Yiddish - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Yiddish 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	3.841x	3.84	0.1120%	631,919
16k	4.158x	4.16	0.1213%	583,788
32k	4.393x	4.40	0.1282%	552,468
64k	4.552x 🏆	4.55	0.1328%	533,206

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: געשעענישן געבוירן נפטר געווארן קאלענדאר צום באמערקן רעפערענצן

Vocab	Tokens	Count
8k	▁געשעענישן ▁געבוירן ▁נפטר ▁געווארן ▁קאלענדאר ▁צום ▁באמערקן ▁רעפערענצן	8
16k	▁געשעענישן ▁געבוירן ▁נפטר ▁געווארן ▁קאלענדאר ▁צום ▁באמערקן ▁רעפערענצן	8
32k	▁געשעענישן ▁געבוירן ▁נפטר ▁געווארן ▁קאלענדאר ▁צום ▁באמערקן ▁רעפערענצן	8
64k	▁געשעענישן ▁געבוירן ▁נפטר ▁געווארן ▁קאלענדאר ▁צום ▁באמערקן ▁רעפערענצן	8

Sample 2: געשעענישן געבוירן 24סטן יאנואר - פרידריך דער גרויסער, מלך פון פרייסן (געש' 28סטן...

Vocab	Tokens	Count
8k	▁געשעענישן ▁געבוירן ▁ 2 4 סטן ▁יאנואר ▁- ▁פרידריך ▁דער ... (+27 more)	37
16k	▁געשעענישן ▁געבוירן ▁ 2 4 סטן ▁יאנואר ▁- ▁פרידריך ▁דער ... (+27 more)	37
32k	▁געשעענישן ▁געבוירן ▁ 2 4 סטן ▁יאנואר ▁- ▁פרידריך ▁דער ... (+25 more)	35
64k	▁געשעענישן ▁געבוירן ▁ 2 4 סטן ▁יאנואר ▁- ▁פרידריך ▁דער ... (+25 more)	35

Sample 3: א מענטש האט אויף יעדער האנט פינף פינגער. זעט אויך פינגער (פוס) אנאטאמיע

Vocab	Tokens	Count
8k	▁א ▁מענטש ▁האט ▁אויף ▁יעדער ▁האנט ▁פינף ▁פ ינגער . ... (+10 more)	20
16k	▁א ▁מענטש ▁האט ▁אויף ▁יעדער ▁האנט ▁פינף ▁פינגער . ▁זעט ... (+6 more)	16
32k	▁א ▁מענטש ▁האט ▁אויף ▁יעדער ▁האנט ▁פינף ▁פינגער . ▁זעט ... (+6 more)	16
64k	▁א ▁מענטש ▁האט ▁אויף ▁יעדער ▁האנט ▁פינף ▁פינגער . ▁זעט ... (+6 more)	16

Key Findings

• Best Compression: 64k achieves 4.552x compression
• Lowest UNK Rate: 8k with 0.1120% 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	21,980	14.42	83,327	13.3%	32.3%
2-gram	Subword	275 🏆	8.10	6,028	68.2%	98.3%
3-gram	Word	61,497	15.91	131,301	6.0%	17.5%
3-gram	Subword	2,102	11.04	45,237	31.8%	72.4%
4-gram	Word	130,494	16.99	212,902	3.8%	10.8%
4-gram	Subword	10,721	13.39	208,071	17.9%	44.3%
5-gram	Word	103,402	16.66	145,493	3.1%	10.1%
5-gram	Subword	36,498	15.16	485,661	10.7%	29.1%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	פון די	13,720
2	איז געווען	11,141
3	אין די	9,304
4	איז א	8,395
5	אין דער	8,145

3-grams (Word):

Rank	N-gram	Count
1	איז געווען א	2,689
2	די ווייב פון	2,393
3	א זון פון	2,168
4	ער איז געווען	1,847
5	איז געווען דער	1,502

4-grams (Word):

Rank	N-gram	Count
1	א זון פון הרב	1,309
2	די ווייב פון רבי	1,223
3	די ווייב פון הרב	967
4	א טאכטער פון הרב	935
5	איז געבוירן געווארן אין	602

5-grams (Word):

Rank	N-gram	Count
1	אנציקלופדיה לחכמי גליציה מאיר וונדר	383
2	ביז צום סוף יאר בלייבן	365
3	צום סוף יאר בלייבן נאך	364
4	און צו זיין מוטער מרת	357
5	אינעם גרעגאריאנישן קאלענדאר ביז צום	336

2-grams (Subword):

Rank	N-gram	Count
1	ן _	767,030
2	_ א	671,834
3	ע ר	443,218
4	ר _	336,097
5	ט _	319,572

3-grams (Subword):

Rank	N-gram	Count
1	_ א י	258,659
2	ע ר _	253,758
3	ו ן _	215,735
4	_ א ו	163,745
5	ן _ א	160,680

