Avar - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Avar 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	3.628x	3.63	0.0828%	245,293
16k	4.030x	4.03	0.0919%	220,825
32k	4.383x	4.39	0.1000%	203,018
64k	4.685x 🏆	4.69	0.1069%	189,944

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: 19-абилеб Октябр — грегорианияб календаралда рекъон къо (високоснияб соналъ — св...

Vocab	Tokens	Count
8k	▁ 1 9 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
16k	▁ 1 9 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
32k	▁ 1 9 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
64k	▁ 1 9 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28

Sample 2: Пинкь яги ГьанамагӀ (латиназул мацӀалда bulla; Bullae) — гӀадамасул лага-черх. л...

Vocab	Tokens	Count
8k	▁п ин кь ▁яги ▁гьан ам агӏ ▁( латиназул ▁мацӏалда ... (+18 more)	28
16k	▁пин кь ▁яги ▁гьан амагӏ ▁( латиназул ▁мацӏалда ▁b ul ... (+15 more)	25
32k	▁пин кь ▁яги ▁гьан амагӏ ▁( латиназул ▁мацӏалда ▁b ul ... (+14 more)	24
64k	▁пинкь ▁яги ▁гьанамагӏ ▁( латиназул ▁мацӏалда ▁b ul la ; ... (+11 more)	21

Sample 3: 22-абилеб Октябр — грегорианияб календаралда рекъон къо (високоснияб соналъ — св...

Vocab	Tokens	Count
8k	▁ 2 2 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
16k	▁ 2 2 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
32k	▁ 2 2 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28
64k	▁ 2 2 - абилеб ▁октябр ▁— ▁грегорианияб ▁календаралда ▁рекъон ... (+18 more)	28

Key Findings

• Best Compression: 64k achieves 4.685x compression
• Lowest UNK Rate: 8k with 0.0828% 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	3,089	11.59	6,523	23.7%	56.2%
2-gram	Subword	424 🏆	8.73	4,120	58.0%	96.7%
3-gram	Word	2,775	11.44	6,745	26.4%	58.9%
3-gram	Subword	3,361	11.71	28,903	23.9%	63.4%
4-gram	Word	8,260	13.01	18,126	17.8%	39.8%
4-gram	Subword	15,393	13.91	119,191	12.7%	37.5%
5-gram	Word	7,813	12.93	15,673	16.8%	39.4%
5-gram	Subword	38,531	15.23	222,134	8.4%	26.5%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	росу буго	710
2	география росу	660
3	мухъалъул росаби	578
4	буго мухъалъул	530
5	мухъалъул росу	523

3-grams (Word):

Rank	N-gram	Count
1	география росу буго	645
2	росу буго мухъалъул	523
3	лъугьа бахъинал гьаруна	368
4	бахъинал гьаруна хвана	358
5	байрамал лъугьа бахъинал	353

4-grams (Word):

Rank	N-gram	Count
1	география росу буго мухъалъул	513
2	лъугьа бахъинал гьаруна хвана	358
3	байрамал лъугьа бахъинал гьаруна	352
4	къо байрамал лъугьа бахъинал	351
5	бахъинал гьаруна хвана ишараби	349

5-grams (Word):

Rank	N-gram	Count
1	къо байрамал лъугьа бахъинал гьаруна	350
2	лъугьа бахъинал гьаруна хвана ишараби	349
3	байрамал лъугьа бахъинал гьаруна хвана	348
4	демография ккола моноэтникияб авар росулъун	305
5	география росу буго мухъалъул марказ	279

2-grams (Subword):

Rank	N-gram	Count
1	а л	85,368
2	л _	64,955
3	л ъ	53,561
4	а _	52,853
5	у л	50,828

3-grams (Subword):

Rank	N-gram	Count
1	у л _	34,266
2	л ъ у	31,682
3	ъ у л	26,429
4	а л ъ	24,583
5	_ г ь	22,014

4-grams (Subword):

Rank	N-gram	Count
1	л ъ у л	25,035
2	ъ у л _	22,571
3	а л ъ у	16,980
4	а л д а	11,684
5	_ г ь е	10,931

