wikilangs/wuu

Wu Chinese - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Wu Chinese 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
16k	1.645x	1.65	0.0470%	189,167
32k	1.914x	1.92	0.0547%	162,652
64k	2.139x 🏆	2.15	0.0612%	145,478

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: 感觉系统（英语：sensory system）是神经系统中处理感觉信息个一部分。感觉系统包括感受器、神经通路搭子大脑中搭感觉知觉有关个部分。

Vocab	Tokens	Count
16k	▁ 感 觉 系统 ( 英语 : s ens ory ... (+35 more)	45
32k	▁ 感觉 系统 ( 英语 : s ens ory ▁system ... (+28 more)	38
64k	▁ 感觉 系统 ( 英语 : sens ory ▁system ) ... (+25 more)	35

Sample 2: 大事记 明代宗为了筹募经费而开始贩卖度牒，直到明末，导致僧尼剧增，寺院林立。 德里苏丹国赛义德王朝锡林德总督巴赫鲁尔·洛迪佔据了德里，赛义德王朝被洛迪王朝取代。...

Vocab	Tokens	Count
16k	▁大事记 ▁明 代 宗 为了 筹 募 经 费 而 ... (+63 more)	73
32k	▁大事记 ▁明代 宗 为了 筹 募 经 费 而 开始 ... (+52 more)	62
64k	▁大事记 ▁明代 宗 为了 筹 募 经费 而 开始 贩卖 ... (+46 more)	56

Sample 3: 吉兰丹州（）是马来西亚拉西马北部个一個州，首府為哥打峇鲁。該州北接泰国，东北为南中国海，西接霹雳州，南临彭亨州，东南为登嘉樓州。吉兰丹国号为Darul Naim...

Vocab	Tokens	Count
16k	▁吉 兰 丹 州 () 是 马来西亚 拉 西 马 ... (+59 more)	69
32k	▁吉 兰 丹 州 () 是马来西亚 拉西 马 北部 个一個 ... (+51 more)	61
64k	▁吉 兰 丹州 () 是马来西亚 拉西 马 北部 个一個 州 ... (+45 more)	55

Key Findings

• Best Compression: 64k achieves 2.139x compression
• Lowest UNK Rate: 16k with 0.0470% 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	1,616 🏆	10.66	2,926	27.3%	67.5%
2-gram	Subword	7,919	12.95	59,139	22.8%	51.4%
3-gram	Word	2,273	11.15	3,242	19.6%	59.2%
3-gram	Subword	27,775	14.76	121,509	9.3%	30.8%
4-gram	Word	5,014	12.29	6,809	13.7%	37.6%
4-gram	Subword	81,103	16.31	233,152	5.5%	16.3%
5-gram	Word	3,786	11.89	5,117	16.4%	41.5%
5-gram	Subword	104,659	16.68	225,092	4.4%	13.3%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	出生 逝世	1,249
2	of the	596
3	2 2	359
4	大事记 中国	331
5	1 1	266

3-grams (Word):

Rank	N-gram	Count
1	2 2 2	234
2	1 1 1	152
3	作词 作曲 编曲	84
4	原唱 作词 作曲	82
5	演唱曲目 原唱 作词	82

4-grams (Word):

Rank	N-gram	Count
1	2 2 2 2	180
2	1 1 1 1	114
3	演唱曲目 原唱 作词 作曲	82
4	原唱 作词 作曲 编曲	82
5	作词 作曲 编曲 排名	73

5-grams (Word):

Rank	N-gram	Count
1	2 2 2 2 2	146
2	1 1 1 1 1	93
3	演唱曲目 原唱 作词 作曲 编曲	82
4	原唱 作词 作曲 编曲 排名	73
5	地区 邮政编码 地区 邮政编码 地区	54

2-grams (Subword):

Rank	N-gram	Count
1	。 _	20,314
2	e _	14,212
3	a n	13,204
4	i n	10,947
5	n _	10,755

3-grams (Subword):

