wikilangs/mn

Mongolian - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Mongolian 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	3.843x	3.84	0.0664%	1,203,793
16k	4.276x	4.28	0.0738%	1,082,049
32k	4.612x	4.61	0.0797%	1,003,132
64k	4.859x 🏆	4.86	0.0839%	952,134

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Акстафа (Ağstafa rayonu) — Азербайжан улсын 8 түмэн хүнтэй район. Засаг захиргаа...

Vocab	Tokens	Count
8k	▁ак ст аф а ▁( a ğ st af a ... (+33 more)	43
16k	▁ак ст афа ▁( a ğ st af a ▁r ... (+31 more)	41
32k	▁ак ст афа ▁( a ğ st af a ▁ray ... (+29 more)	39
64k	▁ак стафа ▁( ağ st af a ▁rayonu ) ▁— ... (+25 more)	35

Sample 2: «Янаг дурлалын дууль» — онд Монгол улсад монгол хэлээр бүтсэн уран сайхны кино. ...

Vocab	Tokens	Count
8k	▁« ян аг ▁дур лалын ▁дуул ь » ▁— ▁онд ... (+15 more)	25
16k	▁« ян аг ▁дурлалын ▁дуул ь » ▁— ▁онд ▁монгол ... (+14 more)	24
32k	▁« ян аг ▁дурлалын ▁дууль » ▁— ▁онд ▁монгол ▁улсад ... (+13 more)	23
64k	▁« ян аг ▁дурлалын ▁дууль » ▁— ▁онд ▁монгол ▁улсад ... (+13 more)	23

Sample 3: Олимпын VIII наадам буюу оны Парисын олимп () нь оны 5 сарын 4-нөөс 7 сарын 27-н...

Vocab	Tokens	Count
8k	▁олимпын ▁v iii ▁наадам ▁буюу ▁оны ▁парисын ▁олимп ▁() ▁нь ... (+27 more)	37
16k	▁олимпын ▁viii ▁наадам ▁буюу ▁оны ▁парисын ▁олимп ▁() ▁нь ▁оны ... (+26 more)	36
32k	▁олимпын ▁viii ▁наадам ▁буюу ▁оны ▁парисын ▁олимп ▁() ▁нь ▁оны ... (+26 more)	36
64k	▁олимпын ▁viii ▁наадам ▁буюу ▁оны ▁парисын ▁олимп ▁() ▁нь ▁оны ... (+26 more)	36

Key Findings

• Best Compression: 64k achieves 4.859x compression
• Lowest UNK Rate: 8k with 0.0664% 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	68,727	16.07	220,179	6.8%	20.8%
2-gram	Subword	413 🏆	8.69	10,809	57.9%	97.3%
3-gram	Word	111,379	16.77	257,301	5.1%	15.7%
3-gram	Subword	3,439	11.75	80,850	22.4%	63.9%
4-gram	Word	225,307	17.78	414,540	3.9%	10.7%
4-gram	Subword	18,056	14.14	452,951	10.9%	35.4%
5-gram	Word	178,177	17.44	286,398	3.6%	10.0%
5-gram	Subword	63,519	15.95	1,205,940	6.3%	22.1%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	р сарын	13,394
2	онд төрсөн	10,821
3	монгол улсын	9,521
4	энэ нь	7,945
5	олон улсын	6,568

3-grams (Word):

Rank	N-gram	Count
1	онд нас барсан	3,190
2	төрсөн онд өнгөрсөн	2,725
3	онд төрсөн онд	2,565
4	тоглогч багийн тоглогч	2,249
5	багийн тоглогч багийн	2,217

4-grams (Word):

Rank	N-gram	Count
1	онд төрсөн онд өнгөрсөн	2,503
2	багийн тоглогч багийн тоглогч	2,210
3	оны зуны олимпод оролцогч	1,481
4	оролцогч оны зуны олимпод	1,046
5	оны 3 р сарын	1,027

5-grams (Word):

Rank	N-gram	Count
1	оролцогч оны зуны олимпод оролцогч	1,046
2	тоглогч багийн тоглогч багийн тоглогч	979
3	багийн тоглогч багийн тоглогч багийн	975
4	оны зуны олимпод оролцогч оны	727
5	хүн онд төрсөн онд өнгөрсөн	679

2-grams (Subword):

Rank	N-gram	Count
1	н _	2,065,189
2	_ б	982,662
3	и й	971,304
4	_ х	933,182
5	а н	813,213

