README.md

metadata

language: ig
language_name: Igbo
language_family: atlantic_yoruba_igbo
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-atlantic_yoruba_igbo
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: 3.745
  - name: best_isotropy
    type: isotropy
    value: 0.8093
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10T00:00:00.000Z

Igbo - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Igbo 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	3.236x	3.24	0.3842%	188,457
16k	3.437x	3.44	0.4081%	177,404
32k	3.614x	3.62	0.4291%	168,744
64k	3.745x 🏆	3.75	0.4447%	162,811

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Duli bu nwere ike izo aka na: Duli, Ardabil, Iran Duli, Hamadan, Iran Duli, Nepa...

Vocab	Tokens	Count
8k	▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)	41
16k	▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)	41
32k	▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)	41
64k	▁du li ▁bu ▁nwere ▁ike ▁izo ▁aka ▁na : ▁du ... (+31 more)	41

Sample 2: Purukotó (Purucotó) bụ asụsụ Cariban na-apụ n'anya . Kaufman debere ya na ngalab...

Vocab	Tokens	Count
8k	▁pu ru ko t ó ▁( pu ru co t ... (+30 more)	40
16k	▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+24 more)	34
32k	▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+22 more)	32
64k	▁puru kot ó ▁( puru co tó ) ▁bụ ▁asụsụ ... (+22 more)	32

Sample 3: Manombai (nke a dị ka Wokam) bụ otu n'ime Asụsụ Aru, nke ndị bi na Aru Islands, ...

Vocab	Tokens	Count
8k	▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo ka ... (+24 more)	34
16k	▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)	33
32k	▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)	33
64k	▁man om bai ▁( nke ▁a ▁dị ▁ka ▁wo kam ... (+23 more)	33

Key Findings

• Best Compression: 64k achieves 3.745x compression
• Lowest UNK Rate: 8k with 0.3842% 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	26,246	14.68	359,156	15.9%	37.9%
2-gram	Subword	280 🏆	8.13	12,173	64.0%	99.0%
3-gram	Word	161,068	17.30	916,288	6.8%	18.8%
3-gram	Subword	2,183	11.09	87,468	30.4%	71.2%
4-gram	Word	532,594	19.02	1,757,879	4.0%	10.9%
4-gram	Subword	11,363	13.47	475,134	17.2%	44.2%
5-gram	Word	559,672	19.09	1,291,016	3.5%	8.9%
5-gram	Subword	42,173	15.36	1,479,265	10.6%	30.3%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	dị ka	140,163
2	a na	112,277
3	ọ bụ	105,148
4	ya na	99,998
5	site na	75,118

3-grams (Word):

Rank	N-gram	Count
1	ma ọ bụ	47,538
2	dị ka onye	33,165
3	dị iche iche	22,236
4	ndi di ndụ	19,640
5	na eme ihe	19,264

4-grams (Word):

Rank	N-gram	Count
1	mmadụ ndi di ndụ	17,108
2	òtù mmadụ ndi di	17,101
3	na eme ihe nkiri	13,842
4	akụkọ ihe mere eme	12,735
5	dị ka onye na	9,212

5-grams (Word):

Rank	N-gram	Count
1	òtù mmadụ ndi di ndụ	17,099
2	onye na eme ihe nkiri	6,973
3	òtù pages with unreviewed translations	4,329
4	e dere n ala ala	4,004
5	ihe e dere n ala	3,927

2-grams (Subword):

Rank	N-gram	Count
1	_ n	5,638,183
2	a _	5,376,024
3	e _	4,318,368
4	n a	2,708,872
5	_ a	2,215,860

3-grams (Subword):

Rank	N-gram	Count
1	_ n a	2,367,266
2	n a _	1,687,800
3	a _ n	1,387,006
4	e _ n	1,187,243
5	_ n k	938,041

4-grams (Subword):

Rank	N-gram	Count
1	_ n a _	1,567,660
2	_ n k e	743,366
3	n k e _	735,578
4	_ n a -	656,811
5	a _ n a	579,489

