README.md

metadata

language: tn
language_name: Tswana
language_family: bantu_southern
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-bantu_southern
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: 4.812
  - name: best_isotropy
    type: isotropy
    value: 0.8424
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-11T00:00:00.000Z

Tswana - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Tswana 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	4.418x	4.42	0.0556%	737,223
16k	4.593x	4.59	0.0578%	709,175
32k	4.727x	4.73	0.0595%	689,022
64k	4.812x 🏆	4.81	0.0606%	676,881

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Need for Speed (NFS) ke motshameko wa motshikinyego o go thomiwang o o dirilweng...

Vocab	Tokens	Count
8k	▁need ▁for ▁spe ed ▁( nf s ) ▁ke ▁motshameko ... (+22 more)	32
16k	▁need ▁for ▁spe ed ▁( nf s ) ▁ke ▁motshameko ... (+19 more)	29
32k	▁need ▁for ▁spe ed ▁( nf s ) ▁ke ▁motshameko ... (+19 more)	29
64k	▁need ▁for ▁speed ▁( nf s ) ▁ke ▁motshameko ▁wa ... (+17 more)	27

Sample 2: Bekkersdal ke toropo ya Gauteng e ko lefatsheng la Aforika Borwa. Metswedi

Vocab	Tokens	Count
8k	▁be k kers dal ▁ke ▁toropo ▁ya ▁gauteng ▁e ▁ko ... (+6 more)	16
16k	▁be k kers dal ▁ke ▁toropo ▁ya ▁gauteng ▁e ▁ko ... (+6 more)	16
32k	▁be k kers dal ▁ke ▁toropo ▁ya ▁gauteng ▁e ▁ko ... (+6 more)	16
64k	▁bekkersdal ▁ke ▁toropo ▁ya ▁gauteng ▁e ▁ko ▁lefatsheng ▁la ▁aforika ... (+3 more)	13

Sample 3: Osaka ke toropo kgolo kwa Japan. E na le baagi ba le

Vocab	Tokens	Count
8k	▁o saka ▁ke ▁toropo ▁kgolo ▁kwa ▁japan . ▁e ▁na ... (+4 more)	14
16k	▁o saka ▁ke ▁toropo ▁kgolo ▁kwa ▁japan . ▁e ▁na ... (+4 more)	14
32k	▁osaka ▁ke ▁toropo ▁kgolo ▁kwa ▁japan . ▁e ▁na ▁le ... (+3 more)	13
64k	▁osaka ▁ke ▁toropo ▁kgolo ▁kwa ▁japan . ▁e ▁na ▁le ... (+3 more)	13

Key Findings

• Best Compression: 64k achieves 4.812x compression
• Lowest UNK Rate: 8k with 0.0556% 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	7,155	12.80	61,361	28.5%	48.8%
2-gram	Subword	191 🏆	7.58	3,179	76.4%	99.6%
3-gram	Word	14,210	13.79	120,191	25.9%	38.6%
3-gram	Subword	1,323	10.37	26,297	38.5%	81.3%
4-gram	Word	23,873	14.54	216,515	24.9%	33.3%
4-gram	Subword	6,088	12.57	134,442	22.1%	55.7%
5-gram	Word	10,743	13.39	157,061	32.2%	39.1%
5-gram	Subword	18,500	14.18	344,305	15.2%	39.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	aforika borwa	32,436
2	toropo ya	30,077
3	ke toropo	29,904
4	ya gauteng	29,770
5	gauteng e	29,736

3-grams (Word):

Rank	N-gram	Count
1	ke toropo ya	29,751
2	ya gauteng e	29,733
3	gauteng e aforika	29,718
4	toropo ya gauteng	29,718
5	e aforika borwa	29,717

4-grams (Word):

Rank	N-gram	Count
1	ya gauteng e aforika	29,718
2	gauteng e aforika borwa	29,717
3	ke toropo ya gauteng	29,716
4	toropo ya gauteng e	29,716
5	mamelodi ke toropo ya	29,700

