README.md

metadata

language: id
language_name: Indonesian
language_family: austronesian_malay
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-austronesian_malay
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: 5.355
  - name: best_isotropy
    type: isotropy
    value: 0.6446
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-13T00:00:00.000Z

Indonesian - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Indonesian 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	4.240x	4.24	0.0805%	2,784,763
16k	4.730x	4.73	0.0899%	2,496,316
32k	5.099x	5.10	0.0969%	2,315,931
64k	5.355x 🏆	5.36	0.1017%	2,205,288

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Marga Karya adalah salah satu desa di kecamatan Kulisusu Barat, Kabupaten Buton ...

Vocab	Tokens	Count
8k	▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+14 more)	24
16k	▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+13 more)	23
32k	▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+12 more)	22
64k	▁marga ▁karya ▁adalah ▁salah ▁satu ▁desa ▁di ▁kecamatan ▁k ulis ... (+12 more)	22

Sample 2: Sukamaju adalah desa di kecamatan Majalaya, Bandung, Jawa Barat, Indonesia. Refe...

Vocab	Tokens	Count
8k	▁suk ama ju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ... (+10 more)	20
16k	▁suk ama ju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ... (+10 more)	20
32k	▁sukamaju ▁adalah ▁desa ▁di ▁kecamatan ▁maj alaya , ▁bandung , ... (+8 more)	18
64k	▁sukamaju ▁adalah ▁desa ▁di ▁kecamatan ▁majalaya , ▁bandung , ▁jawa ... (+7 more)	17

Sample 3: Sukarapih adalah desa di kecamatan Sukarame, Tasikmalaya, Jawa Barat, Indonesia....

Vocab	Tokens	Count
8k	▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+11 more)	21
16k	▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+9 more)	19
32k	▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukar ame , ... (+9 more)	19
64k	▁suk arap ih ▁adalah ▁desa ▁di ▁kecamatan ▁sukarame , ▁tasikmalaya ... (+8 more)	18

Key Findings

• Best Compression: 64k achieves 5.355x compression
• Lowest UNK Rate: 8k with 0.0805% 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	352,571	18.43	3,589,156	5.5%	15.7%
2-gram	Subword	237 🏆	7.89	60,775	71.6%	99.3%
3-gram	Word	1,403,108	20.42	7,774,768	5.0%	12.0%
3-gram	Subword	2,119	11.05	298,247	28.5%	73.3%
4-gram	Word	2,339,176	21.16	12,102,231	6.6%	13.7%
4-gram	Subword	13,149	13.68	1,501,572	14.3%	40.9%
5-gram	Word	1,091,988	20.06	7,805,199	10.1%	20.0%
5-gram	Subword	56,499	15.79	5,070,685	8.4%	25.8%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	pada tahun	570,229
2	pranala luar	330,544
3	bagian dari	220,724
4	salah satu	204,094
5	referensi pranala	188,446

3-grams (Word):

Rank	N-gram	Count
1	referensi pranala luar	188,236
2	merupakan bagian dari	173,920
3	ini juga merupakan	121,570
4	juga merupakan bagian	118,712
5	spesies ini juga	82,513

4-grams (Word):

Rank	N-gram	Count
1	juga merupakan bagian dari	118,623
2	ini juga merupakan bagian	118,088
3	spesies ini juga merupakan	82,160
4	merupakan bagian dari genus	74,651
5	kelas insecta filum arthropoda	71,731

5-grams (Word):

Rank	N-gram	Count
1	ini juga merupakan bagian dari	118,072
2	spesies ini juga merupakan bagian	82,150
3	kelas insecta filum arthropoda dan	71,731
4	filum arthropoda dan kingdom animalia	71,725
5	insecta filum arthropoda dan kingdom	71,724

2-grams (Subword):

Rank	N-gram	Count
1	a n	49,403,346
2	n _	31,170,947
3	a _	27,237,962
4	_ d	23,894,955
5	n g	23,074,425

3-grams (Subword):

Rank	N-gram	Count
1	a n _	24,307,048
2	a n g	11,583,028
3	_ m e	10,082,401
4	_ d a	9,885,365
5	n g _	9,758,169

