README.md

metadata

language: kv
language_name: Komi
language_family: uralic_permian
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-uralic_permian
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: 4.057
  - name: best_isotropy
    type: isotropy
    value: 0.7808
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10T00:00:00.000Z

Komi - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Komi Wikipedia data. We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.

📋 Repository Contents

Models & Assets

• Tokenizers (8k, 16k, 32k, 64k)
• N-gram models (2, 3, 4, 5-gram)
• Markov chains (context of 1, 2, 3, 4 and 5)
• Subword N-gram and Markov chains
• Embeddings in various sizes and dimensions (aligned and unaligned)
• Language Vocabulary
• Language Statistics

Analysis and Evaluation

• 1. Tokenizer Evaluation
• 2. N-gram Model Evaluation
• 3. Markov Chain Evaluation
• 4. Vocabulary Analysis
• 5. Word Embeddings Evaluation
• 6. Morphological Analysis (Experimental)
• 7. Summary & Recommendations
• Metrics Glossary
• Visualizations Index

---

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.121x	3.13	0.1052%	211,919
16k	3.570x	3.58	0.1204%	185,286
32k	3.866x	3.87	0.1303%	171,084
64k	4.057x 🏆	4.06	0.1368%	163,039

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Сизимсё кӧкъямысдасӧд вояс - 781 восянь 790 воӧдз. Медыджыд лоӧмторъяс

Vocab	Tokens	Count
8k	▁сизим сё ▁кӧкъямыс дасӧд ▁вояс ▁- ▁ 7 8 1 ... (+9 more)	19
16k	▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)	17
32k	▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)	17
64k	▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)	17

Sample 2: 451 Патиентия — тайӧ Шонді ылдӧсын астероид. Сылӧн ыджда — 224 км. Патиентия вос...

Vocab	Tokens	Count
8k	▁ 4 5 1 ▁п ат и ент ия ▁— ... (+36 more)	46
16k	▁ 4 5 1 ▁пат и ент ия ▁— ▁тайӧ ... (+32 more)	42
32k	▁ 4 5 1 ▁пат и ентия ▁— ▁тайӧ ▁шонді ... (+26 more)	36
64k	▁ 4 5 1 ▁патиентия ▁— ▁тайӧ ▁шонді ▁ылдӧсын ▁астероид ... (+22 more)	32

Sample 3: Тюмень обласьт тайӧ регион Рочмуын. Видзӧдӧй тшӧтш Ханты-Вӧгул асвеськӧдлан кытш...

Vocab	Tokens	Count
8k	▁т ю мен ь ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ... (+16 more)	26
16k	▁тю мен ь ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ... (+11 more)	21
32k	▁тюмень ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ▁ханты - ... (+9 more)	19
64k	▁тюмень ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ▁ханты - ... (+9 more)	19

Key Findings

• Best Compression: 64k achieves 4.057x compression
• Lowest UNK Rate: 8k with 0.1052% 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	4,415	12.11	14,094	22.1%	55.9%
2-gram	Subword	681 🏆	9.41	6,463	44.2%	94.6%
3-gram	Word	5,552	12.44	19,425	23.7%	51.7%
3-gram	Subword	5,657	12.47	40,644	16.0%	51.0%
4-gram	Word	8,996	13.14	34,620	23.6%	45.0%
4-gram	Subword	24,300	14.57	169,451	9.1%	29.8%
5-gram	Word	6,977	12.77	28,246	27.6%	47.7%
5-gram	Subword	55,081	15.75	319,260	6.7%	22.7%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	ӧд лун	2,382
2	республики коми	1,598
3	республика коми	1,394
4	сикт овмӧдчӧмин	1,392
5	коми республикаса	1,281

3-grams (Word):

Rank	N-gram	Count
1	сыктывкар республика коми	1,059
2	республика коми энциклопедия	811
3	августа г издание	797
4	1 августа г	797
5	на 1 августа	797

4-grams (Word):

Rank	N-gram	Count
1	1 августа г издание	797
2	на 1 августа г	797
3	и л где ты	717
4	жеребцов и л где	714
5	коми историко демографический справочник	704

5-grams (Word):

Rank	N-gram	Count
1	на 1 августа г издание	797
2	жеребцов и л где ты	714
3	республики коми историко демографический справочник	704
4	пункты республики коми историко демографический	704
5	населенные пункты республики коми историко	703

2-grams (Subword):

Rank	N-gram	Count
1	а _	76,965
2	. _	76,956
3	_ к	64,740
4	_ в	54,790
5	, _	52,769

