Upload all models and assets for hu (latest)

README.md

@@ -33,741 +33,198 @@ dataset_info:
 metrics:
   - name: best_compression_ratio
     type: compression
-    value: 4.660
+    value: 4.661
   - name: best_isotropy
     type: isotropy
-    value: 0.7896
+    value: 0.7886
+  - name: best_alignment_r10
+    type: alignment
+    value: 0.9180
   - name: vocabulary_size
     type: vocab
-    value: 0
-generated: 2026-01-13
+    value: 1458224
+generated: 2026-03-04
 ---
 
-# Hungarian - Wikilangs Models
-## Comprehensive Research Report & Full Ablation Study
+# Hungarian — Wikilangs Models
 
-This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Hungarian** Wikipedia data.
-We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **Hungarian** Wikipedia by [Wikilangs](https://wikilangs.org).
 
-## 📋 Repository Contents
+🌐 [Language Page](https://wikilangs.org/languages/hu/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=hu) · 📊 [Full Research Report](RESEARCH_REPORT.md)
 
-### Models & Assets
+## Language Samples
 
-- 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
+Example sentences drawn from the Hungarian Wikipedia corpus:
 
-![Performance Dashboard](visualizations/performance_dashboard.png)
+> magyar nép magyar nyelv Magyarország Magyar állampolgárság Magyar, régi magyar családnév
 
-### Analysis and Evaluation
+> A Tejútrendszer szinonimája a csillagászatban Galaktika, egy tudományos-fantasztikus antológia neve
 
-- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
-- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
-- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
-- [4. Vocabulary Analysis](#4-vocabulary-analysis)
-- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
-- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
-- [7. Summary & Recommendations](#7-summary--recommendations)
-- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
-- [Visualizations Index](#visualizations-index)
+> Óe, japán családnév Óe, kisváros Japánban, Jamagata prefektúrában ÓE, az Óbudai Egyetem rövidítése
 
----
-## 1. Tokenizer Evaluation
+> Szó fogalma a nyelvészetben Szó fogalma a matematikai logikában és a formális nyelvek elméletében Szó fogalma az informatikában Szó fogalma a zenében Szo, japán kana
 
-![Tokenizer Compression](visualizations/tokenizer_compression.png)
+> Memória (biológia) Memória (számítástechnika): Számítástechnikában használják Memória (játék) Párkereső kártyajáték
 
-![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+## Quick Start
 
-![Tokenizer OOV](visualizations/tokenizer_oov.png)
+### Load the Tokenizer
 
-![Total Tokens](visualizations/tokenizer_total_tokens.png)
+```python
+import sentencepiece as spm
 
-### Results
+sp = spm.SentencePieceProcessor()
+sp.Load("hu_tokenizer_32k.model")
 
-| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
-|------------|-------------|---------------|----------|--------------|
-| **8k** | 3.504x | 3.50 | 0.1728% | 3,324,472 |
-| **16k** | 3.921x | 3.92 | 0.1933% | 2,971,202 |
-| **32k** | 4.310x | 4.31 | 0.2125% | 2,702,863 |
-| **64k** | 4.660x 🏆 | 4.66 | 0.2298% | 2,499,703 |
+text = "Elvonás, addiktológia Elvonás, a szóalkotás egy módja"
+tokens = sp.EncodeAsPieces(text)
+ids    = sp.EncodeAsIds(text)
 
-### Tokenization Examples
+print(tokens)  # subword pieces
+print(ids)     # integer ids
 
-Below are sample sentences tokenized with each vocabulary size:
+# Decode back
+print(sp.DecodeIds(ids))
+```
 
-**Sample 1:** `orvlövész, személy → lásd: mesterlövész Orvlövész amerikai akciófilm`
+<details>
+<summary><b>Tokenization examples (click to expand)</b></summary>
+
+**Sample 1:** `Elvonás, addiktológia Elvonás, a szóalkotás egy módja`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁orv l öv ész , ▁személy ▁ → ▁lásd : ... (+12 more)` | 22 |
-| 16k | `▁orv löv ész , ▁személy ▁→ ▁lásd : ▁mester löv ... (+7 more)` | 17 |
-| 32k | `▁orv löv ész , ▁személy ▁→ ▁lásd : ▁mester löv ... (+7 more)` | 17 |
-| 64k | `▁orv lövész , ▁személy ▁→ ▁lásd : ▁mesterlövész ▁orv lövész ... (+2 more)` | 12 |
+| 8k | `▁elv on ás , ▁ad d ikt ológia ▁elv on … (+9 more)` | 19 |
+| 16k | `▁elv on ás , ▁add ikt ológia ▁elv on ás … (+8 more)` | 18 |
+| 32k | `▁elvon ás , ▁add ikt ológia ▁elvon ás , ▁a … (+5 more)` | 15 |
+| 64k | `▁elvon ás , ▁add ikt ológia ▁elvon ás , ▁a … (+4 more)` | 14 |
 
