RESEARCH_REPORT.md

Moroccan Arabic — Full Ablation Study & Research Report

Detailed evaluation of all model variants trained on Moroccan Arabic Wikipedia data by Wikilangs.

👈 Back to README

📋 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	3.481x	3.48	0.0910%	300,053
16k	3.755x	3.76	0.0982%	278,145
32k	3.985x	3.99	0.1041%	262,127
64k	4.172x 🏆	4.18	0.1090%	250,361

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: قريش هيا قبيلة ؤلا أجموع قبلي لي، علا حساب لمصادر لإسلامية، كانت ف مكة ؤ كاينتام...

Vocab	Tokens	Count
8k	▁ق ريش ▁هيا ▁قبيلة ▁ؤلا ▁أج موع ▁ق بلي ▁لي ... (+19 more)	29
16k	▁قريش ▁هيا ▁قبيلة ▁ؤلا ▁أج موع ▁ق بلي ▁لي ، ... (+16 more)	26
32k	▁قريش ▁هيا ▁قبيلة ▁ؤلا ▁أجموع ▁ق بلي ▁لي ، ▁علا ... (+15 more)	25
64k	▁قريش ▁هيا ▁قبيلة ▁ؤلا ▁أجموع ▁قبلي ▁لي ، ▁علا ▁حساب ... (+14 more)	24

Sample 2: آيت ميلك جماعة ترابية قروية كاينة في إقليم اشتوكة آيت باها، جهة سوس ماسة، ساكنين...

Vocab	Tokens	Count
8k	▁آيت ▁ميل ك ▁جماعة ▁ترابية ▁قروية ▁كاينة ▁في ▁إقليم ▁اشتوكة ... (+16 more)	26
16k	▁آيت ▁ميل ك ▁جماعة ▁ترابية ▁قروية ▁كاينة ▁في ▁إقليم ▁اشتوكة ... (+16 more)	26
32k	▁آيت ▁ميل ك ▁جماعة ▁ترابية ▁قروية ▁كاينة ▁في ▁إقليم ▁اشتوكة ... (+16 more)	26
64k	▁آيت ▁ميلك ▁جماعة ▁ترابية ▁قروية ▁كاينة ▁في ▁إقليم ▁اشتوكة ▁آيت ... (+15 more)	25

Sample 3: خديجة بنت علي بن أبي طالب، هي بنت علي بن أبي طالب. مصادر د نسا

Vocab	Tokens	Count
8k	▁خديجة ▁بنت ▁علي ▁بن ▁أبي ▁طالب ، ▁هي ▁بنت ▁علي ... (+7 more)	17
16k	▁خديجة ▁بنت ▁علي ▁بن ▁أبي ▁طالب ، ▁هي ▁بنت ▁علي ... (+7 more)	17
32k	▁خديجة ▁بنت ▁علي ▁بن ▁أبي ▁طالب ، ▁هي ▁بنت ▁علي ... (+7 more)	17
64k	▁خديجة ▁بنت ▁علي ▁بن ▁أبي ▁طالب ، ▁هي ▁بنت ▁علي ... (+7 more)	17

Key Findings

• Best Compression: 64k achieves 4.172x compression
• Lowest UNK Rate: 8k with 0.0910% 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	7,415	12.86	40,208	22.8%	50.4%
2-gram	Subword	428 🏆	8.74	5,913	57.8%	96.3%
3-gram	Word	5,775	12.50	44,139	27.3%	56.7%
3-gram	Subword	3,823	11.90	44,840	23.0%	60.5%
4-gram	Word	8,149	12.99	71,489	27.3%	53.3%
4-gram	Subword	20,320	14.31	222,645	11.9%	35.8%
5-gram	Word	7,702	12.91	59,669	28.3%	52.6%
5-gram	Subword	63,356	15.95	533,903	7.3%	24.8%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	واصلة ل	8,540
2	نسبة د	7,170
3	ف لمغريب	6,310
4	ف إقليم	6,015
5	ف نسبة	4,265

3-grams (Word):

Rank	N-gram	Count
1	ف نسبة د	4,264
2	فيها مصدر و	3,235
3	و نسبة د	2,894
4	مصدر و بايت	2,855
5	اللي خدامين ف	2,761

4-grams (Word):

Rank	N-gram	Count
1	فيها مصدر و بايت	2,855
2	نسبة نّاس اللي خدامين	2,705
3	نّاس اللي خدامين ف	2,595
4	على حساب لإحصاء الرسمي	2,501
5	لمغريب هاد دّوار كينتامي	2,500

5-grams (Word):

Rank	N-gram	Count
1	نسبة نّاس اللي خدامين ف	2,594
2	ف لمغريب هاد دّوار كينتامي	2,500
3	لمغريب هاد دّوار كينتامي ل	2,500
4	هاد دّوار كينتامي ل مشيخة	2,500
5	حساب لإحصاء الرسمي د عام	2,500

