wikilangs/gl

Galician - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Galician 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

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1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.858x	3.86	0.0578%	2,440,248
16k	4.272x	4.27	0.0640%	2,203,809
32k	4.611x	4.61	0.0691%	2,041,816
64k	4.855x 🏆	4.86	0.0728%	1,939,285

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Galería de imaxes do río Lima, en Portugal. Véxase tamén de imaxes de Galicia

Vocab	Tokens	Count
8k	▁galería ▁de ▁imaxes ▁do ▁río ▁li ma , ▁en ▁portugal ... (+7 more)	17
16k	▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)	16
32k	▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)	16
64k	▁galería ▁de ▁imaxes ▁do ▁río ▁lima , ▁en ▁portugal . ... (+6 more)	16

Sample 2: Como topónimo Gurgueiro pode referirse a: En Galiza Gurgueiro, parroquia do conc...

Vocab	Tokens	Count
8k	▁como ▁topónimo ▁g ur gueiro ▁pode ▁referirse ▁a : ▁en ... (+22 more)	32
16k	▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)	27
32k	▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)	27
64k	▁como ▁topónimo ▁gur gueiro ▁pode ▁referirse ▁a : ▁en ▁galiza ... (+17 more)	27

Sample 3: Acontecementos Os escitas fanse co poder en Media (até -625). Nacementos Mortes ...

Vocab	Tokens	Count
8k	▁acontecementos ▁os ▁es ci tas ▁f anse ▁co ▁poder ▁en ... (+32 more)	42
16k	▁acontecementos ▁os ▁es ci tas ▁f anse ▁co ▁poder ▁en ... (+30 more)	40
32k	▁acontecementos ▁os ▁esci tas ▁fanse ▁co ▁poder ▁en ▁media ▁( ... (+28 more)	38
64k	▁acontecementos ▁os ▁escitas ▁fanse ▁co ▁poder ▁en ▁media ▁( até ... (+27 more)	37

Key Findings

• Best Compression: 64k achieves 4.855x compression
• Lowest UNK Rate: 8k with 0.0578% unknown tokens
• Trade-off: Larger vocabularies improve compression but increase model size
• Recommendation: 32k vocabulary provides optimal balance for production use

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2. N-gram Model Evaluation

Results

N-gram	Variant	Perplexity	Entropy	Unique N-grams	Top-100 Coverage	Top-1000 Coverage
2-gram	Word	177,694	17.44	1,599,435	7.3%	19.3%
2-gram	Subword	245 🏆	7.94	20,270	70.9%	99.1%
3-gram	Word	807,045	19.62	3,552,102	4.3%	10.2%
3-gram	Subword	2,104	11.04	147,394	28.3%	74.1%
4-gram	Word	1,759,879	20.75	5,677,444	3.4%	7.5%
4-gram	Subword	12,759	13.64	835,381	12.6%	40.0%
5-gram	Word	1,252,252	20.26	3,696,764	3.5%	8.1%
5-gram	Subword	56,114	15.78	2,862,824	6.8%	23.0%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	a súa	166,299
2	véxase tamén	147,020
3	e a	141,357
4	que se	140,843
5	o seu	139,408

3-grams (Word):

Rank	N-gram	Count
1	notas véxase tamén	95,486
2	lugar da parroquia	82,962
3	da parroquia de	77,119
4	véxase tamén outros	57,984
5	tamén outros artigos	57,948

4-grams (Word):

Rank	N-gram	Count
1	lugar da parroquia de	74,902
2	véxase tamén outros artigos	57,933
3	véxase tamén ligazóns externas	46,507
4	lugares e parroquias lugares	41,059
5	notas véxase tamén outros	37,978

5-grams (Word):

Rank	N-gram	Count
1	notas véxase tamén outros artigos	37,947
2	é un lugar da parroquia	36,571
3	lugares e parroquias lugares de	36,422
4	un lugar da parroquia de	32,976
5	notas véxase tamén ligazóns externas	27,554

2-grams (Subword):

Rank	N-gram	Count
1	e _	15,175,729
2	a _	14,798,960
3	o _	13,352,930
4	_ d	12,106,131
5	s _	11,921,693