-**Sample 2:** `Monterde, település Spanyolországban, Zaragoza tartományban Monterde de Albarrac...`
+**Sample 2:** `Memória (biológia) Memória (számítástechnika): Számítástechnikában használják Me…`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁mont er de , ▁település ▁spanyol országban , ▁zar ag ... (+19 more)` | 29 |
-| 16k | `▁mont er de , ▁település ▁spanyol országban , ▁zar ag ... (+19 more)` | 29 |
-| 32k | `▁mont er de , ▁település ▁spanyol országban , ▁zarag oza ... (+17 more)` | 27 |
-| 64k | `▁monter de , ▁település ▁spanyol országban , ▁zaragoza ▁tartományban ▁monter ... (+14 more)` | 24 |
+| 8k | `▁mem ória ▁( bi ológia ) ▁mem ória ▁( szám … (+23 more)` | 33 |
+| 16k | `▁mem ória ▁( bi ológia ) ▁mem ória ▁( számítás … (+19 more)` | 29 |
+| 32k | `▁memória ▁( bi ológia ) ▁memória ▁( számítás technika ): … (+12 more)` | 22 |
+| 64k | `▁memória ▁( biológia ) ▁memória ▁( számítás technika ): ▁számítástechn … (+10 more)` | 20 |
 
-**Sample 3:** `A Nyoman szóra a következő lapok hivatkozhatnak: Nyoman, a Nyeman folyó belarusz...`
+**Sample 3:** `Óe, japán családnév Óe, kisváros Japánban, Jamagata prefektúrában ÓE, az Óbudai …`
 