4-grams (Subword):

Rank	N-gram	Count
1	_ ד י _	111,007
2	פ ו ן _	108,221
3	_ פ ו ן	105,513
4	א י ן _	97,976
5	_ א י ן	97,190

5-grams (Subword):

Rank	N-gram	Count
1	_ פ ו ן _	105,410
2	_ א י ן _	97,087
3	_ א ו ן _	88,981
4	_ א י ז _	80,703
5	_ ד ע ר _	61,940

Key Findings

• Best Perplexity: 2-gram (subword) with 275
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~29% 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	0.8935	1.858	7.14	157,053	10.6%
1	Subword	1.0780	2.111	9.14	1,976	0.0%
2	Word	0.3611	1.284	2.03	1,117,409	63.9%
2	Subword	0.8485	1.801	5.31	18,020	15.1%
3	Word	0.1429	1.104	1.26	2,254,596	85.7%
3	Subword	0.7997	1.741	3.95	95,571	20.0%
4	Word	0.0521 🏆	1.037	1.08	2,835,381	94.8%
4	Subword	0.6110	1.527	2.70	377,001	38.9%

Generated Text Samples (Word-based)

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

Context Size 1:

1. די שיאיטן און דער אונגווארער רב איבער 400 ווען ער איז רייך פון קיִעוו איר דער
2. פון סערעט וויזשניץ הרב שלמה לעלאווער רבי חיים פון די אויבנדערמאנטע פראדוקטן פאר א שעה קיין
3. אין די געגנט האט זיך ענדליך פארבעסערט אירע צוויי יאר נאכן טויט איז ווען אלע יאהר

Context Size 2:

1. פון די פארן זענען דער אב בית דין איז מזכה סענדער פון ר אברהם פרידמאן איז רבי
2. איז געווען דער איינציגער זון צו זיין פאטער ביי דער צווייטער משגיח רבי מאיר איז נפטר געווארן
3. אין די שפיץ שעה ן פון טאג און גאנצע פעקלעך וויכטיגע פיילס אויסגעשפרייט אויף 5 604 פיס

Context Size 3:

1. איז געווען א שטארקע וואוקס אין קליינע ביזנעסער צו וועלכע די סקאטישע האבן אבער געקרומט מיט די נאז
2. די ווייב פון רבי דוד זיינע ספרים שני המאורות רבי שבתי פון ראשקוב פון די חבריא קדישא פון
3. א זון פון דעם לאנגן הרב יהושע א זון פון ר מרדכי צמח דער בעל העובדא אויך האט

Context Size 4:

1. א זון פון הרב אליעזר הורוויץ רב אין ריגליץ און סטאשאוו א זון פון הרב מנחם מענדיל האגער ה
2. די ווייב פון רבי אברהם יעקב פרידמאן ה אלול ה תרפ ח י ט טבת תשע ג סאדיגורער רבי
3. די ווייב פון הרב דוד שפירא פון סאנוק רבי דוד שפירא ה שבט ה תרל ז כ ב סיון

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _דער_ע"נערען_טי_
2. יזײַ_גנירער_מצול_
3. אזער_רס_ס_ארקרן_

Context Size 2:

1. ן_שפרושים_טיציג,_
2. _איזשעה_אלן_אידי_
3. ער_די_סישטיביט_זע

Context Size 3:

1. _אין_אויף_דער_זיין
2. ער_פילמען:_piel_(ק
3. ון_רפון_נאנציעסן_ד

Context Size 4:

1. _די_היינט_זיידעם_גר
2. פון_סטעיט_נאך_געווא
3. _פון_תורה_אויב_זי"ע

Key Findings

• Best Predictability: Context-4 (word) with 94.8% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (377,001 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	69,606
Total Tokens	3,320,646
Mean Frequency	47.71
Median Frequency	4
Frequency Std Dev	1020.60

Most Common Words

Rank	Word	Frequency
1	די	112,921
2	פון	105,938
3	אין	97,977
4	און	89,450
5	איז	81,968
6	א	72,112
7	דער	63,946
8	האט	50,599
9	ער	32,997
10	צו	30,909

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	בראַזיליאַנער	2
2	ראקכאָולד	2
3	אַראָפּגעוואָרפן	2
4	xai	2
5	גראָק	2
6	מאַננעס	2
7	מאָטאָרספּאָרט	2
8	ספּיר	2
9	באקער	2
10	דזשוהורי	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1137
R² (Goodness of Fit)	0.995903
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	44.7%
Top 1,000	69.3%
Top 5,000	85.0%
Top 10,000	90.4%

Key Findings

• Zipf Compliance: R²=0.9959 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 44.7% of corpus
• Long Tail: 59,606 words needed for remaining 9.6% 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	0.8392	0.3748	N/A	N/A
mono_64d	64	0.8430	0.2765	N/A	N/A
mono_128d	128	0.7897	0.1920	N/A	N/A
aligned_32d	32	0.8392	0.3531	0.0140	0.1620
aligned_64d	64	0.8430 🏆	0.2622	0.0220	0.2260
aligned_128d	128	0.7897	0.1928	0.0940	0.3060

Key Findings

• Best Isotropy: aligned_64d with 0.8430 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2752. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 9.4% R@1 in cross-lingual retrieval.
• Recommendation: 128d aligned for best cross-lingual performance

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6. Morphological Analysis (Experimental)

This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.

