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Kongo - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Kongo 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

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.971x	3.98	0.2014%	98,310
16k	4.333x	4.34	0.2197%	90,112
32k	4.520x 🏆	4.53	0.2292%	86,376

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Tübingen kele kizunga ya Baden-Württemberg, Alemanyi.

Vocab	Tokens	Count
8k	`▁t ü b ing en ▁kele ▁kizunga ▁ya ▁baden - ... (+4 more)`	14
16k	`▁t übingen ▁kele ▁kizunga ▁ya ▁baden - württemberg , ▁alemanyi ... (+1 more)`	11
32k	`▁tübingen ▁kele ▁kizunga ▁ya ▁baden - württemberg , ▁alemanyi .`	10

Sample 2: Ubuntu kele mpila mosi ya Linux. Nkumbu ya Ubuntu (na kikongo: bumuntu to kimunt...

Vocab	Tokens	Count
8k	`▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁l inux . ▁nkumbu ▁ya ... (+27 more)`	37
16k	`▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁linux . ▁nkumbu ▁ya ▁ubuntu ... (+24 more)`	34
32k	`▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁linux . ▁nkumbu ▁ya ▁ubuntu ... (+24 more)`	34

Sample 3: kele suka ya kondi ya Repubilika ya Kôngo. ya kondi

Vocab	Tokens	Count
8k	`▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kôngo . ▁ya ... (+1 more)`	11
16k	`▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kôngo . ▁ya ... (+1 more)`	11
32k	`▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kôngo . ▁ya ... (+1 more)`	11

Key Findings

Best Compression: 32k achieves 4.520x compression
Lowest UNK Rate: 8k with 0.2014% 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	1,534	10.58	3,731	31.2%	75.1%
2-gram	Subword	168 🏆	7.39	1,390	77.7%	99.7%
3-gram	Word	3,413	11.74	6,815	19.8%	55.8%
3-gram	Subword	948	9.89	8,160	43.7%	83.9%
4-gram	Word	6,222	12.60	11,303	15.0%	42.1%
4-gram	Subword	3,320	11.70	28,230	28.1%	63.0%
5-gram	Word	4,107	12.00	7,664	18.9%	48.5%
5-gram	Subword	7,120	12.80	46,201	19.4%	50.9%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	`sambu na`	862
2	`ya kongo`	704
3	`ya bantu`	652
4	`kele na`	649
5	`na yandi`	613

3-grams (Word):

Rank	N-gram	Count
1	`ya kongo ya`	375
2	`repubilika ya kongo`	369
3	`na kati ya`	361
4	`kongo ya dimokalasi`	332
5	`na nima ya`	314

4-grams (Word):

Rank	N-gram	Count
1	`ya kongo ya dimokalasi`	332
2	`repubilika ya kongo ya`	283
3	`ya repubilika ya kongo`	216
4	`mbanza ya kimfumu ya`	181
5	`kimfumu ya kizunga ya`	164

5-grams (Word):

Rank	N-gram	Count
1	`repubilika ya kongo ya dimokalasi`	270
2	`ya repubilika ya kongo ya`	172
3	`mbanza ya kimfumu ya kizunga`	161
4	`ya kimfumu ya kizunga ya`	160
5	`kele mbanza kimfumu ya yinsi`	109

2-grams (Subword):

Rank	N-gram	Count
1	`a _`	66,512
2	`_ y`	30,798
3	`y a`	27,604
4	`_ n`	21,445
5	`_ k`	21,155

3-grams (Subword):

Rank	N-gram	Count
1	`_ y a`	26,479
2	`y a _`	23,101
3	`n a _`	15,424
4	`_ n a`	11,972
5	`a _ k`	11,963

4-grams (Subword):

Rank	N-gram	Count
1	`_ y a _`	22,832
2	`_ n a _`	11,648
3	`a _ y a`	8,946
4	`u _ y a`	6,395
5	`a k a _`	5,692

5-grams (Subword):

Rank	N-gram	Count
1	`a _ y a _`	7,419
2	`u _ y a _`	5,909
3	`_ y a _ k`	5,126
4	`i _ y a _`	4,012
5	`a _ n a _`	3,703

Key Findings

Best Perplexity: 2-gram (subword) with 168
Entropy Trend: Decreases with larger n-grams (more predictable)
Coverage: Top-1000 patterns cover ~51% 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	0.6753	1.597	3.82	13,889	32.5%
1	Subword	1.1440	2.210	8.61	367	0.0%
2	Word	0.2863	1.219	1.74	52,840	71.4%
2	Subword	0.9575	1.942	5.20	3,158	4.3%
3	Word	0.1457	1.106	1.27	91,186	85.4%
3	Subword	0.7225	1.650	3.20	16,384	27.8%
4	Word	0.0715 🏆	1.051	1.11	115,336	92.9%
4	Subword	0.4661	1.381	2.03	52,366	53.4%

