Uzbek - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Uzbek 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	3.671x	3.67	0.0852%	1,947,309
16k	4.048x	4.05	0.0940%	1,765,944
32k	4.351x	4.35	0.1010%	1,642,973
64k	4.579x 🏆	4.58	0.1063%	1,561,057

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: — Braziliyaning Alagoas shtatidagi munisipalitet. Manbalar munitsipalitetlari

Vocab	Tokens	Count
8k	▁— ▁braziliyaning ▁ala go as ▁shtatidagi ▁munisipalitet . ▁manbalar ▁munitsipalitet ... (+1 more)	11
16k	▁— ▁braziliyaning ▁ala go as ▁shtatidagi ▁munisipalitet . ▁manbalar ▁munitsipalitet ... (+1 more)	11
32k	▁— ▁braziliyaning ▁ala go as ▁shtatidagi ▁munisipalitet . ▁manbalar ▁munitsipalitet ... (+1 more)	11
64k	▁— ▁braziliyaning ▁alagoas ▁shtatidagi ▁munisipalitet . ▁manbalar ▁munitsipalitet lari	9

Sample 2: Boztarla — Adıyaman viloyatining Kâhta tumanidagi qishloqlardan biri. Manbalar b...

Vocab	Tokens	Count
8k	▁boz tar la ▁— ▁ad ı y aman ▁viloyatining ▁k ... (+14 more)	24
16k	▁boz tar la ▁— ▁adıyaman ▁viloyatining ▁k â h ta ... (+11 more)	21
32k	▁boz tar la ▁— ▁adıyaman ▁viloyatining ▁k â hta ▁tumanidagi ... (+10 more)	20
64k	▁boz tar la ▁— ▁adıyaman ▁viloyatining ▁kâhta ▁tumanidagi ▁qishloqlardan ▁biri ... (+8 more)	18

Sample 3: — Braziliyaning Para shtatidagi munitsipalitet. Manbalar munitsipalitetlari

Vocab	Tokens	Count
8k	▁— ▁braziliyaning ▁para ▁shtatidagi ▁munitsipalitet . ▁manbalar ▁munitsipalitet lari	9
16k	▁— ▁braziliyaning ▁para ▁shtatidagi ▁munitsipalitet . ▁manbalar ▁munitsipalitet lari	9
32k	▁— ▁braziliyaning ▁para ▁shtatidagi ▁munitsipalitet . ▁manbalar ▁munitsipalitet lari	9
64k	▁— ▁braziliyaning ▁para ▁shtatidagi ▁munitsipalitet . ▁manbalar ▁munitsipalitet lari	9

Key Findings

• Best Compression: 64k achieves 4.579x compression
• Lowest UNK Rate: 8k with 0.0852% 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	144,258	17.14	1,000,611	8.6%	21.5%
2-gram	Subword	306 🏆	8.26	17,282	64.7%	98.6%
3-gram	Word	209,904	17.68	1,395,449	10.7%	21.4%
3-gram	Subword	2,739	11.42	139,644	25.4%	67.7%
4-gram	Word	290,405	18.15	2,129,240	11.2%	22.1%
4-gram	Subword	15,565	13.93	811,800	12.4%	38.1%
5-gram	Word	184,509	17.49	1,485,957	12.3%	25.0%
5-gram	Subword	58,859	15.84	2,792,057	7.1%	25.1%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	aholi punktlari	133,471
2	boʻyicha aholi	102,687
3	tarkibiga kiradi	71,231
4	istiqomat qiladi	66,979
5	aholi istiqomat	65,487

3-grams (Word):

Rank	N-gram	Count
1	boʻyicha aholi punktlari	102,646
2	nafar aholi istiqomat	64,709
3	aholi istiqomat qiladi	62,946
4	aholi punktlari shaharlari	55,710
5	manbalar boʻyicha aholi	44,383

4-grams (Word):

Rank	N-gram	Count
1	nafar aholi istiqomat qiladi	62,574
2	boʻyicha aholi punktlari shaharlari	55,662
3	manbalar boʻyicha aholi punktlari	44,383
4	yangi umumiy katalog asl	32,515
5	umumiy katalog asl nashrida	32,515

5-grams (Word):

Rank	N-gram	Count
1	manbalar boʻyicha aholi punktlari shaharlari	33,698
2	yangi umumiy katalog asl nashrida	32,515
3	aholi zichligi har kvadrat kilometrga	30,929
4	nafar aholi istiqomat qiladi aholi	30,451
5	aholi istiqomat qiladi aholi zichligi	30,448