 | Vocab | Tokens | Count |
 |-------|--------|-------|
-| 8k | `▁a ▁nyom an ▁szó ra ▁a ▁következő ▁lap ok ▁hivatkoz ... (+25 more)` | 35 |
-| 16k | `▁a ▁nyom an ▁szó ra ▁a ▁következő ▁lapok ▁hivatkoz hatnak ... (+23 more)` | 33 |
-| 32k | `▁a ▁nyom an ▁szó ra ▁a ▁következő ▁lapok ▁hivatkoz hatnak ... (+21 more)` | 31 |
-| 64k | `▁a ▁nyom an ▁szóra ▁a ▁következő ▁lapok ▁hivatkoz hatnak : ... (+17 more)` | 27 |
-
-
-### Key Findings
-
-- **Best Compression:** 64k achieves 4.660x compression
-- **Lowest UNK Rate:** 8k with 0.1728% 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
-
-![N-gram Perplexity](visualizations/ngram_perplexity.png)
-
-![N-gram Unique](visualizations/ngram_unique.png)
-
-![N-gram Coverage](visualizations/ngram_coverage.png)
-
-### Results
-
-| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
-|--------|---------|------------|---------|----------------|------------------|-------------------|
-| **2-gram** | Word | 534,583 | 19.03 | 4,267,292 | 5.3% | 13.6% |
-| **2-gram** | Subword | 435 🏆 | 8.77 | 36,188 | 54.2% | 98.1% |
-| **3-gram** | Word | 2,075,420 | 20.98 | 7,553,147 | 2.6% | 6.6% |
-| **3-gram** | Subword | 4,599 | 12.17 | 265,628 | 17.2% | 55.9% |
-| **4-gram** | Word | 4,222,921 | 22.01 | 12,285,779 | 2.7% | 6.1% |
-| **4-gram** | Subword | 30,520 | 14.90 | 1,702,927 | 7.5% | 26.9% |
-| **5-gram** | Word | 3,104,259 | 21.57 | 8,851,426 | 3.4% | 7.3% |
-| **5-gram** | Subword | 140,455 | 17.10 | 6,669,073 | 3.8% | 16.0% |
-
-### Top 5 N-grams by Size
-
-**2-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `és a` | 750,740 |
-| 2 | `hogy a` | 246,908 |
-| 3 | `további információk` | 239,762 |
-| 4 | `és az` | 222,085 |
-| 5 | `volt a` | 210,831 |
-
-**3-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `jegyzetek további információk` | 116,226 |
-| 2 | `népesség a település` | 75,437 |
-| 3 | `személyek elhunyt személyek` | 70,441 |
-| 4 | `született személyek elhunyt` | 69,726 |
-| 5 | `további információk megye` | 43,373 |
-
-**4-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `született személyek elhunyt személyek` | 69,726 |
-| 2 | `a település népességének változása` | 42,715 |
-| 3 | `népesség a település népességének` | 42,581 |
-| 4 | `jegyzetek további információk megye` | 41,857 |
-| 5 | `megyében népesség a település` | 40,991 |
-
-**5-grams (Word):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `népesség a település népességének változása` | 42,500 |
-| 2 | `jegyzetek további információk megye települései` | 39,789 |
-| 3 | `további információk megye települései létrehozott` | 38,604 |
-| 4 | `települései létrehozott francia település cikkek` | 33,554 |
-| 5 | `megye települései létrehozott francia település` | 33,497 |
-
-**2-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ a` | 28,615,318 |
-| 2 | `a _` | 26,126,954 |
-| 3 | `s z` | 20,526,948 |
-| 4 | `t _` | 17,995,334 |
-| 5 | `e l` | 17,138,516 |
-
-**3-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ a _` | 14,744,854 |
-| 2 | `_ s z` | 7,371,389 |
-| 3 | `_ a z` | 5,409,490 |
-| 4 | `é s _` | 5,376,301 |
-| 5 | `s z e` | 5,046,767 |
-
-**4-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ a z _` | 4,706,514 |
-| 2 | `_ é s _` | 4,404,673 |
-| 3 | `_ e g y` | 2,864,622 |
-| 4 | `_ m e g` | 2,653,603 |
-| 5 | `_ s z e` | 2,581,753 |
-
-**5-grams (Subword):**
-
-| Rank | N-gram | Count |
-|------|--------|-------|
-| 1 | `_ s z e r` | 1,290,178 |
