README.md

metadata

language: fon
language_name: Fon
language_family: atlantic_kwa
tags:
  - wikilangs
  - nlp
  - tokenizer
  - embeddings
  - n-gram
  - markov
  - wikipedia
  - feature-extraction
  - sentence-similarity
  - tokenization
  - n-grams
  - markov-chain
  - text-mining
  - fasttext
  - babelvec
  - vocabulous
  - vocabulary
  - monolingual
  - family-atlantic_kwa
license: mit
library_name: wikilangs
pipeline_tag: text-generation
datasets:
  - omarkamali/wikipedia-monthly
dataset_info:
  name: wikipedia-monthly
  description: Monthly snapshots of Wikipedia articles across 300+ languages
metrics:
  - name: best_compression_ratio
    type: compression
    value: 4.124
  - name: best_isotropy
    type: isotropy
    value: 0.6254
  - name: vocabulary_size
    type: vocab
    value: 0
generated: 2026-01-04T00:00:00.000Z

Fon - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

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

---

1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	3.633x	3.64	0.1627%	178,834
16k	3.846x	3.85	0.1723%	168,913
32k	4.057x	4.06	0.1817%	160,142
64k	4.124x 🏆	4.13	0.1847%	157,541

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Koffi Danger, ɔ́ nyí malànhwlɛ̀nvlɛ́tɔ́ Benɛɛ tɔn ɖé wɛ bɔ è jì i ɖò ɖò Gbɔ̀xikɔ...

Vocab	Tokens	Count
8k	▁koffi ▁dan ger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ... (+19 more)	29
16k	▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)	28
32k	▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)	28
64k	▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)	28

Sample 2: Kuwanwangu nyi glekɔxwe ɖokpo nǔ tokpɔnlavi Kwaba tɔn nú tokpɔnla Natitingu tɔn ...

Vocab	Tokens	Count
8k	▁ku wan wan gu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ... (+12 more)	22
16k	▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)	19
32k	▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)	19
64k	▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)	19

Sample 3: Ablu ɔ hwenu e minyɔ̀ alo weziza han ɔ wɛ nɔ nyi mɔ̌. Xixa tɔn

Vocab	Tokens	Count
8k	▁ab lu ▁ɔ ▁hwenu ▁e ▁min yɔ̀ ▁alo ▁weziza ▁han ... (+8 more)	18
16k	▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)	16
32k	▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)	16
64k	▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)	16

Key Findings

• Best Compression: 64k achieves 4.124x compression
• Lowest UNK Rate: 8k with 0.1627% 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	1,671	10.71	7,538	38.1%	71.7%
2-gram	Subword	265 🏆	8.05	2,254	68.9%	98.7%
3-gram	Word	2,808	11.46	12,455	33.4%	62.3%
3-gram	Subword	1,585	10.63	14,789	35.7%	77.3%
4-gram	Word	3,755	11.87	19,739	32.3%	58.3%
4-gram	Subword	5,749	12.49	55,463	22.8%	55.5%
5-gram	Word	2,983	11.54	15,474	34.1%	61.1%
5-gram	Subword	12,261	13.58	96,928	17.0%	44.8%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	tɔn mɛ	7,028
2	mɛ ɖo	3,347
3	tɔn lɛ	2,790
4	mɛ e	2,133
5	dodo tɔn	1,886

3-grams (Word):

Rank	N-gram	Count
1	tɔn mɛ ɖo	2,782
2	jì é ɖěè	1,274
3	ayi e jì	1,171
4	tɔn mɛ é	1,170
5	e jì é	1,168

4-grams (Word):

Rank	N-gram	Count
1	ayi e jì é	1,167
2	e jì é ɖěè	1,157
3	e ɖěè mɛ e	1,134
4	gbɛtɔ e ɖěè mɛ	1,133
5	tɔn mɛ ɖo benɛ	1,090

5-grams (Word):

Rank	N-gram	Count
1	ayi e jì é ɖěè	1,156
2	gbɛtɔ e ɖěè mɛ e	1,133
3	benɛ ayi e jì é	1,064
4	ɖo benɛ ayi e jì	1,060
5	mɛ ɖo benɛ ayi e	1,060

2-grams (Subword):

Rank	N-gram	Count
1	n _	58,568
2	o _	46,161
3	_ t	45,106
4	ɔ n	41,894
5	_ ɖ	36,979

3-grams (Subword):

