Urdu - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Urdu 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	3.434x	3.44	0.1597%	2,494,826
16k	3.731x	3.74	0.1735%	2,296,340
32k	3.936x	3.95	0.1830%	2,176,646
64k	4.066x 🏆	4.08	0.1891%	2,107,362

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: لائژوو چین کا ایک کاؤنٹی سطح شہر جو ژانگجیانگ میں واقع ہے۔ مزید دیکھیے چین فہرست...

Vocab	Tokens	Count
8k	▁لائ ژ وو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ... (+21 more)	31
16k	▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژ ... (+19 more)	29
32k	▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژانگ ... (+18 more)	28
64k	▁لائ ژوو ▁چین ▁کا ▁ایک ▁کاؤنٹی ▁سطح ▁شہر ▁جو ▁ژانگ ... (+18 more)	28

Sample 2: ساروی پاکستان کا ایک آباد مقام جو ضلع لاہور میں واقع ہے۔ مزید دیکھیے پاکستان پاک...

Vocab	Tokens	Count
8k	▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)	26
16k	▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)	26
32k	▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)	26
64k	▁سار وی ▁پاکستان ▁کا ▁ایک ▁آباد ▁مقام ▁جو ▁ضلع ▁لاہور ... (+16 more)	26

Sample 3: انڈونیشیا کی ثقافت سے مراد انڈونیشیا کا ثقافتی ورثہ ہے۔ حوالہ جات ثقافت مشرقی ای...

Vocab	Tokens	Count
8k	▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)	16
16k	▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)	16
32k	▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)	16
64k	▁انڈونیشیا ▁کی ▁ثقافت ▁سے ▁مراد ▁انڈونیشیا ▁کا ▁ثقافتی ▁ورثہ ▁ہے۔ ... (+6 more)	16

Key Findings

• Best Compression: 64k achieves 4.066x compression
• Lowest UNK Rate: 8k with 0.1597% 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	71,880	16.13	920,952	12.1%	28.0%
2-gram	Subword	407 🏆	8.67	31,986	59.9%	96.3%
3-gram	Word	315,178	18.27	2,297,981	8.3%	17.4%
3-gram	Subword	3,547	11.79	203,673	25.5%	63.2%
4-gram	Word	765,780	19.55	4,319,755	7.4%	14.2%
4-gram	Subword	19,593	14.26	1,069,845	12.6%	37.0%
5-gram	Word	617,009	19.23	3,316,122	7.9%	15.7%
5-gram	Subword	75,628	16.21	3,267,447	7.4%	25.5%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	کے لیے	246,197
2	حوالہ جات	212,286
3	واقع ہے	138,739
4	مزید دیکھیے	134,662
5	ہے اور	134,251

3-grams (Word):

Rank	N-gram	Count
1	میں واقع ہے	98,697
2	ہے مزید دیکھیے	91,225
3	ریاستہائے متحدہ امریکا	75,905
4	شہر حوالہ جات	70,046
5	کے شہر حوالہ	69,949

4-grams (Word):

Rank	N-gram	Count
1	کے شہر حوالہ جات	69,947
2	ڈیٹا سے مختلف مختصر	60,477
3	سے مختلف مختصر وضاحت	60,477
4	میں واقع ہے تفصیلات	57,274
5	واقع ہے مزید دیکھیے	56,176

5-grams (Word):

Rank	N-gram	Count
1	ڈیٹا سے مختلف مختصر وضاحت	60,477
2	مطابقت رکھنے والی مختصر تفصیل	36,597
3	ڈیٹا سے مطابقت رکھنے والی	36,597
4	سے مطابقت رکھنے والی مختصر	36,597
5	ریاستہائے متحدہ امریکا کا ایک	32,162

2-grams (Subword):

Rank	N-gram	Count
1	_ ک	8,939,155
2	ے _	7,506,929
3	ی _	7,229,403
4	_ ا	6,895,736
5	_ م	5,580,612

3-grams (Subword):

Rank	N-gram	Count
1	ی ں _	2,526,926
2	ک ے _	2,439,009
3	_ ک ے	2,399,929
4	_ ک ی	2,309,611
5	_ م ی	2,222,538

4-grams (Subword):

Rank	N-gram	Count
1	_ ک ے _	2,395,195
2	م ی ں _	1,913,836
3	_ م ی ں	1,894,953
4	_ ک ی _	1,654,644
5	_ ا و ر	1,206,959

5-grams (Subword):