-| 2 | `_ a z _ e` | 1,248,859 |
-| 3 | `_ é s _ a` | 1,122,375 |
-| 4 | `_ e g y _` | 1,119,120 |
-| 5 | `_ v o l t` | 1,080,101 |
-
-
-### Key Findings
-
-- **Best Perplexity:** 2-gram (subword) with 435
-- **Entropy Trend:** Decreases with larger n-grams (more predictable)
-- **Coverage:** Top-1000 patterns cover ~16% of corpus
-- **Recommendation:** 4-gram or 5-gram for best predictive performance
-
----
-## 3. Markov Chain Evaluation
-
-![Markov Entropy](visualizations/markov_entropy.png)
-
-![Markov Contexts](visualizations/markov_contexts.png)
-
-![Markov Branching](visualizations/markov_branching.png)
-
-### Results
-
-| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
-|---------|---------|-------------|------------|------------------|-----------------|----------------|
-| **1** | Word | 0.9149 | 1.885 | 11.86 | 5,253,585 | 8.5% |
-| **1** | Subword | 1.3264 | 2.508 | 10.40 | 16,190 | 0.0% |
-| **2** | Word | 0.3314 | 1.258 | 2.16 | 62,241,118 | 66.9% |
-| **2** | Subword | 0.6166 | 1.533 | 4.07 | 168,239 | 38.3% |
-| **3** | Word | 0.1296 | 1.094 | 1.28 | 134,211,461 | 87.0% |
-| **3** | Subword | 0.6817 | 1.604 | 4.31 | 684,267 | 31.8% |
-| **4** | Word | 0.0479 🏆 | 1.034 | 1.08 | 171,557,270 | 95.2% |
-| **4** | Subword | 0.7163 | 1.643 | 3.92 | 2,950,554 | 28.4% |
-
-### Generated Text Samples (Word-based)
-
-Below are text samples generated from each word-based Markov chain model:
-
-**Context Size 1:**
-
-1. `a legközelebbi piac volt az amerikai r hernádi judit lánya családjához tartozó veb kranbau hennigsdo...`
-2. `az íreket a belül félprím kanonikus alakja a turistaút mellett támadhatók ám később v vlagyimir ilji...`
-3. `és a krasznojarszki határterület melyből római korból ugyanis a kerlés beszterce naszód vármegyéhez ...`
-
-**Context Size 2:**
-
-1. `és a vérlemezke szám vizsgálatok az eklampsiasok vérének calciumion concentratiójáról bodó richárdda...`
-2. `hogy a tábornagy unokája teschen harmadik hercegének és aragóniai nyelven nyelvjárásban íxar bárója ...`
-3. `további információk görög irodalom története athenaeum november 4 a aguja km 279 36 32 53 2 45`
-
-**Context Size 3:**
-
-1. `jegyzetek további információk színészek született személyek személyek színésznők humoristák york iak...`
-2. `népesség a település népessége az elmúlt években az alábbi módon változott jegyzetek további informá...`
-3. `született személyek elhunyt személyek becsületrend lovagjai tárcaírók származású magyarok emigránsok...`
-
-**Context Size 4:**
-
-1. `született személyek elhunyt személyek nők eurovíziós dalfesztivál pontbejelentői`
-2. `a település népességének változása jegyzetek további információk települései létrehozott spanyol tel...`
-3. `népesség a település népességének változása jegyzetek további információk megye települései létrehoz...`
-
-
-### Generated Text Samples (Subword-based)
-
-Below are text samples generated from each subword-based Markov chain model:
-
-**Context Size 1:**
-
-1. `_bódáműválla_ém_`
-2. `etotéspa_mégóncs`
-3. `apcigyla_em_k)_h`
-
-**Context Size 2:**
-
-1. `_avallés_ma_akasz`
-2. `a_+_céletőbbeild.`
-3. `szettáraminterico`
-
-**Context Size 3:**
+| 8k | `▁ó e , ▁japán ▁család név ▁ó e , ▁kis … (+21 more)` | 31 |
+| 16k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban … (+16 more)` | 26 |
+| 32k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban … (+13 more)` | 23 |
+| 64k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban … (+11 more)` | 21 |
 
