wikilangs/shi

Tachelhit - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Tachelhit 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	3.016x	3.02	1.3945%	407,897
16k	3.301x	3.30	1.5260%	372,731
32k	3.556x	3.56	1.6440%	345,980
64k	3.819x 🏆	3.82	1.7653%	322,212

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Talggut neɣ Algu tga yat tasklut ur iskaren awd yat ugummu, tesker ifrawen zund ...

Vocab	Tokens	Count
8k	▁tal gg ut ▁neɣ ▁al gu ▁tga ▁yat ▁tas klut ... (+29 more)	39
16k	▁tal ggut ▁neɣ ▁algu ▁tga ▁yat ▁tasklut ▁ur ▁iskar en ... (+22 more)	32
32k	▁talggut ▁neɣ ▁algu ▁tga ▁yat ▁tasklut ▁ur ▁iskaren ▁awd ▁yat ... (+20 more)	30
64k	▁talggut ▁neɣ ▁algu ▁tga ▁yat ▁tasklut ▁ur ▁iskaren ▁awd ▁yat ... (+19 more)	29

Sample 2: 1 000 iga yan umḍan imqquṛn, ism ns s tmaziɣt igat ifḍ (s tfinaɣ : ⵉⴼⴹ). Msmun a...

Vocab	Tokens	Count
8k	▁ 1 ▁ 0 0 0 ▁iga ▁yan ▁umḍan ▁imqquṛn ... (+30 more)	40
16k	▁ 1 ▁ 0 0 0 ▁iga ▁yan ▁umḍan ▁imqquṛn ... (+29 more)	39
32k	▁ 1 ▁ 0 0 0 ▁iga ▁yan ▁umḍan ▁imqquṛn ... (+29 more)	39
64k	▁ 1 ▁ 0 0 0 ▁iga ▁yan ▁umḍan ▁imqquṛn ... (+29 more)	39

Sample 3: Iga Q yan sg iskkiln n ugmmay alatin n tmaziɣt. Tisaɣulin amaziɣ tamaziɣt

Vocab	Tokens	Count
8k	▁iga ▁q ▁yan ▁sg ▁iskkiln ▁n ▁ugmmay ▁alatin ▁n ▁tmaziɣt ... (+4 more)	14
16k	▁iga ▁q ▁yan ▁sg ▁iskkiln ▁n ▁ugmmay ▁alatin ▁n ▁tmaziɣt ... (+4 more)	14
32k	▁iga ▁q ▁yan ▁sg ▁iskkiln ▁n ▁ugmmay ▁alatin ▁n ▁tmaziɣt ... (+4 more)	14
64k	▁iga ▁q ▁yan ▁sg ▁iskkiln ▁n ▁ugmmay ▁alatin ▁n ▁tmaziɣt ... (+4 more)	14

Key Findings

• Best Compression: 64k achieves 3.819x compression
• Lowest UNK Rate: 8k with 1.3945% 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	1,027	10.00	23,236	45.7%	81.7%
2-gram	Subword	255 🏆	7.99	3,781	68.8%	99.0%
3-gram	Word	1,698	10.73	46,052	39.0%	76.4%
3-gram	Subword	1,284	10.33	29,091	35.1%	84.7%
4-gram	Word	3,109	11.60	90,307	35.2%	68.9%
4-gram	Subword	3,344	11.71	117,787	23.5%	73.6%
5-gram	Word	3,900	11.93	100,603	35.2%	65.7%
5-gram	Subword	5,685	12.47	238,802	18.6%	68.5%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	tgmiḍi n	30,047
2	n usggʷas	27,406
3	umḍan n	26,921
4	n imzdaɣn	25,250
5	tlkm tgmiḍi	24,096

3-grams (Word):

Rank	N-gram	Count
1	tlkm tgmiḍi n	24,096
2	tamattayt n usɣiws	16,122
3	tasmirit tamattayt n	15,740
4	umḍan n imzdaɣn	14,946
5	g tlkm tgmiḍi	12,050

4-grams (Word):

Rank	N-gram	Count
1	tasmirit tamattayt n usɣiws	15,739
2	g tlkm tgmiḍi n	12,050
3	ad i trfiqt n	8,924
4	uḍwwaṛ ad i trfiqt	8,917
5	umḍan n imzdaɣn nns	8,916

5-grams (Word):

Rank	N-gram	Count
1	uḍwwaṛ ad i trfiqt n	8,916
2	amatay n imzdaɣn tasmirit tamattayt	8,910
3	imzdaɣn tasmirit tamattayt n usɣiws	8,910
4	n imzdaɣn tasmirit tamattayt n	8,910
5	ilkm umḍan n imzdaɣn nns	8,904

