RESEARCH_REPORT.md

Tachelhit — Full Ablation Study & Research Report

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

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📋 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	3.017x	3.02	1.3938%	408,453
16k	3.301x	3.30	1.5252%	373,263
32k	3.557x	3.56	1.6432%	346,460
64k	3.819x 🏆	3.82	1.7643%	322,672

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Sstekk iga yan ugḍiḍ imẓẓin. Assaɣ Tuzduɣt Tasnalɣa (morphologie) Tisaɣulin Msmu...

Vocab	Tokens	Count
8k	▁s ste kk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ... (+19 more)	29
16k	▁s ste kk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ... (+19 more)	29
32k	▁s stekk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ▁tasnalɣa ... (+18 more)	28
64k	▁sstekk ▁iga ▁yan ▁ugḍiḍ ▁imẓẓin . ▁assaɣ ▁tuzduɣt ▁tasnalɣa ▁( ... (+17 more)	27

Sample 2: Asimwas iga ass wiss Smmus g ussan n imalass. Tisaɣulin

Vocab	Tokens	Count
8k	▁as im was ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ... (+3 more)	13
16k	▁as imwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass ... (+2 more)	12
32k	▁asimwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass . ... (+1 more)	11
64k	▁asimwas ▁iga ▁ass ▁wiss ▁smmus ▁g ▁ussan ▁n ▁imalass . ... (+1 more)	11

Sample 3: Turdut (S turdut: اردو ) tga tutlayt nna s sawaln ayt Bakistan d Lhnd. Isuɣal

Vocab	Tokens	Count
8k	▁tur dut ▁( s ▁tur dut : ▁ا ر دو ... (+14 more)	24
16k	▁tur dut ▁( s ▁tur dut : ▁ار دو ▁) ... (+13 more)	23
32k	▁turdut ▁( s ▁turdut : ▁اردو ▁) ▁tga ▁tutlayt ▁nna ... (+9 more)	19
64k	▁turdut ▁( s ▁turdut : ▁اردو ▁) ▁tga ▁tutlayt ▁nna ... (+8 more)	18

Key Findings

• Best Compression: 64k achieves 3.819x compression
• Lowest UNK Rate: 8k with 1.3938% 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	1,027	10.00	23,244	45.7%	81.7%
2-gram	Subword	255 🏆	7.99	3,782	68.8%	99.0%
3-gram	Word	1,698	10.73	46,062	39.0%	76.4%
3-gram	Subword	1,284	10.33	29,101	35.1%	84.7%
4-gram	Word	3,109	11.60	90,318	35.2%	68.9%
4-gram	Subword	3,345	11.71	117,821	23.5%	73.6%
5-gram	Word	3,900	11.93	100,607	35.2%	65.7%
5-gram	Subword	5,689	12.47	238,898	18.6%	68.5%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	tgmiḍi n	30,047
2	n usggʷas	27,406
3	umḍan n	26,921
4	n imzdaɣn	25,250
5	tlkm tgmiḍi	24,096

3-grams (Word):

Rank	N-gram	Count
1	tlkm tgmiḍi n	24,096
2	tamattayt n usɣiws	16,122
3	tasmirit tamattayt n	15,740
4	umḍan n imzdaɣn	14,946
5	g tlkm tgmiḍi	12,050

4-grams (Word):

Rank	N-gram	Count
1	tasmirit tamattayt n usɣiws	15,739
2	g tlkm tgmiḍi n	12,050
3	ad i trfiqt n	8,924
4	uḍwwaṛ ad i trfiqt	8,917
5	umḍan n imzdaɣn nns	8,916

5-grams (Word):

Rank	N-gram	Count
1	uḍwwaṛ ad i trfiqt n	8,916
2	n imzdaɣn tasmirit tamattayt n	8,910
3	amatay n imzdaɣn tasmirit tamattayt	8,910
4	imzdaɣn tasmirit tamattayt n usɣiws	8,910
5	ilkm umḍan n imzdaɣn nns	8,904

2-grams (Subword):

Rank	N-gram	Count
1	n _	653,950
2	_ n	401,960
3	_ t	358,450
4	_ i	253,361
5	t a	205,185

3-grams (Subword):

Rank	N-gram	Count
1	_ n _	294,525
2	_ t a	132,562
3	n _ t	104,642
4	a n _	103,515
5	_ ɣ _	101,882

