README.md

metadata

language: ady
language_name: ADY
language_family: caucasian_northwest
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - monolingual
  - family-caucasian_northwest
license: mit
library_name: wikilangs
pipeline_tag: feature-extraction
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: 4.231
  - name: best_isotropy
    type: isotropy
    value: 0.473
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03T00:00:00.000Z

ADY - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on ADY 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	3.442x	3.45	0.1638%	134,283
16k	3.798x	3.80	0.1808%	121,676
32k	4.231x 🏆	4.24	0.2014%	109,215

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Шъхьафит — Ашэ псыхъо иджабгъу нэпкъы тес Адыгэ къуадж. районым хахьэ. Хым зы пэ...

Vocab	Tokens	Count
8k	▁шъхьафит ▁— ▁ашэ ▁псыхъо ▁иджабгъу ▁нэпкъы ▁тес ▁адыгэ ▁къуадж . ... (+7 more)	17
16k	▁шъхьафит ▁— ▁ашэ ▁псыхъо ▁иджабгъу ▁нэпкъы ▁тес ▁адыгэ ▁къуадж . ... (+7 more)	17
32k	▁шъхьафит ▁— ▁ашэ ▁псыхъо ▁иджабгъу ▁нэпкъы ▁тес ▁адыгэ ▁къуадж . ... (+7 more)	17

Sample 2: thumb Америкэ - чӀынэлъэшхухэр Iут зэхэт (Къыблэ Америкэмрэ, Ишъхъэрэмрэ) Тыгъэк...

Vocab	Tokens	Count
8k	▁thumb ▁америкэ ▁- ▁чӏы нэлъэ шхухэр ▁i ут ▁зэхэт ▁( ... (+17 more)	27
16k	▁thumb ▁америкэ ▁- ▁чӏынэлъэшхухэр ▁i ут ▁зэхэт ▁( къыблэ ▁америкэмрэ ... (+13 more)	23
32k	▁thumb ▁америкэ ▁- ▁чӏынэлъэшхухэр ▁i ут ▁зэхэт ▁( къыблэ ▁америкэмрэ ... (+11 more)	21

Sample 3: thumb Мамуныр мэз псэушъхьэхэмэ а щыщ. Мамунхэр чыг дэпшэиэным лъэшэу Мамуным и ...

Vocab	Tokens	Count
8k	▁thumb ▁мамун ыр ▁мэз ▁псэушъхьэхэмэ ▁а ▁щыщ . ▁мамун хэр ... (+22 more)	32
16k	▁thumb ▁мамуныр ▁мэз ▁псэушъхьэхэмэ ▁а ▁щыщ . ▁мамунхэр ▁ч ыг ... (+14 more)	24
32k	▁thumb ▁мамуныр ▁мэз ▁псэушъхьэхэмэ ▁а ▁щыщ . ▁мамунхэр ▁чыг ▁дэпшэиэным ... (+10 more)	20

Key Findings

• Best Compression: 32k achieves 4.231x compression
• Lowest UNK Rate: 8k with 0.1638% 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	418	8.71	593	45.3%	100.0%
2-gram	Subword	399 🏆	8.64	2,072	57.0%	97.4%
3-gram	Word	706	9.46	922	33.9%	100.0%
3-gram	Subword	2,788	11.44	11,614	24.5%	65.1%
4-gram	Word	2,848	11.48	3,264	13.1%	44.3%
4-gram	Subword	10,651	13.38	35,316	12.4%	39.6%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	нэбгырэ млн	169
2	къехъу щэпсэу	104
3	картым тетэу	100
4	м къехъу	89
5	дло м	87

3-grams (Word):

Rank	N-gram	Count
1	м къехъу щэпсэу	76
2	къехъу щэпсэу хэгэгум	70
3	адыгэ республикэм и	48
4	дло м хахьэ	44
5	м хахьэ хэгъэгу	39

4-grams (Word):

Rank	N-gram	Count
1	м къехъу щэпсэу хэгэгум	45
2	дло м хахьэ хэгъэгу	39
3	еуропэм хэт къэралыгъу къэлэ	23
4	америкэм ит къэралыгъу къэлэ	19
5	азием ит къэралыгъу къэлэ	18

2-grams (Subword):

Rank	N-gram	Count
1	г ъ	9,349
2	ъ э	9,255
3	э _	8,719
4	м _	7,823
5	э р	6,778

3-grams (Subword):

Rank	N-gram	Count
1	г ъ э	4,967
2	_ к ъ	4,149
3	э м _	3,582
4	ы г ъ	3,357
5	э р _	3,016

4-grams (Subword):

Rank	N-gram	Count
1	ы г ъ э	1,903
2	х э р _	1,450
3	а г ъ э	1,351
4	х э м _	1,305
5	_ к ъ э	1,289

