README.md

metadata

language: el
language_name: Greek
language_family: greek
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - feature-extraction
  - sentence-similarity
  - tokenization
  - n-grams
  - markov-chain
  - text-mining
  - fasttext
  - babelvec
  - vocabulous
  - vocabulary
  - monolingual
  - family-greek
license: mit
library_name: wikilangs
pipeline_tag: text-generation
datasets:
  - omarkamali/wikipedia-monthly
dataset_info:
  name: wikipedia-monthly
  description: Monthly snapshots of Wikipedia articles across 300+ languages
metrics:
  - name: best_compression_ratio
    type: compression
    value: 4.872
  - name: best_isotropy
    type: isotropy
    value: 0.8028
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-10T00:00:00.000Z

Greek - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Greek Wikipedia data. We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.

📋 Repository Contents

Models & Assets

• Tokenizers (8k, 16k, 32k, 64k)
• N-gram models (2, 3, 4, 5-gram)
• Markov chains (context of 1, 2, 3, 4 and 5)
• Subword N-gram and Markov chains
• Embeddings in various sizes and dimensions (aligned and unaligned)
• Language Vocabulary
• Language Statistics

Analysis and Evaluation

• 1. Tokenizer Evaluation
• 2. N-gram Model Evaluation
• 3. Markov Chain Evaluation
• 4. Vocabulary Analysis
• 5. Word Embeddings Evaluation
• 6. Morphological Analysis (Experimental)
• 7. Summary & Recommendations
• Metrics Glossary
• Visualizations Index

---

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.621x	3.62	0.0471%	2,711,752
16k	4.087x	4.09	0.0531%	2,402,524
32k	4.519x	4.52	0.0587%	2,172,769
64k	4.872x 🏆	4.87	0.0633%	2,015,689

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: .ms είναι ο top-level domain κωδικός για το Μοντσερράτ στο Διαδίκτυο. Δείτε επίσ...

Vocab	Tokens	Count
8k	▁. ms ▁είναι ▁ο ▁top - level ▁domain ▁κω δικόσ ... (+30 more)	40
16k	▁. ms ▁είναι ▁ο ▁top - level ▁domain ▁κωδικόσ ▁για ... (+21 more)	31
32k	▁. ms ▁είναι ▁ο ▁top - level ▁domain ▁κωδικόσ ▁για ... (+21 more)	31
64k	▁. ms ▁είναι ▁ο ▁top - level ▁domain ▁κωδικόσ ▁για ... (+19 more)	29

Sample 2: Το Φόππολο (ιταλικά: Foppolo) είναι ιταλικός δήμος στην Επαρχία του Μπέργκαμο, σ...

Vocab	Tokens	Count
8k	▁το ▁φ όπ πο λο ▁( ιταλικά : ▁f op ... (+32 more)	42
16k	▁το ▁φ όπ πο λο ▁( ιταλικά : ▁f op ... (+28 more)	38
32k	▁το ▁φ όπ πο λο ▁( ιταλικά : ▁f op ... (+25 more)	35
64k	▁το ▁φ όπ πο λο ▁( ιταλικά : ▁f op ... (+21 more)	31

Sample 3: Το Λε Τορ () είναι γαλλική κοινότητα στο νομό της Ερ, στη διοικητική περιοχή της...

Vocab	Tokens	Count
8k	▁το ▁λε ▁τορ ▁() ▁είναι ▁γαλλική ▁κοινότητα ▁στο ▁νομό ▁τησ ... (+15 more)	25
16k	▁το ▁λε ▁τορ ▁() ▁είναι ▁γαλλική ▁κοινότητα ▁στο ▁νομό ▁τησ ... (+14 more)	24
32k	▁το ▁λε ▁τορ ▁() ▁είναι ▁γαλλική ▁κοινότητα ▁στο ▁νομό ▁τησ ... (+13 more)	23
64k	▁το ▁λε ▁τορ ▁() ▁είναι ▁γαλλική ▁κοινότητα ▁στο ▁νομό ▁τησ ... (+13 more)	23