-1. `_a_tor_anni_volsen`
-2. `_szólósításai_form`
-3. `_az_amika_végzeti_`
+</details>
 
-**Context Size 4:**
+### Load Word Embeddings
 
-1. `_az_le_a_muzsikus_b`
-2. `_és_4-i_egyet_érkez`
-3. `_egy_kir._idős_diss`
+```python
+from gensim.models import KeyedVectors
 
+# Aligned embeddings (cross-lingual, mapped to English vector space)
+wv = KeyedVectors.load("hu_embeddings_128d_aligned.kv")
 
-### 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 (2,950,554 contexts)
-- **Recommendation:** Context-3 or Context-4 for text generation
-
----
-## 4. Vocabulary Analysis
-
-![Zipf's Law](visualizations/zipf_law.png)
-
-![Top Words](visualizations/top20_words.png)
-
-![Coverage Curve](visualizations/vocab_coverage.png)
-
-### Statistics
-
-| Metric | Value |
-|--------|-------|
-| Vocabulary Size | 2,314,804 |
-| Total Tokens | 210,700,540 |
-| Mean Frequency | 91.02 |
-| Median Frequency | 4 |
-| Frequency Std Dev | 11249.15 |
-
-### Most Common Words
-
-| Rank | Word | Frequency |
-|------|------|-----------|
-| 1 | a | 15,266,391 |
-| 2 | az | 4,841,770 |
-| 3 | és | 4,422,301 |
-| 4 | is | 1,350,461 |
-| 5 | egy | 1,181,563 |
-| 6 | hogy | 978,556 |
-| 7 | volt | 963,293 |
-| 8 | 1 | 909,318 |
-| 9 | nem | 804,148 |
-| 10 | 2 | 677,083 |
-
-### Least Common Words (from vocabulary)
-
-| Rank | Word | Frequency |
-|------|------|-----------|
-| 1 | vichyvel | 2 |
-| 2 | ftpf | 2 |
-| 3 | hakeimi | 2 |
-| 4 | ixkun | 2 |
-| 5 | demannt | 2 |
-| 6 | summercamp | 2 |
-| 7 | madguy | 2 |
-| 8 | meisterleistung | 2 |
-| 9 | copín | 2 |
-| 10 | transparentete | 2 |
-
-### Zipf's Law Analysis
-
-| Metric | Value |
-|--------|-------|
-| Zipf Coefficient | 0.9342 |
-| R² (Goodness of Fit) | 0.996484 |
-| Adherence Quality | **excellent** |
-
-### Coverage Analysis
-
-| Top N Words | Coverage |
-|-------------|----------|
-| Top 100 | 25.6% |
-| Top 1,000 | 45.5% |
-| Top 5,000 | 61.8% |
-| Top 10,000 | 69.0% |
-
-### Key Findings
-
-- **Zipf Compliance:** R²=0.9965 indicates excellent adherence to Zipf's law
-- **High Frequency Dominance:** Top 100 words cover 25.6% of corpus
-- **Long Tail:** 2,304,804 words needed for remaining 31.0% coverage
-
----
-## 5. Word Embeddings Evaluation
-
-![Embedding Isotropy](visualizations/embedding_isotropy.png)
-
-![Similarity Matrix](visualizations/embedding_similarity.png)
-
-![t-SNE Words](visualizations/tsne_words.png)
-
-![t-SNE Sentences](visualizations/tsne_sentences.png)
-
-
-### 5.1 Cross-Lingual Alignment
-
-![Alignment Quality](visualizations/embedding_alignment_quality.png)
-
-![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
-
-
-### 5.2 Model Comparison
-
-| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
-|-------|-----------|----------|------------------|---------------|----------------|
-| **mono_32d** | 32 | 0.7896 | 0.3549 | N/A | N/A |
-| **mono_64d** | 64 | 0.7843 | 0.2900 | N/A | N/A |
-| **mono_128d** | 128 | 0.7205 | 0.2280 | N/A | N/A |
-| **aligned_32d** | 32 | 0.7896 🏆 | 0.3731 | 0.3780 | 0.7580 |
-| **aligned_64d** | 64 | 0.7843 | 0.2877 | 0.5600 | 0.8860 |
-| **aligned_128d** | 128 | 0.7205 | 0.2242 | 0.7160 | 0.9400 |
+similar = wv.most_similar("word", topn=5)
+for word, score in similar:
+    print(f"  {word}: {score:.3f}")
+```
 
