Occitan - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Occitan 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	3.591x	3.59	0.0580%	1,039,006
16k	3.939x	3.94	0.0637%	947,222
32k	4.234x	4.24	0.0684%	881,100
64k	4.442x 🏆	4.44	0.0718%	839,942

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Lucas Reiner (n. es un actor e productor de cinèma american. american a Los Ange...

Vocab	Tokens	Count
8k	▁lu cas ▁rein er ▁( n . ▁es ▁un ▁actor ... (+11 more)	21
16k	▁lu cas ▁rein er ▁( n . ▁es ▁un ▁actor ... (+11 more)	21
32k	▁lucas ▁rein er ▁( n . ▁es ▁un ▁actor ▁e ... (+10 more)	20
64k	▁lucas ▁reiner ▁( n . ▁es ▁un ▁actor ▁e ▁productor ... (+9 more)	19

Sample 2: Altwis es un vilatjòt, e comuna soïssa, situat dins lo districte d'Hochdorf, e l...

Vocab	Tokens	Count
8k	▁alt w is ▁es ▁un ▁vilat j òt , ▁e ... (+29 more)	39
16k	▁alt wis ▁es ▁un ▁vilat j òt , ▁e ▁comuna ... (+26 more)	36
32k	▁alt wis ▁es ▁un ▁vilatjòt , ▁e ▁comuna ▁soïssa , ... (+24 more)	34
64k	▁alt wis ▁es ▁un ▁vilatjòt , ▁e ▁comuna ▁soïssa , ... (+22 more)	32

Sample 3: Puebla de la Calzada es un municipi de la província espanhòla de Badajoz e de la...

Vocab	Tokens	Count
8k	▁pu eb la ▁de ▁la ▁cal z ada ▁es ▁un ... (+19 more)	29
16k	▁pu eb la ▁de ▁la ▁cal zada ▁es ▁un ▁municipi ... (+15 more)	25
32k	▁puebla ▁de ▁la ▁cal zada ▁es ▁un ▁municipi ▁de ▁la ... (+13 more)	23
64k	▁puebla ▁de ▁la ▁calzada ▁es ▁un ▁municipi ▁de ▁la ▁província ... (+12 more)	22

Key Findings

• Best Compression: 64k achieves 4.442x compression
• Lowest UNK Rate: 8k with 0.0580% 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	40,858	15.32	382,833	16.0%	32.3%
2-gram	Subword	256 🏆	8.00	9,724	69.1%	99.1%
3-gram	Word	99,251	16.60	691,705	13.3%	25.5%
3-gram	Subword	2,095	11.03	74,490	29.1%	73.4%
4-gram	Word	144,878	17.14	1,152,073	14.3%	26.4%
4-gram	Subword	11,826	13.53	411,965	14.7%	42.1%
5-gram	Word	78,202	16.25	807,722	17.0%	32.1%
5-gram	Subword	46,870	15.52	1,292,254	8.9%	27.4%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	de la	213,501
2	de l	104,259
3	es una	53,804
4	e la	52,175
5	dins lo	51,918

3-grams (Word):

Rank	N-gram	Count
1	es una comuna	35,541
2	e monuments personalitats	35,308
3	monuments personalitats ligadas	31,330
4	e la region	31,001
5	ligams extèrnes nòtas	30,871

4-grams (Word):

Rank	N-gram	Count
1	e monuments personalitats ligadas	31,329
2	luòcs e monuments personalitats	29,121
3	ligadas amb la comuna	28,401
4	personalitats ligadas amb la	28,400
5	monuments personalitats ligadas amb	27,977

5-grams (Word):

Rank	N-gram	Count
1	personalitats ligadas amb la comuna	28,398
2	e monuments personalitats ligadas amb	27,976
3	monuments personalitats ligadas amb la	27,973
4	luòcs e monuments personalitats ligadas	27,614
5	demografia luòcs e monuments personalitats	27,263

2-grams (Subword):

Rank	N-gram	Count
1	a _	3,234,853
2	e _	3,172,126
3	s _	3,063,418
4	_ d	3,054,602
5	_ l	2,264,276

3-grams (Subword):

Rank	N-gram	Count
1	_ d e	2,037,812
2	d e _	1,436,433
3	_ l a	884,600
4	l a _	867,260
5	a s _	793,636

4-grams (Subword):

Rank	N-gram	Count
1	_ d e _	1,401,348
2	_ l a _	689,470
3	d e _ l	434,454
4	i o n _	370,419
5	a _ d e	370,367

5-grams (Subword):

Rank	N-gram	Count
1	_ d e _ l	432,360
2	e _ l a _	285,044
3	d e _ l a	266,977
4	s _ d e _	254,104
5	a _ d e _	253,709

Key Findings

• Best Perplexity: 2-gram (subword) with 256
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~27% 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	0.9719	1.961	8.13	622,832	2.8%
1	Subword	0.8487	1.801	5.90	5,768	15.1%
2	Word	0.3692	1.292	2.14	5,057,510	63.1%
2	Subword	0.7604	1.694	4.98	33,998	24.0%
3	Word	0.1556	1.114	1.32	10,822,711	84.4%
3	Subword	0.7496	1.681	4.18	169,232	25.0%
4	Word	0.0609 🏆	1.043	1.10	14,277,493	93.9%
4	Subword	0.6863	1.609	3.33	707,794	31.4%

Generated Text Samples (Word-based)

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

Context Size 1:

1. de comunas vesinas e solidaritat s auçant quitament d una comuna veire tanben ligams extèrnes nòtas
2. la corona mas es l intoxicacion son concentradas de govèrn francés livre premier estudi meninosa d
3. e posicion relativa istòria l entorn istòria revòlta del grand glise est attestée semble que depend

Context Size 2:

1. de la municipalitat qu es connectat e diferents ph es segon la definicion d un rai de
2. de l arnm pòrta l anèl latin digitus annularis det de l industria unica de l union
3. es una proprietat sus la luna esquèrra vinheta moïses trencant las taules de la nauta marna e

Context Size 3:

1. es una comuna francesa del departament de tarn e garona ligams extèrnes nòtas de gironda de la regio...
2. e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas e referéncias de ...
3. monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas de la nauta garona ...