5-grams (Word):

Rank	N-gram	Count
1	ya gauteng e aforika borwa	29,717
2	ke toropo ya gauteng e	29,714
3	toropo ya gauteng e aforika	29,706
4	mamelodi ke toropo ya gauteng	29,700
5	borwa mamelodi ke toropo ya	29,699

2-grams (Subword):

Rank	N-gram	Count
1	a _	935,894
2	e _	661,328
3	o _	427,244
4	l e	283,587
5	_ m	267,742

3-grams (Subword):

Rank	N-gram	Count
1	_ l e	169,796
2	l e _	163,890
3	n g _	148,572
4	w a _	147,301
5	y a _	133,144

4-grams (Subword):

Rank	N-gram	Count
1	_ y a _	122,807
2	_ l e _	121,639
3	e n g _	86,110
4	_ g o _	81,757
5	a _ b o	80,508

5-grams (Subword):

Rank	N-gram	Count
1	o _ y a _	65,761
2	_ y a _ g	42,726
3	_ k w a _	39,822
4	a _ g o _	37,584
5	k a _ b o	37,508

Key Findings

• Best Perplexity: 2-gram (subword) with 191
• 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	0.9132	1.883	6.97	102,696	8.7%
1	Subword	1.0155	2.022	7.94	975	0.0%
2	Word	0.3523	1.277	2.10	714,400	64.8%
2	Subword	0.9918	1.989	6.26	7,740	0.8%
3	Word	0.1700	1.125	1.38	1,497,396	83.0%
3	Subword	0.9000	1.866	4.58	48,443	10.0%
4	Word	0.0886 🏆	1.063	1.16	2,060,334	91.1%
4	Subword	0.6744	1.596	2.97	221,611	32.6%

Generated Text Samples (Word-based)

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

Context Size 1:

1. ya ntlha wa citylife ka beilby porteus bishopo wa batjho ba ba amegang mo melawaneng ya
2. le balatedi bale mo dipolelong tsa itsholelo le tlhaeletsano pula botswana e diragalang bonnyane le ...
3. e aforika borwa mamelodi ke marang rang a le 357 quoting from the namibian via africabib

Context Size 2:

1. aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika
2. toropo ya gauteng e aforika borwa e tshwenyegile ka ditlamorago tse di nnang kwa kgaolong ya kweneng
3. ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke

Context Size 3:

1. ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke torop...
2. ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gaute...
3. toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke toropo y...

Context Size 4:

1. ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gaute...
2. toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke toropo y...
3. ke toropo ya gauteng e aforika borwa mamelodi ke toropo ya gauteng e aforika borwa mamelodi ke torop...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _tlanga_ssatllhe
2. asophopolotlesha
3. eg,_ne_kgipave_d

Context Size 2:

1. a_mo_tlhabews_fet
2. e_neiratse_le_e_k
3. o_tekgo_ke_e_bof_

Context Size 3:

1. _le_e_a_nna_e_tor_
2. le_dipape_fa_tswa_
3. ng_e_aforika_di_mo

Context Size 4:

1. _ya_borwa._mamelodi
2. _le_mme_a_bonakgoba
3. eng_of_ethiopia_re,

Key Findings

• Best Predictability: Context-4 (word) with 91.1% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (221,611 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	51,001
Total Tokens	3,021,722
Mean Frequency	59.25
Median Frequency	4
Frequency Std Dev	1394.66

Most Common Words

Rank	Word	Frequency
1	ya	122,910
2	le	122,280
3	e	120,451
4	a	105,517
5	go	82,599
6	ka	70,434
7	ba	60,026
8	ne	54,685
9	o	51,263
10	ke	50,884

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	komit	2
2	duduzane	2
3	marčetić	2
4	prijedor	2
5	dnevne	2
6	novosti	2
7	greifenseelauf	2
8	makithing	2
9	benet	2
10	linnen	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1378
R² (Goodness of Fit)	0.995228
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	57.2%
Top 1,000	76.6%
Top 5,000	89.3%
Top 10,000	93.6%