3-grams (Subword):

Rank	N-gram	Count
1	_ к о	26,805
2	ы с ь	25,301
3	ъ я с	23,484
4	_ — _	22,691
5	_ в о	20,230

4-grams (Subword):

Rank	N-gram	Count
1	ы с ь _	16,760
2	к о м и	15,656
3	_ к о м	15,118
4	ъ я с _	13,192
5	л ы с ь	12,862

5-grams (Subword):

Rank	N-gram	Count
1	_ к о м и	14,450
2	к о м и _	10,888
3	л ы с ь _	9,228
4	с ы к т ы	6,769
5	ы к т ы в	6,764

Key Findings

• Best Perplexity: 2-gram (subword) with 681
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~23% 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	0.6233	1.540	3.71	115,439	37.7%
1	Subword	0.4379	1.355	4.01	7,808	56.2%
2	Word	0.1549	1.113	1.31	426,513	84.5%
2	Subword	0.5508	1.465	3.55	31,340	44.9%
3	Word	0.0585	1.041	1.11	556,965	94.2%
3	Subword	0.5879	1.503	3.07	111,343	41.2%
4	Word	0.0316 🏆	1.022	1.06	612,330	96.8%
4	Subword	0.4947	1.409	2.22	341,894	50.5%

Generated Text Samples (Word-based)

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

Context Size 1:

1. коми пермяцко русский словарь грузинского языка ч ʧ сі чи ті vos сійӧ вӧлі лӧсьӧдавны каникуласигас
2. да 535 morinda phyllireoides sert austro caledon 49 м изд во 45 80 4 ньыль 5
3. сыктывкар коми республикаса почёта грамота коми литературы и муниципальное устройство республики ком...

Context Size 2:

1. ӧд лун коми кыв автономия панысьяс асшӧрлун шедӧдӧмын пайыс эм уналӧн кызьӧд воясӧ виз рочӧн княжпог...
2. республики коми историко демографический справочник сыктывкар история коми с древнейших времен до ко...
3. республика коми энциклопедия в 3 х тт емва да эжва юяс бокын но сикт грездъяс сикт сӧвет

Context Size 3:

1. сыктывкар республика коми административно территориальное деление на 1 августа г издание пятое сыкты...
2. республика коми энциклопедия сыктывкар т 1 3 ыстӧдъяс республикалӧн сиктъяс сикт грезд сикт овмӧдчӧм...
3. на 1 августа г издание шестое официальное гу тфи рк сыктывкар 278 с изьва мулӧн ин нимъяс топонимия

Context Size 4:

1. 1 августа г издание шестое официальное гу тфи рк сыктывкар 278 с сикт грезд сикт овмӧдчӧмин грездъяс...
2. на 1 августа г издание пятое сыктывкар республика коми энциклопедия в 3 тт сыктывкар ыстӧдъяс вылыс ...
3. и л где ты живешь населенные пункты республики коми историко демографический справочник сыктывкар ис...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _ктыджӧйисечмико
2. алломахъя_млазат
3. сопаын_кило_жез_

Context Size 2:

1. а_талӧны_jʊ_23932
2. ._спублавканбур_о
3. _кмын_шойдъяслаын

Context Size 3:

1. _кокнижнӧй_уджаліс
2. ысьясӧ_сьыс_—_пемӧ
3. _—_коми_сарина_тэ_

Context Size 4:

1. ысь_18-ӧд_лун_лои_п
2. коми_музейӧн»,_арав
3. _комияса_кыв_(tod._

Key Findings

• Best Predictability: Context-4 (word) with 96.8% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (341,894 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	41,073
Total Tokens	725,042
Mean Frequency	17.65
Median Frequency	3
Frequency Std Dev	140.79

Most Common Words

Rank	Word	Frequency
1	коми	13,968
2	да	11,866
3	сыктывкар	5,358
4	и	5,043
5	а	4,697
6	ӧд	4,292
7	тӧлысь	4,290
8	в	4,031
9	лун	4,030
10	сикт	3,821

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	былины	2
2	символыс	2
3	палуоя	2
4	байтэрек	2
5	кухмо	2
6	хэнкок	2
7	тычы	2
8	идеал	2
9	университетъясысь	2
10	елюенэ	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0595
R² (Goodness of Fit)	0.993095
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	26.6%
Top 1,000	59.7%
Top 5,000	79.2%
Top 10,000	86.7%