Context Size 4:

1. e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas dels vòges
2. luòcs e monuments personalitats ligadas amb la comuna véser tanben ligams extèrnes nòtas de normandi...
3. ligadas amb la comuna véser tanben ligams extèrnes nòtas de normandia de la marga

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _daurenèrd'anaio
2. art_deabesime,_p
3. e_uzarive_se_ge_

Context Size 2:

1. a_doppsi_morlà_10
2. e_menregièrnasist
3. s_panar_mil_de_pl

Context Size 3:

1. _desfistòria_cap_a
2. de_jacque_dismeniv
3. _la_(∗)_régions_en

Context Size 4:

1. _de_la_grat_de_la_c
2. _la_fibrairie_e_avi
3. de_lieux_forcèt_l'a

Key Findings

• Best Predictability: Context-4 (word) with 93.9% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (707,794 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	298,767
Total Tokens	19,561,503
Mean Frequency	65.47
Median Frequency	4
Frequency Std Dev	3567.99

Most Common Words

Rank	Word	Frequency
1	de	1,412,762
2	la	704,581
3	e	516,061
4	d	382,843
5	en	367,851
6	lo	364,128
7	l	357,372
8	a	301,072
9	es	226,360
10	un	196,170

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	shonkinita	2
2	piròp	2
3	lherzolita	2
4	miéj	2
5	mangiato	2
6	ignaure	2
7	langfors	2
8	accouplés	2
9	theodiscus	2
10	nyamuragira	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0382
R² (Goodness of Fit)	0.998226
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	45.3%
Top 1,000	64.4%
Top 5,000	78.5%
Top 10,000	84.0%

Key Findings

• Zipf Compliance: R²=0.9982 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 45.3% of corpus
• Long Tail: 288,767 words needed for remaining 16.0% 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	0.7759	0.3605	N/A	N/A
mono_64d	64	0.7311	0.2808	N/A	N/A
mono_128d	128	0.7021	0.2184	N/A	N/A
aligned_32d	32	0.7759 🏆	0.3741	0.2480	0.6180
aligned_64d	64	0.7311	0.2733	0.3600	0.7300
aligned_128d	128	0.7021	0.2172	0.5080	0.8180

Key Findings

• Best Isotropy: aligned_32d with 0.7759 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2874. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 50.8% 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	0.170	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	arrecebèva, auroish, apròhe
-s	sfrf, suris, saëns
-ma	manqueront, mahlkirch, maçacans
-c	colomberiis, chaohusaurus, campanhard
-b	bièle, brixey, bartl
-m	mcgowan, manqueront, mahlkirch
-p	pennante, pousser, pisuerga
-ca	campanhard, casalabriva, castelpers

Productive Suffixes

Suffix	Examples
-s	suris, kohs, colomberiis
-a	goja, fonologica, arrecebèva
-e	pennante, bièle, podiosalicone
-t	manqueront, projèct, convertissent
-n	mcgowan, esteron, réligion
-as	termonuclearas, taças, refractàrias
-es	ecoulettes, vongnes, neuffontaines
-on	esteron, réligion, diferencièron

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
itat	2.00x	167 contexts	pitat, gitat, itata
acio	2.06x	128 contexts	acion, bacio, racion
ogra	1.83x	133 contexts	dogra, logran, lograr
raci	1.80x	136 contexts	racim, oraci, braci
tats	2.06x	67 contexts	stats, états, etats
ntre	1.86x	105 contexts	antre, entre, intre
énci	2.13x	49 contexts	éncia, réncia, siéncia
icio	1.84x	83 contexts	licio, vicios, bricio
stra	1.35x	282 contexts	stray, strat, strad
lita	1.67x	94 contexts	litas, elita, clita
anbe	2.53x	19 contexts	anben, tanbe, tanben
tanb	2.49x	19 contexts	tanbn, tanbe, tanban

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	210 words	conreats, cippus
-a	-s	171 words	annexis, annuentes
-p	-s	168 words	palays, prébois
-s	-s	126 words	senˈtises, sevas
-c	-a	119 words	casalta, conoguda
-a	-a	101 words	abjura, abominabla
-b	-s	94 words	brindas, barangays
-c	-e	94 words	colloverge, coroe
-p	-a	91 words	partidària, plantada
-m	-s	80 words	meus, majusculas

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
velhiment	velhi-me-nt	7.5	me
hermaphroditism	hermaphroditi-s-m	7.5	s
tuscaloosa	tuscaloo-s-a	7.5	s
drepanocitòsi	drepanocitò-s-i	7.5	s
sarrasiet	sarrasi-e-t	7.5	e
acomplisca	acompli-s-ca	7.5	s
daissarem	daissar-e-m	7.5	e
condusent	condus-e-nt	7.5	e
étroussat	étrous-s-at	7.5	s
garrwanas	garrw-an-as	7.5	an
prehistoria	p-re-historia	7.5	historia
cerevisiae	cerevisi-a-e	7.5	a
billinghurst	billinghur-s-t	7.5	s
europeans	europe-an-s	7.5	an
cherquesses	cherques-s-es	7.5	s

6.6 Linguistic Interpretation

Automated Insight: The language Occitan 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 (4.44x)
N-gram	2-gram	Lowest perplexity (256)
Markov	Context-4	Highest predictability (93.9%)
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: 2026-01-10 18:02:02