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Pali - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

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

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	2.300x 🏆	2.30	1.0840%	94,738

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1:

वलहि एका सनातन ग्राम अत्थि, ईमा पतिठ्ठापना अंतो सोरठ पदेश।

Vocab	Tokens	Count
8k	`▁वलहि ▁एका ▁सनातन ▁ग्राम ▁अत्थि , ▁ईमा ▁पतिठ्ठापना ▁अंतो ▁सोरठ ... (+2 more)`	12

Sample 2: +दक्षिण क्यारोलिनाSouth Carolina 125px 125px 300px संदरिभ

Vocab	Tokens	Count
8k	`▁+ दक्षिण ▁क्यारोलिना south ▁carolina ▁ 1 2 5 px ... (+11 more)`	21

Sample 3:

+वासिंगटन डि सिWashington, D.C. 125px 125px 300px वासिंगटन डि सि अभिञ्ञाणा

Vocab	Tokens	Count
8k	`▁+ वासिंगटन ▁डि ▁सि washington , ▁d . c . ... (+19 more)`	29

Key Findings

Best Compression: 8k achieves 2.300x compression
Lowest UNK Rate: 8k with 1.0840% 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	266 🏆	8.05	416	54.4%	100.0%
2-gram	Subword	827	9.69	2,901	42.1%	88.9%
3-gram	Word	349	8.45	534	49.9%	100.0%
3-gram	Subword	3,441	11.75	9,002	21.6%	58.3%
4-gram	Word	1,582	10.63	1,950	21.7%	63.4%
4-gram	Subword	8,498	13.05	20,231	15.6%	40.0%
5-gram	Word	1,377	10.43	1,660	22.3%	68.6%
5-gram	Subword	9,937	13.28	21,227	15.3%	35.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	`प्रकाश स्तंभ`	223
2	`yā pana`	189
3	`pana bhikkhunī`	187
4	`टापू समूह`	98
5	`sikkhā karaṇīyā`	75

3-grams (Word):

Rank	N-gram	Count
1	`yā pana bhikkhunī`	187
2	`बालिआरिक टापू समूह`	64
3	`प्रकाश स्तंभ 120px`	62
4	`टापू समूह बालिआरिक`	32
5	`समूह बालिआरिक टापू`	32

4-grams (Word):

Rank	N-gram	Count
1	`बालिआरिक टापू समूह बालिआरिक`	32
2	`टापू समूह बालिआरिक टापू`	32
3	`समूह बालिआरिक टापू समूह`	32
4	`frameless upright 0 2`	29
5	`upright 0 2 link`	25

5-grams (Word):

Rank	N-gram	Count
1	`बालिआरिक टापू समूह बालिआरिक टापू`	32
2	`टापू समूह बालिआरिक टापू समूह`	32
3	`frameless upright 0 2 link`	25
4	`upright 0 2 link frameless`	25
5	`0 2 link frameless upright`	25

2-grams (Subword):

Rank	N-gram	Count
1	`a ṃ`	1,530
2	`, _`	1,307
3	`p a`	1,306
4	`ṃ _`	1,294
5	`ā _`	1,256

3-grams (Subword):

Rank	N-gram	Count
1	`a ṃ _`	1,183
2	`k k h`	938
3	`i k k`	900
4	`_ p a`	621
5	`_ b h`	560

4-grams (Subword):

Rank	N-gram	Count
1	`i k k h`	881
2	`_ b h i`	455
3	`b h i k`	453
4	`h i k k`	453
5	`k k h u`	452

5-grams (Subword):

Rank	N-gram	Count
1	`b h i k k`	453
2	`h i k k h`	453
3	`_ b h i k`	450
4	`i k k h u`	449
5	`k k h u n`	436

Key Findings

Best Perplexity: 2-gram (word) with 266
Entropy Trend: Decreases with larger n-grams (more predictable)
Coverage: Top-1000 patterns cover ~36% 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	0.4100	1.329	2.11	10,593	59.0%
1	Subword	0.9525	1.935	6.62	2,113	4.7%
2	Word	0.1078	1.078	1.17	22,259	89.2%
2	Subword	0.4969	1.411	2.61	13,978	50.3%
3	Word	0.0355	1.025	1.05	25,920	96.5%
3	Subword	0.3495	1.274	1.73	36,519	65.0%
4	Word	0.0185 🏆	1.013	1.03	27,208	98.1%
4	Subword	0.1994	1.148	1.34	63,113	80.1%

Generated Text Samples (Word-based)

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

Context Size 1:

है पूर्ण उदच्यते उत्पन्न होता है गले में किसी मनुष्य अथवा तकनीक है भ्रूण को पार
के समान ही रह जाता है उसके नीचे क्रमश देवकी और स्वभाव और बहुतों के थे
में लिखा गया है अंडा एक स्वतन्त्र एक कालाग्नि नामक एक एव बुद्धत्तं ति मञ्ञति सन्दब्भा

Context Size 2:

प्रकाश स्तंभ 120px आन्दलूसिया मालागा मारबिआ प्रकाश स्तंभ देल बाखो दे पोरतमान मूर्किया का कारतागेना ओ...
yā pana bhikkhunī nānappakārakaṃ kayavikkayaṃ samāpajjeyya nissaggiyaṃ pācittiyaṃ aññacetāpana sikkh...
pana bhikkhunī paripuṇṇavīsativassaṃ kumāribhūtaṃ dve vassāni chasu dhammesu sikkhitasikkhaṃ saṅghen...