Generated Text Samples (Word-based)

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

Context Size 1:

ya 8 bantu ya kimpwanza mosi ya saint lucia saint marin serbie slovakia slovenia solomon islands
na provense ya kutadila ntalu ya kutwadisa mpi bo na mayi na ouganda bantu ya dibulu
kele mbanza goma mpi yo mutindu yina vandaka me tulaka yandi kele kaka na biro ya

Context Size 2:

sambu na kisalu mpi kutomisa bima ya nkaka ke sala nde bantu yina vandaka kumonisa nsoba ya
ya kongo rdc category sénateur ya kasai na baluba ne mvuta ya bantu ya nkaka ya me
ya bantu ya mubulu na mvu bo me binga sambu na kuzabisa luzayisu yayi kusalama na kutadila

Context Size 3:

ya kongo ya dimokalasi mbanza mfumu ya kizunga jiangsu ya sina ya sina
na kati ya bazulunalu yina salaka mambu ya yimbi mpe kimbeni yina ke vuandaka na mukidi ya nzadi
repubilika ya kongo ya dimokalasi sambu bo kezabaka nde nzo nkanda vandaka kufuta yves piron mpi sam...

Context Size 4:

repubilika ya kongo ya dimokalasi ya kati
ya kongo ya dimokalasi category guvernere ya tshopo category lubutuku na zaire category avocat congo...
ya repubilika ya kongo yandi vuandaka muene ya brazzaville ti kuna ná ntumua ya ntete ya repubilika ...

Generated Text Samples (Subword-based)

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

Context Size 1:

_nakama_mbo_mba_
alaba_a_yi_yikin
n_yo_yasa_yingar

Context Size 2:

a_keles)_ta._ba_y
_yan_john_luzwalb
ya_ntu_ya_yandimo

Context Size 3:

_ya_kizunga_na_ket
ya_kusalu_ya_los_k
na_nkandakaataka_k

Context Size 4:

_ya_kuponaka_kimban
_na_ntinu,_kinkundi
a_ya_repubilika_ya_

Key Findings

Best Predictability: Context-4 (word) with 92.9% predictability
Branching Factor: Decreases with context size (more deterministic)
Memory Trade-off: Larger contexts require more storage (52,366 contexts)
Recommendation: Context-3 or Context-4 for text generation

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	6,048
Total Tokens	147,208
Mean Frequency	24.34
Median Frequency	3
Frequency Std Dev	343.74

Most Common Words

Rank	Word	Frequency
1	ya	22,856
2	na	11,712
3	kele	3,421
4	yandi	2,406
5	yina	2,286
6	mpi	2,116
7	ke	1,925
8	bantu	1,619
9	bo	1,431
10	ti	1,160

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	lukwikulu	2
2	kinama	2
3	bangolo	2
4	difuta	2
5	mbatukulu	2
6	kifumba	2
7	weto	2
8	metangama	2
9	dieumerci	2
10	xoon	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1604
R² (Goodness of Fit)	0.989979
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	59.2%
Top 1,000	85.9%
Top 5,000	98.6%
Top 10,000	0.0%

Key Findings

Zipf Compliance: R²=0.9900 indicates excellent adherence to Zipf's law
High Frequency Dominance: Top 100 words cover 59.2% of corpus
Long Tail: -3,952 words needed for remaining 100.0% 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	0.1871 🏆	0.4938	N/A	N/A
mono_64d	64	0.0298	0.4879	N/A	N/A
mono_128d	128	0.0037	0.5051	N/A	N/A
aligned_32d	32	0.1871	0.5017	0.0140	0.0900
aligned_64d	64	0.0298	0.4772	0.0120	0.1260
aligned_128d	128	0.0037	0.4863	0.0100	0.1280

Key Findings

Best Isotropy: mono_32d with 0.1871 (more uniform distribution)
Semantic Density: Average pairwise similarity of 0.4920. Lower values indicate better semantic separation.
Alignment Quality: Aligned models achieve up to 1.4% 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	-0.048	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
`-m`	mphotho, motángo, mapi
`-k`	kimenga, kontina, kubutukaka
`-ba`	balongoki, bavière, baministre
`-b`	bzl, balongoki, bavière
`-ku`	kubutukaka, kusadisa, kufua
`-n`	nima, nzundu, ndalama
`-ma`	mapi, maulalo, manimba
`-ki`	kimenga, kimama, kinkita