4-grams (Subword):

Rank	N-gram	Count
1	a n g _	7,357,567
2	k a n _	6,126,631
3	_ m e n	5,037,208
4	_ d a n	4,774,956
5	d a n _	4,773,132

5-grams (Subword):

Rank	N-gram	Count
1	_ d a n _	4,636,764
2	y a n g _	4,351,516
3	_ y a n g	4,285,242
4	n g a n _	2,901,020
5	p a d a _	2,514,636

Key Findings

• Best Perplexity: 2-gram (subword) with 237
• 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

---

3. Markov Chain Evaluation

Results

Context	Variant	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	Word	0.8593	1.814	13.13	2,962,654	14.1%
1	Subword	0.4616	1.377	5.47	82,539	53.8%
2	Word	0.4377	1.354	2.70	38,824,783	56.2%
2	Subword	0.4117	1.330	2.64	451,148	58.8%
3	Word	0.1831	1.135	1.41	104,699,371	81.7%
3	Subword	0.4412	1.358	2.78	1,192,939	55.9%
4	Word	0.0695 🏆	1.049	1.12	147,775,338	93.1%
4	Subword	0.5403	1.454	3.03	3,321,158	46.0%

Generated Text Samples (Word-based)

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

Context Size 1:

1. dan inlandsch schrijver semua lautan mazhab chicago medical top akan tetapi istrinya musik dan yogya...
2. yang dirilis pada tahun pembangunan talud pembangunan ii orthomolybdate feo h2 lebih mudah teroksida...
3. di india jakarta ichtiar baru seri televisi abc nbc selama lamanya yang mendorong serta melumasi lap...

Context Size 2:

1. pada tahun dengan bubarnya laskar jihad by noorhaidi hasan s ip center sikka 4 maria sharapova dan
2. pranala luar film rusia tahun berikutnya penyelidik ufo dapat berupa kuantitatif misalnya dalam bent...
3. bagian dari ordo diptera kelas insecta filum arthropoda dan kingdom animalia larva kumbang ini biasa...

Context Size 3:

1. merupakan bagian dari genus bulbophyllum nama ilmiah dari spesies ini didasarkan pada laporan dua or...
2. referensi pranala luar air alps armada air alps telah mencakup pesawat berikut ini per agustus armad...
3. ini juga merupakan bagian dari genus menemerus dan ordo araneae nama ilmiah dari spesies ini pertama...

Context Size 4:

1. juga merupakan bagian dari genus neoitamus ordo diptera kelas insecta filum arthropoda dan kingdom a...
2. ini juga merupakan bagian dari ordo poales spesies cyperus paniceus sendiri merupakan bagian dari ge...
3. spesies ini juga merupakan bagian dari genus megopis ordo coleoptera kelas insecta filum arthropoda ...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. ani_arasebeladan
2. _kani,_"tatryang
3. ng_mbantundidmbk

Context Size 2:

1. angnyangala_pest.
2. n_gi,_ision_untif
3. a_bah_res_porah_a

Context Size 3:

1. an_:_dan_ada_yang_
2. angan_mencanyimnya
3. _merurandah_dengka

Context Size 4:

1. ang_dirand_prättige
2. kan_dises_p._london
3. _menyata_panason_me

Key Findings

• Best Predictability: Context-4 (word) with 93.1% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (3,321,158 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	1,224,888
Total Tokens	195,423,598
Mean Frequency	159.54
Median Frequency	4
Frequency Std Dev	8831.73

Most Common Words

Rank	Word	Frequency
1	dan	4,660,597
2	yang	4,306,521
3	di	3,661,285
4	pada	2,304,326
5	dari	2,124,901
6	dengan	1,729,322
7	ini	1,681,032
8	untuk	1,457,185
9	dalam	1,438,758
10	adalah	1,350,786

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	melanthiales	2
2	trilliales	2
3	medeolaceae	2
4	alstroemeriales	2
5	burmanniales	2
6	amaryllidales	2
7	dioscoreanae	2
8	arecanae	2
9	mewstadz	2
10	bithorax	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0756
R² (Goodness of Fit)	0.989157
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	29.0%
Top 1,000	56.7%
Top 5,000	76.2%
Top 10,000	83.0%