3-grams (Subword):

Rank	N-gram	Count
1	_ d e	6,968,387
2	d e _	6,412,643
3	o s _	4,111,887
4	_ c o	3,620,668
5	a s _	3,409,570

4-grams (Subword):

Rank	N-gram	Count
1	_ d e _	5,450,285
2	_ e n _	1,570,834
3	c i ó n	1,543,944
4	o _ d e	1,530,833
5	_ q u e	1,455,346

5-grams (Subword):

Rank	N-gram	Count
1	_ q u e _	1,344,486
2	o _ d e _	1,307,199
3	s _ d e _	1,120,819
4	c i ó n _	1,079,093
5	a _ d e _	1,051,617

Key Findings

• Best Perplexity: 2-gram (subword) with 245
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~23% of corpus
• Recommendation: 4-gram or 5-gram for best predictive performance

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3. Markov Chain Evaluation

Results

Context	Variant	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	Word	1.0271	2.038	12.90	1,350,064	0.0%
1	Subword	1.1629	2.239	7.96	10,661	0.0%
2	Word	0.4319	1.349	2.66	17,398,522	56.8%
2	Subword	0.6671	1.588	4.32	84,803	33.3%
3	Word	0.1909	1.142	1.44	46,200,692	80.9%
3	Subword	0.6991	1.624	4.08	366,443	30.1%
4	Word	0.0765 🏆	1.054	1.14	66,305,200	92.4%
4	Subword	0.6839	1.606	3.51	1,494,151	31.6%

Generated Text Samples (Word-based)

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

Context Size 1:

1. de outubro josé manuel fernández novoa médico e impulsou a altura inerme ante o en polo
2. a mellor de sabedoría pervivindo algúns atribúenlle a proa cafè món e coli estas características ori...
3. e a cal será desmontado en hábitats de vanuatu nacionais e os tempos rexistrados participación a

Context Size 2:

1. a súa orixe no mercado invernal o manchester united fc do racing club de ferrol onde antigamente
2. véxase tamén outros artigos dólar internacional é unha substancia derivada da norma xurídica ditada ...
3. e a segunda guerra mundial voou por primeira vez a vida dedicándose a promover abertamente a bandeir...

Context Size 3:

1. notas véxase tamén bibliografía bradbury mark becoming somaliland james currey isbn michael schoiswo...
2. lugar da parroquia de nantón no concello de fisterra san paio de carreira monte da cidá é un
3. da parroquia de augas santas no concello de lugo san amaro lugar da parroquia de cobres no concello

Context Size 4:

1. lugar da parroquia de san pedro de antealtares da mesma cidade compostela dise que compuxo esta pía ...
2. véxase tamén outros artigos lugares de nigrán de nigrán de fútbol do cd lalín do algeciras cf do ad
3. véxase tamén ligazóns externas de en lingua francesa de francia da arte do alemán ao francés da univ...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _gon_axianto_fab
2. abre_po_douraba_
3. elá_s_121_dola_d

Context Size 2:

1. e_pertide_recació
2. a_mán,_e_acipobro
3. o_cruque_seta._e_

Context Size 3:

1. _de_direculta_desc
2. de_libra_súa_idena
3. os_aneirashi,_unha

Context Size 4:

1. _de_marticide_on_d.
2. _en_funciosos_ríxid
3. ción_regreira_da_ac

Key Findings

• Best Predictability: Context-4 (word) with 92.4% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,494,151 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	625,330
Total Tokens	87,603,878
Mean Frequency	140.09
Median Frequency	4
Frequency Std Dev	9798.07

Most Common Words

Rank	Word	Frequency
1	de	5,468,609
2	a	2,599,768
3	e	2,303,284
4	o	2,069,024
5	en	1,636,097
6	que	1,373,339
7	do	1,309,653
8	da	1,272,492
9	no	696,789
10	un	677,110

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	nodulisporium	2
2	sylviforme	2
3	cladosporioides	2
4	ccnsc	2
5	bessels	2
6	espertina	2
7	esperpenta	2
8	faïence	2
9	malecoloxía	2
10	clappi	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0034
R² (Goodness of Fit)	0.997371
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	39.7%
Top 1,000	60.3%
Top 5,000	76.2%
Top 10,000	82.6%