-### Key Findings
+### Load N-gram Model
 
-- **Best Isotropy:** aligned_32d with 0.7896 (more uniform distribution)
-- **Semantic Density:** Average pairwise similarity of 0.2930. Lower values indicate better semantic separation.
-- **Alignment Quality:** Aligned models achieve up to 71.6% R@1 in cross-lingual retrieval.
-- **Recommendation:** 128d aligned for best cross-lingual performance
+```python
+import pyarrow.parquet as pq
 
----
-## 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 | **-0.542** | 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 |
-|--------|----------|
-| `-s` | szedhessék, sejttípusban, szemszínű |
-| `-k` | kóborlónak, kőalappal, küldetéseikben |
-| `-m` | meklēt, morarano, megbüntethették |
-| `-a` | ammaniti, aurignacian, aranybaglyok |
-| `-t` | tagként, távhőtermelő, terepviszony |
-| `-b` | bolondozott, buga, birkózással |
-| `-ma` | manbij, macham, magánszínházakban |
-| `-e` | elaborate, elitegyetemek, eugène |
-
-#### Productive Suffixes
-| Suffix | Examples |
-|--------|----------|
-| `-t` | cseréphéjazat, tagként, irritációkat |
-| `-k` | kóborlónak, érbetegségek, szedhessék |
-| `-n` | vardaman, pihenőhelyükön, sejttípusban |
-| `-a` | hera, buga, philosophya |
-| `-l` | hurból, lavel, vranishtnál |
-| `-s` | francoizmus, nativizálás, öndiagnózis |
-| `-i` | ammaniti, lendvai, diófalvi |
-| `-e` | elaborate, piauiense, eugène |
-
-### 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 |
-|------|----------|------------------|----------|
-| `mber` | 1.61x | 605 contexts | ember, umber, ámber |
-| `epül` | 1.89x | 164 contexts | repül, repüle, repülő |
-| `erül` | 1.60x | 344 contexts | terül, kerül, merül |
-| `örté` | 2.09x | 79 contexts | törté, körtés, sörtéi |
-| `ület` | 1.50x | 362 contexts | fület, sz��let, ízület |
-| `atás` | 1.41x | 443 contexts | katás, fatás, hatás |
-| `rtén` | 2.05x | 57 contexts | artén, értény, történ |
-| `ítot` | 1.62x | 161 contexts | ított, sított, vított |
-| `ítás` | 1.38x | 376 contexts | sítás, újítás, ámítás |
-| `ormá` | 1.46x | 267 contexts | ormán, ormát, dormán |
-| `alál` | 1.43x | 226 contexts | talál, halál, valál |
-| `lepü` | 2.81x | 14 contexts | telepü, telepük, települ |
-
-### 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` | `-k` | 118 words | kinyissanak, konowalik |
-| `-s` | `-t` | 87 words | szvetlánát, szkádit |
-| `-k` | `-t` | 84 words | konceptalbumokat, kevesebbért |
-| `-k` | `-l` | 84 words | kártyacsomagokkal, karmáról |
-| `-s` | `-l` | 84 words | szénül, szőnyeggyárból |
-| `-s` | `-k` | 81 words | sóraktárnak, számolhatnánk |
-| `-s` | `-n` | 80 words | sarrewerden, sumbawán |
-| `-s` | `-a` | 77 words | sserunkuma, sztalina |
-| `-k` | `-a` | 77 words | kruczynska, kivételszámba |
-| `-m` | `-k` | 75 words | manhunterek, megbetegedéseik |
-
-### 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 |
-|------|-----------------|------------|------|
-| családira | **`család-i-ra`** | 7.5 | `i` |
-| xantofilek | **`xantofi-l-ek`** | 7.5 | `l` |
-| marinaviale | **`marinavi-al-e`** | 7.5 | `al` |
-| castillának | **`castillá-n-ak`** | 7.5 | `n` |
-| karakterjének | **`karakterjé-n-ek`** | 7.5 | `n` |
-| kampánystábjának | **`kampánystábjá-n-ak`** | 7.5 | `n` |
-| nyelveire | **`nyelve-i-re`** | 7.5 | `i` |
-| távharcban | **`távharc-ba-n`** | 7.5 | `ba` |