Rank	N-gram	Count
1	_ م ی ں _	1,841,071
2	_ ا و ر _	1,180,320
3	_ ا ی ک _	540,019
4	_ ہ ے ۔ _	533,595
5	ن _ ک ے _	281,812

Key Findings

• Best Perplexity: 2-gram (subword) with 407
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~26% 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	0.7878	1.726	10.02	931,321	21.2%
1	Subword	1.0778	2.111	8.71	12,123	0.0%
2	Word	0.4142	1.333	2.63	9,325,685	58.6%
2	Subword	0.7107	1.637	4.70	105,552	28.9%
3	Word	0.2036	1.152	1.51	24,486,673	79.6%
3	Subword	0.6567	1.576	3.92	496,416	34.3%
4	Word	0.0977 🏆	1.070	1.19	36,918,721	90.2%
4	Subword	0.6391	1.557	3.25	1,947,168	36.1%

Generated Text Samples (Word-based)

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

Context Size 1:

1. کے خلاف شمالی افریقی ارکان پارلیمنٹ جان جیکب آباد مقامات ڈیٹا سے قبل مسیح rishi 24
2. میں اس پر پہچان ایک وسیع تحقیق کی جاتی ہے اور دیگر نے بنا مزید دیکھیے
3. کی سپریم کورٹ سی پی سی اے حسینہ اور 626 0 126 نے مسترد کرتے ہوئے

Context Size 2:

1. کے لیے دو فرسٹ کلاس کرکٹ میں ان کی نمائندگی کرتے ہیں اور طنز کرتے اور حقانیت
2. حوالہ جات بیرونی روابط طرطلیان کا معما بنی ہوئی ایک ترقی یافتہ ڈویژن فور کے لیے حملہ
3. واقع ہے مزید دیکھیے لتھووینیا فہرست لتھووینیا کے نامکمل مضامین ڈیٹا سے مختلف مختصر وضاحت کی پیدائشیں

Context Size 3:

1. میں واقع ہے تفصیلات ییپچس ضلع کا رقبہ 53 944 مربع کلومیٹر ہے اس کی مجموعی آبادی 6
2. ہے مزید دیکھیے جرمنی کی ریاستیں 16 بھارت کی ریاستیں بلحاظ آبادی حوالہ جات میں قائم ہونے والے
3. ریاستہائے متحدہ امریکا ریاستہائے متحدہ امریکا کا ایک ٹاؤن شپ جو کلنٹن کاؤنٹی اوہائیو اوہائیو 61 310 ...

Context Size 4:

1. کے شہر حوالہ جات میں آباد ہونے والے مقامات ڈیٹا سے مختلف مختصر وضاحت کے آباد مقامات میں مرگ
2. ڈیٹا سے مختلف مختصر وضاحت مزاحیہ ڈراما فلمیں فلمیں متحدہ میں زنا کے بارے میں فلمیں فلمیں سے تخلیق
3. میں واقع ہے تفصیلات لا شاپیل این والگوڈیمار کا رقبہ 108 02 مربع کلومیٹر ہے اور اس کی مجموعی

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _16_دیکاس_مد_کے_
2. ا_اتھروساہے_لے۔_
3. یں_tad_ا_نیا_205

Context Size 2:

1. _کی_پان_ال_ہور_بر
2. ے_ان_میں_معا_مغرب
3. ی_ول_گار_پیدارکھی

Context Size 3:

1. یں_کا_کورپینیجرینڈ
2. کے_بلندیر_بِکری_علی
3. _کے_تھے۔_ابھ_انھوں

Context Size 4:

1. _کے_نتیجے_میں_واقع_
2. میں_جہاں_i_tehsil_w
3. _میں_وشون-شوگر_پار،

Key Findings

• Best Predictability: Context-4 (word) with 90.2% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (1,947,168 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	395,742
Total Tokens	58,544,950
Mean Frequency	147.94
Median Frequency	4
Frequency Std Dev	7177.12

Most Common Words

Rank	Word	Frequency
1	کے	2,399,956
2	میں	1,897,386
3	کی	1,727,992
4	اور	1,185,297
5	ہے	1,085,895
6	سے	991,149
7	کا	802,866
8	نے	660,570
9	اس	581,153
10	پر	570,105

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	yarns	2
2	anika	2
3	dailystar	2
4	دامنیوں	2
5	دریاچۂ	2
6	murgap	2
7	دیمقراطیت	2
8	الممتنعة	2
9	کرداراے	2
10	قیطابای	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1596
R² (Goodness of Fit)	0.989996
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	40.8%
Top 1,000	67.7%
Top 5,000	84.7%
Top 10,000	89.9%