Productive Suffixes

Suffix	Examples
`-a`	kimenga, tendula, nima
`-e`	laurène, jenerale, bavière
`-ka`	kubutukaka, twadisaka, vwandaka
`-i`	tournoi, balongoki, mapi
`-s`	chinois, awards, mois
`-o`	mphotho, motángo, mpozo
`-u`	nzundu, banduku, dibuku
`-n`	installation, radiodiffusion, american

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
`anga`	1.59x	42 contexts	sanga, tanga, nanga
`angu`	1.34x	33 contexts	hangu, kangu, wangu
`kand`	1.75x	12 contexts	kanda, kandy, nkandu
`tion`	1.77x	11 contexts	option, action, motion
`unga`	1.38x	23 contexts	zunga, lunga, tunga
`ambu`	1.31x	26 contexts	sambu, mambu, wambu
`ndak`	1.61x	12 contexts	vandaka, bandaka, fundaka
`alak`	1.60x	12 contexts	palaki, talaka, salaka
`laka`	1.65x	11 contexts	kulaka, talaka, bulaka
`kisa`	1.63x	10 contexts	kisaka, vukisa, kisalu
`anza`	1.52x	12 contexts	sanza, kanza, banza
`bans`	1.65x	8 contexts	bansi, banswa, bansaka

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
`-k`	`-a`	369 words	kimenga, kontina
`-k`	`-ka`	114 words	kubutukaka, kusalaka
`-m`	`-a`	102 words	mbânza, manimba
`-ba`	`-a`	90 words	bandînga, bafwana
`-k`	`-la`	55 words	kufokula, kubokila
`-n`	`-a`	48 words	nima, ndalama
`-k`	`-i`	48 words	kasi, kimosi
`-ba`	`-i`	46 words	balongoki, bankengi
`-b`	`-a`	46 words	bandînga, beba
`-ba`	`-e`	45 words	bavière, baministre

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
kudibingaka	`ku-di-bingaka`	7.5	`bingaka`
twadisama	`twadis-a-ma`	7.5	`a`
kesalamaka	`kesalam-a-ka`	7.5	`a`
tungamaka	`tunga-ma-ka`	7.5	`ma`
kendimaka	`kendim-a-ka`	7.5	`a`
commandant	`command-a-nt`	7.5	`a`
nwaninaka	`nwanin-a-ka`	7.5	`a`
kukutanaka	`kukutan-a-ka`	7.5	`a`
entrepreneuriat	`entrepreneuri-a-t`	7.5	`a`
azərbaycan	`azərbayc-a-n`	7.5	`a`
nsambukila	`nsambu-ki-la`	7.5	`ki`
kudibanza	`ku-di-banza`	7.5	`banza`
twadisaka	`twadis-a-ka`	7.5	`a`
acheulean	`acheule-a-n`	7.5	`a`
championnat	`championn-a-t`	7.5	`a`

6.6 Linguistic Interpretation

Automated Insight: The language Kongo 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	32k BPE	Best compression (4.52x)
N-gram	2-gram	Lowest perplexity (168)
Markov	Context-4	Highest predictability (92.9%)
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

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.

Maintainer

Omar Kamali - Omneity Labs

Citation

If you use these models in your research, please cite:

bibtex

@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.

wikilangs/kg

Kongo - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

📋 Repository Contents

Models & Assets

Analysis and Evaluation

1. Tokenizer Evaluation

Results

Tokenization Examples

Key Findings

2. N-gram Model Evaluation

Results

Top 5 N-grams by Size

Key Findings

3. Markov Chain Evaluation

Results

Generated Text Samples (Word-based)

Generated Text Samples (Subword-based)

Key Findings

4. Vocabulary Analysis

Statistics

Most Common Words

Least Common Words (from vocabulary)

Zipf's Law Analysis

Coverage Analysis

Key Findings

5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

5.2 Model Comparison

Key Findings

6. Morphological Analysis (Experimental)

6.1 Productivity & Complexity

6.2 Affix Inventory (Productive Units)

Productive Prefixes

Productive Suffixes

6.3 Bound Stems (Lexical Roots)

6.4 Affix Compatibility (Co-occurrence)

6.5 Recursive Morpheme Segmentation

6.6 Linguistic Interpretation

7. Summary & Recommendations

Production Recommendations

Appendix: Metrics Glossary & Interpretation Guide

Tokenizer Metrics

N-gram Model Metrics

Markov Chain Metrics

Vocabulary & Zipf's Law Metrics

Word Embedding Metrics

General Interpretation Guidelines

Visualizations Index

About This Project

Data Source

Project

Maintainer

Citation

License

Links

wikilangs/
kg