Context Size 3:

yā pana bhikkhunī āsandiṃ vā pallaṅkaṃ vā paribhuñjeyya pācittiyaṃ suttakantanasikkhāpadaṃ 43 yā pan...
बालिआरिक टापू समूह इबिसा और फोरमैनतेरा तागोमागो प्रकाश स्तंभ बालिआरिक टापू समूह मेनोरका सिउतादेया प्...
प्रकाश स्तंभ 120px गालिसिया केप ओमे प्रकाश स्तंभ 120px बालिआरिक टापू समूह माखोरका केप गरोस प्रकाश स्...

Context Size 4:

समूह बालिआरिक टापू समूह इबिसा और फोरमैनतेरा पोएनसा प्रकाश स्तंभ बालिआरिक टापू समूह बालिआरिक टापू समू...
टापू समूह बालिआरिक टापू समूह माखोरका पोरतो कोलोम प्रकाश स्तंभ बालिआरिक टापू समूह बालिआरिक टापू समूह ...
बालिआरिक टापू समूह बालिआरिक टापू समूह माखोरका केप बलांक प्रकाश स्तंभ 120px बालिआरिक टापू समूह बालिआर...

Generated Text Samples (Subword-based)

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

Context Size 1:

_paṃ,_सिक्खामकूपनेत्तिभ_र
aexy_स्तंभकी_o_sikki
i._कृष्णवासमूहम्_suṇat

Context Size 2:

aṃ_–_"आभीर_एका_शुभदर्शी
,_na_(कम्प्युटर_शून्य:_मिसि
padaṃ_pāṇijabaṇīy

Context Size 3:

aṃ_bhikkhuniyo_bhi
kkhuni_cells_theva
ikkhā_evamerittikk

Context Size 4:

ikkhāpadaṃ_43._yā_p
_bhikkhāpadaṃ_1._yā
hikkhā_kareyya_‘‘ap

Key Findings

Best Predictability: Context-4 (word) with 98.1% predictability
Branching Factor: Decreases with context size (more deterministic)
Memory Trade-off: Larger contexts require more storage (63,113 contexts)
Recommendation: Context-3 or Context-4 for text generation

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	3,395
Total Tokens	23,559
Mean Frequency	6.94
Median Frequency	3
Frequency Std Dev	20.62

Most Common Words

Rank	Word	Frequency
1	है	395
2	के	395
3	में	356
4	vā	314
5	से	276
6	और	265
7	हैं	261
8	bhikkhunī	254
9	प्रकाश	229
10	स्तंभ	224

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	नामान्तरण	2
2	मिलाबट	2
3	घटनाओं	2
4	वेदिक	2
5	शबदस्स	2
6	दरुश	2
7	गरिंथात	2
8	अनूसार	2
9	करविन	2
10	हिंदू	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.8293
R² (Goodness of Fit)	0.980447
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	37.8%
Top 1,000	75.0%
Top 5,000	0.0%
Top 10,000	0.0%

Key Findings

Zipf Compliance: R²=0.9804 indicates excellent adherence to Zipf's law
High Frequency Dominance: Top 100 words cover 37.8% of corpus
Long Tail: -6,605 words needed for remaining 100.0% 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	0.0330 🏆	0.5234	N/A	N/A
mono_64d	64	0.0047	0.5510	N/A	N/A
mono_128d	128	0.0008	0.5621	N/A	N/A
aligned_32d	32	0.0330	0.5288	0.0240	0.1377
aligned_64d	64	0.0047	0.5520	0.0240	0.1257
aligned_128d	128	0.0008	0.5541	0.0180	0.1437

Key Findings

Best Isotropy: mono_32d with 0.0330 (more uniform distribution)
Semantic Density: Average pairwise similarity of 0.5452. Lower values indicate better semantic separation.
Alignment Quality: Aligned models achieve up to 2.4% 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	0.910	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
`-स`	सच्चानीति, स्टेम, संवाद
`-प`	प्रवृति, पाताल, प्रभुने
`-sa`	saṅghikaṃ, samayā, sambhuñjeyya
`-pa`	paṭiggahetabbaṃ, paṭisevato, pakkameyya
`-पर`	परिपूर्णतम, परायण, परवर्ती
`-an`	announcement, anniversary, and
`-vi`	via, vikappaṃ, vinassā
`-मह`	महावग्गो, महाविराट्के, महेश्वर