5-grams (Subword):

Rank	N-gram	Count
1	л ъ у л _	22,224
2	а л ъ у л	15,591
3	я л ъ у л	7,776
4	а л д а _	7,381
5	_ б у г о	5,843

Key Findings

• Best Perplexity: 2-gram (subword) with 424
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~26% 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	0.6594	1.579	3.57	90,954	34.1%
1	Subword	1.1677	2.247	9.26	1,148	0.0%
2	Word	0.1264	1.092	1.22	323,475	87.4%
2	Subword	0.9998	2.000	5.69	10,625	0.0%
3	Word	0.0288	1.020	1.04	392,122	97.1%
3	Subword	0.7938	1.734	3.67	60,414	20.6%
4	Word	0.0121 🏆	1.008	1.02	406,770	98.8%
4	Subword	0.5607	1.475	2.33	221,366	43.9%

Generated Text Samples (Word-based)

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

Context Size 1:

1. ва испан фонология цогидал туркиял мацӏаз чанго шагьрияб гӏумру яшавалда хурхарал феодализм социум с...
2. буго республикалъул рутул мухъ буго шартіияб рикікіеналдалъун гьабураб бищун це б грузинский алфавит...
3. бугеб муниципалияб гӏуцӏи гъорлӏе рачуна чӏужуялда хурхарал цогидал киналго хвана ишараби мугъчӏваял...

Context Size 2:

1. росу буго мухъалъул марказ лъаратӏаса 22 км лъ жанубияб бакъбаккудехун ралъдал гьурматӏаса 968 метра...
2. география росу буго мухъалъул марказ лъаратӏаса 0 5 41 9 12 гуржиял 617 401 253 10 0
3. буго мухъалъул центер уркарахъалдаса бакътӏерхьудехун демография референсал мухъалъул росаби мухъ ро...

Context Size 3:

1. география росу буго мухъалъул марказ лъаратӏаса 22 км алъ демография ккола моноэтникияб авар росулъу...
2. росу буго мухъалъул центер уркарахъалдаса жанубияб бакътӏерхьудехун ралъдал гьурматӏаса борхалъи буг...
3. лъугьа бахъинал гьаруна хвана ишараби мугъчӏваял гь балагье трактат адабият тайпаби изданиял

Context Size 4:

1. география росу буго мухъалъул марказ лъаратӏаса 5 км алъ шималалиябгин бакъбаккудехун аваргӏоралъул ...
2. байрамал лъугьа бахъинал гьаруна хвана ишараби мугъчӏваял гь балагье
3. къо байрамал лъугьа бахъинал гьаруна хвана ишараби мугъчӏваял гь балагье

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _ссва_—_1_вадаре
2. ан._ия_в._тӏавар
3. лдацӏиялъухъуск;

Context Size 2:

1. алдастияб_6_киябр
2. л_джибацӏаниякеап
3. лъул_бакъго_рахъе

Context Size 3:

1. ул_намен_гьеб_раса
2. лъулго_справенция)
3. ъул_яги_перации_«г

Context Size 4:

1. лъул_ассив_гьел_ккв
2. ъул_ківар_география
3. алъулалде._борхалъу

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 (221,366 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	34,315
Total Tokens	413,611
Mean Frequency	12.05
Median Frequency	3
Frequency Std Dev	77.17

Most Common Words

Rank	Word	Frequency
1	ва	7,138
2	буго	5,684
3	бугеб	2,903
4	ккола	2,872
5	росу	2,838
6	мухъалъул	2,671
7	гьеб	2,178
8	росдал	1,902
9	the	1,812
10	цо	1,800

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	уркутамахьи	2
2	континуумалде	2
3	къулецӏмаги	2
4	гьаркӏасуниб	2
5	махӏарги	2
6	пилибхиталъул	2
7	заповедникалда	2
8	пилибхит	2
9	лъалъадул	2
10	хӏанчӏи	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.9572
R² (Goodness of Fit)	0.993745
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	23.1%
Top 1,000	51.6%
Top 5,000	74.2%
Top 10,000	83.6%