2-grams (Subword):

Rank	N-gram	Count
1	ا ل	348,897
2	_ ل	282,523
3	ة _	230,243
4	_ ا	221,714
5	_ م	157,830

3-grams (Subword):

Rank	N-gram	Count
1	_ ا ل	216,894
2	_ ف _	84,068
3	ا ت _	64,715
4	_ و _	60,577
5	ي ة _	60,370

4-grams (Subword):

Rank	N-gram	Count
1	_ د ي ا	48,269
2	د ي ا ل	48,014
3	ي ا ل _	33,434
4	د _ ا ل	33,075
5	_ م ن _	29,173

5-grams (Subword):

Rank	N-gram	Count
1	_ د ي ا ل	47,884
2	د ي ا ل _	33,006
3	_ ع ل ى _	19,658
4	_ ا ل ل ي	18,939
5	ا ل ل ي _	18,733

Key Findings

• Best Perplexity: 2-gram (subword) with 428
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~25% 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	0.8581	1.813	5.40	180,421	14.2%
1	Subword	1.1243	2.180	8.36	2,159	0.0%
2	Word	0.2267	1.170	1.49	973,633	77.3%
2	Subword	0.8165	1.761	5.10	18,051	18.4%
3	Word	0.0619	1.044	1.10	1,450,643	93.8%
3	Subword	0.8035	1.745	4.14	92,103	19.7%
4	Word	0.0207 🏆	1.014	1.04	1,595,675	97.9%
4	Subword	0.6627	1.583	2.87	381,563	33.7%

Generated Text Samples (Word-based)

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

Context Size 1:

1. ف ايكنان نقص ب سباب غتيال لماليك لأمازيغي ؤ ولّا على المنطق والبحث كان حتا زلزال
2. و نسبة نّاس اللي سبق ليهوم مصادر ربحو جايزة أحسن 10 سنين موراها تولّا لحكم الداتي
3. د لميداليات ف إقليم لحوز جهة مراكش آسفي ف المغرب من بعد باللي كان نتر خيالي

Context Size 2:

1. واصلة ل 3 ف لعقد ديال عوام كيوافق ف تّقويم لهيجري ؤ ف تّقويم لڭريڭوري بدا نهار
2. نسبة د الشوماج واصلة ل 6 6 044 0 290 يوكطوتانية هيدروجين 7 7 و لخصوبة لكاملة
3. ف لمغريب هاد دّوار كينتامي ل مشيخة أيت قضني لي كتضم 9 د دّواور لعاداد د سّكان

Context Size 3:

1. ف نسبة د التسكويل واصلة ل 90 8 و نسبة د لأمية واصلة ل 50 33 لخدمة ف
2. فيها مصدر و بايت على حساب النوع د لحنش التشلال التنفوسي فشلان لكبدة لكوما و bites a d
3. و نسبة د الشوماج واصلة ل 18 4 و لموعدّال د لعمر عند الجواج اللولاني هوّ 23 87

Context Size 4:

1. نسبة نّاس اللي خدامين في لقطاع لخاص 39 1 مصادر الرباط سلا القنيطرة قروية ف إقليم لخميسات مسكونين ف
2. نّاس اللي خدامين ف لپريڤي 57 1 مصادر الرباط سلا القنيطرة قروية ف إقليم سيدي إيفني جهة ݣلميم واد
3. على حساب لإحصاء الرسمي د عام نوطات مصادر ف لمغريب ف إقليم تارودانت زادهوم داريجابوت

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _أو_جة_م_-اسبش_د
2. الاف_ف،_عية_لحدا
3. لعة_ل_وعبر،_اليب

Context Size 2:

1. الجديات)._عنصاد_ا
2. _لخمسيوسيحطولا_صر
3. ة_ديال_لهي_بزرقة_

Context Size 3:

1. _اللي_خمائيات_ديال
2. _ف_لجمهورية_الطابل
3. ات_(گاع_ل_من_مابين

Context Size 4:

1. _ديال_المرسى_ديال_ا
2. ديالهوم_مصادر_فيهم_
3. يال_شيحد_من_بعد_فـ_

Key Findings

• Best Predictability: Context-4 (word) with 97.9% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (381,563 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	79,667
Total Tokens	2,057,009
Mean Frequency	25.82
Median Frequency	4
Frequency Std Dev	518.98

Most Common Words

Rank	Word	Frequency
1	ف	84,381
2	و	60,856
3	د	60,420
4	ديال	32,966
5	من	29,503
6	ل	23,808
7	على	19,757
8	لي	18,777
9	ب	17,745
10	اللي	17,410