Key Findings

• Zipf Compliance: R²=0.9952 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 57.2% of corpus
• Long Tail: 41,001 words needed for remaining 6.4% 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	0.8424	0.3285	N/A	N/A
mono_64d	64	0.8282	0.2689	N/A	N/A
mono_128d	128	0.7325	0.2225	N/A	N/A
aligned_32d	32	0.8424 🏆	0.3391	0.0640	0.3560
aligned_64d	64	0.8282	0.2702	0.1760	0.5100
aligned_128d	128	0.7325	0.2209	0.2840	0.6440

Key Findings

• Best Isotropy: aligned_32d with 0.8424 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2751. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 28.4% 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	-0.020	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
-ma	marcia, mahlatse, magudumana
-m	moinjineere, marcia, membrane
-s	sejaneng, still, stratification
-b	bontshiwang, busiwa, bongz
-a	adaptations, ausi, aug
-di	diitsholelo, distinguished, dikhwaere
-mo	moinjineere, motlabogi, monkeybone
-t	thapisitsweng, thakanyo, tedx

Productive Suffixes

Suffix	Examples
-e	christine, ratilwe, legotlhe
-ng	sejaneng, thapisitsweng, bontshiwang
-a	otjozondjupa, zuma, marcia
-g	rosberg, sejaneng, thapisitsweng
-s	vermeers, adaptations, focuses
-o	diitsholelo, phatlalatso, thakanyo
-n	zeaxanthin, stratification, defection
-i	shwahili, ausi, cpi

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
tion	2.63x	39 contexts	action, motion, notion
tsen	2.13x	60 contexts	tseno, tsene, tsena
tlho	1.79x	96 contexts	tlhoa, tlhopo, tlhora
tshw	2.08x	46 contexts	tshwa, ntshwa, tshweu
otlh	1.78x	67 contexts	otlhe, yotlhe, sotlhe
tshe	1.76x	68 contexts	ntshe, tsheko, tshele
lhop	2.30x	24 contexts	tlhopo, tlhopa, tlhopha
otsw	1.86x	43 contexts	otswa, rotswe, motswe
hoph	2.25x	21 contexts	tlhopha, tlhopho, tlhophe
mets	1.81x	43 contexts	metso, metsi, metse
wana	1.98x	30 contexts	swana, mowana, ntwana
gwag	2.28x	18 contexts	ngwag, gwaga, ngwago

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
-t	-g	121 words	tlhodileng, tlileng
-t	-ng	120 words	tlhodileng, tlileng
-t	-a	111 words	tshwaetswa, tsenngwa
-t	-e	108 words	takirambudde, togolese
-s	-e	95 words	setswerre, segololwane
-b	-i	93 words	bogasi, bukhari
-b	-e	90 words	blaze, banyamulenge
-di	-o	84 words	ditshenolo, dikago
-b	-g	83 words	benefitting, buang
-b	-ng	81 words	benefitting, buang

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
botshepegi	botshepe-g-i	7.5	g
kgatlhego	kgatlhe-g-o	7.5	g
prehistoric	p-re-historic	7.5	historic
watergate	water-ga-te	7.5	ga
eletsegang	eletseg-a-ng	7.5	a
malahlela	malah-le-la	7.5	le
botswanago	botswana-g-o	7.5	g
ditlhagala	ditlhag-a-la	7.5	a
motshidisi	motshi-di-si	7.5	di
bosimegeng	bosimeg-e-ng	7.5	e
northeast	northea-s-t	7.5	s
diphethogo	diphetho-g-o	7.5	g
rwandaise	rwanda-i-se	7.5	i
utlwaleng	utlwa-le-ng	7.5	le
kgatlhile	kgatlh-i-le	7.5	i

6.6 Linguistic Interpretation

Automated Insight: The language Tswana 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

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.81x)
N-gram	2-gram	Lowest perplexity (191)
Markov	Context-4	Highest predictability (91.1%)
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

---

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 01:22:30