-| guadalcanalt | **`guadalcan-al-t`** | 7.5 | `al` |
-| palesztínai | **`palesztín-a-i`** | 7.5 | `a` |
-| idénymunkákon | **`idénymunká-k-on`** | 7.5 | `k` |
-| képzőművészeknek | **`képzőművészek-n-ek`** | 7.5 | `n` |
-| paakkanen | **`paakka-n-en`** | 7.5 | `n` |
-| körlapnak | **`körlap-n-ak`** | 7.5 | `n` |
-| férfimunkások | **`férfimunká-s-ok`** | 7.5 | `s` |
-
-### 6.6 Linguistic Interpretation
-
-> **Automated Insight:**
-The language Hungarian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+df = pq.read_table("hu_3gram_word.parquet").to_pandas()
+print(df.head())
+```
 
----
-## 7. Summary & Recommendations
+## Models Overview
 
 ![Performance Dashboard](visualizations/performance_dashboard.png)
 
-### Production Recommendations
-
-| Component | Recommended | Rationale |
-|-----------|-------------|-----------|
-| Tokenizer | **64k BPE** | Best compression (4.66x) |
-| N-gram | **2-gram** | Lowest perplexity (435) |
-| Markov | **Context-4** | Highest predictability (95.2%) |
-| 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
-
-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 |
+| Category | Assets |
+|----------|--------|
+| Tokenizers | BPE at 8k, 16k, 32k, 64k vocab sizes |
+| N-gram models | 2 / 3 / 4 / 5-gram (word & subword) |
+| Markov chains | Context 1–5 (word & subword) |
+| Embeddings | 32d, 64d, 128d — mono & aligned |
+| Vocabulary | Full frequency list + Zipf analysis |
+| Statistics | Corpus & model statistics JSON |
+
+## Metrics Summary
+
+| Component | Model | Key Metric | Value |
+|-----------|-------|------------|-------|
+| Tokenizer | 8k BPE | Compression | 3.50x |
+| Tokenizer | 16k BPE | Compression | 3.92x |
+| Tokenizer | 32k BPE | Compression | 4.31x |
+| Tokenizer | 64k BPE | Compression | 4.66x 🏆 |
+| N-gram | 2-gram (subword) | Perplexity | 429 🏆 |
+| N-gram | 2-gram (word) | Perplexity | 362,917 |
+| N-gram | 3-gram (subword) | Perplexity | 4,501 |
+| N-gram | 3-gram (word) | Perplexity | 1,261,909 |
+| N-gram | 4-gram (subword) | Perplexity | 29,749 |
+| N-gram | 4-gram (word) | Perplexity | 2,487,801 |
+| N-gram | 5-gram (subword) | Perplexity | 135,455 |
+| N-gram | 5-gram (word) | Perplexity | 1,806,602 |
+| Markov | ctx-1 (subword) | Predictability | 0.0% |
+| Markov | ctx-1 (word) | Predictability | 5.8% |
+| Markov | ctx-2 (subword) | Predictability | 32.3% |
+| Markov | ctx-2 (word) | Predictability | 68.8% |
+| Markov | ctx-3 (subword) | Predictability | 23.4% |
+| Markov | ctx-3 (word) | Predictability | 88.4% |
+| Markov | ctx-4 (subword) | Predictability | 25.2% |
+| Markov | ctx-4 (word) | Predictability | 96.0% 🏆 |
+| Vocabulary | full | Size | 1,458,224 |
+| Vocabulary | full | Zipf R² | 0.9963 |
+| Embeddings | mono_32d | Isotropy | 0.7886 |
+| Embeddings | mono_64d | Isotropy | 0.7831 |
+| Embeddings | mono_128d | Isotropy | 0.7114 |
+| Embeddings | aligned_32d | Isotropy | 0.7886 🏆 |
+| Embeddings | aligned_64d | Isotropy | 0.7831 |
+| Embeddings | aligned_128d | Isotropy | 0.7114 |
+| Alignment | aligned_32d | R@1 / R@5 / R@10 | 36.0% / 62.8% / 75.2% |
+| Alignment | aligned_64d | R@1 / R@5 / R@10 | 53.2% / 77.0% / 85.6% |
+| Alignment | aligned_128d | R@1 / R@5 / R@10 | 63.0% / 85.4% / 91.8% 🏆 |
+
+📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**
 