Key Findings

• Best Compression: 64k achieves 4.872x compression
• Lowest UNK Rate: 8k with 0.0471% 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	254,029	17.95	2,414,487	7.3%	17.4%
2-gram	Subword	443 🏆	8.79	26,716	56.5%	96.8%
3-gram	Word	1,488,610	20.51	5,529,817	1.9%	6.3%
3-gram	Subword	3,933	11.94	250,216	24.2%	59.6%
4-gram	Word	3,845,615	21.87	9,144,193	1.3%	3.9%
4-gram	Subword	22,210	14.44	1,519,855	12.8%	34.2%
5-gram	Word	2,910,168	21.47	5,914,525	1.4%	4.2%
5-gram	Subword	87,887	16.42	5,267,290	7.2%	20.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	από το	323,213
2	από την	290,152
3	με την	252,647
4	από τον	241,108
5	για την	198,175

3-grams (Word):

Rank	N-gram	Count
1	κατά τη διάρκεια	71,561
2	παραπομπές εξωτερικοί σύνδεσμοι	62,539
3	τη διάρκεια της	34,723
4	για πρώτη φορά	29,480
5	σύμφωνα με την	25,173

4-grams (Word):

Rank	N-gram	Count
1	κατά τη διάρκεια της	32,537
2	από το έως το	20,094
3	κατά τη διάρκεια του	19,453
4	γαλλική κοινότητα στο νομό	16,152
5	είναι γαλλική κοινότητα στο	16,142

5-grams (Word):

Rank	N-gram	Count
1	είναι γαλλική κοινότητα στο νομό	16,142
2	γαλλική κοινότητα στο νομό της	10,798
3	σύμφωνα με την απογραφή του	8,977
4	προβλήματα οργανικής χημείας ν α	5,103
5	οργανικής χημείας ν α πετάση	5,103

2-grams (Subword):

Rank	N-gram	Count
1	ς _	20,530,109
2	_ τ	20,509,338
3	τ ο	15,006,596
4	ο υ	13,459,949
5	α _	12,791,705

3-grams (Subword):

Rank	N-gram	Count
1	_ τ ο	9,583,813
2	ο υ _	7,426,167
3	_ κ α	6,229,911
4	α ι _	5,946,159
5	_ τ η	5,812,762

4-grams (Subword):

Rank	N-gram	Count
1	_ τ ο υ	4,854,974
2	τ ο υ _	3,990,563
3	_ κ α ι	3,906,895
4	κ α ι _	3,870,183
5	_ τ ο _	3,120,828

5-grams (Subword):

Rank	N-gram	Count
1	_ κ α ι _	3,856,808
2	_ τ ο υ _	3,836,821
3	_ τ η ς _	2,888,245
4	_ τ η ν _	1,890,516
5	_ α π ό _	1,864,707

Key Findings

• Best Perplexity: 2-gram (subword) with 443
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~21% 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	0.9344	1.911	11.28	2,374,710	6.6%
1	Subword	1.0861	2.123	7.80	13,425	0.0%
2	Word	0.4145	1.333	2.61	26,731,768	58.6%
2	Subword	0.7185	1.645	5.31	104,621	28.2%
3	Word	0.1946	1.144	1.46	69,637,387	80.5%
3	Subword	0.8000	1.741	4.75	555,743	20.0%
4	Word	0.0819 🏆	1.058	1.15	101,596,464	91.8%
4	Subword	0.7130	1.639	3.67	2,639,831	28.7%

Generated Text Samples (Word-based)

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

Context Size 1:

1. του άγραφος νόμος και εκλογές κερδίζει το ο ν ευστρατίου κώστας καραπατής έλληνας αγωνιστής του οίκο...
2. και βασανίστηκε σε αντίθεση με τον στρυμόνα ο βοναπάρτης κάλεσε σε κομματικό μάθημα φυκολογία harvey...
3. το μπρύγκεν κάηκε τρεις πήχεις και τους τύπους κλειδώματος πολλές προσπάθειες ευχρηστίας υπηρετεί ως...