5-grams (Subword):

Rank	N-gram	Count
1	_ n k e _	722,504
2	_ n d ị _	399,246
3	_ i h e _	373,739
4	_ n a - e	351,252
5	a _ n a _	349,914

Key Findings

• Best Perplexity: 2-gram (subword) with 280
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~30% 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	0.8607	1.816	9.02	510,524	13.9%
1	Subword	1.0714	2.101	7.32	6,437	0.0%
2	Word	0.3599	1.283	2.38	4,598,546	64.0%
2	Subword	0.7215	1.649	4.70	47,137	27.9%
3	Word	0.1996	1.148	1.52	10,914,867	80.0%
3	Subword	0.6901	1.613	3.94	221,281	31.0%
4	Word	0.1054 🏆	1.076	1.21	16,623,256	89.5%
4	Subword	0.6621	1.582	3.28	871,504	33.8%

Generated Text Samples (Word-based)

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

Context Size 1:

1. na mmemme ahụ n ọtụtụ ndị dugara na abụọ nke 302 west sepik province nke a
2. nke na kaduna kama nke ndị agha ebumnuche na ndị na otu a na ya olulu
3. n ime ndị o kwuru na ahụ na eto ya niile na dholuo okpukpe n etiti

Context Size 2:

1. dị ka nke abụọ marathon nke etiopia onye otu bọọdụ na achọ ọfịs dabere na ike araromire
2. a na enyo enyo ébé ọ bi na ya jide nche anwụ nke all progressives congress apc
3. ọ bụ akụkụ nke machar colony akụkụ nke usoro nke na ezere ọkwa nna ya bụ 531

Context Size 3:

1. ma ọ bụ tin ore ihe ndị fọdụrụ na german army dina na nzuzo na eduga na nkwupụta
2. dị ka onye edemede na onye na ezisa ozi ọma na ghana ebe ọ mmụta akwụkwọ na adịbeghị
3. dị iche iche nke a ga enyocha n ihu nyocha nke chọpụtara ụzọ agha oke ala nke dara

Context Size 4:

1. òtù mmadụ ndi di ndụ òtù pages with unreviewed translations __lead_section__ áká_ịkẹngạ thumb ihe ej...
2. na eme ihe nkiri kacha mma na ọrụ dị mkpa nke ala ala dị n ibéetiti ahụ áká_èkpè thumb
3. akụkọ ihe mere eme na muizenberg cape town mbipụta abụ m na efe efe carapace doo wop girls of

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _i_natọ_nọ_ndona
2. a_ngbụ_ondiy_ma_
3. e_i_nnropana-e_ụ

Context Size 2:

1. _ng_porosii_nke_a
2. a_ọdụ_na_ka_hasụ_
3. e_12.2,_ndihe_ọzọ

Context Size 3:

1. _na-ụdị_nwunyere_o
2. na_nke_na_gọzi_na_
3. a_nke_umuagest_6_k

Context Size 4:

1. _na_baltham_taa_aː_
2. _nke_12,_ndị_burugb
3. nke_ọrụ_egypt_mara_

Key Findings

• Best Predictability: Context-4 (word) with 89.5% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (871,504 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	220,608
Total Tokens	24,129,478
Mean Frequency	109.38
Median Frequency	4
Frequency Std Dev	5866.90

Most Common Words

Rank	Word	Frequency
1	na	2,239,768
2	nke	735,052
3	n	615,909
4	ihe	410,419
5	ndị	405,283
6	ọ	395,253
7	ya	384,400
8	a	339,042
9	dị	325,019
10	onye	319,693

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	agbalagbo	2
2	akpalagu	2
3	okwule	2
4	otuogene	2
5	ovili	2
6	anyansi	2
7	ifediorah	2
8	chidalu	2
9	okebo	2
10	pdna	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.2680
R² (Goodness of Fit)	0.992771
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	50.1%
Top 1,000	75.8%
Top 5,000	88.4%
Top 10,000	91.8%

