README.md

metadata

language: ms
language_name: Malay
language_family: austronesian_malay
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - feature-extraction
  - sentence-similarity
  - tokenization
  - n-grams
  - markov-chain
  - text-mining
  - fasttext
  - babelvec
  - vocabulous
  - vocabulary
  - monolingual
  - family-austronesian_malay
license: mit
library_name: wikilangs
pipeline_tag: text-generation
datasets:
  - omarkamali/wikipedia-monthly
dataset_info:
  name: wikipedia-monthly
  description: Monthly snapshots of Wikipedia articles across 300+ languages
metrics:
  - name: best_compression_ratio
    type: compression
    value: 5.467
  - name: best_isotropy
    type: isotropy
    value: 0.759
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10T00:00:00.000Z

Malay - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Malay 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	4.396x	4.40	0.0792%	1,649,775
16k	4.887x	4.89	0.0880%	1,484,029
32k	5.238x	5.24	0.0943%	1,384,577
64k	5.467x 🏆	5.47	0.0985%	1,326,382

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Patavan merupakan sebuah kawasan yang terletak di Iran. Rujukan di Kaunti Sowme'...

Vocab	Tokens	Count
8k	▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+10 more)	20
16k	▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+9 more)	19
32k	▁p ata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran ... (+8 more)	18
64k	▁pata van ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ▁di ▁iran . ... (+7 more)	17

Sample 2: Elikesik, Alanya merupakan sebuah kawasan yang terletak di Turki. Lihat juga Dae...

Vocab	Tokens	Count
8k	▁e lik es ik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ... (+13 more)	23
16k	▁e lik es ik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ... (+13 more)	23
32k	▁e lik esik , ▁al anya ▁merupakan ▁sebuah ▁kawasan ▁yang ... (+12 more)	22
64k	▁e lik esik , ▁alanya ▁merupakan ▁sebuah ▁kawasan ▁yang ▁terletak ... (+11 more)	21

Sample 3: Mélagues ialah komun di jabatan di Aveyron selatan Perancis. Lihat juga Komun di...

Vocab	Tokens	Count
8k	▁m é lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁av ... (+17 more)	27
16k	▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)	21
32k	▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)	21
64k	▁mé lagu es ▁ialah ▁komun ▁di ▁jabatan ▁di ▁aveyron ▁selatan ... (+11 more)	21

Key Findings

• Best Compression: 64k achieves 5.467x compression
• Lowest UNK Rate: 8k with 0.0792% 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	154,800	17.24	1,398,741	9.5%	21.6%
2-gram	Subword	230 🏆	7.85	34,956	72.4%	99.3%
3-gram	Word	325,387	18.31	2,569,328	10.9%	21.5%
3-gram	Subword	1,970	10.94	177,272	30.0%	74.6%
4-gram	Word	377,074	18.52	3,858,678	13.0%	24.9%
4-gram	Subword	11,507	13.49	899,517	15.2%	43.5%
5-gram	Word	161,998	17.31	2,522,183	16.9%	31.6%
5-gram	Subword	45,772	15.48	2,934,104	9.2%	28.3%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	pada tahun	182,854
2	merupakan sebuah	180,992
3	terletak di	177,681
4	yang terletak	164,205
5	pautan luar	142,071

3-grams (Word):

Rank	N-gram	Count
1	yang terletak di	145,449
2	rujukan pautan luar	85,486
3	komun di jabatan	62,009
4	ini juga merupakan	48,368
5	merupakan sebuah kawasan	47,805

4-grams (Word):

Rank	N-gram	Count
1	kayu yang telah ditebang	47,171
2	pada batang kayu hidup	47,170
3	kayu dan dapat menyebabkan	47,170
4	hidup atau kayu yang	47,158
5	atau kayu yang telah	47,158

5-grams (Word):

Rank	N-gram	Count
1	pada batang kayu hidup atau	47,158
2	kayu hidup atau kayu yang	47,158
3	hidup atau kayu yang telah	47,158
4	atau kayu yang telah ditebang	47,158
5	batang kayu hidup atau kayu	47,158

2-grams (Subword):