Rank	N-gram	Count
1	t h e	3,901
2	_ t h	3,488
3	_ — _	3,447
4	_ o f	3,437
5	_ - _	3,310

4-grams (Subword):

Rank	N-gram	Count
1	_ o f _	3,134
2	t h e _	3,085
3	_ t h e	2,842
4	— _ — _	2,489
5	_ — _ —	2,487

5-grams (Subword):

Rank	N-gram	Count
1	_ t h e _	2,564
2	_ — _ — _	2,487
3	— _ — _ —	1,986
4	a t i o n	1,684
5	。 _ 出 生 _	1,567

Key Findings

• Best Perplexity: 2-gram (word) with 1,616
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~13% 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	0.2252	1.169	1.67	213,385	77.5%
1	Subword	1.9391	3.835	30.25	12,723	0.0%
2	Word	0.0575	1.041	1.10	342,915	94.2%
2	Subword	0.5697	1.484	2.77	384,552	43.0%
3	Word	0.0189	1.013	1.03	360,203	98.1%
3	Subword	0.2223	1.167	1.47	1,063,474	77.8%
4	Word	0.0074 🏆	1.005	1.01	353,710	99.3%
4	Subword	0.1256	1.091	1.23	1,559,569	87.4%

Generated Text Samples (Word-based)

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

Context Size 1:

1. of depression 个经济衰退开始 伊拉世界范围内造成了巨大创伤 导致普遍个失业搭贫困 富兰克林 皮尔斯franklin 民主党 乔治 唐宁搭唐宁街个典故 分类 microsoft windo...
2. the honourable privy 分类 作家 評論員 朱立熙 前華視副總 與劉文正同班 鄭啟明 中華民國風工程學會理事長 曾任國立海洋大學河海工程系副教授 淡大土木工程系副教授 教授 杜秉明 ...
3. 英语 new jersey 是美国新泽西州个最大高等学府 是一所公立研究型大学 渠个主校区垃拉佛罗里达州个首府 塔拉哈西 英语 the interpreter all the world cup 法語...

Context Size 2:

1. 出生 逝世 伊莎贝拉一世 西班牙卡斯蒂利亚女王 4年 0 06 0 39 0 24 3 38 0 206 58 64
2. of the population converted into years of amor en los tiempos del cólera 英文 love in all
3. 2 2 2 2 6 美國永久居民 1 4 4 4 4 5 百萬人 23 4 97 百萬人

Context Size 3:

1. 2 2 2 1 4 6 5 6 3 3 4 2 3 3 3 3 3 3
2. 1 1 1 1 1 2 2 3 windows macos gpl 主页 arcadeflex 0 36 13 多种街机系统 java
3. 作词 作曲 编曲 排名 互投 1 李克勤 李维嘉 谢谢你的爱 刘德华 林秋离 熊美玲 johnny yim 5 7 haya乐团 张大大

Context Size 4:

1. 2 2 2 2 赛艇 17px fisa 4 5 6 4 4 8 8 苏诗丁 5 3 6 5
2. 1 1 1 1 2 3 1 1 5 2 6 3 4 2 1 1 1 1 1
3. 演唱曲目 原唱 作词 作曲 编曲 排名 互投 1 赵 传 李 锐 大地 beyond 刘卓辉 黄家驹 terence teo 7

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _仙子因为北京车获胜拉该地形成美
2. atha）_l_00_22_-关
3. e_-_425_skherorl

Context Size 2:

1. 。_澳大利」〔glonoël_f_
2. e_'comande_handri
3. an_rw-hyd_gires_v

Context Size 3:

1. the_flee_y_特色词汇_我—
2. _theffide）是由两条有得公共
3. _—_—_3.30%_參加高中社區服

Context Size 4:

1. _of_the_nakara_ou_k
2. the_boy_adley,_clau
3. _the_warraglypha》（日

Key Findings

• Best Predictability: Context-4 (word) with 99.3% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,559,569 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	32,292
Total Tokens	241,506
Mean Frequency	7.48
Median Frequency	3
Frequency Std Dev	50.87