Key Findings

• Zipf Compliance: R²=0.9931 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 26.6% of corpus
• Long Tail: 31,073 words needed for remaining 13.3% 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	0.7808	0.3587	N/A	N/A
mono_64d	64	0.5590	0.3120	N/A	N/A
mono_128d	128	0.1539	0.3129	N/A	N/A
aligned_32d	32	0.7808 🏆	0.3525	0.0260	0.1300
aligned_64d	64	0.5590	0.3133	0.0460	0.1960
aligned_128d	128	0.1539	0.3018	0.0580	0.2120

Key Findings

• Best Isotropy: aligned_32d with 0.7808 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3252. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 5.8% R@1 in cross-lingual retrieval.
• Recommendation: 128d aligned for best cross-lingual performance

---

6. Morphological Analysis (Experimental)

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

6.1 Productivity & Complexity

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	1.101	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
-к	калипсо, как, куратовалысь
-с	сартас, связь, сапёрнӧй
-п	пещераа, пансигӧн, партияӧн
-в	волывлӧма, вичӧн, выльног
-м	мыжы, медасьлісны, мирон
-т	турьев, тышкасьӧмлӧн, таын
-ко	комсь, колльӧдӧны, корӧмаӧсь
-s	semperflorens, scabrifolia, sz

Productive Suffixes

Suffix	Examples
-н	айясыслӧн, вичӧн, пансигӧн
-а	волывлӧма, жанетта, пещераа
-с	сартас, бӧраныс, гӧгӧрсьыс
-a	trullifolia, dresslerara, carinilabia
-ӧн	айясыслӧн, вичӧн, пансигӧн
-ь	куратовалысь, связь, ль
-яс	квенъяс, войтыръяс, геологъяс
-сь	куратовалысь, комсь, лӧсьӧдӧмлысь

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
лӧма	2.03x	47 contexts	олӧма, вӧлӧма, кылӧма
ӧдӧм	1.80x	62 contexts	тӧдӧм, ӧлӧдӧм, ӧшӧдӧм
ръяс	1.67x	76 contexts	уръяс, юръяс, юӧръяс
існы	2.08x	23 contexts	юлісны, олісны, кулісны
ӧлыс	2.30x	15 contexts	тӧлыс, пӧлыс, йӧлыс
ӧдъя	1.98x	23 contexts	мӧдъяс, юкӧдъяс, инӧдъяс
дъяс	1.62x	39 contexts	садъяс, андъяс, видъяс
въяс	1.62x	38 contexts	увъяс, овъяс, левъяс
отыр	1.91x	21 contexts	котыр, котыра, котырӧ
исто	2.02x	15 contexts	истор, исток, истоки
стор	1.89x	16 contexts	истор, пастор, простор
коты	1.93x	15 contexts	котыр, котыра, котырӧ

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
-к	-н	88 words	кн, кипрушевлӧн
-п	-н	70 words	предприятиеяслӧн, поэмаясын
-к	-а	68 words	кипасалӧма, косьювомса
-с	-н	64 words	семуковын, сборникъясын
-к	-с	64 words	коммунистъяс, кыръясыс
-с	-а	61 words	ставмирса, сорта
-п	-а	61 words	пырӧма, пылаева
-в	-н	60 words	вӧркутаын, войын
-п	-с	58 words	примитіс, поэтъясӧс
-в	-ы	58 words	восьтны, вуджны

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
странанас	страна-н-ас	7.5	н
парткомса	партко-м-са	7.5	м
скрипкаӧн	скрипк-а-ӧн	7.5	а
институтлысь	институт-лы-сь	6.0	институт
висьтавсьӧ	висьтав-сь-ӧ	6.0	висьтав
кальӧлысь	кальӧ-лы-сь	6.0	кальӧ
авторлысь	автор-лы-сь	6.0	автор
ветлысьяслы	ветлысь-яс-лы	6.0	ветлысь
войскаясӧн	войска-яс-ӧн	6.0	войска
абхазияын	абхаз-ия-ын	6.0	абхаз
национальносьт	национально-сь-т	6.0	национально
пемӧслысь	пемӧс-лы-сь	6.0	пемӧс
ӧтувтчӧмӧн	ӧтувтчӧм-ӧн	4.5	ӧтувтчӧм
пемӧсъясӧс	пе-мӧсъясӧс	4.5	мӧсъясӧс
балтикаса	балтика-са	4.5	балтика

6.6 Linguistic Interpretation

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

Note on Idiomaticity: The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.06x)
N-gram	2-gram	Lowest perplexity (681)
Markov	Context-4	Highest predictability (96.8%)
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

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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: 2026-01-10 08:51:50