wikilangs/zgh

Standard Moroccan Tamazight - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Standard Moroccan Tamazight 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

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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

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Results

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: thumb ⴱⵉ ⴱⵉ ⵙⵉ ⵏⵖ BBC (ⵙ ⵜⵏⴳⵍⵉⵣⵜ: British Broadcasting Corporation) ⵉⵙⴰⵖⵓⵍⵏ

Sample 2: ⴰⴳⴰⴷⴰⵣ ⴰⴼⵕⴰⵏⵚⵉⵚ ⵉⴳⴰ ⴰⴳⴷⵓⵣ ⴷ ⴰⵙⴷⴷⵉ ⵏ ⵡⴰⵙⵖⵏⵣⵉ ⴳ ⵜⴰⴷⴷⵓⵔⵜ ⵜⴰⴼⵕⴰⵏⵚⵉⵚⵜ, ⵏ ⵓⵔⵍⵢⴰⵏⵣ ⴰⵎⴰⵢ...

Sample 3: ⵄⴱⴷⵍⴼⵜⵜⴰⵃ ⵙⵙⵉⵙⵉ (ⵙ ⵜⴰⵄⵕⴰⴱⵜ: عبد الفتاح السيسي), ⵉⵍⵓⵍ ⴳ 19 ⵏⵓⵡⴰⵏⴱⵉⵔ ⴳ ⵜⵇⴰⵀⵉⵔⵜ, ⵉⴳ...

Key Findings

• —Best Compression: 64k achieves 3.844x compression
• —Lowest UNK Rate: 8k with 0.9549% 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

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Results

Top 5 N-grams by Size

2-grams (Word):

3-grams (Word):

4-grams (Word):

5-grams (Word):

2-grams (Subword):

3-grams (Subword):

4-grams (Subword):

5-grams (Subword):

Key Findings

• —Best Perplexity: 2-gram (subword) with 278
• —Entropy Trend: Decreases with larger n-grams (more predictable)
• —Coverage: Top-1000 patterns cover ~65% of corpus
• —Recommendation: 4-gram or 5-gram for best predictive performance

3. Markov Chain Evaluation

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Results

Generated Text Samples (Word-based)

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

Context Size 1:

1. 1.ⵏ ⵜⴰⵚⵚⵓⵕⵜ ⵜⴰⵎⵏⴰⴹⵜ ⵏ 800 ⵏ ⵜⴰⵎⴹⵉⵜ ⵙ ⵜⴳⵎⵉⴹⵉ ⵏ ⵍⵎⵏⵣⵍ ⵜⴰⵙⴳⴰ ⵏ ⵓⵙⵍⵎⴷ 95 ⵏ
2. 2.ⴳ ⵜⵍⴽⵎ ⵜⴳⵎⵉⴹⵉ ⵏ ⵜⵎⵍⵙⴰ ⵢⴰⴹⵏⵉ ⵣⵓⵏ ⴷ 11 ⵏ ⵢⵉⵡⵍ ⴰⵎⵣⵡⴰⵔⵓ 33 85 37 5
3. 3.ⴷ ⵉⵔⴰⵔ ⵍⵎⵖⵔⵉⴱ ⵉⴹⴼⴰⵕ ⵓⵙⵓⵏ ⵉⵎⵓⵏⵏ ⵢⵉⵍⵉ ⴳ ⵓⵙⵉⴹⵏ ⴰⵎⴰⴷⴷⵓⴷ ⵏ ⵜⴳⵍⴷⵉⵜ ⵜⴰⵙⴰⵄⵓⴷⵉⵜ ⴳ ⵜⴳⵔⴰⵡⵜ ⵏ

Context Size 2:

1. 1.ⵜⴳⵎⵉⴹⵉ ⵏ ⵎⴷⴷ ⵏⵏⴰ ⵥⴹⴰⵕⵏⵉⵏ ⵉ ⵜⵡⵓⵔⵉ 53 52 ⴳ ⴰⵢⵜ ⵄⵍⵍⴰ ⵏⵏⴰ ⴳ ⵍⵍⴰⵏ 5 ⵏ
2. 2.ⵏ ⵓⵙⴳⴳⵯⴰⵙ démographiques et socio économiques de la population et de l habitat de ⵜⴰⵙⵎⵉⵔⵉⵜ ⵜⴰⵎⴰⵜⵜⴰⵢⵜ...
3. 3.ⵓⵎⴹⴰⵏ ⵏ ⵉⵎⵣⴷⴰⵖⵏ ⵏⵏⵙ 75 ⵏ ⵜⵡⵜⵎⵉⵏ ⵜⴰⵡⵊⵉⵡⵉⵏ ⵉⵡⵍ ⴷ ⵜⴰⵔⵡⴰ ⴳ ⴳⴰⵏ ⵡⵉⵏⴰ ⵢⵉⵡⵍⵏ ⴳ ⵓⵙⵓⵏ

Context Size 3:

1. 1.ⵜⵍⴽⵎ ⵜⴳⵎⵉⴹⵉ ⵏ ⵜⴰⵔⵙⴽⴽⵉⵍⵜ 50 98 ⴳⵔ ⵉⵔⴱⴰⵏ ⴷ ⵜⵔⴱⴰⵜⵉⵏ ⵏⵏⴰ ⵖⵓⵔ ⴳⵔ 6 ⴷ 11 ⵏ ⵓⵙⴳⴳⵯⴰⵙ
2. 2.ⵓⵎⴹⴰⵏ ⵏ ⵉⵎⵣⴷⴰⵖⵏ ⵏⵏⵙ 122 ⵏ ⵓⵎⵣⴷⴰⵖ ⴳ ⵓⵙⵉⴹⵏ ⴰⵎⴰⴷⴷⵓⴷ ⵏ ⵓⵙⴳⴳⵯⴰⵙ démographiques et socio économiques de la
3. 3.ⵜⴰⵎⴰⵜⵜⴰⵢⵜ ⵏ ⵓⵙⵖⵉⵡⵙ ⴰⵕⵛⵉⴼ 14 ⵖⵓⵛⵜ ⵜⵉⵙⵏⴰⴷⴷⴰⴷⵉⵏ ⵜⵉⵙⵏⴰⴷⴷⴰⴷⵉⵏ ⵜⵉⵎⴰⵜⴰⵢⵉⵏ ⵉⴳⴳⵯⵉⵣ ⵓⵎⴹⴰⵏ ⵏ ⵉⵎⵣⴷⴰⵖⵏ ⵏ ⵜⴰⵖⵣⵓⵜ ⵙ...

Context Size 4:

1. 1.ⵜⴰⵙⵎⵉⵔⵉⵜ ⵜⴰⵎⴰⵜⵜⴰⵢⵜ ⵏ ⵓⵙⵖⵉⵡⵙ ⴰⵕⵛⵉⴼ 14 ⵖⵓⵛⵜ ⵜⵉⵙⵏⴰⴷⴷⴰⴷⵉⵏ ⵜⵉⵙⵏⴰⴷⴷⴰⴷⵉⵏ ⵜⵉⵎⴰⵜⴰⵢⵉⵏ ⵉⴳⴳⵯⵉⵣ ⵓⵎⴹⴰⵏ ⵏ ⵉⵎⵣⴷⴰⵖⵏ ⵏ...
2. 2.ⴳ ⵜⵍⴽⵎ ⵜⴳⵎⵉⴹⵉ ⵏ ⵎⴷⴷ ⵏⵏⴰ ⵥⴹⴰⵕⵏⵉⵏ ⵉ ⵜⵡⵓⵔⵉ 55 29 ⴳ ⴰⵢⵜ ⴱⵏ ⵄⴱⴱⵓ ⴰⵔ ⵏⵉⵜ ⵙⵡⵓⵔⵉⵏ ⵏⵉⵖ
3. 3.ⵓⵎⴹⴰⵏ ⵏ ⵉⵎⵣⴷⴰⵖⵏ ⵏⵏⵙ 390 ⵏ ⵓⵎⵣⴷⴰⵖ ⴳ ⵓⵙⵉⴹⵏ ⴰⵎⴰⴷⴷⵓⴷ ⵏ ⵓⵙⴳⴳⵯⴰⵙ démographiques et socio économiques de la...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. 1._ⵜⴰⵢⵢⵉⵏⴰⵔ_ⴱⵜⴰⵏ_ⵓ
2. 2.ⴰⵖⵉⵜ_ⵖⵜ_ⵇⵏ_ⴳ_non
3. 3.ⵏ_ⵉⵇⵜⴰ_ⵏ_ⵓⵎⴹⵏ_ⴼⵜ

