README.md

metadata

language: ce
language_name: Chechen
language_family: caucasian_northeast
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - feature-extraction
  - sentence-similarity
  - tokenization
  - n-grams
  - markov-chain
  - text-mining
  - fasttext
  - babelvec
  - vocabulous
  - vocabulary
  - monolingual
  - family-caucasian_northeast
license: mit
library_name: wikilangs
pipeline_tag: text-generation
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: 3.737
  - name: best_isotropy
    type: isotropy
    value: 0.8747
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-03T00:00:00.000Z

Chechen - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Chechen 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	2.792x	2.80	0.9605%	541,154
16k	3.113x	3.12	1.0708%	485,447
32k	3.423x	3.43	1.1775%	441,435
64k	3.737x 🏆	3.74	1.2855%	404,354

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Бейца (Бихор) Бейца (Клуж) Бейца (Марамуреш) Бейца (Муреш) Бейца (Хунедоара) Бей...

Vocab	Tokens	Count
8k	▁бей ца ▁( б их ор ) ▁бей ца ▁( ... (+30 more)	40
16k	▁бей ца ▁( б ихор ) ▁бей ца ▁( к ... (+24 more)	34
32k	▁бей ца ▁( бихор ) ▁бей ца ▁( клуж ) ... (+20 more)	30
64k	▁бейца ▁( бихор ) ▁бейца ▁( клуж ) ▁бейца ▁( ... (+14 more)	24

Sample 2: Киякты (Актобен область) Киякты (Мангистаунан область)

Vocab	Tokens	Count
8k	▁к ия кт ы ▁( акт обен ▁область ) ▁к ... (+10 more)	20
16k	▁к ия кты ▁( акт обен ▁область ) ▁к ия ... (+8 more)	18
32k	▁кия кты ▁( актобен ▁область ) ▁кия кты ▁( ман ... (+3 more)	13
64k	▁кия кты ▁( актобен ▁область ) ▁кия кты ▁( мангистаунан ... (+2 more)	12

Sample 3: ХӀаджали (40° 14' N 47° 16' E), (Бардан кӀошт) ХӀаджали (40° 27' N 47° 05' E), (...

Vocab	Tokens	Count
8k	▁хӏа дж али ▁( 4 0 ° ▁ 1 4 ... (+44 more)	54
16k	▁хӏадж али ▁( 4 0 ° ▁ 1 4 ' ... (+42 more)	52
32k	▁хӏадж али ▁( 4 0 ° ▁ 1 4 ' ... (+40 more)	50
64k	▁хӏадж али ▁( 4 0 ° ▁ 1 4 ' ... (+40 more)	50

Key Findings

• Best Compression: 64k achieves 3.737x compression
• Lowest UNK Rate: 8k with 0.9605% 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	3,390	11.73	113,212	22.9%	62.3%
2-gram	Subword	435 🏆	8.77	6,171	54.5%	98.0%
3-gram	Word	4,361	12.09	176,983	18.9%	57.8%
3-gram	Subword	2,517	11.30	59,082	23.1%	68.3%
4-gram	Word	5,357	12.39	387,928	16.4%	55.1%
4-gram	Subword	6,651	12.70	339,742	15.1%	48.5%
5-gram	Word	5,776	12.50	363,840	15.2%	53.7%
5-gram	Subword	11,240	13.46	966,556	12.7%	40.2%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	нах беха	1,039,295
2	беха меттигаш	953,014
3	билгалдахарш хьажоргаш	387,484
4	климат кхузахь	314,080
5	кхузахь климат	293,860

3-grams (Word):

Rank	N-gram	Count
1	нах беха меттигаш	952,977
2	климат кхузахь климат	274,749
3	кӏоштан нах беха	256,927
4	бахархой билгалдахарш хьажоргаш	156,557
5	ред а м	153,110

4-grams (Word):

Rank	N-gram	Count
1	кӏоштан нах беха меттигаш	256,923
2	лелаш ду сахьтан аса	134,397
3	нийса лелаш ду сахьтан	134,397
4	сахьтан аса йу utc	133,768
5	ду сахьтан аса йу	133,768

5-grams (Word):

