CDO - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on CDO 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-gram)
• Markov chains (context of 1, 2, 3 and 4)
• Subword N-gram and Markov chains
• Embeddings in various sizes and dimensions
• 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. Summary & Recommendations
• Metrics Glossary
• Visualizations Index

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1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
32k	2.562x	2.54	0.0007%	298,320
64k	2.796x 🏆	2.77	0.0007%	273,367

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Pender Gông (Ĭng-ngṳ̄: Pender County) sê Mī-guók North Carolina gì siŏh ciáh gôn...

Vocab	Tokens	Count
32k	▁pen der ▁gông ▁( ĭng - ngṳ̄ : ▁pen der ... (+19 more)	29
64k	▁pender ▁gông ▁( ĭng - ngṳ̄ : ▁pender ▁county ) ... (+17 more)	27

Sample 2: `Duâi dâi

Chók-sié

Guó-sié

分類:1170 nièng-dâi`

Vocab	Tokens	Count
32k	▁duâi ▁dâi ▁chók - sié ▁guó - sié ▁分類 : ... (+7 more)	17
64k	▁duâi ▁dâi ▁chók - sié ▁guó - sié ▁分類 : ... (+7 more)	17

Sample 3: 1000 nièng-dâi téng 1000 nièng 1 nguŏk 1 hô̤ kăi-sṳ̄, gáu 1009 nièng 12 nguŏk 31...

Vocab	Tokens	Count
32k	▁ 1 0 0 0 ▁nièng - dâi ▁téng ▁ ... (+36 more)	46
64k	▁ 1 0 0 0 ▁nièng - dâi ▁téng ▁ ... (+36 more)	46

Key Findings

• Best Compression: 64k achieves 2.796x compression
• Lowest UNK Rate: 32k with 0.0007% 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	Perplexity	Entropy	Unique N-grams	Top-100 Coverage	Top-1000 Coverage
2-gram	2,092 🏆	11.03	13,738	34.2%	70.6%
2-gram	517 🏆	9.01	13,773	57.4%	92.0%
3-gram	6,902	12.75	35,914	23.0%	49.3%
3-gram	2,154	11.07	33,837	33.3%	72.5%
4-gram	16,500	14.01	75,913	16.0%	37.8%
4-gram	6,830	12.74	94,271	22.4%	53.8%

Top 5 N-grams by Size

2-grams:

Rank	N-gram	Count
1	分類 :	17,792
2	̤ ng	9,653
3	. 分類	8,000
4	- guók	7,750
5	- sié	7,747

3-grams:

Rank	N-gram	Count
1	. 分類 :	8,000
2	gì siŏh ciáh	5,565
3	- ngṳ ̄	4,336
4	mī - guók	3,641
5	gâe ̤ ng	3,480

4-grams:

Rank	N-gram	Count
1	sê mī - guók	3,211
2	gì siŏh ciáh gông	3,000
3	ciáh gông . 分類	3,000
4	gông . 分類 :	3,000
5	siŏh ciáh gông .	3,000

Key Findings

• Best Perplexity: 2-gram with 517
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~54% of corpus
• Recommendation: 4-gram or 5-gram for best predictive performance

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3. Markov Chain Evaluation

Results

Context	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	0.2803	1.214	3.49	48,699	72.0%
1	0.3942	1.314	4.02	31,614	60.6%
2	0.1991	1.148	1.83	169,503	80.1%
2	0.3616	1.285	2.00	127,156	63.8%
3	0.1556	1.114	1.42	308,939	84.4%
3	0.2179	1.163	1.54	253,902	78.2%
4	0.0983 🏆	1.071	1.21	437,205	90.2%
4	0.1764 🏆	1.130	1.38	389,634	82.4%

Generated Text Samples

Below are text samples generated from each Markov chain model:

Context Size 1:

1. - hū siék gì siŏh ciáh gông . 分類 : chĭng - uăng - pū -
2. ̤ k nâ sáng ĕu - ngiòng ( 螺洲路 ) guōng - dŏng - dōi -
3. gì dâ ̤ 18 艭 ngiê - guók - guó - dók “ . chók -

Context Size 2:

1. 分類 : 1370 nièng - dâi gì lùng - dŭng - ngŏk liù - giù - dôi
2. ̤ ng hók - gióng , dâi - biēu gê ̤ ṳng - sāng - dōng gâe
3. . 分類 : 200 nièng - dâi - mā 分類 : 1300年代

Context Size 3:

1. . 分類 : minnesota gì gông
2. gì siŏh ciáh dê - ngék - chê . 分類 : hù - báe ̤ k - chiă
3. - ngṳ ̄ : lafayette county ) sê mī - guók gì buô - hông gì sṳ ̆

Context Size 4:

1. sê mī - guók colorado gì siŏh ciáh gông . 分類 : florida gì gông
2. gông . 分類 : michigan gì gông
3. siŏh ciáh gông . 分類 : indiana gì gông

Key Findings

• Best Predictability: Context-4 with 90.2% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (389,634 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	12,714
Total Tokens	590,881
Mean Frequency	46.47
Median Frequency	3
Frequency Std Dev	447.20

Most Common Words

Rank	Word	Frequency
1	gì	24,268
2	分類	17,794
3	ng	16,472
4	sê	15,967
5	siŏh	9,713
6	guók	9,302
7	gông	9,087
8	sié	8,595
9	nièng	7,825
10	dâi	7,699

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	燈泡厰	2
2	搪瓷厰	2
3	保溫瓶厰	2
4	啤酒厰	2
5	福大機械厰	2
6	抗生素厰	2
7	kbo	2
8	우주항공청	2
9	cho	2
10	chit	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.3995
R² (Goodness of Fit)	0.979429
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	55.6%
Top 1,000	90.8%
Top 5,000	97.1%
Top 10,000	99.1%

Key Findings

• Zipf Compliance: R²=0.9794 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 55.6% of corpus
• Long Tail: 2,714 words needed for remaining 0.9% coverage

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5. Word Embeddings Evaluation

Model Comparison

Model	Vocab Size	Dimension	Avg Norm	Std Norm	Isotropy
mono_32d	7,009	32	4.149	1.118	0.5460 🏆
mono_64d	7,009	64	4.243	1.106	0.2037
mono_128d	7,009	128	4.233	1.119	0.0381
embeddings_enhanced	0	0	0.000	0.000	0.0000

Key Findings

• Best Isotropy: mono_32d with 0.5460 (more uniform distribution)
• Dimension Trade-off: Higher dimensions capture more semantics but reduce isotropy
• Vocabulary Coverage: All models cover 7,009 words
• Recommendation: 100d for balanced semantic capture and efficiency

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6. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	32k BPE	Best compression (2.80x) with low UNK rate
N-gram	5-gram	Lowest perplexity (517)
Markov	Context-4	Highest predictability (90.2%)
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},
  publisher = {HuggingFace},
  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

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Generated by Wikilangs Models Pipeline

Report Date: 2025-12-28 16:25:16