Context Size 2:

1. από το πανί και τον βιότοπο της κέντρο είναι το δεύτερο όσκαρ β τέλεσε τη θεία της
2. από την αστυνομία ενώ είναι διαθέσιμο σε 409 αγώνες σκοράροντας 4 γκολ σε όλες τις έδρες δηλαδή
3. με την οργάνωση και επέκταση των ορίων λειτουργίας των διαδικασιών η εταιρεία το δίκτυο αποχέτευσης ...

Context Size 3:

1. κατά τη διάρκεια της οποίας προέτρεψε να παραδοθούν αφού πρωτύτερα συμφώνησαν να μην ενημερώσουν τον...
2. παραπομπές εξωτερικοί σύνδεσμοι ψηφιακό αρχείο των δημοσιεύσεων του χ σάιμον με τα πλήρη ίσια μαλλιά...
3. τη διάρκεια της βασιλείας του τσάρου πέτρου α τα ελεύθερα οικόπεδα αγοράστηκαν και το μια μεταλλική ...

Context Size 4:

1. κατά τη διάρκεια της δεκαετίας του 20 τάφηκε μαζί με την σύζυγο του αυγούστα κόρτενεϋ 8 φεβρουαρίου ...
2. από το έως το με εξαίρεση εκείνες του μετά την έξωση του όθωνα κατά τη διάρκεια των φιλορωσικών ανατ...
3. κατά τη διάρκεια του χειμώνα μεταξύ της τελευταίας κυριακής του οκτωβρίου μέχρι τη 1 00 utc της τελε...

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _ικαθεσυπν_μμε_a
2. ας._ησο_πόδύπίαι
3. ούν_πού_κόπρες_ό

Context Size 2:

1. ς_εξε_μος_αντρώτο
2. _τη_για_ήταχματην
3. το_ναι_από_τοντις

Context Size 3:

1. _του_αναλίαρχές_αλ
2. ου_έγκροτεχνολούν_
3. _καιρισμοι_/σεβαιω

Context Size 4:

1. _τους_χρήση_ο_πτερύ
2. του_της_ανακάλυψη_σ
3. _και_τους_δικτίνας_

Key Findings

• Best Predictability: Context-4 (word) with 91.8% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (2,639,831 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	1,039,940
Total Tokens	132,061,031
Mean Frequency	126.99
Median Frequency	4
Frequency Std Dev	9123.56

Most Common Words

Rank	Word	Frequency
1	του	4,095,731
2	και	3,886,615
3	το	3,228,440
4	της	2,987,569
5	η	1,958,228
6	την	1,895,055
7	από	1,882,149
8	ο	1,862,872
9	με	1,655,296
10	τον	1,304,224

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	ωσμωπροστατευτικά	2
2	ορμπέκη	2
3	hidronor	2
4	jpp	2
5	liebrand	2
6	οϊρατσουμέ	2
7	χασιχίτο	2
8	σεϊσι	2
9	τακατσουκασά	2
10	κατσιρέλο	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	0.9498
R² (Goodness of Fit)	0.997066
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	38.6%
Top 1,000	55.9%
Top 5,000	71.4%
Top 10,000	78.0%

Key Findings

• Zipf Compliance: R²=0.9971 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 38.6% of corpus
• Long Tail: 1,029,940 words needed for remaining 22.0% 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	0.8028	0.3648	N/A	N/A
mono_64d	64	0.7821	0.3021	N/A	N/A
mono_128d	128	0.7303	0.2408	N/A	N/A
aligned_32d	32	0.8028 🏆	0.3775	0.2640	0.6820
aligned_64d	64	0.7821	0.2965	0.4780	0.8720
aligned_128d	128	0.7303	0.2330	0.6560	0.9100