Key Findings

• Zipf Compliance: R²=0.9892 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 29.0% of corpus
• Long Tail: 1,214,888 words needed for remaining 17.0% 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	0.6145	0.3978	N/A	N/A
mono_64d	64	0.6446	0.3232	N/A	N/A
mono_128d	128	0.6017	0.2493	N/A	N/A
aligned_32d	32	0.6145	0.3840	0.5320	0.8980
aligned_64d	64	0.6446 🏆	0.3083	0.7760	0.9520
aligned_128d	128	0.6017	0.2548	0.8760	0.9860

Key Findings

• Best Isotropy: aligned_64d with 0.6446 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3196. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 87.6% 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	-0.225	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
-s	sarmizegetusa, sounkyoensis, savoia
-a	anakboru, analdie, aerotaxi
-ma	mantellodon, mariarosa, manaruh
-m	mengahruskan, muhadatsatul, menyalahkan
-k	khathib, kunžak, kar98k
-p	parungi, perusaahaan, pinsot
-b	burdi, bumbong, bagiab
-t	theridioides, teymourtash, talana

Productive Suffixes

Suffix	Examples
-a	westa, grazilla, lefkosia
-n	discrimination, jörn, mengahruskan
-s	gomphrenoides, zimdars, sounkyoensis
-i	parungi, burdi, aerotaxi
-e	analdie, herne, iratsume
-an	mengahruskan, fatchurohman, perusaahaan
-ya	bungkusnya, oksidatifnya, berkembangbiaknya
-r	vbr, legitimator, sattar

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
engu	1.64x	240 contexts	cengu, dengu, wengu
ebag	2.05x	77 contexts	sebag, tebag, lebaga
gkan	1.83x	118 contexts	ingkan, ongkan, tigkan
ebua	2.11x	62 contexts	sebua, ebuah, zebua
rkan	1.74x	146 contexts	arkan, mrkan, erkan
egar	1.61x	200 contexts	jegar, degar, cegar
rseb	2.00x	68 contexts	terseb, ersebut, trsebut
njad	2.11x	51 contexts	njadi, anjad, anjadi
ingk	1.37x	376 contexts	singk, hingk, ingky
menj	1.88x	63 contexts	menju, menja, menje
terb	1.49x	188 contexts	terbai, terbis, terbat
nnya	1.65x	106 contexts	annya, ionnya, lannya

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
-p	-n	117 words	pankorben, pembumian
-p	-a	104 words	puncumania, paradera
-s	-a	93 words	sylviatata, saaka
-a	-a	85 words	ajidarma, anisotricha
-k	-n	82 words	kipin, kylián
-p	-an	81 words	pembumian, pacinan
-k	-a	78 words	kreuta, kepemimpinanya
-m	-n	77 words	mistakon, mengkonsentrasikan
-s	-n	74 words	sajikdan, saefudin
-t	-a	72 words	tubicinella, typhlonyphia

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
coelosphaeridae	coelosphaerid-a-e	7.5	a
iguanidae	iguanid-a-e	7.5	a
wijayaanwar	wijayaanw-a-r	7.5	a
kerarajaan	keraraj-a-an	7.5	a
pandjhoerit	pandjhoer-i-t	7.5	i
fauthouxsandrine	fauthouxsandri-n-e	7.5	n
retnowati	retnow-a-ti	7.5	a
encontrar	encontr-a-r	7.5	a
prasekolah	p-ra-sekolah	7.5	sekolah
penamamaan	penama-ma-an	7.5	ma
samatorsemarang	samatorsemar-a-ng	7.5	a
keshavrao	keshavr-a-o	7.5	a
mencatatnya	mencatat-n-ya	7.5	n
interkelasi	interke-la-si	7.5	la
siberpunk	siberpu-n-k	7.5	n

6.6 Linguistic Interpretation

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

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (5.35x)
N-gram	2-gram	Lowest perplexity (237)
Markov	Context-4	Highest predictability (93.1%)
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

---

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: 2026-01-13 19:55:01