Rank	N-gram	Count
1	a n	20,347,605
2	n _	12,967,523
3	a _	9,650,615
4	n g	8,591,886
5	e r	8,563,362

3-grams (Subword):

Rank	N-gram	Count
1	a n _	10,591,409
2	a n g	4,452,343
3	n g _	3,798,949
4	_ m e	3,721,748
5	_ d a	3,505,831

4-grams (Subword):

Rank	N-gram	Count
1	k a n _	2,911,587
2	a n g _	2,824,382
3	_ m e n	1,732,841
4	d a n _	1,723,759
5	_ d a n	1,689,159

5-grams (Subword):

Rank	N-gram	Count
1	_ d a n _	1,659,387
2	y a n g _	1,558,610
3	_ y a n g	1,536,001
4	p a d a _	1,174,037
5	n g a n _	1,075,874

Key Findings

• Best Perplexity: 2-gram (subword) with 230
• 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

---

3. Markov Chain Evaluation

Results

Context	Variant	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	Word	0.8903	1.854	12.05	1,355,695	11.0%
1	Subword	1.3613	2.569	10.50	18,334	0.0%
2	Word	0.4107	1.329	2.43	16,287,373	58.9%
2	Subword	0.5296	1.444	3.13	192,441	47.0%
3	Word	0.1577	1.116	1.33	39,552,428	84.2%
3	Subword	0.5062	1.420	3.06	602,656	49.4%
4	Word	0.0565 🏆	1.040	1.09	52,397,468	94.4%
4	Subword	0.5777	1.492	3.09	1,842,490	42.2%

Generated Text Samples (Word-based)

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

Context Size 1:

1. dan berukuran kecil jepun pautan luar pesisir permaisuri imperial jepun dia juga berkhidmat di pulau...
2. di sini pada perlawanan 213 kampung rujukan pautan luar the kebar district south park hyo song
3. yang paling muda bahagian barat daya perancis lihat juga komun di rantau ini mempunyai ketahanan sel...

Context Size 2:

1. pada tahun album lagu tema dua taman negara namadgi nil desperandum dan rock im park kwang jae
2. merupakan sebuah sistem perakaman bunyi sehingga syarikat yang sepatutnya dia kemudian diganti naman...
3. terletak di daerah schmalkalden meiningen thüringen jerman perbandaran di león senarai kawasan perba...

Context Size 3:

1. yang terletak di wilayah hajdú bihar di timur hungary di hungary perbandaran di hungary hu kazár
2. rujukan pautan luar national portal of india bagli
3. komun di jabatan oise di utara perancis lihat juga komun di jabatan cantal di selatan tengah austral...

Context Size 4:

1. kayu yang telah ditebang rujukan the world of jewel beetles bellamy c l 25 aug kumbang
2. kayu dan dapat menyebabkan kerosakan pada batang kayu hidup atau kayu yang telah ditebang rujukan ti...
3. pada batang kayu hidup atau kayu yang telah ditebang rujukan the world of jewel beetles bellamy c l ...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. an_il_mekuardan_
2. _rum_bun_an_pawa
3. nyarekomert_ris,

Context Size 2:

1. ang_turuparri_ton
2. n_merun,_85*_esik
3. a_bahur,_"udperja

Context Size 3:

1. an_perkan_diberkan
2. ang_dia_/_bum_man_
3. ng_timusi_mina_ada

Context Size 4:

1. kan_syed_muar_roorg
2. ang_ditebangsa._mel
3. _mengen,_arya_acara

Key Findings

• Best Predictability: Context-4 (word) with 94.4% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,842,490 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	605,972
Total Tokens	70,999,280
Mean Frequency	117.17
Median Frequency	4
Frequency Std Dev	4627.23

Most Common Words

Rank	Word	Frequency
1	dan	1,667,877
2	di	1,592,069
3	yang	1,542,694
4	pada	780,935
5	dalam	669,363
6	dengan	531,319
7	ini	520,575
8	untuk	476,200
9	sebagai	428,345
10	dari	375,471

