Vietnamese - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Vietnamese 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	3.647x	3.65	0.1376%	4,322,437
16k	3.775x	3.77	0.1424%	4,176,769
32k	3.851x	3.85	0.1453%	4,093,428
64k	3.900x 🏆	3.90	0.1471%	4,042,743

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Siphona scutellata là một loài ruồi trong họ Tachinidae. Chú thích Liên kết ngoà...

Vocab	Tokens	Count
8k	▁si ph ona ▁sc ut ell ata ▁là ▁một ▁loài ... (+12 more)	22
16k	▁si ph ona ▁scut ellata ▁là ▁một ▁loài ▁ruồi ▁trong ... (+9 more)	19
32k	▁si ph ona ▁scut ellata ▁là ▁một ▁loài ▁ruồi ▁trong ... (+9 more)	19
64k	▁siph ona ▁scutellata ▁là ▁một ▁loài ▁ruồi ▁trong ▁họ ▁tach ... (+7 more)	17

Sample 2: Kocaali là một xã thuộc huyện Ergani, tỉnh Diyarbakır, Thổ Nhĩ Kỳ. Dân số thời đ...

Vocab	Tokens	Count
8k	▁k oc a ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er ... (+31 more)	41
16k	▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er g ... (+29 more)	39
32k	▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er g ... (+28 more)	38
64k	▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁erg ani ... (+24 more)	34

Sample 3: Glipidiomorpha riesei là một loài bọ cánh cứng trong họ Mordellidae. Loài này đư...

Vocab	Tokens	Count
8k	▁gl ip idi omorpha ▁r ies ei ▁là ▁một ▁loài ... (+24 more)	34
16k	▁gl ip idi omorpha ▁r ies ei ▁là ▁một ▁loài ... (+24 more)	34
32k	▁gl ip idi omorpha ▁ries ei ▁là ▁một ▁loài ▁bọ ... (+21 more)	31
64k	▁gl ip idi omorpha ▁riesei ▁là ▁một ▁loài ▁bọ ▁cánh ... (+19 more)	29

Key Findings

• Best Compression: 64k achieves 3.900x compression
• Lowest UNK Rate: 8k with 0.1376% 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	106,320	16.70	2,695,824	10.1%	25.1%
2-gram	Subword	409 🏆	8.67	93,876	59.2%	96.0%
3-gram	Word	890,077	19.76	9,913,320	6.8%	13.5%
3-gram	Subword	2,984	11.54	411,919	25.5%	66.3%
4-gram	Word	2,796,979	21.42	22,248,727	6.3%	11.5%
4-gram	Subword	16,513	14.01	1,959,172	13.3%	41.5%
5-gram	Word	2,571,700	21.29	19,242,355	7.4%	13.5%
5-gram	Subword	69,615	16.09	6,377,982	8.7%	27.1%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	là một	1,495,225
2	chú thích	852,707
3	tham khảo	804,096
4	một loài	728,551
5	trong họ	711,111

3-grams (Word):

Rank	N-gram	Count
1	là một loài	722,924
2	liên kết ngoài	620,713
3	loài này được	453,066
4	chú thích liên	440,159
5	thích liên kết	440,150

4-grams (Word):

Rank	N-gram	Count
1	chú thích liên kết	440,133
2	thích liên kết ngoài	439,810
3	được mô tả năm	384,043
4	chú thích tham khảo	365,017
5	vật được mô tả	363,438

5-grams (Word):

Rank	N-gram	Count
1	chú thích liên kết ngoài	439,801
2	vật được mô tả năm	363,377
3	tả khoa học đầu tiên	335,608
4	khoa học đầu tiên năm	309,398
5	đầu tiên năm chú thích	263,309

2-grams (Subword):

Rank	N-gram	Count
1	_ t	44,618,705
2	n g	36,466,380
3	_ c	30,008,094
4	n _	29,116,380
5	g _	27,402,011

3-grams (Subword):

Rank	N-gram	Count
1	n g _	27,209,259
2	_ t h	17,092,068
3	_ t r	10,431,331
4	_ c h	9,946,202
5	n h _	9,905,520

4-grams (Subword):