Key Findings

• Best Perplexity: 2-gram (subword) with 399
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~40% 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	0.4365	1.353	2.10	22,306	56.3%
1	Subword	1.4909	2.811	10.56	410	0.0%
2	Word	0.0764	1.054	1.12	46,305	92.4%
2	Subword	1.1481	2.216	5.61	4,325	0.0%
3	Word	0.0240	1.017	1.03	51,243	97.6%
3	Subword	0.7541	1.687	2.97	24,260	24.6%
4	Word	0.0128 🏆	1.009	1.02	52,387	98.7%
4	Subword	0.4304	1.348	1.86	72,077	57.0%

Generated Text Samples (Word-based)

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

Context Size 1:

1. и 13 мэ ащыщэу адыгэр сыдигъокіи адыгэ къуаж ипшъэ итхьапӏэ иблэгъожъхэм афэгъэхьыгъэ мифхэр къызэра...
2. адыгэ хьатыкъуай унагъохэр тыркуем и плакат ныбэрынхьэблэ адыгэбзэ жэбзэ къабзэ ежь ныпым зызиушъомб...
3. м ахахьэ хэгъэгу шавкат мирзияев къэрал лӏышъхьэр кӏокӏо къызбэч кавказ заом ыпэкӏэ щыӏагъэхэмрэ якъ...

Context Size 2:

1. нэбгырэ млн 10 фэдиз тешӏагъэу анатолием ахэр агъэкощыгъэх тхыгъэ зэфэшъхьафхэм мэхьанэу каноничност...
2. къехъу щэпсэу я 84 хэгэгум 93 030 км я 26 испаныбзэр ащ нэмыкӏэу регионыбзэхэр иӏэх дло м
3. картым тетэу бразилие къыблэ америкэм ыгу ит германиер аустриер словакиер руманиер украинэр сербиер ...

Context Size 3:

1. м къехъу щэпсэу хэгэгум 2 149 690 км арапыбз сауд арабиер арап къэралыгъомэ ащыщмэ анахь хэгъэгу ащы...
2. къехъу щэпсэу хэгэгум 140 800 км непали дло м хахьэ хэгъэгу хассанал болкиах географие азием и гъунэ...
3. адыгэ республикэм и къэралыгъо премие илауреат дунэе адыгэ академием иакадемик къалэу шъачэ поселкэу...

Context Size 4:

1. м къехъу щэпсэу хэгэгум 9 596 960 км китаибзэр дло м хахьэ хэгъэгу эмомали рахмон къэрал тхьэматэр к...
2. дло м хахьэ хэгъэгу джоко видодо гуадзэр юсуф калла географие океан шъэфымымрэ инд океанымрэ азфагу ...
3. еуропэм хэт къэралыгъу къэлэ париж нэбгырэ млн 66 м къехъу щэпсэу хэгэгум 9 984 670 км я 2 англыбзэ

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _фим_хъэрикъолам
2. эм_илъу_-м_бэхь_
3. ышъэпсым_илнине_

Context Size 2:

1. гъэгъэ_асэу_ɡʲadə
2. ъэхьэухэм_епхъухь
3. э_хэгьэмрэ_щыпӏэ-

Context Size 3:

1. гъэ_уахэмрэ,_къыуи
2. _къалэбилэжъ_зэпхъ
3. эм_ыгугъэкон_къаук

Context Size 4:

1. ыгъэуцохэр_чэзыу-чэ
2. хэр_нэхъин_динхэр_з
3. агъэр_гъэп,_англыбз

Key Findings

• Best Predictability: Context-4 (word) with 98.7% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (72,077 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	7,032
Total Tokens	44,503
Mean Frequency	6.33
Median Frequency	3
Frequency Std Dev	22.13

Most Common Words

Rank	Word	Frequency
1	и	1,013
2	адыгэ	666
3	м	489
4	илъэсым	398
5	ащ	391
6	я	309
7	ары	271
8	нэбгырэ	247
9	а	243
10	ыкӏи	211

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	рсфср	2
2	серийнэ	2
3	ныбжьыкӏэхэри	2
4	зэратебэнагъэр	2
5	хираганэ	2
6	катаканэ	2
7	сербыбзэм	2
8	къыздикӏыгъэр	2
9	тыванбзэ	2
10	къызыл	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.7821
R² (Goodness of Fit)	0.977951
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	29.3%
Top 1,000	60.6%
Top 5,000	90.9%
Top 10,000	0.0%

Key Findings

• Zipf Compliance: R²=0.9780 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 29.3% of corpus
• Long Tail: -2,968 words needed for remaining 100.0% coverage

---

5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

Note: Multilingual alignment visualization not available for this language.