6.1 Productivity & Complexity

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	-0.653	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
-א	אבֿות, אויפגעקלערטע, אויסצושפרייטן
-מ	מעדל, מעכוצעף, מיעוטים
-ה	האסאקאוו, המחדש, המאוחדת
-ב	באדינוג, בפה, בוכה
-פ	פאלעסיע, פאָרויס, פרינציפל
-קא	קאראקטיר, קאבאל, קאמיוניטי
-בא	באדינוג, באַטײַט, באַזונדערע
-גע	געווארענע, געשעט, געהרגט

Productive Suffixes

Suffix	Examples
-ן	יומן, טעלעפאָן, רעקאדירן
-ער	93סטער, שעפעטיווקער, כאראקטער
-ר	93סטער, קאראקטיר, שעפעטיווקער
-ע	פאלעסיע, סוואליאווע, אויפגעקלערטע
-ט	דערהויפּט, אנשטאלט, אריבערגעטוישט
-ען	צוריקגעווינען, פיליפיניען, אריינצונעמען
-ס	סענסארס, פאָרויס, מאָדיפֿיקאַציעס
-ג	באדינוג, סטאַרטינג, פרובירנדיג

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
אנגע	1.82x	57 contexts	לאנגע, טאנגע, אנגעל
שראל	2.40x	18 contexts	ישראל, ישראלי, לישראל
עווע	1.59x	84 contexts	געווע, מעווע, סטעווע
יבער	1.49x	102 contexts	ליבער, טיבער, ציבער
געוו	1.57x	62 contexts	געווע, געוון, געוויס
דישע	1.67x	47 contexts	ידישע, ײדישע, מאדישע
ידיש	1.80x	33 contexts	אידיש, ידישע, יידיש
ייער	1.53x	62 contexts	אייער, פייער, מייער
נדער	1.33x	94 contexts	אנדער, ענדער, אנדערט
יינע	1.41x	70 contexts	ריינע, דיינע, ניינע
נגען	1.74x	26 contexts	הענגען, גאנגען, שענגען
קומע	1.62x	27 contexts	קומען, קומעט, באקומע

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
-א	-ן	361 words	אנצייכענען, אויסגעשריבן
-פ	-ן	176 words	פאקוסירן, פקדון
-א	-ט	176 words	אוניוועריסטעט, איזאלירט
-א	-ע	135 words	אראמישע, אראביקע
-א	-ר	105 words	אדר, אגריקולטורער
-פ	-ע	99 words	פארדייטע, פרייליכסטע
-פ	-ט	93 words	פאָרמאַט, פובליצירט
-פ	-ר	91 words	פאראמעדיקער, פאנגער
-א	-ער	91 words	אגריקולטורער, אינטערוויוער
-א	-ען	90 words	אנצייכענען, אריבערציען

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
באוואוינטסטע	באוואוינט-ס-טע	7.5	ס
אויסגעלאסן	אויסגעלא-ס-ן	7.5	ס
דרויסנדיקסטע	דרויסנדיק-ס-טע	7.5	ס
וועלטטייל	וועלטטי-י-ל	7.5	י
באוויסטזיין	באוויסטז-י-ין	7.5	י
באַוווּסט	באַוווּ-ס-ט	7.5	ס
אייברשטען	אייברש-ט-ען	7.5	ט
בעסמעדרעש	בעסמעדר-ע-ש	7.5	ע
איוואניטש	אי-ווא-ניטש	6.0	ניטש
מיטאַרבעטערס	מיטאַרבעט-ער-ס	6.0	מיטאַרבעט
דרייווערס	דרייוו-ער-ס	6.0	דרייוו
דעמאלסטיקער	דעמאלסט-יק-ער	6.0	דעמאלסט
בלייבנדיק	בלייב-נד-יק	6.0	בלייב
גלייבנדיג	גלייב-נד-יג	6.0	גלייב
שרייבנדיק	שרייב-נד-יק	6.0	שרייב

6.6 Linguistic Interpretation

Automated Insight: The language Yiddish 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 (4.55x)
N-gram	2-gram	Lowest perplexity (275)
Markov	Context-4	Highest predictability (94.8%)
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: 2026-01-11 05:37:12