Key Findings

• Zipf Compliance: R²=0.9974 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 39.7% of corpus
• Long Tail: 615,330 words needed for remaining 17.4% coverage

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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	0.8055	0.3709	N/A	N/A
mono_64d	64	0.7807	0.2987	N/A	N/A
mono_128d	128	0.7103	0.2440	N/A	N/A
aligned_32d	32	0.8055 🏆	0.3769	0.4140	0.7540
aligned_64d	64	0.7807	0.2912	0.5720	0.8760
aligned_128d	128	0.7103	0.2400	0.7240	0.9400

Key Findings

• Best Isotropy: aligned_32d with 0.8055 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3036. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 72.4% R@1 in cross-lingual retrieval.
• Recommendation: 128d aligned for best cross-lingual performance

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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	-0.648	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	avalíen, ardre, autocompracencia
-s	subxéneros, saybrook, societys
-ma	marcharían, mandiargues, malenkov
-c	comú, citizens, celestron
-m	muñozdianteira, monoamino, meszaros
-p	papovaviridae, próvaí, phani
-t	taragaza, trenque, top
-b	bharani, battuto, baninter

Productive Suffixes

Suffix	Examples
-s	subxéneros, illiers, citizens
-a	kottila, 5ha, muñozdianteira
-e	violone, fable, papovaviridae
-o	firmamento, everxetismo, battuto
-os	subxéneros, sáibaos, avetouros
-n	avalíen, celestron, jamin
-as	xeodas, criovolcánicas, kangas
-es	lifesciences, exemplares, remontadores

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
icas	1.91x	173 contexts	icase, micas, icasa
ació	1.77x	148 contexts	ación, nació, lació
emen	1.59x	249 contexts	jemen, emene, iemen
ntos	1.72x	87 contexts	untos, óntos, antos
atur	1.49x	156 contexts	datur, ature, satur
orma	1.34x	257 contexts	torma, ormal, porma
oqui	1.75x	64 contexts	toqui, coqui, noqui
ncia	1.45x	152 contexts	ūncia, uncia, encia
naci	1.62x	84 contexts	nacif, nacin, nacio
ific	1.33x	192 contexts	ifici, ificar, unifica
cció	1.70x	55 contexts	acció, lecció, acción
roqu	1.62x	67 contexts	roqué, roque, croque

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
-c	-s	203 words	craniais, codiciadas
-a	-s	175 words	angers, aeroplanos
-p	-s	144 words	predis, pags
-a	-a	122 words	adenda, avicennia
-p	-a	121 words	penichaira, paralaia
-c	-a	117 words	cabreiresa, camisasca
-c	-o	105 words	canonízao, córnico
-e	-s	105 words	escravos, eppes
-s	-s	104 words	solsticiais, solicitamos
-c	-e	101 words	citándose, creedence

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
unbekannt	unbekan-n-t	7.5	n
gestalten	gestal-te-n	7.5	te
acanthium	acanth-i-um	7.5	i
matjhabeng	matjhab-e-ng	7.5	e
manufactúraa	manufactúr-a-a	7.5	a
bibliorum	biblio-r-um	7.5	r
andersens	ander-se-ns	7.5	se
contrataran	contrata-ra-n	7.5	ra
anacharsis	anachar-s-is	7.5	s
aavasaksa	aavasak-s-a	7.5	s
endorfinas	endorfi-n-as	7.5	n
cuestiónanse	cuestión-an-se	7.5	an
albacetenses	albaceten-s-es	7.5	s
toplumsal	toplum-s-al	7.5	s
synaxarium	synaxar-i-um	7.5	i

6.6 Linguistic Interpretation

Automated Insight: The language Galician shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

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7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.85x)
N-gram	2-gram	Lowest perplexity (245)
Markov	Context-4	Highest predictability (92.4%)
Embeddings	100d	Balanced semantic capture and isotropy

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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: 2026-01-13 08:28:57