Key Findings

• Best Isotropy: aligned_32d with 0.8028 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3025. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 65.6% 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	-0.798	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
-α	αβρανσάν, απόχρεμψη, αποφέροντάς
-σ	συνειδητοποιήσετε, στίβενσον, σπειροτόμησης
-a	ayodhya, addicted, apocolo
-s	superdome, sembrich, sibling
-κ	κίτσεβο, κλειδώνω, κινοσάκι
-κα	καριστάνιου, κασιγουαμπάρα, καλλιρροη
-ε	ελληνοαλβανικών, επανεξετάζει, ενοργάνιση
-μ	μάστερινγκ, μεθυλοβουτανονιτρίλιοασκήσεις, μπαλάφα

Productive Suffixes

Suffix	Examples
-ς	νεπαλέζους, 125ος, μεθυλοβουτανονιτρίλιοασκήσεις
-ν	ελληνοαλβανικών, νταγκάν, αβρανσάν
-α	οκτωβρίουεφημερίδα, προσωπίδα, τζιτζιμπίρα
-ι	χότζι, φρύξουσι, υπονομεύεται
-ος	125ος, φιλαθλος, μπατιστάτος
-ο	ζηρίνειο, κίτσεβο, ριβονουκλεοτίδιο
-ου	καριστάνιου, ατταβύρου, βερεγγάριου
-ης	φαρέλης, απόρθητης, σπειροτόμησης

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
ικών	2.20x	163 contexts	δικών, νικών, οικών
ικής	2.14x	156 contexts	ιικής, τικής, πικής
ότητ	2.07x	175 contexts	κότητα, νότητα, ἑνότητα
ικές	1.96x	135 contexts	νικές, μικές, δικές
ιστι	1.52x	338 contexts	μιστι, ιστική, πιστιν
ατος	1.90x	92 contexts	ματος, αίατος, υπατος
ανικ	1.44x	370 contexts	δανικα, δανικό, μανικά
ήθηκ	1.93x	81 contexts	ψήθηκε, λήθηκε, μυήθηκε
ολογ	1.40x	399 contexts	ολογρ, υπολογ, οδολογ
πίση	2.06x	48 contexts	πίσης, επίση, έπίσης
ατικ	1.38x	317 contexts	ατικέ, ατικά, φατική
οποι	1.45x	200 contexts	τοποι, οποιά, οποιο

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
-α	-ς	188 words	αφηγησεις, ανύπανδρους
-κ	-ς	153 words	καλλιοντζής, κωστούλης
-σ	-ς	127 words	στηις, σοβαρώς
-ε	-ς	116 words	ενελικτικός, επιμορφωτικούς
-μ	-ς	110 words	μεταξάςπρωταγωνιστικός, μπούσεβιτς
-α	-ν	104 words	αιτωλίαν, απονεμηθέν
-κ	-ν	68 words	κηρύκειον, κατακάηκαν
-μ	-ν	65 words	μπιέγκαν, μεταβλητών
-ε	-ν	65 words	εξεπόνησαν, ερείπωσαν
-α	-α	65 words	αυτοκρατόρισσα, αυστραλια

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
έτοςχιόνι	έτοςχιό-ν-ι	7.5	ν
περισσεία	περισσ-ε-ία	7.5	ε
αἰγινήτου	αἰγινή-τ-ου	7.5	τ
αντιψυχωσικών	αντιψυχωσι-κ-ών	7.5	κ
λανγκλουά	λανγκλ-ου-ά	6.0	λανγκλ
μπουνάκιας	μπουνάκ-ια-ς	6.0	μπουνάκ
γιαλούρης	γιαλούρη-ς	4.5	γιαλούρη
εφαρμόζεις	εφαρμόζει-ς	4.5	εφαρμόζει
internationalοι	international-οι	4.5	international
λοξότητας	λοξότητα-ς	4.5	λοξότητα
δομινικανικής	δομινικανική-ς	4.5	δομινικανική
aθλητικός	aθλητικό-ς	4.5	aθλητικό
επηρεασμένης	επηρεασμένη-ς	4.5	επηρεασμένη
σελτζουκικός	σελτζουκικό-ς	4.5	σελτζουκικό
modernisme	modernism-e	4.5	modernism