Rank	N-gram	Count
1	нийса лелаш ду сахьтан аса	134,397
2	ду сахьтан аса йу utc	133,768
3	лелаш ду сахьтан аса йу	133,768
4	индексаш кӏоштан нах беха меттигаш	122,584
5	аьхка йовха хуьлу ткъа ӏа	113,661

2-grams (Subword):

Rank	N-gram	Count
1	а _	10,875,281
2	. _	9,874,426
3	н _	8,151,111
4	а н	7,675,531
5	р а	6,751,030

3-grams (Subword):

Rank	N-gram	Count
1	а н _	4,716,126
2	_ — _	2,941,993
3	р а _	2,306,576
4	а ш _	2,292,649
5	а х ь	2,054,431

4-grams (Subword):

Rank	N-gram	Count
1	т а н _	1,577,468
2	а х а р	1,505,060
3	а _ м е	1,193,821
4	а х ь _	1,177,180
5	_ м е т	1,177,138

5-grams (Subword):

Rank	N-gram	Count
1	_ м е т т	1,166,495
2	м е т т и	1,154,656
3	е т т и г	1,154,628
4	а _ м е т	1,067,312
5	_ н а х _	1,048,954

Key Findings

• Best Perplexity: 2-gram (subword) with 435
• 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	0.6776	1.600	4.20	526,205	32.2%
1	Subword	0.9453	1.926	9.06	1,550	5.5%
2	Word	0.1950	1.145	1.49	2,194,953	80.5%
2	Subword	0.9623	1.948	7.39	14,021	3.8%
3	Word	0.0756	1.054	1.15	3,239,505	92.4%
3	Subword	0.8389	1.789	4.99	103,540	16.1%
4	Word	0.0367 🏆	1.026	1.08	3,672,181	96.3%
4	Subword	0.7073	1.633	3.29	516,039	29.3%

Generated Text Samples (Word-based)

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

Context Size 1:

1. а зонехьклимат тверской области бахархойн дукхалла бахархойн дукхалла бахархойн дукхалла климат йу л...
2. нах беха меттигаш жудецан нах беха меттигаш штатан йукъахь дара кочими монки гуайкура перику индейн ...
3. беха меттигаш нах беха меттигаш нисйина нах беха меттигаш нисйина нах беха меттигаш кӏоштан индексаш...

Context Size 2:

1. нах беха меттигаш нах беха меттигаш лаха калифорни штатан йукъахь йу бахархой билгалдахарш литератур...
2. беха меттигаш воеводаллин нах беха меттигаш нисйина нах беха меттигаш нисйина нах беха меттигаш нах ...
3. билгалдахарш хьажоргаш спас деменскан кӏошт калугин областан спас деменскан кӏоштара дӏатесна эвла б...

Context Size 3:

1. нах беха меттигаш кӏоштан нах беха меттигаш штатан нах беха меттигаш штатан нах беха меттигаш штатан...
2. климат кхузахь климат йу лаьттайуккъера хӏордан барамехь йекъа а йовха ӏа шийла ца хуьйлат а галкина...
3. кӏоштан нах беха меттигаш штатан нах беха меттигаш нах беха меттигаш нисйина нах беха меттигаш нисйи...

Context Size 4:

1. лелаш ду сахьтан аса йу utc 3 билгалдахарш хьажоргаш устьян кӏоштан индексаш кӏоштан нах беха меттиг...
2. нийса лелаш ду сахьтан аса йу utc 3 билгалдахарш хьажоргаш приморскан кӏоштан индексаш областан прим...
3. ду сахьтан аса йу utc 7 билгалдахарш мохк

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _йаду_—_н_бще_вх
2. анташ_гахахарха_
3. нцалальталарклус

Context Size 2:

1. а_хила_дуьлинецес
2. ._у-фактябра_эххь
3. н_йоккъах_бехь_ст

Context Size 3:

1. ан_областан_сизал_
2. _—_январь_современ
3. ра_хьолехьажоргаш_

Context Size 4:

1. тан_асан_коммунан_х
2. ахарш_хьажоргаши_(д
3. а_меттигаш_коммунан

Key Findings

• Best Predictability: Context-4 (word) with 96.3% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (516,039 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	238,347
Total Tokens	67,032,110
Mean Frequency	281.24
Median Frequency	3
Frequency Std Dev	8160.67