5.2 Model Comparison

Model	Dimension	Isotropy	Semantic Density	Alignment R@1	Alignment R@10
mono_32d	32	0.4730 🏆	0.4239	N/A	N/A
mono_64d	64	0.2201	0.4040	N/A	N/A
mono_128d	128	0.0372	0.3952	N/A	N/A

Key Findings

• Best Isotropy: mono_32d with 0.4730 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.4077. Lower values indicate better semantic separation.
• Alignment Quality: No aligned models evaluated in this run.
• Recommendation: 128d aligned for best cross-lingual performance

---

6. Morphological Analysis (Experimental)

⚠️ Warning: This language shows low morphological productivity. The statistical signals used for this analysis may be noisy or less reliable than for morphologically rich languages.

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	0.000	Low morphological productivity	⚠️ Likely unreliable
Idiomaticity Gap	-1.000	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
-къ	къыщыхъу, къуаджэхэу, къэбарым
-зэ	зэман, зэдаштэгъэ, зэпэух
-къы	къыщыхъу, къыщыфэфедэщтхэу, къызыхэкӏыгъэр

Productive Suffixes

Suffix	Examples
-э	ятхьэ, урысыбзэ, чылэ
-м	такъырым, шапхъэхэм, къэбарым
-р	латвиер, сыхьатыр, министр
-эр	курдхэр, щыгъынхэр, мэхъошхэр
-эм	шапхъэхэм, япэм, урымыбзэм
-эу	алфавитэу, илъхэу, игъэкӏотыгъэу
-хэр	курдхэр, щыгъынхэр, мэхъошхэр
-рэ	къагъэлъагъуэрэ, зыгорэ, цӏэмрэ

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
тыгъ	1.78x	28 contexts	тыгъэ, итыгъ, тыгъу
ъагъ	2.15x	14 contexts	пчъагъ, лъагъо, пчъагъэ
агъэ	1.54x	41 contexts	тхагъэ, благъэ, пчагъэ
эпкъ	1.74x	25 contexts	нэпкъ, тхэпкъ, лъэпкъ
къуа	2.16x	10 contexts	къуае, къуажэ, къуадж
ъхьэ	1.78x	16 contexts	шъхьэ, пшъхьэ, шъхьэм
дыгэ	1.82x	14 contexts	адыгэ, адыгэм, иадыгэ
эхэр	1.56x	21 contexts	бэхэр, усэхэр, ынэхэр
шъхь	1.49x	24 contexts	шъхьэ, пшъхьэ, шъхьэм
псэу	1.57x	19 contexts	щыпсэу, щэпсэу, сыпсэу
ыгъо	1.56x	19 contexts	цыгъо, мыгъо, пщыгъо
гъэх	1.65x	14 contexts	багъэх, хъугъэх, ежагъэх

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
-къ	-э	96 words	къэлэмымкӏэ, къалэмэ
-къ	-р	64 words	къор, къуаджэхэр
-къ	-м	56 words	къалэм, къумбылым
-къ	-эр	52 words	къуаджэхэр, къэбархэр
-зэ	-р	42 words	зэготхэр, зэхэтхэр
-зэ	-м	41 words	зэхэзгъэуцуагъэхэм, зэӏукӏэгъум
-къ	-эм	36 words	къалэм, къуаджэхэм
-зэ	-эр	34 words	зэготхэр, зэхэтхэр
-къ	-эу	34 words	къыхэкӏыгъэу, къэгъэлъэгъонэу
-зэ	-э	31 words	зэ, зэригъэфэгъэ

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
щыпсэухэрэр	щыпс-эу-хэр-эр	7.5	щыпс
америкэмрэ	америк-эм-рэ	6.0	америк
океанымрэ	океан-ым-рэ	6.0	океан
литературэмрэ	литератур-эм-рэ	6.0	литератур
бзылъфыгъэмрэ	бзылъфыгъ-эм-рэ	6.0	бзылъфыгъ
адыгабзэмрэ	адыгабз-эм-рэ	6.0	адыгабз
хыплъыжьымрэ	хыплъыжь-ым-рэ	6.0	хыплъыжь
алфавитэу	алфавит-эу	4.5	алфавит
цӏыкӏухэр	цӏыкӏу-хэр	4.5	цӏыкӏу
исурэтхэр	исурэт-хэр	4.5	исурэт
шӏыпӏэхэр	шӏыпӏэ-хэр	4.5	шӏыпӏэ
шӏэныгъэм	шӏэныгъ-эм	4.5	шӏэныгъ
къыпыщылъ	къы-пыщылъ	4.5	пыщылъ
пэблагъэу	пэблагъ-эу	4.5	пэблагъ
ишъхъэрэмрэ	ишъхъ-эр-эм-рэ	4.5	ишъхъ

6.6 Linguistic Interpretation

Automated Insight: The language ADY appears to be more isolating or has a highly fixed vocabulary. Word-level models perform nearly as well as subword models, indicating fewer productive morphological processes.

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	32k BPE	Best compression (4.23x)
N-gram	2-gram	Lowest perplexity (399)
Markov	Context-4	Highest predictability (98.7%)
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

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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: 2026-01-03 05:00:02