Key Findings

• Zipf Compliance: R²=0.9937 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 23.1% of corpus
• Long Tail: 24,315 words needed for remaining 16.4% 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	0.8604	0.3207	N/A	N/A
mono_64d	64	0.7367	0.2711	N/A	N/A
mono_128d	128	0.2721	0.2530	N/A	N/A
aligned_32d	32	0.8604 🏆	0.3335	0.0200	0.1400
aligned_64d	64	0.7367	0.2791	0.0280	0.1780
aligned_128d	128	0.2721	0.2649	0.0820	0.2540

Key Findings

• Best Isotropy: aligned_32d with 0.8604 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2870. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 8.2% 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	0.488	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
-ба	батӏалъуда, батӏа, бахъинаро

Productive Suffixes

Suffix	Examples
-л	субтропикиял, кумикал, риччалел
-а	лъараца, тіалъиялда, анатолиялдаса
-ул	агьлулъиялъул, кипралъул, урарталъул
-ъул	агьлулъиялъул, кипралъул, урарталъул
-лъул	агьлулъиялъул, кипралъул, урарталъул
-да	тіалъиялда, текстазда, батӏалъуда
-ал	кумикал, туарегал, я́сал
-ги	тахшагьарлъунги, яги, фортисги

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
алъу	1.88x	101 contexts	алъул, далъун, малъун
ялъу	2.05x	41 contexts	ялъул, ялъуни, аялъул
ьабу	2.11x	29 contexts	гьабу, гьабун, кьабун
агьа	1.75x	59 contexts	багьа, дагьа, шагьав
иялъ	1.85x	36 contexts	химиялъ, биялъул, армиялъ
анал	1.48x	70 contexts	канал, ханал, данал
иялд	1.69x	36 contexts	сиялда, азиялде, азиялда
огра	1.87x	22 contexts	географ, фотограф, этнограф
азда	1.67x	31 contexts	гьазда, ишазда, раздан
налд	1.64x	31 contexts	иналда, доналд, иналде
гъор	2.15x	13 contexts	гъорлі, гъорлъ, гъорлӏ
лдас	2.01x	15 contexts	лдаса, ялдаса, алдаса

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
-ба	-л	36 words	багьадурасул, бакътӏерхьул
-ба	-а	34 words	багъа, батӏалъана
-ба	-ул	17 words	багьадурасул, бакътӏерхьул
-ба	-ун	16 words	бахчун, бахъбаккудехун
-ба	-да	16 words	бащалъуда, балазда
-ба	-ал	11 words	бахӏсал, бакъбаккулал
-ба	-ъул	8 words	бавариялъул, баталйоналъул
-ба	-лда	8 words	бахъиялда, бахшалда
-ба	-ги	6 words	бакӏалъулги, бахӏарзабиги
-ба	-лъул	6 words	бавариялъул, баталйоналъул

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
къуръаналги	къуръан-ал-ги	6.0	къуръан
ханасдаги	ханас-да-ги	6.0	ханас
элементалги	элемент-ал-ги	6.0	элемент
гьелъулги	гьел-ъул-ги	6.0	гьел
гьармониялда	гьармония-лда	4.5	гьармония
гьолокьги	гьолокь-ги	4.5	гьолокь
хьондасебги	хьондасеб-ги	4.5	хьондасеб
районазул	районаз-ул	4.5	районаз
аскаразда	аскараз-да	4.5	аскараз
экономикаги	экономика-ги	4.5	экономика
процессазул	процессаз-ул	4.5	процессаз
насрудиницаги	насрудиница-ги	4.5	насрудиница
бугиланги	бугилан-ги	4.5	бугилан
рагьаразул	рагьараз-ул	4.5	рагьараз
минскалъул	минска-лъул	4.5	минска

6.6 Linguistic Interpretation

Automated Insight: The language Avar 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.

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7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.69x)
N-gram	2-gram	Lowest perplexity (424)
Markov	Context-4	Highest predictability (98.8%)
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: 2026-01-03 18:29:30