Most Common Words

Rank	Word	Frequency
1	а	1,815,637
2	нах	1,049,193
3	беха	1,039,696
4	меттигаш	968,757
5	йу	814,157
6	м	798,557
7	климат	741,272
8	в	736,957
9	билгалдахарш	631,076
10	с	588,454

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	эмпачадо	2
2	энано	2
3	эскопетал	2
4	эскриторио	2
5	макариос	2
6	эроика	2
7	скирринг	2
8	зигуинчор	2
9	зигуиншор	2
10	люксембургхо	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.8633
R² (Goodness of Fit)	0.948539
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	41.8%
Top 1,000	83.4%
Top 5,000	96.8%
Top 10,000	97.8%

Key Findings

• Zipf Compliance: R²=0.9485 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 41.8% of corpus
• Long Tail: 228,347 words needed for remaining 2.2% coverage

---

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	0.8747	0.3629	N/A	N/A
mono_64d	64	0.8592	0.2868	N/A	N/A
mono_128d	128	0.7998	0.2691	N/A	N/A
aligned_32d	32	0.8747 🏆	0.3562	0.0120	0.0960
aligned_64d	64	0.8592	0.3007	0.0320	0.2180
aligned_128d	128	0.7998	0.2615	0.1100	0.3620

Key Findings

• Best Isotropy: aligned_32d with 0.8747 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3062. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 11.0% 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

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	0.335	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
-ка	каркаусь, кассагумахи, кафка
-ко	костровскан, коховка, колумбехь

Productive Suffixes

Suffix	Examples
-а	ледара, жиховка, масленка
-н	галийн, кувшин, самодийн
-о	белшево, санторо, эрцо
-ан	тӏаьрсиган, менделеевскан, костровскан
-во	белшево, миллерово, горяново
-ка	жиховка, масленка, раковка
-ово	миллерово, горяново, атынаково
-ки	недниковски, новокубански, ибараки

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
архо	2.00x	121 contexts	архон, лархо, тархо
исто	1.91x	130 contexts	мисто, чисто, исток
галд	2.88x	16 contexts	галда, галдо, галдун
ргаш	2.28x	34 contexts	ургаш, воргаш, мургаш
харх	2.14x	41 contexts	йахарх, хархув, мухарх
икин	1.84x	62 contexts	викин, рикин, бикин
халл	1.55x	92 contexts	халле, халль, халла
рхой	2.30x	19 contexts	лархой, сурхой, ахархой
лгал	2.36x	17 contexts	билгал, билгало, билгала
игаш	2.34x	17 contexts	бигаш, цигаш, эхигаш
етти	1.73x	42 contexts	бетти, нетти, петтит
ттиг	1.96x	25 contexts	меттиг, гаттиг, ме́ттиг

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
-ко	-а	44 words	комната, колохта
-ка	-о	40 words	кастелларо, карманково
-ка	-а	38 words	казчана, кажа
-ко	-о	35 words	корково, кощейково
-ка	-н	27 words	кассон, капланецкан
-ко	-н	23 words	конкистадоран, коюнлун
-ко	-во	17 words	корково, кощейково
-ка	-во	16 words	карманково, каптырево
-ка	-ан	15 words	капланецкан, каштан
-ко	-ан	13 words	конкистадоран, котован

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
евдокимовски	евдокимовс-ки	4.5	евдокимовс
заказникан	заказник-ан	4.5	заказник
череповецан	череповец-ан	4.5	череповец
господиново	господин-ово	4.5	господин
вайнахана	вайнаха-на	4.5	вайнаха
воротынскан	воротынск-ан	4.5	воротынск
кинофильман	кинофильм-ан	4.5	кинофильм
дийцаршна	дийцарш-на	4.5	дийцарш
театрашка	театраш-ка	4.5	театраш
федотован	федотов-ан	4.5	федотов
веселовка	веселов-ка	4.5	веселов
маядыково	маядык-ово	4.5	маядык
ходоровка	ходоров-ка	4.5	ходоров
новиковски	новиковс-ки	4.5	новиковс
меженашна	меженаш-на	4.5	меженаш

6.6 Linguistic Interpretation

Automated Insight: The language Chechen 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.

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (3.74x)
N-gram	2-gram	Lowest perplexity (435)
Markov	Context-4	Highest predictability (96.3%)
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 20:55:32