2-grams (Subword):

Rank	N-gram	Count
1	n _	653,867
2	_ n	401,914
3	_ t	358,373
4	_ i	253,323
5	t a	205,156

3-grams (Subword):

Rank	N-gram	Count
1	_ n _	294,487
2	_ t a	132,536
3	n _ t	104,627
4	a n _	103,501
5	_ ɣ _	101,865

4-grams (Subword):

Rank	N-gram	Count
1	_ n _ u	84,430
2	t _ n _	67,376
3	_ n _ i	61,495
4	_ n _ t	56,122
5	n _ u s	52,239

5-grams (Subword):

Rank	N-gram	Count
1	_ n _ u s	51,413
2	m z d a ɣ	46,710
3	g g ʷ a s	34,963
4	s g g ʷ a	34,938
5	_ n n a _	34,315

Key Findings

• Best Perplexity: 2-gram (subword) with 255
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~68% 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	0.6330	1.551	4.06	76,235	36.7%
1	Subword	1.2937	2.452	10.38	803	0.0%
2	Word	0.2598	1.197	1.65	308,778	74.0%
2	Subword	1.0718	2.102	6.52	8,338	0.0%
3	Word	0.0839	1.060	1.19	508,428	91.6%
3	Subword	0.8300	1.778	3.82	54,347	17.0%
4	Word	0.0475 🏆	1.033	1.13	601,160	95.2%
4	Subword	0.5641	1.478	2.43	207,735	43.6%

Generated Text Samples (Word-based)

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

Context Size 1:

1. n ayt twayya nna dar irgazn d amatay n usggʷas niɣ uggar ɣ ɛin ijri s
2. ɣ uḍwwaṛ innazwan yili ɣ lmɣrib iḍfaṛ uḍwwaṛ ad i twuri tannayin tisaɣulin ɣ llan 4
3. d 11 n tarwuri 2 ig unammas n tznit tamnaḍt n iḍuṛan ilkm wawtay nnsn iẓḍiṛn

Context Size 2:

1. tgmiḍi n uslmd 92 86 gr irban d trbatin nna dar 15 n usggʷas démographiques et socio
2. n usggʷas démographiques et socio économiques de la population rurale hors nomades par douar selon l...
3. umḍan n imzdaɣn n usun ad 20 n iḍuṛan ilkm umḍan n twjiwin s 32 7 gr

Context Size 3:

1. tlkm tgmiḍi n uslmd 100 gr irban d trbatin nna dar gr 6 d 11 n usggʷas ɣ
2. tamattayt n usɣiws tannayin tisaɣulin ɣ lmɣrib ɣ tsga n lḥuz n lḥuz n lḥuz n lḥuz n
3. tasmirit tamattayt n usɣiws ɣ iga umḍan n imawaḍn 224 n umzdaɣ gisn 581 n iwtman d 329

Context Size 4:

1. tasmirit tamattayt n usɣiws ɣ iga umḍan n imawaḍn 236 n umzdaɣ gisn 110 n iwtman d 101 n
2. g tlkm tgmiḍi n uslmd 89 66 gr irban d trbatin nna dar gr 6 d 11 n usggʷas
3. ad i trfiqt n ayt iɛzman nna ɣ llan 4 n iḍuṛan ilkm umḍan n imzdaɣn nns 251 n

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _5_wtaṛsɣ_t_tana
2. aḍas_tm_aɣnaphon
3. nn_nn_puriquriɣ_

Context Size 2:

1. n_muḍwwawtmas_soc
2. _n_et_tamklattamk
3. _tawtmadin_tlkm_u

Context Size 3:

1. _n_imzdaɣn_nit_soc
2. _tarwurin_i_trfiqt
3. n_tawuri._tluḥarch

Context Size 4:

1. _n_usɣiws._aṛcif,_1
2. t_n_iwtman_d_23.95_
3. _n_imzdaɣn_n_iwtman

Key Findings

• Best Predictability: Context-4 (word) with 95.2% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (207,735 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	31,610
Total Tokens	2,378,642
Mean Frequency	75.25
Median Frequency	4
Frequency Std Dev	1969.69

Most Common Words

Rank	Word	Frequency
1	n	294,685
2	ɣ	101,988
3	d	64,374
4	s	34,997
5	nna	34,361
6	imzdaɣn	31,398
7	dar	30,865
8	gr	30,721
9	tgmiḍi	30,050
10	usggʷas	28,210

