Comprehensive Research Report & Full Ablation Study
This repository contains NLP models trained and evaluated by Wikilangs, specifically on Assamese 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)
হ ল পদ্মভূষণ ভাৰতৰ তৃতীয় সৰ্বোচ্চ অসামৰিক সন্মান পদ্মশ্ৰী লাভ কৰে তেখেতে অভিনয় কৰে চনত তেওঁৰ নিজাক...
Context Size 3:
ব্যৱহাৰ কৰা হয় msa এ smtp প্ৰটোকলত প্ৰদান কৰা গন্তব্যস্থানৰ ঠিকনা নিৰ্ধাৰণ কৰে বাৰ্তা হেডাৰৰ পৰা নহ...
হ ব পাৰে অসমৰ কবি লেখক জীৱন নৰহে আত্মজীৱনীমূলক গ্ৰন্থখনক নতুন প্ৰজন্মৰ সাহসৰ দলিল বুলি অভিহিত কৰে অৰ...
বুলি কোৱা হয় ৰাক্ষসসকলক প্ৰায় পৰাধীন সৈনিকৰ ৰূপত দেখুৱা হৈছিল পিছে কিছু ৰাক্ষসে অত্যন্ত বল অৰ্জন ক...
Context Size 4:
তথ্য সংগ্ৰহ বাহ্যিক সংযোগ cornell university e book library of classic texts on mechanical design an...
বুলি গণ্য কৰা হয় আৰু কোমল গ আৰু কোমল ধ স্বৰসমূহ কম্পনৰ সৈতে অন্দোলিত পৰিবেশিত হয় সকলো পাঁচটা স্বৰ
স্নাতক ডিগ্ৰী লাভ কৰে সেই একেই দীন দয়াল উপাধ্যায় কলেজৰ পৰা সামাজিক কাম চনত ১৯ বছৰ বয়সত ছেম অল্টমে...
Generated Text Samples (Subword-based)
Below are text samples generated from each subword-based Markov chain model:
Context Size 1:
_ধান_লা_শক্তি।_হাসিদ্ধ_তাকা
ৰক্ষ_নিজনগোৱাহালচ্চিত্ৰ_মবাবে
কথাই_ইছিল_জীৱন_ডিয়_বা
Context Size 2:
ৰ_আৰু_লাউ।_অসংখ্যক_ভাৰত
ত_জ্ঞানৰ_কৃপ,_কেন্দ্ৰটোৰ_পৰা
_আৰু_মোৰ_পৰা_উৰুলিয়ান_শা
Context Size 3:
আৰু_বীৰেন্দ্ৰ_মোডীৰ_নিগমৰ_প্ৰয়া
_আৰু_অৰ্থ।_দেৱালয়খনৰ_পিছ
_কৰিবলৈ_অস্বীকাৰ_হোৱা_মতবাদ
Context Size 4:
_আৰু_পাম_তেল_আৰু_পিপিপি_আৰু
ছিল।_কুমাৰীত্ব_পৰীক্ষাৰ_অংগ_আৰু
_কৰা_দুখৰ_আৰু_তেওঁৰ_ছিলভাৰ
Key Findings
Best Predictability: Context-4 (word) with 97.2% predictability
Branching Factor: Decreases with context size (more deterministic)
Memory Trade-off: Larger contexts require more storage (3,437,009 contexts)
Recommendation: Context-3 or Context-4 for text generation
4. Vocabulary Analysis
Zipf's Law
Top Words
Coverage Curve
Statistics
Metric
Value
Vocabulary Size
225,407
Total Tokens
9,007,362
Mean Frequency
39.96
Median Frequency
4
Frequency Std Dev
792.95
Most Common Words
Rank
Word
Frequency
1
আৰু
247,463
2
কৰা
93,923
3
হয়
87,716
4
কৰে
78,599
5
এই
64,931
6
তেওঁ
64,613
7
পৰা
53,636
8
কৰিছিল
51,623
9
বাবে
50,799
10
চনত
49,149
Least Common Words (from vocabulary)
Rank
Word
Frequency
1
পাণ্ডুবংশী
2
2
মনুমেণ্টছ
2
3
ছিৰপুৰৰ
2
4
swfl
2
5
manhunt
2
6
megamodel
2
7
গ্লেডৰেগ্চ
2
8
কিস
2
9
পদাইভীৰণ
2
10
বিগিল
2
Zipf's Law Analysis
Metric
Value
Zipf Coefficient
1.0094
R² (Goodness of Fit)
0.989782
Adherence Quality
excellent
Coverage Analysis
Top N Words
Coverage
Top 100
25.5%
Top 1,000
50.9%
Top 5,000
71.9%
Top 10,000
79.7%
Key Findings
Zipf Compliance: R²=0.9898 indicates excellent adherence to Zipf's law
High Frequency Dominance: Top 100 words cover 25.5% of corpus
Long Tail: 215,407 words needed for remaining 20.3% coverage
5. Word Embeddings Evaluation
Embedding Isotropy
Similarity Matrix
t-SNE Words
t-SNE Sentences
5.1 Cross-Lingual Alignment
Alignment Quality
Multilingual t-SNE
5.2 Model Comparison
Model
Dimension
Isotropy
Semantic Density
Alignment R@1
Alignment R@10
mono_32d
32
0.8458
0.3637
N/A
N/A
mono_64d
64
0.8547 🏆
0.2742
N/A
N/A
mono_128d
128
0.8359
0.2093
N/A
N/A
aligned_32d
32
0.8458
0.3735
0.0580
0.3060
aligned_64d
64
0.8547
0.2836
0.1180
0.3960
aligned_128d
128
0.8359
0.2075
0.1480
0.4820
Key Findings
Best Isotropy: mono_64d with 0.8547 (more uniform distribution)
Semantic Density: Average pairwise similarity of 0.2853. Lower values indicate better semantic separation.