Rank	N-gram	Count
1	ɔ n _	27,349
2	t ɔ n	25,832
3	_ t ɔ	24,140
4	_ ɖ o	19,620
5	ɖ o _	17,028

4-grams (Subword):

Rank	N-gram	Count
1	_ t ɔ n	23,518
2	t ɔ n _	22,408
3	_ ɖ o _	16,782
4	_ m ɛ _	10,812
5	k p ɔ n	8,817

5-grams (Subword):

Rank	N-gram	Count
1	_ t ɔ n _	20,896
2	_ t o k p	8,408
3	t o k p ɔ	8,400
4	o k p ɔ n	8,400
5	t ɔ n _ m	7,246

Key Findings

• Best Perplexity: 2-gram (subword) with 265
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~45% 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	0.7272	1.655	4.51	24,791	27.3%
1	Subword	1.2806	2.429	14.66	265	0.0%
2	Word	0.2756	1.210	1.70	111,357	72.4%
2	Subword	1.1501	2.219	7.00	3,884	0.0%
3	Word	0.1152	1.083	1.21	188,520	88.5%
3	Subword	0.7806	1.718	3.61	27,160	21.9%
4	Word	0.0471 🏆	1.033	1.08	227,466	95.3%
4	Subword	0.5178	1.432	2.22	98,034	48.2%

Generated Text Samples (Word-based)

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

Context Size 1:

1. tɔn mɛ wli hwe ɔ huzu tokpɔnlavi agɔnkanmɛ tɔn bo ɖyɔ ɛ ylɔ ɛ ɖo yovogbè
2. ɖo tokpɔn alibori e nɔ̀ kpénukún tovixixa wǔ é kpo hɛnnu mɛ bo nɔ nyì do
3. e ɖo lé e é mɛ xwédo 1 lɛ nukɔnnɔtɔ hwɛxo tɔn ayi e yovo hwan

Context Size 2:

1. tɔn mɛ ɖò totaligbé gbadahweji benɛɛtò tɔn lɛ mi na mɔ xogbè to ɔ tɔn ɖo tantɔn
2. mɛ ɖo benɛ ayi e jì é ɖěè lěè akpɔkpɔ ɖé ɖe ɔ è sɔ ɛ ɖɛmɛnu
3. mɛ e lɛ́zun gletoxo do sɛ̀nxwĭ jí sin azan ayizin 6 xwejisùn léxwé tɔn mɛ toxoɖɔgbɛ tɔn

Context Size 3:

1. tɔn mɛ ɖo atacora e lɛ́ nyi gletoxo do sɛ̀nxwĭ jí sin azan ayizin 6 xwejisùn lé xwélé
2. jì é ɖěè zinvie ɖo tokpɔnlavi zinvié tɔn mɛ ɖo benɛɛto mɛ bo nyi sɔmi sɔmi ɖɛ̌mɛnu lɛ
3. ayi e jì é ɖěè tokpɔnlávì tayaku tɔn ɔ nyi tokpɔnlavi ɖokpo ɖo wò 10 ě ɖo tokpɔnla

Context Size 4:

1. ayi e jì é ɖěè dovogon ɖo tokpɔnlavi zogbodomey tɔn mɛ ɖo zou e lɛ́ nyí gletoxo ɖò sɛ̀nxwí
2. e jì é ɖěè bouhanrou ɖo tokpɔnlavi gomparou tɔn mɛ ɖo alibori e lɛ́ nyi gletoxo ɖo sɛ̀nxwĭ jí
3. e ɖěè mɛ e axɔsuxwe insae instad e nɔ̀n kpé nunkún tovixixa wǔ é lɛn xɔta 248 nǔ gbɛtɔ

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _be,._ɖo_ɖěè_e"_
2. nɔn_kuɖoudoku_to
3. o_é_mbe_gblɛn_ɖò

Context Size 2:

1. n_kan)_xɔtan_è_ɖo
2. o_tɔnla_akanɖie_ɖ
3. _tokpé_dodo_tɛntr

Context Size 3:

1. ɔn_atlant_dolore_t
2. tɔn_ɖó_azinkpo_ɔ,_
3. _tɔn_ɔ_tɔn_léxwé_d

Context Size 4:

1. _tɔn._ɖo_tokpɔn_atu
2. tɔn_lɛ_sin_azǎn_20ɔ
3. _ɖo_tokpɔnlavi_tɔn,

Key Findings

• Best Predictability: Context-4 (word) with 95.3% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (98,034 contexts)
• Recommendation: Context-3 or Context-4 for text generation

---

4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	11,148
Total Tokens	363,048
Mean Frequency	32.57
Median Frequency	3
Frequency Std Dev	405.71

Most Common Words

Rank	Word	Frequency
1	tɔn	23,451
2	ɖo	16,822
3	e	15,001
4	mɛ	14,011
5	é	10,488
6	ɔ	10,251
7	lɛ	8,160
8	nyi	5,259
9	nɔ	5,214
10	ɖò	4,492

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	rust	2
2	gnu	2
3	programme	2
4	java	2
5	api	2
6	columns	2
7	break	2
8	inside	2
9	avoid	2
10	greek	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1833
R² (Goodness of Fit)	0.993854
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	63.7%
Top 1,000	86.2%
Top 5,000	95.8%
Top 10,000	99.4%

Key Findings

• Zipf Compliance: R²=0.9939 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 63.7% of corpus
• Long Tail: 1,148 words needed for remaining 0.6% 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	0.6254 🏆	0.3950	N/A	N/A
mono_64d	64	0.3309	0.3691	N/A	N/A
mono_128d	128	0.0582	0.3829	N/A	N/A
aligned_32d	32	0.6254	0.3991	0.0100	0.1180
aligned_64d	64	0.3309	0.3687	0.0300	0.1420
aligned_128d	128	0.0582	0.3777	0.0520	0.2300

Key Findings

• Best Isotropy: mono_32d with 0.6254 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3821. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 5.2% 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	0.364	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

Productive Suffixes

Suffix	Examples
-mɛ	akwɛnyanumɛ, mimɛ, wùnmɛ

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
okpo	1.55x	21 contexts	xokpo, yokpo, lokpo
ɖokp	1.57x	16 contexts	ɖokpɔ, ɖokpò, ɖokpó
ɔnyi	1.72x	12 contexts	sɔnyi, lɔnyiji, ɖɔnyitɔ
plɔn	1.72x	12 contexts	kplɔn, kplɔnnǔ, kplɔnyi
mɛnu	1.74x	10 contexts	dɛmɛnu, wemɛnu, ɖɛmɛnu
ntɔn	1.41x	16 contexts	tantɔn, tǎntɔn, xɔntɔn
ligb	1.67x	9 contexts	aligbo, taligbé, taligbe
pɔnl	1.58x	10 contexts	kpɔnla, tokpɔnlá, tòkpɔnlà
hwen	1.42x	13 contexts	hwenù, hwenu, hwenú
igbe	1.53x	10 contexts	jigbe, yigbe, igbere
ukun	1.53x	9 contexts	wukun, nukun, bukunbé
tokp	1.59x	8 contexts	tokpn, tokpo, tokpa

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
liberiatòmɛ	liberiatò-mɛ	4.5	liberiatò
gabɔntomɛ	gabɔnto-mɛ	4.5	gabɔnto
jɔwunjɔjamɛ	jɔwunjɔja-mɛ	4.5	jɔwunjɔja
flanségbèmɛ	flanségbè-mɛ	4.5	flanségbè
kplekplemɛ	kplekple-mɛ	4.5	kplekple
flanségbémɛ	flanségbé-mɛ	4.5	flanségbé
senegaltòmɛ	senegaltò-mɛ	4.5	senegaltò
flansetomɛ	flanseto-mɛ	4.5	flanseto
kplékplémɛ	kplékplé-mɛ	4.5	kplékplé
avɔɖesinukunmɛ	avɔɖesinukun-mɛ	1.5	avɔɖesinukun
zogbodomɛ	zogbodo-mɛ	1.5	zogbodo
nùkplɔnmɛ	nùkplɔn-mɛ	1.5	nùkplɔn
kotoklomɛ	kotoklo-mɛ	1.5	kotoklo
adakplamɛ	adakpla-mɛ	1.5	adakpla
azɔnzunmɛ	azɔnzun-mɛ	1.5	azɔnzun

6.6 Linguistic Interpretation

Automated Insight: The language Fon 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 (4.12x)
N-gram	2-gram	Lowest perplexity (265)
Markov	Context-4	Highest predictability (95.3%)
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

---

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

---

Generated by Wikilangs Models Pipeline

Report Date: 2026-01-04 14:47:03