3-grams (Subword):

Rank	N-gram	Count
1	й н _	630,284
2	и й н	596,746
3	ы н _	466,998
4	_ б а	433,581
5	а н _	329,680

4-grams (Subword):

Rank	N-gram	Count
1	и й н _	582,887
2	_ б а й	257,407
3	г и й н	207,050
4	_ н ь _	172,147
5	_ б о л	171,235

5-grams (Subword):

Rank	N-gram	Count
1	г и й н _	202,980
2	л и й н _	88,000
3	_ б о л о	85,950
4	_ о н д _	83,407
5	и й н _ х	73,490

Key Findings

• Best Perplexity: 2-gram (subword) with 413
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~22% 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	0.9549	1.938	9.40	425,053	4.5%
1	Subword	1.2682	2.409	7.77	6,078	0.0%
2	Word	0.3001	1.231	1.76	3,989,426	70.0%
2	Subword	0.6382	1.556	4.13	47,189	36.2%
3	Word	0.0919	1.066	1.16	7,019,958	90.8%
3	Subword	0.7262	1.654	4.12	194,932	27.4%
4	Word	0.0319 🏆	1.022	1.05	8,133,129	96.8%
4	Subword	0.6730	1.594	3.05	802,473	32.7%

Generated Text Samples (Word-based)

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

Context Size 1:

1. нь нийгмийн болон анадолугийн их хурлын тогтоолоор албан ёсны цахим холбоос article from the coup d
2. онд нас бие монгол нь ангилж нэрлэж болно оху ын төлөөлөгч эсэргүүцлийн хандлага нь нарийвчлал бага
3. оны 5 танхим нба гийн аваргаар онд бнмау ын холбооны нэгдсэн хөдөлгөөн багатай боловч жон лиллигийн

Context Size 2:

1. р сарын 1 нд компьень хотод төрсөн америкийн мэргэжлийн хөлбөмбөгийн карьераа онд серие виченца бага...
2. монгол улсын засгийн газар сонгуульд ялснаар важпи энэтхэг улсын карнатака мужийн үндэс нь морзе код...
3. энэ нь ажиллуулах боломжтой болгосон ромын эзэн хаан вильхельмийн нийгэмлэг гэдэг нэртэй болжээ хоёу...

Context Size 3:

1. онд нас барсан америкийн геологич хүний үүслийн судлаач бөгөөд палеонтолог олон жил нью йорк дахь нү...
2. онд төрсөн онд өнгөрсөн хаан хүн монголын түүх үндэстэн зуунд төрсөн онд өнгөрсөн түрэгийн хаад зуун...
3. тоглогч багийн тоглогч 05 багийн тоглогч багийн тоглогч багийн тоглогч багийн тоглогч марсель багийн...

Context Size 4:

1. багийн тоглогч багийн тоглогч багийн тоглогч онд төрсөн онд өнгөрсөн хүн улс төрч байгаль орчны сайд
2. оны зуны олимпод оролцогч оны зуны олимпод оролцогч онд төрсөн онд өнгөрсөн улсын жанжин улсын улс т...
3. оролцогч оны зуны олимпод оролцогч хамгаалагч хотспур багийн тоглогч лигийн тоглогч ирландчууд

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _нги_тэ_ты_хүдай
2. аагэмьтой_штөлго
3. ндөөлар_дгөөван_

Context Size 2:

1. н_ол_бай_бөмжилца
2. _бөглог_он_өөрсөн
3. ий_сургийн_цай._ч

Context Size 3:

1. йн_түүхээс_их_бөги
2. ийн_фран_гарахарим
3. ын_холбоотой_бөмбө

Context Size 4:

1. ийн_улсын_отограмма
2. _байршилын_үед_холб
3. гийн_босгодог._эдий

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 (802,473 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	188,243
Total Tokens	9,012,621
Mean Frequency	47.88
Median Frequency	4
Frequency Std Dev	695.98

Most Common Words

Rank	Word	Frequency
1	нь	175,668
2	онд	84,299
3	оны	67,254
4	юм	49,881
5	улсын	48,832
6	байна	43,613
7	сарын	43,501
8	болон	40,408
9	байсан	38,901
10	их	36,525

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	аюудайн	2
2	хэргииг	2
3	сайханчдыг	2
4	нимсан	2
5	агваандоной	2
6	гольдоны	2
7	тожин	2
8	хэшидэй	2
9	иезуитүүдийн	2
10	хванчкара	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0408
R² (Goodness of Fit)	0.986627
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	22.0%
Top 1,000	51.5%
Top 5,000	73.5%
Top 10,000	81.3%