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	ختيلال	2
2	تسطية	2
3	التخمار	2
4	لمركزين	2
5	تعلاف	2
6	الروضيو	2
7	رِد	2
8	وينغز	2
9	تايغرز	2
10	كلتة	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.0203
R² (Goodness of Fit)	0.998917
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	38.4%
Top 1,000	62.8%
Top 5,000	77.7%
Top 10,000	84.1%

Key Findings

• Zipf Compliance: R²=0.9989 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 38.4% of corpus
• Long Tail: 69,667 words needed for remaining 15.9% 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	0.8215 🏆	0.3275	N/A	N/A
mono_64d	64	0.8006	0.2538	N/A	N/A
mono_128d	128	0.6555	0.2039	N/A	N/A
aligned_32d	32	0.8215	0.3276	0.0080	0.1080
aligned_64d	64	0.8006	0.2565	0.0380	0.2000
aligned_128d	128	0.6555	0.2044	0.0440	0.2420

Key Findings

• Best Isotropy: mono_32d with 0.8215 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.2623. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 4.4% 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	1.121	High formulaic/idiomatic 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	القوميين, الاحتياطية, الطابلة
-ل	لعضان, لكرواتي, لعاميد
-ت	تقرا, تحقيقات, تشارلي
-م	ميطاكا, معاهد, موليكيلة
-لم	لمحلولة, لمقبولين, لمطلوق
-و	والهيئات, والطرقان, وبطريقة
-الم	المركب, المعروفين, المناخية
-ب	بنشليخة, بيئات, بلمارشالية

Productive Suffixes

Suffix	Examples
-ت	والهيئات, تحقيقات, لبويرات
-ة	بنشليخة, وبطريقة, عشبة
-ات	والهيئات, تحقيقات, لبويرات
-ن	لعضان, والطرقان, القوميين
-ية	أكترية, الاحتياطية, والاشتراكية
-ا	ميطاكا, تقرا, سيينا
-ي	ؤطوماتيكي, لكرواتي, سينتشي
-ين	القوميين, پيسّين, مشهورين

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
انية	1.84x	68 contexts	سانية, تانية, غانية
النا	1.79x	63 contexts	الناي, الناس, النار
لمغر	2.03x	30 contexts	لمغرب, المغرب, لمغربي
جماع	1.89x	37 contexts	جماعة, إجماع, جماعي
اللو	1.66x	61 contexts	اللون, اللور, اللوز
الات	1.59x	65 contexts	صالات, حالات, سالات
مغري	2.11x	18 contexts	مغرية, مغريب, لمغريب
دهوم	2.19x	16 contexts	ضدهوم, يردهوم, جهدهوم
إحصا	2.09x	17 contexts	إحصاء, لإحصا, إحصائي
حصاء	2.23x	14 contexts	إحصاء, ليحصاء, لإحصاء
قليم	2.08x	16 contexts	إقليم, فقليم, اقليم
لجوا	1.76x	26 contexts	لجواب, الجوا, لجواد

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	-ة	281 words	الرواقية, القهوة
-ل	-ة	184 words	لفريسة, للمنصة
-ال	-ت	170 words	المجموعات, الصوتيات
-ال	-ات	164 words	المجموعات, الصوتيات
-ال	-ية	142 words	الرواقية, السيادية
-ل	-ت	131 words	لقمقومات, لپوطوات
-ل	-ات	125 words	لقمقومات, لپوطوات
-ل	-ن	124 words	لعيّان, لخيشوميين
-ال	-ن	119 words	الكربون, الفريقين
-ل	-ية	116 words	لعدمية, لبيولوجية

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
والعمالات	و-ال-عمالات	7.5	عمالات
والراشيدية	و-ال-راشيدية	7.5	راشيدية
والمشروبات	و-ال-مشروبات	7.5	مشروبات
والمؤرخين	و-ال-مؤرخين	7.5	مؤرخين
والمسيحية	و-ال-مسيحية	7.5	مسيحية
فالسعودية	ف-ال-سعودية	7.5	سعودية
بالفرنسية	ب-ال-فرنسية	7.5	فرنسية
بالكيلوݣرام	ب-ال-كيلوݣرام	7.5	كيلوݣرام
والأساتذة	و-ال-أساتذة	7.5	أساتذة
والأقاليم	و-ال-أقاليم	7.5	أقاليم
باللاتينية	ب-ال-لاتينية	7.5	لاتينية
باليونانية	ب-ال-يونانية	7.5	يونانية
لبزقوليين	لبزقول-ي-ين	7.5	ي
فالجورنال	ف-ال-جورنال	7.5	جورنال
بالصيناعة	ب-ال-صيناعة	7.5	صيناعة

6.6 Linguistic Interpretation

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

Note on Idiomaticity: The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.

---

7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.17x)
N-gram	2-gram	Lowest perplexity (428)
Markov	Context-4	Highest predictability (97.9%)
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

---

👈 Back to README

Generated by Wikilangs Pipeline · 2026-03-02 12:03:50