wikilangs/szy

Sakizaya - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Sakizaya 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	3.383x	3.39	0.1851%	601,273
16k	3.613x	3.61	0.1977%	563,108
32k	3.789x	3.79	0.2073%	536,850
64k	3.882x 🏆	3.88	0.2124%	524,017

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: (kamu nu hulam：照顧) diput tu babalaki. 照顧老人。 malalitin tu ihekalay atu zumaay a n...

Vocab	Tokens	Count
8k	▁( kamu ▁nu ▁hulam : 照 顧 ) ▁d iput ... (+16 more)	26
16k	▁( kamu ▁nu ▁hulam : 照顧 ) ▁d iput ▁tu ... (+14 more)	24
32k	▁( kamu ▁nu ▁hulam : 照顧 ) ▁diput ▁tu ▁babalaki ... (+12 more)	22
64k	▁( kamu ▁nu ▁hulam : 照顧 ) ▁diput ▁tu ▁babalaki ... (+11 more)	21

Sample 2: (kasatubangan:u kamu nu Hulam:被殖民、被奴隸 pasatubangan:讓他做奴隸)

Vocab	Tokens	Count
8k	▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+17 more)	27
16k	▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+17 more)	27
32k	▁( kas atu bangan : u ▁kamu ▁nu ▁hulam : ... (+16 more)	26
64k	▁( kas atubangan : u ▁kamu ▁nu ▁hulam : 被 ... (+9 more)	19

Sample 3: kamu nu hulam：掉下 tinaku a kamu mihetik 掉下 mihetik kaku tu kalisiw i ginko. 我去銀行提...

Vocab	Tokens	Count
8k	▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+29 more)	39
16k	▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+26 more)	36
32k	▁kamu ▁nu ▁hulam : 掉 下 ▁tinaku ▁a ▁kamu ▁mih ... (+26 more)	36
64k	▁kamu ▁nu ▁hulam : 掉下 ▁tinaku ▁a ▁kamu ▁mihetik ▁ ... (+21 more)	31

Key Findings

• Best Compression: 64k achieves 3.882x compression
• Lowest UNK Rate: 8k with 0.1851% 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	8,778	13.10	36,425	17.4%	45.6%
2-gram	Subword	254 🏆	7.99	27,613	77.3%	95.0%
3-gram	Word	11,965	13.55	51,761	13.4%	44.7%
3-gram	Subword	1,471	10.52	60,255	37.3%	81.6%
4-gram	Word	18,427	14.17	98,389	13.3%	43.1%
4-gram	Subword	6,740	12.72	170,144	17.8%	54.2%
5-gram	Word	13,641	13.74	78,197	15.0%	47.2%
5-gram	Subword	20,122	14.30	280,627	10.5%	36.0%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	a tademaw	9,781
2	a mihcaan	6,305
3	sa u	4,975
4	idaw ku	4,643
5	ku tademaw	4,369

3-grams (Word):

Rank	N-gram	Count
1	kamu nu hulam	1,808
2	nasulitan nasakamuan atu	1,789
3	namakayniay a nasulitan	1,789
4	a nasulitan nasakamuan	1,789
5	nasakamuan atu natinengan	1,757

4-grams (Word):

Rank	N-gram	Count
1	a nasulitan nasakamuan atu	1,789
2	namakayniay a nasulitan nasakamuan	1,778
3	nasulitan nasakamuan atu natinengan	1,755
4	atu zumaay a natinengan	1,673
5	tu ihekalay atu zumaay	1,466

5-grams (Word):

Rank	N-gram	Count
1	namakayniay a nasulitan nasakamuan atu	1,778
2	a nasulitan nasakamuan atu natinengan	1,755
3	tu ihekalay atu zumaay a	1,465
4	malalitin tu ihekalay atu zumaay	1,463
5	ihekalay atu zumaay a natinengan	1,462

2-grams (Subword):

Rank	N-gram	Count
1	u _	357,853
2	a n	299,562
3	a _	290,493
4	a y	241,409
5	_ a	215,000

3-grams (Subword):

Rank	N-gram	Count
1	a y _	143,914
2	_ a _	137,006
3	a n _	126,871
4	t u _	101,083
5	_ s a	100,121