Key Findings

• Zipf Compliance: R²=0.9866 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 22.0% of corpus
• Long Tail: 178,243 words needed for remaining 18.7% 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	0.8474 🏆	0.3711	N/A	N/A
mono_64d	64	0.8353	0.2813	N/A	N/A
mono_128d	128	0.8031	0.2224	N/A	N/A
aligned_32d	32	0.8474	0.3608	0.0800	0.3720
aligned_64d	64	0.8353	0.2867	0.0800	0.4280
aligned_128d	128	0.8031	0.2290	0.1740	0.5200

Key Findings

• Best Isotropy: mono_32d with 0.8474 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2919. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 17.4% 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	-0.547	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
-а	аарцаг, аргад, апулиа
-х	хүрэлцэхүйц, хё, ханцуйны
-б	бнрау, багаад, баянзүрхулсын
-с	сурагчидтай, сэтэлж, субстраттай
-ха	ханцуйны, ханноверийн, хасан
-т	туулах, телескопыг, тансаглал
-к	кэмби, кронбергийн, кмтаван
-ба	багаад, баянзүрхулсын, баттулгахөвсгөл

Productive Suffixes

Suffix	Examples
-н	мөнхтөрзавхан, латеран, гуалин
-йн	яшкулийн, кронбергийн, ерөөлтийн
-г	аарцаг, мессежийг, телескопыг
-ын	баянзүрхулсын, дизайнерын, харрисын
-д	давшаад, багаад, аргад
-й	всемирный, сурагчидтай, зориулалтай
-р	мейнор, нууцлалаар, конр
-с	гулагаас, шанс, хараалаас

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
гуул	1.71x	228 contexts	угуул, гууль, гуульд
байс	2.78x	18 contexts	байса, байсн, байсаг
айса	2.10x	44 contexts	байса, хайса, кайса
йсан	2.07x	40 contexts	айсан, хийсан, зайсан
йгуу	2.43x	22 contexts	уйгуур, байгуу, байгуул
нгол	1.78x	68 contexts	ангол, нгола, онгол
олбо	1.91x	49 contexts	олбол, толбо, колбо
лсан	1.74x	63 contexts	улсан, үлсан, алсан
үүлэ	1.38x	187 contexts	үүлэн, үүлээ, шүүлэг
агаа	1.40x	140 contexts	агаан, цагаа, жагаа
ргуу	1.56x	79 contexts	шаргуу, аргууд, шургуу
сург	2.31x	18 contexts	сурга, сурган, сургах

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
-б	-н	133 words	бичсэнчлэн, баясахын
-х	-н	122 words	хүлэгүгийн, хашлагдсан
-с	-н	118 words	сүсэглэн, станцийн
-а	-н	106 words	адамирангийн, абатсүхийн
-т	-н	103 words	тонуулын, талстжисан
-м	-н	75 words	металлын, миникомпьютерын
-х	-г	66 words	хуйраг, хүрснийг
-д	-н	65 words	дармаагийн, дамдингийн
-х	-й	64 words	хугархай, хаштай
-к	-н	64 words	кондратьевын, кантабрийн

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
борлуулагчдаараа	борлуулагчда-ар-аа	7.5	ар
нийлэгжүүлнэ	нийлэгжүүл-н-э	7.5	н
өргөжүүлнэ	өргөжүүл-н-э	7.5	н
сургуулилт	сургуули-л-т	7.5	л
тэмүжиний	тэмүжи-н-ий	7.5	н
халдлаганд	халдлага-н-д	7.5	н
ойлгоцгоож	ойлгоцго-о-ж	7.5	о
бэрнаскони	бэрнаско-н-и	7.5	н
бүжигчний	бүжигч-н-ий	7.5	н
өрнөдийнхний	өрнөдийнх-н-ий	7.5	н
наносекунд	наносеку-н-д	7.5	н
биндусара	биндус-ар-а	7.5	ар
диоксидыг	диокси-д-ыг	7.5	д
товчоодыг	товчоо-д-ыг	7.5	д
хадгалалтанд	хадгалалта-н-д	7.5	н

6.6 Linguistic Interpretation

Automated Insight: The language Mongolian 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 (4.86x)
N-gram	2-gram	Lowest perplexity (413)
Markov	Context-4	Highest predictability (96.8%)
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: 2026-01-10 13:03:40