2-grams (Subword):

Rank	N-gram	Count
1	a _	8,473,406
2	i _	8,057,286
3	a r	7,652,474
4	l a	7,619,051
5	a n	7,333,858

3-grams (Subword):

Rank	N-gram	Count
1	l a r	4,022,470
2	a n _	2,638,804
3	d a _	2,516,594
4	i d a	2,211,006
5	g a n	2,200,072

4-grams (Subword):

Rank	N-gram	Count
1	n i n g	1,559,456
2	i n g _	1,556,517
3	l a r i	1,513,348
4	l a r _	1,478,041
5	i d a _	1,328,422

5-grams (Subword):

Rank	N-gram	Count
1	n i n g _	1,484,174
2	l a r i _	771,549
3	g a n . _	672,707
4	d a g i _	557,890
5	a d i . _	529,582

Key Findings

• Best Perplexity: 2-gram (subword) with 306
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~25% 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	0.8649	1.821	9.91	1,734,204	13.5%
1	Subword	1.1817	2.268	7.66	9,225	0.0%
2	Word	0.3006	1.232	1.88	17,159,887	69.9%
2	Subword	0.6573	1.577	4.58	70,636	34.3%
3	Word	0.1029	1.074	1.20	32,224,146	89.7%
3	Subword	0.7355	1.665	4.35	323,343	26.4%
4	Word	0.0379 🏆	1.027	1.06	38,723,206	96.2%
4	Subword	0.7076	1.633	3.60	1,405,500	29.2%

Generated Text Samples (Word-based)

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

Context Size 1:

1. va pele vafoti muhammad stadioni 1 b neilson denyse julien près de antropología e 5 dan
2. bilan jamoaviy koʻrgazmalarini oʻtkazgan faqat tana aʼzosi boʻlgan juftlik bahslarida chempion boʻlg...
3. u oʻzining isteʼdodlar va viruslar qoʻzgʻatadigan yuqumli dasturlar bbc worldwide goʻzallik iffat qu...

Context Size 2:

1. boʻyicha aholi punktlari shaharlari shaharlar ipak yoʻli yaqinida joylashgan lawang kidul masjidi us...
2. aholi punktlari shaharlari tashkil etilgan u mexanika boʻyicha mutaxassis avval amerikada keyin ahol...
3. tarkibiga kiradi aholisi 779 nafarga yetadi o ni qoʻshilishi bilan stansiya ichidan uning sirtiga ch...

Context Size 3:

1. boʻyicha aholi punktlari shaharlari metropolitan hududlari
2. nafar aholi istiqomat qiladi aholi zichligi har kvadrat kilometrga 20 7 nafar kishi geografiyasi may...
3. aholi istiqomat qiladi aholi zichligi har kvadrat kilometrga 20 8 nafar kishi geografiyasi maydoni 3...

Context Size 4:

1. nafar aholi istiqomat qiladi geografiyasi hududi ramslaning hududi kmdir dengiz sathidan oʻrtacha m ...
2. manbalar boʻyicha aholi punktlari shaharlari shaharlari shaharlar
3. yangi umumiy katalog asl nashrida ngc 845 yangi umumiy katalog asl nashrida mavjud manbalar havolala...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _boshatoraskeyid
2. a_miladig‘rir_uc
3. ini_pefartilafr_

Context Size 2:

1. a_younkty_fausta_
2. i_1-1)_mena_oliga
3. lar_si_jahayratbo

Context Size 3:

1. lardan,_shundan_sh
2. an_edi._(_)_rivojl
3. da_u_lood_(milgan_

Context Size 4:

1. ning_oʻrtacha_aholi
2. ing_asosiyon)_stadi
3. lar_va_federn_klubi

Key Findings

• Best Predictability: Context-4 (word) with 96.2% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,405,500 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	722,817
Total Tokens	48,635,987
Mean Frequency	67.29
Median Frequency	4
Frequency Std Dev	1990.25

Most Common Words

Rank	Word	Frequency
1	va	1,184,048
2	bilan	369,678
3	u	280,225
4	manbalar	272,147
5	aholi	258,250
6	uchun	237,429
7	joylashgan	206,009
8	1	194,151
9	boʻyicha	181,987
10	boʻlgan	170,867