4-grams (Subword):

Rank	N-gram	Count
1	_ n u _	84,566
2	_ t u _	65,522
3	_ k u _	59,832
4	a y _ a	54,817
5	y _ a _	47,865

5-grams (Subword):

Rank	N-gram	Count
1	a y _ a _	47,058
2	_ a t u _	22,206
3	t a d e m	21,403
4	a d e m a	21,335
5	d e m a w	21,328

Key Findings

• Best Perplexity: 2-gram (subword) with 254
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~36% 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	0.4793	1.394	3.89	158,896	52.1%
1	Subword	2.1979	4.588	29.06	6,068	0.0%
2	Word	0.2677	1.204	1.80	616,064	73.2%
2	Subword	0.5459	1.460	2.59	176,243	45.4%
3	Word	0.1031	1.074	1.20	1,105,652	89.7%
3	Subword	0.2326	1.175	1.58	456,451	76.7%
4	Word	0.0342 🏆	1.024	1.06	1,321,192	96.6%
4	Subword	0.1897	1.141	1.47	718,822	81.0%

Generated Text Samples (Word-based)

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

Context Size 1:

1. a kamu nu sakizaya 940 sejek 9 位由執政黨與反對黨分別任命之參議員組成 任期五年 每五年舉行一次普選 malawi sa cacay ademiad mapatay im...
2. nu u miliyaway a cidekay 南島語族 saan ya a kawaw panay有專屬的工作
3. tu 報刊會涼 u siwkay nu sakizaya 鄒族 cou uici itan 卑南 triyatriyaran 阿美 bu a sapaluma

Context Size 2:

1. a tademaw silecaday a lalangawan lisin kamu atu kabanaan si kalilidan tumuk saca babalaki mililid tu...
2. a mihcaan u nananuman nikaidaw atu sapatakekal hamin i cung ku u pu se su wi alesen
3. sa u moyan putiput tina dadiw sa nasulitan ni tuku sayun nay pabalucu ay a cidekay ku

Context Size 3:

1. kamu nu hulam a pu ha ce a kakitidaan atu nu sakay kinkuay i paris 巴黎 kina i
2. a nasulitan nasakamuan atu natinengan lists of national basketball association sapuyu en nba u amis ...
3. nasulitan nasakamuan atu natinengan 參考來源 ː malaalitin tu i hekalay atu zumaay a natinengan list of c...

Context Size 4:

1. a nasulitan nasakamuan atu natinengan lists of national basketball association players alvan adams 阿...
2. namakayniay a nasulitan nasakamuan atu natinengan 撒奇萊雅族語詞典 原住民族委員會線上字詞典 花蓮縣政府
3. nasulitan nasakamuan atu natinengan 中國高等植物資料庫全庫 中國科學院微生物研究所 行政院原住民族委員會 原住民族藥用植物 花序數位典藏國家型科技計畫 應用服務分項...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. abu_mit_in._iw-b
2. _uzay_ng”,isasan
3. ude_cihcatu_a_ay

Context Size 2:

1. u_macay_a_nida_pi
2. anaydaw-mici_paan
3. a_casa_luayinipah

Context Size 3:

1. ay_izaw_nan_藝術家mis
2. _a_nidaw_masa_mica
3. an_cuduc_tu_pyria_

Context Size 4:

1. _nu_siyhu_ku_kapah_
2. _tu_takuwanikeliday
3. _ku_akuti’_nu_baluc

Key Findings

• Best Predictability: Context-4 (word) with 96.6% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (718,822 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	51,046
Total Tokens	1,702,988
Mean Frequency	33.36
Median Frequency	3
Frequency Std Dev	928.70

Most Common Words

Rank	Word	Frequency
1	a	138,739
2	nu	85,232
3	tu	70,354
4	ku	61,136
5	u	60,011
6	sa	38,061
7	i	34,413
8	atu	22,437
9	tademaw	19,177
10	ci	13,592

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	lengat	2
2	屋頂的裂縫	2
3	pulukelin	2
4	kulisimas	2
5	pingki	2
6	matulakay	2
7	kalimicu	2
8	的未來	2
9	pisasapi	2
10	sadihkuay	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1985
R² (Goodness of Fit)	0.993933
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	49.3%
Top 1,000	75.3%
Top 5,000	88.1%
Top 10,000	92.1%