Most Common Words

Rank	Word	Frequency
1	of	3,198
2	the	3,043
3	英语	2,743
4	分类	2,491
5	2	2,396
6	1	2,018
7	大事记	1,930
8	出生	1,790
9	逝世	1,772
10	3	1,615

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	衢化公司电石厂	2
2	浙江大成	2
3	温州佳运	2
4	队数	2
5	绍兴塔牌	2
6	舟山舟峰	2
7	台州王野	2
8	义乌土木建设	2
9	杭州天业电子	2
10	天业电子	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.8530
R² (Goodness of Fit)	0.995865
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	25.0%
Top 1,000	46.3%
Top 5,000	67.8%
Top 10,000	78.3%

Key Findings

• Zipf Compliance: R²=0.9959 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 25.0% of corpus
• Long Tail: 22,292 words needed for remaining 21.7% 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	0.6410	0.3758	N/A	N/A
mono_64d	64	0.2896	0.3654	N/A	N/A
mono_128d	128	0.0637	0.3638	N/A	N/A
aligned_32d	32	0.6410 🏆	0.3750	0.0500	0.2840
aligned_64d	64	0.2896	0.3749	0.0680	0.3380
aligned_128d	128	0.0637	0.3655	0.0820	0.3460

Key Findings

• Best Isotropy: aligned_32d with 0.6410 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3701. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 8.2% 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	2.111	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
-s	saidu, sakigake, scientists
-m	mas, musume, missionary
-a	apparatus, at, angel
-c	christi, christensen, cotillard
-b	barnes, brassica, bushou
-p	plutocracy, parti, parent
-t	towns, translated, tellabs
-d	duels, dieu, diadem

Productive Suffixes

Suffix	Examples
-s	barnes, rigs, enemies
-e	verte, sakigake, musume
-n	watson, christensen, wigan
-a	brassica, barbara, patricia
-on	watson, baron, anderson
-r	soccer, ratzinger, isomer
-y	plutocracy, way, missionary
-t	parent, at, hurt

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
族自治州	2.45x	13 contexts	甘南藏族自治州, 海南藏族自治州, 甘孜藏族自治州
atio	1.98x	18 contexts	ratio, oratio, ratios
tion	1.91x	17 contexts	motion, action, nation
我是歌手	2.43x	7 contexts	我是歌手第八季, 我是歌手第四季, 我是歌手第三季
是歌手第	2.43x	7 contexts	我是歌手第八季, 我是歌手第四季, 我是歌手第三季

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	-s	29 words	points, primates
-c	-s	29 words	chinois, comptes
-s	-s	25 words	shakespeares, seuss
-c	-n	25 words	chuushin, callaghan
-c	-e	24 words	course, complete
-m	-s	23 words	maximus, meiers
-a	-n	23 words	asunción, anderson
-a	-s	23 words	antilles, arts
-p	-n	23 words	ponn, prachachon
-s	-e	21 words	serie, soreyuke

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
玛理诺marino	玛理诺mar-in-o	7.5	in
submitted	submit-t-ed	7.5	t
australasia	australa-s-ia	7.5	s
gilbertese	gilbert-es-e	6.0	gilbert
interests	inter-es-ts	6.0	inter
alchemists	alchemist-s	4.5	alchemist
nobunagas	nobunaga-s	4.5	nobunaga
christian	christi-an	4.5	christi
wikipedias	wikipedia-s	4.5	wikipedia
governments	government-s	4.5	government
productions	production-s	4.5	production
entertainmentna	entertainment-na	4.5	entertainment
childrens	children-s	4.5	children
publishers	publisher-s	4.5	publisher
assessment	a-s-sessment	4.5	sessment

6.6 Linguistic Interpretation

Automated Insight: The language Wu Chinese 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 (2.14x)
N-gram	2-gram	Lowest perplexity (1,616)
Markov	Context-4	Highest predictability (99.3%)
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 04:47:13