Rank	N-gram	Count
1	n g _ t	4,408,233
2	_ v à _	3,874,748
3	_ l à _	3,858,257
4	c ủ a _	3,768,746
5	_ c ủ a	3,768,226

5-grams (Subword):

Rank	N-gram	Count
1	_ c ủ a _	3,765,562
2	_ đ ư ợ c	3,314,830
3	đ ư ợ c _	3,299,257
4	_ m ộ t _	3,246,287
5	_ n ă m _	3,101,391

Key Findings

• Best Perplexity: 2-gram (subword) with 409
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~27% 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	0.7563	1.689	9.23	2,640,968	24.4%
1	Subword	1.2157	2.323	15.79	34,963	0.0%
2	Word	0.4386	1.355	3.10	24,350,395	56.1%
2	Subword	0.5203	1.434	3.02	551,811	48.0%
3	Word	0.2736	1.209	1.81	75,436,653	72.6%
3	Subword	0.4089	1.328	2.69	1,667,382	59.1%
4	Word	0.1518 🏆	1.111	1.33	136,713,102	84.8%
4	Subword	0.4863	1.401	2.89	4,478,768	51.4%

Generated Text Samples (Word-based)

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

Context Size 1:

1. là tổng thống như đường sắt bắc iwate grulla morioka thống shad striper rượt đuổi theo
2. và lavrov đã đi hết các xơ cứng trong hiệp hòa thảo loài khác biệt hiệu
3. của mình mang tên đàn đạo đền một mạng nicaragua 3 năm vật hoang mạc thiên

Context Size 2:

1. là một loài hymenoptera trong họ noctuidae chú thích tham khảo bay kazakhstan không tìm thấy tại
2. chú thích liên kết ngoài vật được mô tả năm vật bolivia vật brasil vật colombia vật
3. một loài bướm đêm trong họ cửu lý hương loài boswellia trong tôn giáo nào giáo dục

Context Size 3:

1. là một loài bọ cánh cứng trong họ melandryidae loài này được werderm mô tả khoa học năm
2. liên kết ngoài c vật được mô tả năm es hemianemia eximia
3. loài này được baker labat schatz mô tả khoa học đầu tiên năm chú thích tham khảo vật

Context Size 4:

1. chú thích liên kết ngoài vật được mô tả năm vật đặc hữu đài loan đài loan thuộc nhật
2. vật được mô tả năm vật đặc hữu trung quốc kim lũ mai tai hùm đơn loài vật được
3. khoa học đầu tiên năm chú thích liên kết ngoài vật được mô tả năm đêm indonesia đêm philippines

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _lef_19_kỳ.wanhe
2. n_terìng_đã_phủ_
3. h_"_ayroarọcá_m_

Context Size 2:

1. _thuệsố_đã_vấn_vù
2. ng_thuộc_nhịu_đầu
3. _của_nh_sác_prit_

Context Size 3:

1. ng_đã_bị_bệnh_lại_
2. _thắng_của_hampus_
3. _trang_3_joon,_nhữ

Context Size 4:

1. ng_tăng_ánh_quyết_c
2. _và_những_có_một_cầ
3. _là_volume_shop,_tâ

Key Findings

• Best Predictability: Context-4 (word) with 84.8% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (4,478,768 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	1,088,012
Total Tokens	275,589,508
Mean Frequency	253.30
Median Frequency	4
Frequency Std Dev	12931.56

Most Common Words

Rank	Word	Frequency
1	là	3,896,221
2	và	3,888,002
3	của	3,770,649
4	năm	3,541,374
5	được	3,324,385
6	một	3,283,880
7	trong	2,847,858
8	có	2,266,526
9	các	2,260,160
10	người	1,505,528

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	bíchhạnh	2
2	dâuliên	2
3	lụanguyễn	2
4	zeltiq	2
5	côtobin	2
6	novitskiy	2
7	tarelkin	2
8	齋堂	2
9	zhāitáng	2
10	chatral	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.5197
R² (Goodness of Fit)	0.977671
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	35.9%
Top 1,000	79.0%
Top 5,000	91.3%
Top 10,000	93.6%