6.6 Linguistic Interpretation

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

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.87x)
N-gram	2-gram	Lowest perplexity (443)
Markov	Context-4	Highest predictability (91.8%)
Embeddings	100d	Balanced semantic capture and isotropy

---

Appendix: Metrics Glossary & Interpretation Guide

This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.

Tokenizer Metrics

Compression Ratio

Definition: The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.

Intuition: Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.

What to seek: Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.

Average Token Length (Fertility)

Definition: Mean number of characters per token produced by the tokenizer.

Intuition: Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.

What to seek: Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.

Unknown Token Rate (OOV Rate)

Definition: Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.

Intuition: Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.

What to seek: Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.

N-gram Model Metrics

Perplexity

Definition: Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.

Intuition: If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.

What to seek: Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.

Entropy

Definition: Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.

Intuition: High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.

What to seek: Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.

Coverage (Top-K)

Definition: Percentage of corpus occurrences explained by the top K most frequent n-grams.

Intuition: High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.

What to seek: Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.

Markov Chain Metrics

Average Entropy

Definition: Mean entropy across all contexts, measuring average uncertainty in next-word prediction.

Intuition: Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).

What to seek: Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.

Branching Factor

Definition: Average number of unique next tokens observed for each context.

Intuition: High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).

What to seek: Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.

Predictability

Definition: Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.

Intuition: 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.

What to seek: Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.

Vocabulary & Zipf's Law Metrics

Zipf's Coefficient

Definition: The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.

Intuition: A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.

What to seek: Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.

R² (Coefficient of Determination)

Definition: Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.

Intuition: R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.

What to seek: R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.

Vocabulary Coverage

Definition: Cumulative percentage of corpus tokens accounted for by the top N words.

Intuition: Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.

What to seek: Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.

Word Embedding Metrics

Isotropy

Definition: Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.

Intuition: High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.

What to seek: Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.

Average Norm

Definition: Mean magnitude (L2 norm) of word vectors in the embedding space.

Intuition: Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.

What to seek: Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).

Cosine Similarity

Definition: Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).

Intuition: Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.

What to seek: Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.

t-SNE Visualization

Definition: t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.

Intuition: Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.

What to seek: Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.

General Interpretation Guidelines

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

Visualizations Index

Visualization	Description
Tokenizer Compression	Compression ratios by vocabulary size
Tokenizer Fertility	Average token length by vocabulary
Tokenizer OOV	Unknown token rates
Tokenizer Total Tokens	Total tokens by vocabulary
N-gram Perplexity	Perplexity by n-gram size
N-gram Entropy	Entropy by n-gram size
N-gram Coverage	Top pattern coverage
N-gram Unique	Unique n-gram counts
Markov Entropy	Entropy by context size
Markov Branching	Branching factor by context
Markov Contexts	Unique context counts
Zipf's Law	Frequency-rank distribution with fit
Vocab Frequency	Word frequency distribution
Top 20 Words	Most frequent words
Vocab Coverage	Cumulative coverage curve
Embedding Isotropy	Vector space uniformity
Embedding Norms	Vector magnitude distribution
Embedding Similarity	Word similarity heatmap
Nearest Neighbors	Similar words for key terms
t-SNE Words	2D word embedding visualization
t-SNE Sentences	2D sentence embedding visualization
Position Encoding	Encoding method comparison
Model Sizes	Storage requirements
Performance Dashboard	Comprehensive performance overview

---

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

---

Generated by Wikilangs Models Pipeline

Report Date: 2026-01-10 02:57:50