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	石塘社	2
2	燕尾脊	2
3	ekapadashirshasana	2
4	danjae	2
5	sinminhoe	2
6	국가	2
7	kukka	2
8	muhurtam	2
9	sasayan	2
10	norr	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1100
R² (Goodness of Fit)	0.989051
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	30.1%
Top 1,000	59.6%
Top 5,000	78.8%
Top 10,000	85.2%

Key Findings

• Zipf Compliance: R²=0.9891 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 30.1% of corpus
• Long Tail: 595,972 words needed for remaining 14.8% 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	0.7590 🏆	0.3685	N/A	N/A
mono_64d	64	0.7509	0.3058	N/A	N/A
mono_128d	128	0.6593	0.3026	N/A	N/A
aligned_32d	32	0.7590	0.3657	0.4180	0.7660
aligned_64d	64	0.7509	0.3027	0.6580	0.9240
aligned_128d	128	0.6593	0.3113	0.7120	0.9240

Key Findings

• Best Isotropy: mono_32d with 0.7590 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3261. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 71.2% 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	0.114	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	shapie, skalstad, smps
-a	a602, altomare, ablan
-ma	maunoury, mascheix, marzian
-k	kimbirila, krasnopolye, khairune
-m	metropolitans, mpumnosemerahtimbalan, mesoamerican
-p	patronnya, piscatori, pagalungan
-b	badoglio, bougaâ, baturuyuk
-t	tabuk, thomalla, terlelap

Productive Suffixes

Suffix	Examples
-n	rundengan, nyongbyon, ablan
-a	thomalla, gombaknya, patronnya
-s	metropolitans, smps, cynops
-an	rundengan, ablan, pagalungan
-e	altomare, shapie, stadsgemeente
-i	piscatori, pipaltari, molinari
-ya	gombaknya, patronnya, prosidurnya
-r	toner, headmaster, wijsmuller

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
angk	2.06x	551 contexts	angku, angky, angka
ngka	2.08x	409 contexts	ingka, engka, ongka
ingg	2.12x	247 contexts	ningg, tingg, ingga
ukan	2.12x	227 contexts	gukan, pukan, bukan
embe	1.99x	165 contexts	membe, bembe, embed
rkan	2.25x	90 contexts	erkan, orkan, arkan
memb	2.34x	73 contexts	membe, memba, member
enja	1.99x	152 contexts	penja, senja, kenja
meny	2.36x	63 contexts	ameny, menya, menye
ebag	2.54x	42 contexts	sebag, kebagu, sebagi
mber	1.76x	217 contexts	imber, amber, umber
ndar	1.71x	238 contexts	ndara, indar, undar

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	-n	126 words	perkaitan, penyelerasan
-k	-n	108 words	kalinin, kaputihan
-p	-an	100 words	perkaitan, penyelerasan
-p	-a	100 words	polyandra, pencuba
-s	-a	90 words	sayura, sametha
-m	-n	87 words	mininggalkan, mazan
-k	-an	80 words	kaputihan, kiniéran
-s	-n	78 words	suthan, satarazwan
-s	-s	78 words	sponsors, suvalmas
-a	-a	66 words	ammochloa, avaa

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
qinghuang	qinghu-a-ng	7.5	a
tordehumos	tordehum-o-s	7.5	o
pulangnya	pulang-n-ya	7.5	n
dialirkan	dialir-k-an	7.5	k
penggertak	pengger-ta-k	7.5	ta
christiansted	christians-t-ed	7.5	t
epikurean	epikur-e-an	7.5	e
unitedpathum	unitedpat-h-um	7.5	h
chandrasena	chandrase-n-a	7.5	n
panggungnya	panggung-n-ya	7.5	n
sanskritnya	sanskrit-n-ya	7.5	n
bibliografinya	bibliografi-n-ya	7.5	n
kondiadou	kondiad-o-u	7.5	o
azmatkhan	azmatk-h-an	7.5	h
karakteristiknya	karakteristik-n-ya	7.5	n

6.6 Linguistic Interpretation

Automated Insight: The language Malay shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (5.47x)
N-gram	2-gram	Lowest perplexity (230)
Markov	Context-4	Highest predictability (94.4%)
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

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Generated by Wikilangs Models Pipeline

Report Date: 2026-01-10 19:01:46