Key Findings

• Zipf Compliance: R²=0.9777 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 35.9% of corpus
• Long Tail: 1,078,012 words needed for remaining 6.4% coverage

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5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

5.2 Model Comparison

Model	Dimension	Isotropy	Semantic Density	Alignment R@1	Alignment R@10
mono_32d	32	0.8322	0.4208	N/A	N/A
mono_64d	64	0.8116	0.3302	N/A	N/A
mono_128d	128	0.7892	0.2753	N/A	N/A
aligned_32d	32	0.8322 🏆	0.4041	0.4880	0.8640
aligned_64d	64	0.8116	0.3384	0.7280	0.9680
aligned_128d	128	0.7892	0.2727	0.8360	0.9820

Key Findings

• Best Isotropy: aligned_32d with 0.8322 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3403. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 83.6% 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	-0.502	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	sinothomisus, sportowe, sprogøe
-t	thổvàng, trilion, tháitô
-a	amorín, aerolindigia, awardchoice
-m	minhphạm, mớimtvca, mutungi
-c	coccomelia, clacton, clatratum
-b	batmagnai, bejt, balep
-k	karepura, kỳtriệu, kronthaler
-ma	marovt, mayran, marghanna

Productive Suffixes

Suffix	Examples
-s	sinothomisus, orestes, trochanteralis
-a	coccomelia, karepura, nuichua
-e	pilosellae, orée, sportowe
-n	oreodendron, gaggabutan, clacton
-is	trochanteralis, neoconis, mononalis
-i	batmagnai, weinmanntái, eesi
-us	sinothomisus, brimidius, eudelus
-es	orestes, pseudaspilates, wingates

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
atio	2.64x	168 contexts	tatio, natio, fatio
opte	2.62x	135 contexts	opted, opter, copte
nter	2.01x	355 contexts	enter, inter, unter
trưở	2.86x	60 contexts	trưởn, trưởnɡ, trưởng
tướn	2.93x	45 contexts	tướng, tướngm, 4tướng
pter	2.21x	106 contexts	ptero, opter, apter
ceae	3.35x	20 contexts	aceae, ficeae, biceae
rưởn	2.86x	32 contexts	trưởn, rưởng, trưởnɡ
huyệ	1.59x	353 contexts	huyệt, huyện, chuyệ
nhiề	2.15x	75 contexts	nhiền, nhiềy, nhiềm
uyễn	2.16x	59 contexts	quyễn, duyễn, nuyễn
huyể	2.06x	28 contexts	chuyể, huyển, thuyểt

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	-a	126 words	pnmburucuya, praeangulata
-p	-s	122 words	pedicellatus, polyotis
-c	-s	116 words	cicindeloides, constrictiflorus
-s	-a	108 words	sungka, serbica
-c	-a	103 words	chensa, conardia
-s	-s	103 words	sacodes, sulamitis
-a	-s	99 words	ardys, airplanes
-a	-a	90 words	akassa, attenuatella
-m	-s	86 words	matles, moyennes
-m	-a	78 words	meryta, mātaatua

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
tolarucan	tolaruc-a-n	7.5	a
kazusensis	kazusen-s-is	7.5	s
jāgarābhivamsa	jāgarābhivam-s-a	7.5	s
alagappapuram	alagappapur-a-m	7.5	a
krickenbach	krickenb-a-ch	7.5	a
speculaas	specu-la-as	7.5	la
namsskogan	namsskog-a-n	7.5	a
mündersbach	mündersb-a-ch	7.5	a
quadrisetosus	quadriseto-s-us	7.5	s
thắngshonan	thắngshon-a-n	7.5	a
atrivenata	atrive-na-ta	7.5	na
hochiensis	hochien-s-is	7.5	s
outermost	outermo-s-t	7.5	s
xuechengensis	xuechengen-s-is	7.5	s
mesypochrysa	mesypochry-s-a	7.5	s

6.6 Linguistic Interpretation

Automated Insight: The language Vietnamese 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 (3.90x)
N-gram	2-gram	Lowest perplexity (409)
Markov	Context-4	Highest predictability (84.8%)
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: 2026-01-18 17:40:28