Key Findings

• Zipf Compliance: R²=0.9928 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 50.1% of corpus
• Long Tail: 210,608 words needed for remaining 8.2% 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	0.8093	0.4233	N/A	N/A
mono_64d	64	0.7925	0.3195	N/A	N/A
mono_128d	128	0.7531	0.2578	N/A	N/A
aligned_32d	32	0.8093 🏆	0.4482	0.2740	0.7140
aligned_64d	64	0.7925	0.3263	0.4540	0.8100
aligned_128d	128	0.7531	0.2597	0.6140	0.8900

Key Findings

• Best Isotropy: aligned_32d with 0.8093 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3391. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 61.4% 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	-0.708	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
-a	agathon, aboudia, ankusha
-m	mertsalov, millionaire, müttererholungsverein
-n	naimdb, nasril, nwpl
-ma	malitereihe, matsumoto, mackerdhuj
-s	schnee, shabaka, shuaibiu
-b	beloved, bourguiba, brunhild
-k	kechie, kareem, kilolo
-e	edekọrọ, eribake, edremoda

Productive Suffixes

Suffix	Examples
-e	kechie, millionaire, ghọtahie
-a	yulia, hekka, bourguiba
-s	hypochlorous, pleiades, morcus
-n	müttererholungsverein, fleischman, agathon
-i	wabehi, hajjaji, adefarati
-r	mountaineer, leaver, br
-o	turbo, wamco, kilolo
-t	chiat, rajput, zuidoost

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
atio	2.41x	79 contexts	ation, ratio, patio
fric	2.53x	46 contexts	afric, frick, friche
nati	2.46x	46 contexts	natij, inati, natie
epụt	2.22x	64 contexts	kepụta, ndepụt, mepụta
alit	1.92x	109 contexts	alita, alito, palit
kwad	2.39x	40 contexts	kwadi, kwado, kwada
wany	1.95x	71 contexts	wanyä, nwany, wanye
gbas	2.08x	54 contexts	gbasa, egbas, ịgbasa
nwan	1.93x	73 contexts	nwany, enwan, nwana
ụtar	2.04x	56 contexts	ụtara, ụtarị, tụtara
ọpụt	1.94x	68 contexts	ọpụta, kọpụta, họpụta
nwet	2.21x	39 contexts	nweta, nwetụ, nwete

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
-a	-a	92 words	amazônia, arema
-m	-e	74 words	montefiore, mmachineke
-m	-s	70 words	marthinus, missionaries
-m	-a	69 words	mgbasasa, mëhneja
-a	-e	69 words	adae, adamorobe
-s	-s	66 words	schreiners, strives
-a	-s	62 words	antiperspirants, autonomous
-s	-e	55 words	stalemate, sute
-k	-a	53 words	kadina, katọkwara
-s	-a	51 words	spelaea, shadia

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
avanzadoras	avanzador-a-s	7.5	a
commutata	commu-ta-ta	7.5	ta
starfruit	starfru-i-t	7.5	i
johnsonmain	johnsonm-a-in	7.5	a
maniapoto	maniapo-t-o	7.5	t
hollywoodland	hollywoodl-an-d	7.5	an
camptoceras	camptoce-ra-s	7.5	ra
expressway	express-wa-y	7.5	wa
minnijean	minnij-e-an	7.5	e
multiflora	multifl-o-ra	7.5	o
christened	christe-n-ed	7.5	n
westfälisch	westfälis-c-h	7.5	c
caballero	ca-baller-o	6.0	baller
personnel	person-ne-l	6.0	person
ameringer	ameri-ng-er	6.0	ameri

6.6 Linguistic Interpretation

Automated Insight: The language Igbo 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 (3.75x)
N-gram	2-gram	Lowest perplexity (280)
Markov	Context-4	Highest predictability (89.5%)
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: 2026-01-10 05:45:06