Key Findings

• Zipf Compliance: R²=0.9939 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 49.3% of corpus
• Long Tail: 41,046 words needed for remaining 7.9% 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	0.7206	0.3585	N/A	N/A
mono_64d	64	0.6971	0.2873	N/A	N/A
mono_128d	128	0.4883	0.2402	N/A	N/A
aligned_32d	32	0.7206 🏆	0.3548	0.0300	0.1480
aligned_64d	64	0.6971	0.2750	0.0520	0.2520
aligned_128d	128	0.4883	0.2443	0.0700	0.2960

Key Findings

• Best Isotropy: aligned_32d with 0.7206 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2934. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 7.0% 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	0.310	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
-ma	masakiketay, mabunal, mata目
-ka	kadiceman, kasikawaw, kaniket
-pa	pabelien, pakalaliw, pacukeday
-sa	saicelangan, sakatu, sakaudipan
-mi	mipelu, mipuputay, mingaayay
-a	ak, amuawaw, anuyaan
-s	saicelangan, sʉhlʉnganʉ, sakatu
-m	mipelu, muoli, masakiketay

Productive Suffixes

Suffix	Examples
-n	pabelien, saicelangan, anuyaan
-an	saicelangan, anuyaan, kadiceman
-ay	umahicaay, masakiketay, mipuputay
-y	umahicaay, masakiketay, mipuputay
-a	yaciyana, yita, esperança
-ng	pisasing, ninaimelang, inng
-g	pisasing, ninaimelang, inng
-u	mipelu, sakatu, swu

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
ulit	1.96x	76 contexts	sulit, kulit, asulit
atin	1.96x	71 contexts	latin, yatin, matin
inen	1.96x	69 contexts	yinen, bineng, tineng
tade	2.10x	42 contexts	tadek, taden, tadem
dema	2.08x	40 contexts	demaw, demad, demak
emia	2.16x	34 contexts	emiad, demia, demiad
awan	1.69x	92 contexts	tawan, dawan, awang
tine	2.29x	27 contexts	tineng, atineng, utineng
demi	2.21x	29 contexts	demia, demied, kudemi
hcaa	2.19x	28 contexts	ihcaan, mihcaa, mhcaan
anan	1.56x	108 contexts	canan, nanan, panan
anat	2.28x	18 contexts	canata, kanatl, kanata

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
-ma	-y	218 words	mapasimaay, mapatidengay
-ma	-ay	211 words	mapasimaay, mapatidengay
-ka	-n	148 words	kasaupuan, kalalulan
-ka	-an	141 words	kasaupuan, kalalulan
-sa	-n	122 words	sakalihalayan, sakayduhan
-mi	-y	120 words	mitatibay, micacuy
-mi	-ay	116 words	mitatibay, mibelinay
-pa	-n	114 words	pazen, pasilisian
-sa	-an	93 words	sakalihalayan, sakayduhan
-sa	-y	72 words	sapisahemay, sakasiidaay

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
nikuwanay	nikuw-an-ay	7.5	an
asasemaan	asase-ma-an	7.5	ma
maytebanay	mayteb-an-ay	7.5	an
sakaputun	sakapu-tu-n	7.5	tu
sapaiyuwan	sapaiyu-w-an	7.5	w
kasasudang	kasasu-da-ng	7.5	da
binacadana	binacad-an-a	7.5	an
nipikisaan	nipikis-a-an	7.5	a
lalaliyunan	lalaliyu-n-an	7.5	n
tadatabaki	ta-da-tabaki	7.5	tabaki
namakaadih	na-ma-kaadih	7.5	kaadih
amasasetul	a-ma-sasetul	7.5	sasetul
mamamelawan	ma-ma-melawan	7.5	melawan
tadaadidi	ta-da-adidi	7.5	adidi
malalawlaw	malalaw-l-aw	7.5	l

6.6 Linguistic Interpretation

Automated Insight: The language Sakizaya 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 (3.88x)
N-gram	2-gram	Lowest perplexity (254)
Markov	Context-4	Highest predictability (96.6%)
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 00:15:31