-an	61 words	maximilian, marhalangan
-pa	-a	57 words	pasca, pasadana
-sa	-a	40 words	samentara, sangapiga
-ma	-ang	38 words	malang, marancang

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
paporangan	pa-pora-ng-an	7.5	pora
marpandangan	mar-pa-ndang-an	7.5	ndang
bagasanna	bagas-an-na	6.0	bagas
pasabolas	pa-sa-bolas	6.0	bolas
mandurung	man-duru-ng	6.0	duru
sasabagas	sa-sa-bagas	6.0	bagas
sabalikna	sa-balik-na	6.0	balik
marlainan	mar-lain-an	6.0	lain
panilaian	pa-nilai-an	6.0	nilai
mardongan	mar-dong-an	6.0	dong
margontian	mar-gonti-an	6.0	gonti
mandefinision	man-definisi-on	6.0	definisi
pemerintahan	pemerintah-an	4.5	pemerintah
margandak	mar-gandak	4.5	gandak
habitatna	habitat-na	4.5	habitat

6.6 Linguistic Interpretation

Automated Insight: The language Batak Mandailing 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 (5.21x)
N-gram	2-gram	Lowest perplexity (193)
Markov	Context-4	Highest predictability (98.8%)
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 19:44:07