Key Findings

• Zipf Compliance: R²=0.9900 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 40.8% of corpus
• Long Tail: 385,742 words needed for remaining 10.1% 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	0.7965 🏆	0.3746	N/A	N/A
mono_64d	64	0.7804	0.3072	N/A	N/A
mono_128d	128	0.7411	0.2584	N/A	N/A
aligned_32d	32	0.7965	0.3667	0.0900	0.3980
aligned_64d	64	0.7804	0.3243	0.1900	0.5220
aligned_128d	128	0.7411	0.2599	0.2640	0.6360

Key Findings

• Best Isotropy: mono_32d with 0.7965 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3152. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 26.4% 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	-0.387	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
-ال	الازہار, الحسينی, الكرخی
-ا	الازہار, اعادی, اسکن
-م	موزولینی, مشہورہ, میلوم
-ب	بیٹاہے, بمبی, بدیشگرام
-س	سکھانے, سگاچے, سووموٹو
-ک	کورینتینے, کتابتِ, کٹمور
-و	وملل, وففینگے, وولڈز
-ت	تبصروں, تحس, ترسیلات

Productive Suffixes

Suffix	Examples
-ی	نمامی, اعادی, بمبی
-ا	آئیڈیلا, چھیڈا, سنگڑا
-ن	اسکن, ڑککن, لعبدالرحمن
-s	anthonys, carpets, condoles
-n	usenon, areairon, bannerman
-e	linkage, lafitte, ampère
-ں	پنجابیوں, بیروتژاں, تبصروں
-ر	الازہار, کٹمور, خائر

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
ھارت	2.29x	63 contexts	ہھارت, پھارت, دھارت
مریک	2.26x	41 contexts	امریک, مریکی, مریکہ
اؤنٹ	2.16x	43 contexts	ماؤنٹ, گاؤنٹ, کاؤنٹ
کاؤن	2.21x	39 contexts	کاؤنا, کاؤنی, کاؤنٹ
اریخ	1.86x	54 contexts	فاریخ, تاریخ, تاریخٰ
لاقو	2.48x	18 contexts	علاقو, الاقو, لاقوۃ
ھلاڑ	2.91x	11 contexts	ڈھلاڑ, کھلاڑ, لھلاڑی
اقوا	2.16x	27 contexts	اقوام, جاقوا, اقوال
ختلف	2.31x	20 contexts	اختلف, يختلف, مختلف
ختصر	2.07x	23 contexts	اختصر, مختصر, مختصرا
الاق	1.77x	39 contexts	الاقو, الاقصي, الاقصى
تحدہ	2.47x	11 contexts	متحدہ, 1متحدہ, المتحدہ

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
-ال	-ی	59 words	النفیسی, الأولی
-ا	-ا	45 words	اورلینزلوویزیانا, اماٹیلا
-ا	-ی	43 words	انڈیانامیامی, النفیسی
-س	-ی	35 words	سریمورالی, سیارچوی
-ک	-ی	32 words	کندی, کولاتیری
-ال	-ن	31 words	الوالدين, الیکزاندرپشکن
-ال	-ہ	28 words	العربیہ, السیارہ
-م	-ی	26 words	مہرؤلی, مورنسی
-ب	-ی	24 words	بحیری, بریطانی
-ا	-ن	23 words	ابوالرین, انبان

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
ایکسیلنسی	ایکسیلن-س-ی	7.5	س
ریٹروگریڈ	ریٹروگر-ی-ڈ	7.5	ی
گراؤنڈبیونس	گراؤنڈبیو-ن-س	7.5	ن
کوستنجویکانہ	کوستنجویک-ان-ہ	7.5	ان
اورتعلیمی	اور-تعلیم-ی	6.0	تعلیم
مالمزبیری	م-الم-زبیری	6.0	زبیری
composers	composer-s	4.5	composer
نصیرآبادی	نصیرآباد-ی	4.5	نصیرآباد
تھیوبالڈس	تھیوبالڈ-س	4.5	تھیوبالڈ
ہائیڈریٹس	ہائیڈریٹ-س	4.5	ہائیڈریٹ
پیرالمپکس	پیرالمپک-س	4.5	پیرالمپک
dwellings	dwelling-s	4.5	dwelling
violations	violation-s	4.5	violation
positional	position-al	4.5	position
oscillators	oscillator-s	4.5	oscillator

6.6 Linguistic Interpretation

Automated Insight: The language Urdu 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 (4.07x)
N-gram	2-gram	Lowest perplexity (407)
Markov	Context-4	Highest predictability (90.2%)
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: 2026-01-11 06:46:29