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	tdarwinit	2
2	talmuqqdimt	2
3	ttawnn	2
4	taggrgist	2
5	umdgar	2
6	uqṛiḍ	2
7	dearborn	2
8	ghosts	2
9	tremblay	2
10	tmmndl	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.2849
R² (Goodness of Fit)	0.988016
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	69.6%
Top 1,000	90.6%
Top 5,000	95.6%
Top 10,000	97.3%

Key Findings

• Zipf Compliance: R²=0.9880 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 69.6% of corpus
• Long Tail: 21,610 words needed for remaining 2.7% 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	0.7173	0.3723	N/A	N/A
mono_64d	64	0.5707	0.3238	N/A	N/A
mono_128d	128	0.2225	0.3121	N/A	N/A
aligned_32d	32	0.7173 🏆	0.3624	0.0140	0.0980
aligned_64d	64	0.5707	0.3343	0.0280	0.1200
aligned_128d	128	0.2225	0.3186	0.0400	0.1960

Key Findings

• Best Isotropy: aligned_32d with 0.7173 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3372. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 4.0% 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	-0.041	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
-t	tugt, tattuyt, tyyuga
-i	issiks, imẓyann, izdg
-ta	tattuyt, taryal, tamaẓuẓt
-a	azdawan, amazɣ, afnsu
-u	utin, uswaɣ, uzzugz
-l	lmṣalḥa, lbkr, lmujawharat
-ti	tizrigin, tidzi, timdst
-m	maskurt, mḥda, mmaggarn

Productive Suffixes

Suffix	Examples
-n	utin, azdawan, tɣmriwin
-t	tugt, tattuyt, trifiyt
-a	tyyuga, mḥda, tssa
-in	utin, tɣmriwin, ɣwin
-s	issiks, chaouis, nations
-i	inlbi, uɣri, igiddi
-e	conduite, historique, déchirée
-an	azdawan, zyyan, franslyan

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
adda	1.65x	52 contexts	addad, wadda, jadda
ggʷa	1.63x	43 contexts	aggʷa, ḥggʷa, zggʷar
ggar	1.94x	22 contexts	iggar, uggar, ggarn
ugga	1.94x	21 contexts	uggar, uggan, yugga
wuri	1.68x	30 contexts	twuri, iswuri, swurin
tion	2.09x	14 contexts	notion, action, nation
ɣrib	1.80x	20 contexts	aɣrib, mɣrib, lɣribi
lati	1.61x	27 contexts	latin, latif, mulati
matt	1.60x	26 contexts	matta, tmatti, umatta
mɣri	1.79x	13 contexts	tmɣri, mɣrib, imɣri
atio	1.86x	8 contexts	nation, nations, national
mata	1.45x	14 contexts	amata, smata, umata

6.4 Affix Compatibility (Co-occurrence)

This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.

Prefix	Suffix	Frequency	Examples
-t	-t	610 words	tifrirt, tdrfit
-i	-n	465 words	ittmttatn, ibṛbbachn
-t	-n	321 words	ttyussanin, tigtfulin
-t	-in	263 words	ttyussanin, tigtfulin
-l	-a	84 words	lbṛaṭla, lɛnabsa
-t	-a	65 words	tiṛṛuyṣa, tzuna
-i	-an	45 words	inultan, ilawan
-a	-i	39 words	adarazi, abriṭani
-a	-n	38 words	agwensan, agaman
-l	-t	32 words	lfwarat, lfuqqiyyat

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
tasnmḍant	tasnmḍ-an-t	7.5	an
africaine	africa-in-e	7.5	in
ttyawssannin	ttyawssan-n-in	7.5	n
ittyurnan	ittyur-n-an	7.5	n
ittusɣẓnn	ittusɣẓ-n-n	7.5	n
zzuzzarnit	zzuzzar-n-it	7.5	n
tutlayyin	tutlay-y-in	7.5	y
ttaggʷanin	ttaggʷa-n-in	7.5	n
marocaines	maroca-in-es	7.5	in
government	governme-n-t	7.5	n
ttussiḍannt	ttussiḍ-an-nt	7.5	an
ittyawstay	ittyaws-t-ay	7.5	t
patrimoine	patrimo-in-e	7.5	in
ittuzdaɣn	it-tu-zdaɣn	6.0	zdaɣn
tinsmunin	ti-nsmun-in	6.0	nsmun

6.6 Linguistic Interpretation

Automated Insight: The language Tachelhit 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 (3.82x)
N-gram	2-gram	Lowest perplexity (255)
Markov	Context-4	Highest predictability (95.2%)
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 20:02:34