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	eversleigh	2
2	bundlening	2
3	thesigerning	2
4	haggleton	2
5	domli	2
6	xatibani	2
7	katakumite	2
8	apistomorpha	2
9	colucci	2
10	guerrio	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0071
R² (Goodness of Fit)	0.991725
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	21.2%
Top 1,000	48.2%
Top 5,000	67.9%
Top 10,000	75.6%

Key Findings

• Zipf Compliance: R²=0.9917 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 21.2% of corpus
• Long Tail: 712,817 words needed for remaining 24.4% 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	0.7694 🏆	0.3417	N/A	N/A
mono_64d	64	0.7319	0.2924	N/A	N/A
mono_128d	128	0.6469	0.2679	N/A	N/A
aligned_32d	32	0.7694	0.3486	0.2540	0.6100
aligned_64d	64	0.7319	0.3022	0.3600	0.7600
aligned_128d	128	0.6469	0.2627	0.5040	0.8100

Key Findings

• Best Isotropy: mono_32d with 0.7694 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3026. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 50.4% 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	-0.006	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	aminofenollar, alimkul, ashtarxoniylardan
-s	stolyarov, signalnaya, sovutish
-ma	macewan, matodir, majduddin
-m	munosabatlaridir, macewan, matodir
-k	konseysao, kuzatuvdagi, kello
-b	boqiya, boatengning, bacsinszky
-t	triangulorum, tarantelloyoʻlboshlovchi, totning
-ba	bacsinszky, barbaraʼ, baholangan

Productive Suffixes

Suffix	Examples
-a	signalnaya, uncda, boqiya
-i	ruhiyati, oʻrtogʻini, semizligi
-ng	sashaning, boatengning, garmonning
-g	sashaning, boatengning, garmonning
-n	gʻishtin, zararsizlantiriladigan, macewan
-an	zararsizlantiriladigan, macewan, lushan
-ni	oʻrtogʻini, shlezvigni, hitini
-ga	umidga, diskiga, yupiterga

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
rnin	2.47x	350 contexts	rnini, rning, barnin
inin	2.05x	623 contexts	minin, inini, zinin
anin	1.72x	759 contexts	ganin, yanin, manin
oʻlg	2.47x	58 contexts	koʻlga, qoʻlga, oʻlgan
ʻlga	2.36x	68 contexts	koʻlga, qoʻlga, oʻlgan
idag	1.82x	211 contexts	idagi, idaga, ridagi
hlar	1.64x	291 contexts	shlar, ihlar, shlari
manb	2.30x	44 contexts	manba, manbam, 3manba
hgan	1.83x	113 contexts	shgan, chgan, shgani
nbal	2.39x	35 contexts	inbal, manbal, nbalar
ilad	1.59x	198 contexts	gilad, iladi, bilad
oyla	1.80x	101 contexts	joyla, oylar, koyla

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
-s	-a	145 words	semiusta, sammitlarda
-t	-a	115 words	tritonda, torgovlya
-k	-a	105 words	konka, kalva
-b	-a	100 words	beldumgʻaza, ballantiophora
-s	-i	100 words	samkni, stantsiyalaridagi
-k	-i	97 words	karetkasi, kriminalistikasi
-a	-a	97 words	akvabogʻda, ahvazga
-t	-i	96 words	tayinlandiyangi, tayinlamadi
-s	-n	85 words	shohmuroddan, slain
-b	-i	84 words	bukowski, butasi

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
spidometriga	spidometr-i-ga	7.5	i
synesthesia	synesthes-i-a	7.5	i
kavaleriyada	kavaleriy-a-da	7.5	a
tesaliyadagi	tesaliya-da-gi	7.5	da
dogʻistondagi	dogʻiston-da-gi	7.5	da
oilalarda	oilal-ar-da	7.5	ar
kamroqdir	kamroqd-i-r	7.5	i
anguilladagi	anguilla-da-gi	7.5	da
qashgʻariya	qashgʻar-i-ya	7.5	i
aggressiv	aggress-i-v	7.5	i
oshirishlariga	oshirishlar-i-ga	7.5	i
hempcrete	hempcre-t-e	7.5	t
misolidir	misolid-i-r	7.5	i
oʻzgarishlarini	oʻzgarishlar-i-ni	7.5	i
raqobatchini	raqobatch-i-ni	7.5	i

6.6 Linguistic Interpretation

Automated Insight: The language Uzbek 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 (4.58x)
N-gram	2-gram	Lowest perplexity (306)
Markov	Context-4	Highest predictability (96.2%)
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: 2026-01-11 07:14:31