4-grams (Subword):

Rank	N-gram	Count
1	_ n _ u	84,436
2	t _ n _	67,385
3	_ n _ i	61,498
4	_ n _ t	56,134
5	n _ u s	52,239

5-grams (Subword):

Rank	N-gram	Count
1	_ n _ u s	51,413
2	m z d a ɣ	46,710
3	g g ʷ a s	34,963
4	s g g ʷ a	34,938
5	_ n n a _	34,315

Key Findings

• Best Perplexity: 2-gram (subword) with 255
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~68% 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	0.6330	1.551	4.06	76,272	36.7%
1	Subword	1.2927	2.450	10.38	804	0.0%
2	Word	0.2598	1.197	1.65	308,953	74.0%
2	Subword	1.0716	2.102	6.52	8,341	0.0%
3	Word	0.0840	1.060	1.19	508,729	91.6%
3	Subword	0.8300	1.778	3.82	54,358	17.0%
4	Word	0.0475 🏆	1.033	1.13	601,513	95.2%
4	Subword	0.5642	1.479	2.43	207,789	43.6%

Generated Text Samples (Word-based)

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

Context Size 1:

1. n twtmin ɣ tsga n lfṛaṛḥa nna mi ilkm umḍan n ayt ɛli n tarskkilt 43
2. ɣ tmnaḍt n urtzaɣ taḍwwaṛḍt n tarwuri 2 aslmd g tlkm tgmiḍi n ism n isrɣinn
3. d ublulls dar gr d lli tmmal tflwit yaḍn ngr adrar n bni matar m sidi

Context Size 2:

1. tgmiḍi n tarskkilt 70 82 gr mddn nna dar gr 6 d 11 n usggʷas niɣ uggar
2. n usggʷas 28 48 dar tsdnan 3 5 aslmd g tlkm tgmiḍi n 35 1 ig unammas
3. umḍan n imzdaɣn n lmɣrib ɣ tsga n trudant n fas amknas ɣ lmɣrib iḍfaṛ uḍwwaṛ ad

Context Size 3:

1. tlkm tgmiḍi n uslmd 91 97 gr irban d trbatin nna dar gr 6 d 11 n usggʷas
2. tamattayt n usɣiws aṛcif 14 ɣuct tisnaddadin tisnaddadin timatayin iggʷiz umḍan n imzdaɣn n tamyawas...
3. tasmirit tamattayt n usɣiws tisaɣulin isɣwan yaḍnin tasmirit tamattayt n usɣiws ɣ iga umḍan n imawaḍ...

Context Size 4:

1. tasmirit tamattayt n usɣiws aṛcif 14 ɣuct tisnaddadin tisnaddadin timatayin iɣli umḍan n imzdaɣn n a...
2. g tlkm tgmiḍi n uslmd 98 7 gr irban d trbatin nna dar gr 6 d 11 n usggʷas
3. ad i trfiqt n ifrdaw tiɣanimin nna ɣ llan 20 n iḍuṛan ilkm umḍan n imzdaɣn nns 997 n

Generated Text Samples (Subword-based)

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

Context Size 1:

1. _sgmawimda_tabir
2. an_4422._uwafarg
3. n),_nartan_49_an

Context Size 2:

1. n_des_ig_twuṭṭa_u
2. _n_10.09_n_uḍwwaṛ
3. _tɛṛanbattamaslmd

Context Size 3:

1. _n_umḍan_d_imir_an
2. _tamatay_n_i_tugt_
3. n_tznit_taru_260_n

Context Size 4:

1. _n_umzdaɣn_tasga_n_
2. t_n_ayt_baha_ɣ_lli_
3. _n_iɣ_isggʷasn_d_tr

Key Findings

• Best Predictability: Context-4 (word) with 95.2% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (207,789 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	31,623
Total Tokens	2,378,986
Mean Frequency	75.23
Median Frequency	4
Frequency Std Dev	1969.53

Most Common Words

Rank	Word	Frequency
1	n	294,723
2	ɣ	102,005
3	d	64,397
4	s	35,003
5	nna	34,361
6	imzdaɣn	31,398
7	dar	30,865
8	gr	30,722
9	tgmiḍi	30,050
10	usggʷas	28,210