Alignment Quality: Aligned models achieve up to 14.8% 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
-0.495
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
Productive Suffixes
Suffix
Examples
-ৰ
কেথলিকসকলৰ, স্বপ্ৰচাৰ, ৱিণ্টাৰ
-াৰ
স্বপ্ৰচাৰ, ৱিণ্টাৰ, অফকাটাৰ
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
ther
3.36x
64 contexts
theri, there, other
ress
3.34x
44 contexts
press, dress, duress
nter
3.33x
38 contexts
inter, enter, wynter
vers
3.15x
47 contexts
verso, versa, verse
atio
3.32x
37 contexts
ratio, fatio, nation
indi
3.24x
39 contexts
hindi, indie, india
ment
3.24x
38 contexts
cement, moment, mental
stor
3.25x
35 contexts
storm, jstor, story
ctio
3.33x
32 contexts
action, auction, faction
iver
3.16x
26 contexts
liver, giver, river
ersi
3.21x
20 contexts
persia, persie, yersin
mber
3.17x
18 contexts
amber, number, member
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.
No significant affix co-occurrences detected.
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
চেটেছুয়াৰাৰ
চেটেছুয়-াৰ-াৰ
3.0
চেটেছুয়
সীতাৰামায়াৰ
সীতাৰামায়-াৰ
1.5
সীতাৰামায়
ৰাজ্কুমাৰ
ৰাজ্কুম-াৰ
1.5
ৰাজ্কুম
প্ৰতিৰক্ষাৰ
প্ৰতিৰক্ষ-াৰ
1.5
প্ৰতিৰক্ষ
দত্তবৰুৱাৰ
দত্তবৰুৱ-াৰ
1.5
দত্তবৰুৱ
বিষ্ণুৰাভাৰ
বিষ্ণুৰাভ-াৰ
1.5
বিষ্ণুৰাভ
বদৌপায়াৰ
বদৌপায়-াৰ
1.5
বদৌপায়
হাছলেংগাৰ
হাছলেংগ-াৰ
1.5
হাছলেংগ
চিজাৰিয়াৰ
চিজাৰিয়-াৰ
1.5
চিজাৰিয়
কুকুৰাঝাৰ
কুকুৰাঝ-াৰ
1.5
কুকুৰাঝ
মন্দাৱস্থাৰ
মন্দাৱস্থ-াৰ
1.5
মন্দাৱস্থ
আত্মপ্ৰতিষ্ঠাৰ
আত্মপ্ৰতিষ্ঠ-াৰ
1.5
আত্মপ্ৰতিষ্ঠ
কেঁচাগোল্লাৰ
কেঁচাগোল্ল-াৰ
1.5
কেঁচাগোল্ল
ফ্ৰণ্টিয়াৰ
ফ্ৰণ্টিয়-াৰ
1.5
ফ্ৰণ্টিয়
যিহোচূৱাৰ
যিহোচূৱ-াৰ
1.5
যিহোচূৱ
6.6 Linguistic Interpretation
Automated Insight:
The language Assamese 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
Performance Dashboard
Production Recommendations
Component
Recommended
Rationale
Tokenizer
64k BPE
Best compression (4.54x)
N-gram
2-gram
Lowest perplexity (2,308)
Markov
Context-4
Highest predictability (97.2%)
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
Compare within model families: Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
Consider trade-offs: Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
Context matters: Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
Corpus influence: All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
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.