Context Size 2:

1. 1.ⵏ_ⴷ_ⵉⴳⴳⵉⵙⵙ_3_ⴰⵍ_ⴰ
2. 2._ⵏ_ⴰⵎⴰⵏ_ⴳ_ⵓⵙⵙⴰⵖⵏ_
3. 3._ⵜⵡⵓⵔ_6_ⴽⵓⴷⴰⵖ,_ⵉⵥ

Context Size 3:

1. 1._ⵏ_ⵜⴰⵙⵡⵉⵏ_ⵉⵙⴽⴰⵔⵏⵜ_
2. 2._ⵜⴰⵡⵓⵔⵉ_4.52%_ⴳⵔ_6
3. 3._ⴳ_ⵍⵍⴰⵏ_ⵡⵉⵏ:_ⵉⵡⵜⵎⵉ

Context Size 4:

1. 1._ⵏ_ⵓⵍⴰ_ⴳ_ⴳⴰⵏ_ⵡⵉⵏⴰ_ⵢ
2. 2.ⵜ_ⵏ_ⵓⵙⵖⵉⵡⵙ._ⴰⵕⵛⵉⴼ,_
3. 3._ⵏ_ⵉⵡⵜⵎⴰⵏ_ⴷ_24.85,_

Key Findings

• —Best Predictability: Context-4 (word) with 95.5% predictability
• —Branching Factor: Decreases with context size (more deterministic)
• —Memory Trade-off: Larger contexts require more storage (223,378 contexts)
• —Recommendation: Context-3 or Context-4 for text generation

4. Vocabulary Analysis

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Statistics

Most Common Words

Least Common Words (from vocabulary)

Zipf's Law Analysis

Coverage Analysis

Key Findings

• —Zipf Compliance: R²=0.9914 indicates excellent adherence to Zipf's law
• —High Frequency Dominance: Top 100 words cover 67.5% of corpus
• —Long Tail: 25,191 words needed for remaining 3.3% coverage

5. Word Embeddings Evaluation

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5.1 Cross-Lingual Alignment

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5.2 Model Comparison

Key Findings

• —Best Isotropy: mono_32d with 0.7259 (more uniform distribution)
• —Semantic Density: Average pairwise similarity of 0.3340. Lower values indicate better semantic separation.
• —Alignment Quality: Aligned models achieve up to 3.6% 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

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

Productive Suffixes

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.

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.

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).

6.6 Linguistic Interpretation

Automated Insight:

The language Standard Moroccan Tamazight 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

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Production Recommendations

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. 1.Compare within model families: Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
2. 2.Consider trade-offs: Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
3. 3.Context matters: Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
4. 4.Corpus influence: All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
5. 5.Language-specific patterns: Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.

Visualizations Index

About This Project

Data Source

Models trained on wikipedia-monthly - a monthly snapshot of Wikipedia articles across 300+ languages.

Project

A project by [Wikilangs](https://wikilangs.org) - 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 --- Generated by Wikilangs Models Pipeline

Report Date: 2026-01-11 05:56:32