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	tdarwinit	2
2	talmuqqdimt	2
3	ttawnn	2
4	taggrgist	2
5	umdgar	2
6	uqṛiḍ	2
7	dearborn	2
8	ghosts	2
9	tremblay	2
10	tmmndl	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.2850
R² (Goodness of Fit)	0.988028
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	69.6%
Top 1,000	90.6%
Top 5,000	95.5%
Top 10,000	97.3%

Key Findings

• Zipf Compliance: R²=0.9880 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 69.6% of corpus
• Long Tail: 21,623 words needed for remaining 2.7% 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	0.6948	0.3782	N/A	N/A
mono_64d	64	0.5226	0.3533	N/A	N/A
mono_128d	128	0.2352	0.3437	N/A	N/A
aligned_32d	32	0.6948 🏆	0.3868	0.0060	0.0540
aligned_64d	64	0.5226	0.3472	0.0240	0.1280
aligned_128d	128	0.2352	0.3345	0.0360	0.1780

Key Findings

• Best Isotropy: aligned_32d with 0.6948 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.3573. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 3.6% 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	-0.041	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
-t	tmurrant, taɣwwaɣt, tuwuri
-i	ill, itturray, iɛisayn
-ta	taɣwwaɣt, tagnsant, tabrruct
-a	anmmassu, asaki, atayn
-u	umzizwr, uɣnja, umdlu
-l	lmuddn, lmɣrib, lbadiɛ
-ti	timqqit, tisutam, tinglizt
-m	mggrn, mennawt, magẓnt

Productive Suffixes

Suffix	Examples
-n	atayn, mggrn, krnun
-t	tmurrant, priest, taɣwwaɣt
-a	uɣnja, phoenicia, iɣrruba
-in	ɛalawiyyin, bdrnin, irwin
-s	ghosts, yuns, palmas
-i	asaki, bani, tuwuri
-e	became, institute, neige
-an	dan, ubrkan, uljmɛan

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
adda	1.68x	52 contexts	addan, hadda, wadda
ggar	1.97x	22 contexts	uggar, ggarn, iggar
ggʷa	1.62x	43 contexts	ḥggʷa, aggʷa, zggʷar
ugga	1.91x	21 contexts	uggar, uggan, tugga
wuri	1.70x	30 contexts	twuri, tuwuri, twwuri
tion	2.05x	14 contexts	nation, notion, action
matt	1.64x	26 contexts	matta, nmatti, amattu
lati	1.61x	27 contexts	latif, latin, talati
ɣrib	1.76x	20 contexts	aɣrib, mɣrib, lmɣrib
mɣri	1.77x	13 contexts	tmɣri, imɣri, mɣrib
ddad	1.62x	14 contexts	ḥddad, addad, addadn
mata	1.54x	14 contexts	smata, amata, umata

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
-t	-t	636 words	takrrayt, tusnaktant
-i	-n	489 words	ittajjan, igatn
-t	-n	323 words	tunisian, tmttawin
-t	-in	264 words	tmttawin, tmdinin
-l	-a	101 words	lqliɛa, lɛmaṛa
-t	-a	71 words	tggʷra, tawayya
-i	-an	60 words	ittajjan, ixxan
-a	-n	60 words	aẓuran, ayncṭayn
-l	-t	39 words	lmɛiṭat, luṭilat
-a	-an	39 words	aẓuran, alilan

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
magrebini	magreb-in-i	7.5	in
mzaraynin	mzaray-n-in	7.5	n
imaynutnin	imaynut-n-in	7.5	n
tiɣrmanin	tiɣrm-an-in	7.5	an
ikkattinn	ikkatt-in-n	7.5	in
tisntutin	tisntu-t-in	7.5	t
tasnmḍant	tasnmḍ-an-t	7.5	an
tuɣnijinin	tuɣnij-in-in	7.5	in
ittyawnna	ittyaw-n-na	7.5	n
tinidlisn	t-in-idlisn	7.5	idlisn
fransisku	fransis-k-u	7.5	k
gibraltar	gibral-t-ar	7.5	t
iblḥsanin	iblḥsa-n-in	7.5	n
ittyurnan	ittyur-n-an	7.5	n
africaines	africa-in-es	7.5	in

6.6 Linguistic Interpretation

Automated Insight: The language Tachelhit 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 (3.82x)
N-gram	2-gram	Lowest perplexity (255)
Markov	Context-4	Highest predictability (95.2%)
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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Generated by Wikilangs Pipeline · 2026-03-02 12:00:43