Productive Suffixes

Suffix	Examples
`-aṃ`	saṅghikaṃ, dhammaṃ, nālaṃ
`-ṃ`	saṅghikaṃ, dhammaṃ, nālaṃ
`-a`	wikimania, acchindāpeyya, uddhareyya
`-ya`	acchindāpeyya, uddhareyya, pakkameyya
`-ā`	vuccamānā, āpannā, cetāpetvā
`-na`	saññācikena, saṅghikena, dhammena
`-yo`	bhikkhuniyo, māyyāyo, ayyāyo
`-yā`	samayā, dubbalyā, karaṇīyā

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
`eyya`	1.78x	8 contexts	seyyaṃ, cāveyya, kareyya
`ikkh`	1.65x	6 contexts	sikkhā, sikkhaṃ, bhikkhu
`kkhu`	1.78x	5 contexts	bhikkhu, bhikkhuṃ, bhikkhunī
`añña`	1.76x	3 contexts	aññaṃ, aññatra, anaññaṃ

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
`-pa`	`-a`	20 words	pakkameyya, paggaṇheyya
`-sa`	`-a`	19 words	sambhuñjeyya, saññācikena
`-sa`	`-ṃ`	15 words	saṅghikaṃ, saṅghādisesaṃ
`-pa`	`-ṃ`	15 words	paṭiggahetabbaṃ, paraṃ
`-pa`	`-ya`	14 words	pakkameyya, paggaṇheyya
`-sa`	`-aṃ`	13 words	saṅghikaṃ, saṅghādisesaṃ
`-pa`	`-aṃ`	12 words	paṭiggahetabbaṃ, paraṃ
`-sa`	`-ā`	10 words	samayā, saṅghādisesā
`-vi`	`-a`	8 words	via, vivekaññeva
`-sa`	`-ya`	7 words	sambhuñjeyya, saṃvaṇṇeyya

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
communities	`communi-ti-es`	3.0	`communi`
ukkhittakāya	`ukkhittak-ā-ya`	3.0	`ukkhittak`
bhaginīnaṃ	`bhaginīn-aṃ`	1.5	`bhaginīn`
vūpasamāya	`vūpasamā-ya`	1.5	`vūpasamā`
ubbhatasmiṃ	`ubbhatasmi-ṃ`	1.5	`ubbhatasmi`
sahadhammena	`sa-hadhammena`	1.5	`hadhammena`
sattarasa	`sattaras-a`	1.5	`sattaras`
pañcakkhattuṃ	`pañcakkhattu-ṃ`	1.5	`pañcakkhattu`
dvattikkhattuṃ	`dvattikkhattu-ṃ`	1.5	`dvattikkhattu`
susaṃvutā	`susaṃvut-ā`	1.5	`susaṃvut`
सीहनादवग्गो	`स-ीहनादवग्गो`	1.5	`ीहनादवग्गो`
sannidhikārakaṃ	`sannidhikārak-aṃ`	1.5	`sannidhikārak`
pattavaggo	`pa-ttavaggo`	1.5	`ttavaggo`
desessāmīti	`desessāmī-ti`	1.5	`desessāmī`
सुत्तपिटक	`स-ुत्तपिटक`	1.5	`ुत्तपिटक`

6.6 Linguistic Interpretation

Automated Insight: The language Pali 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	8k BPE	Best compression (2.30x)
N-gram	2-gram	Lowest perplexity (266)
Markov	Context-4	Highest predictability (98.1%)
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

Compare within model families: Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
Consider trade-offs: Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
Context matters: Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
Corpus influence: All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
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:

bibtex

@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.

wikilangs/pi

Pali - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

📋 Repository Contents

Models & Assets

Analysis and Evaluation

1. Tokenizer Evaluation

Results

Tokenization Examples

Key Findings

2. N-gram Model Evaluation

Results

Top 5 N-grams by Size

Key Findings

3. Markov Chain Evaluation

Results

Generated Text Samples (Word-based)

Generated Text Samples (Subword-based)

Key Findings

4. Vocabulary Analysis

Statistics

Most Common Words

Least Common Words (from vocabulary)

Zipf's Law Analysis

Coverage Analysis

Key Findings

5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

5.2 Model Comparison

Key Findings

6. Morphological Analysis (Experimental)

6.1 Productivity & Complexity

6.2 Affix Inventory (Productive Units)

Productive Prefixes

Productive Suffixes

6.3 Bound Stems (Lexical Roots)

6.4 Affix Compatibility (Co-occurrence)

6.5 Recursive Morpheme Segmentation

6.6 Linguistic Interpretation

7. Summary & Recommendations

Production Recommendations

Appendix: Metrics Glossary & Interpretation Guide

Tokenizer Metrics

N-gram Model Metrics

Markov Chain Metrics

Vocabulary & Zipf's Law Metrics

Word Embedding Metrics

General Interpretation Guidelines

Visualizations Index

About This Project

Data Source

Project

Maintainer

Citation

License

Links

wikilangs/
pi