 ---
-## About This Project
-
-### Data Source
 
-Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+## About
 
-### Project
+Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.
 
-A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
-
-### Maintainer
-
-[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+A project by **[Wikilangs](https://wikilangs.org)** · Maintainer: [Omar Kamali](https://omarkamali.com) · [Omneity Labs](https://omneitylabs.com)
 
 ### 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},
+  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}
+  url       = {https://huggingface.co/wikilangs},
   institution = {Omneity Labs}
 }
 ```
 
-### License
-
-MIT License - Free for academic and commercial use.
-
 ### Links
 
-- 🌐 Website: [wikilangs.org](https://wikilangs.org)
-- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
-- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
-- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🌐 [wikilangs.org](https://wikilangs.org)
+- 🌍 [Language page](https://wikilangs.org/languages/hu/)
+- 🎮 [Playground](https://wikilangs.org/playground/?lang=hu)
+- 🤗 [HuggingFace models](https://huggingface.co/wikilangs)
+- 📊 [wikipedia-monthly dataset](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 [Omar Kamali](https://huggingface.co/omarkamali)
 - 🤝 Sponsor: [Featherless AI](https://featherless.ai)
----
-*Generated by Wikilangs Models Pipeline*
 
-*Report Date: 2026-01-13 20:45:23*
+**License:** MIT — free for academic and commercial use.
+
+---
+*Generated by Wikilangs Pipeline · 2026-03-04 18:37:58*

RESEARCH_REPORT.md

@@ -0,0 +1,686 @@
+# Hungarian — Full Ablation Study & Research Report
+
+Detailed evaluation of all model variants trained on **Hungarian** Wikipedia data by [Wikilangs](https://wikilangs.org).
+
+👈 [Back to README](README.md)
+
+## 📋 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
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.505x | 3.51 | 0.1725% | 3,328,421 |
+| **16k** | 3.921x | 3.92 | 0.1930% | 2,974,904 |
+| **32k** | 4.311x | 4.31 | 0.2122% | 2,705,982 |
+| **64k** | 4.661x 🏆 | 4.66 | 0.2295% | 2,502,482 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Elvonás, addiktológia Elvonás, a szóalkotás egy módja`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁elv on ás , ▁ad d ikt ológia ▁elv on ... (+9 more)` | 19 |
+| 16k | `▁elv on ás , ▁add ikt ológia ▁elv on ás ... (+8 more)` | 18 |
+| 32k | `▁elvon ás , ▁add ikt ológia ▁elvon ás , ▁a ... (+5 more)` | 15 |
+| 64k | `▁elvon ás , ▁add ikt ológia ▁elvon ás , ▁a ... (+4 more)` | 14 |
+
+**Sample 2:** `Memória (biológia) Memória (számítástechnika): Számítástechnikában használják Me...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mem ória ▁( bi ológia ) ▁mem ória ▁( szám ... (+23 more)` | 33 |
+| 16k | `▁mem ória ▁( bi ológia ) ▁mem ória ▁( számítás ... (+19 more)` | 29 |
+| 32k | `▁memória ▁( bi ológia ) ▁memória ▁( számítás technika ): ... (+12 more)` | 22 |
+| 64k | `▁memória ▁( biológia ) ▁memória ▁( számítás technika ): ▁számítástechn ... (+10 more)` | 20 |
+
+**Sample 3:** `Óe, japán családnév Óe, kisváros Japánban, Jamagata prefektúrában ÓE, az Óbudai ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ó e , ▁japán ▁család név ▁ó e , ▁kis ... (+21 more)` | 31 |
+| 16k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban ... (+16 more)` | 26 |
+| 32k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban ... (+13 more)` | 23 |
+| 64k | `▁ó e , ▁japán ▁családnév ▁ó e , ▁kisváros ▁japánban ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.661x compression
+- **Lowest UNK Rate:** 8k with 0.1725% 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
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 362,917 | 18.47 | 2,207,301 | 5.4% | 14.2% |
+| **2-gram** | Subword | 429 🏆 | 8.74 | 22,141 | 54.6% | 98.3% |
+| **3-gram** | Word | 1,261,909 | 20.27 | 3,510,384 | 2.1% | 6.2% |
+| **3-gram** | Subword | 4,501 | 12.14 | 188,349 | 17.4% | 56.2% |
+| **4-gram** | Word | 2,487,801 | 21.25 | 5,404,855 | 2.0% | 5.1% |
+| **4-gram** | Subword | 29,749 | 14.86 | 1,214,042 | 7.6% | 26.9% |
+| **5-gram** | Word | 1,806,602 | 20.78 | 3,707,959 | 2.3% | 5.8% |
+| **5-gram** | Subword | 135,455 | 17.05 | 4,635,566 | 3.7% | 15.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `és a` | 381,304 |
+| 2 | `hogy a` | 127,431 |
+| 3 | `és az` | 111,129 |
+| 4 | `a magyar` | 107,955 |
+| 5 | `volt a` | 102,268 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `személyek elhunyt személyek` | 25,908 |
+| 2 | `született személyek elhunyt` | 25,567 |
+| 3 | `madarai madarai madarai` | 21,522 |
+| 4 | `jegyzetek további információk` | 19,348 |
+| 5 | `kisbolygók listája jegyzetek` | 13,405 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `született személyek elhunyt személyek` | 25,567 |
+| 2 | `madarai madarai madarai madarai` | 17,618 |
+| 3 | `listája jegyzetek naprendszer kisbolygói` | 13,129 |
+| 4 | `kisbolygók listája jegyzetek naprendszer` | 13,128 |
+| 5 | `kapcsolódó szócikkek kisbolygók listája` | 13,112 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `madarai madarai madarai madarai madarai` | 14,553 |
+| 2 | `kisbolygók listája jegyzetek naprendszer kisbolygói` | 13,128 |
+| 3 | `kapcsolódó szócikkek kisbolygók listája jegyzetek` | 13,034 |
+| 4 | `szócikkek kisbolygók listája jegyzetek naprendszer` | 12,763 |
+| 5 | `a naprendszer kisbolygóövében található aszteroida` | 12,084 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a` | 14,447,157 |
+| 2 | `a _` | 13,088,817 |
+| 3 | `s z` | 10,335,605 |
+| 4 | `t _` | 8,923,690 |
+| 5 | `e l` | 8,560,877 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a _` | 7,569,874 |
+| 2 | `_ s z` | 3,684,895 |
+| 3 | `_ a z` | 2,740,812 |
+| 4 | `é s _` | 2,565,914 |
+| 5 | `s z e` | 2,500,891 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a z _` | 2,372,557 |
+| 2 | `_ é s _` | 2,184,467 |
+| 3 | `_ e g y` | 1,442,169 |
+| 4 | `_ m e g` | 1,293,813 |
+| 5 | `. _ a _` | 1,289,446 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a z _ e` | 641,291 |
+| 2 | `_ s z e r` | 617,289 |
+| 3 | `_ é s _ a` | 566,317 |
+| 4 | `_ e g y _` | 545,352 |
+| 5 | `_ v o l t` | 535,186 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 429
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~16% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.9424 | 1.922 | 10.97 | 3,256,935 | 5.8% |
+| **1** | Subword | 1.1652 | 2.243 | 8.26 | 11,176 | 0.0% |
+| **2** | Word | 0.3123 | 1.242 | 2.03 | 35,712,539 | 68.8% |
+| **2** | Subword | 0.6772 | 1.599 | 4.69 | 92,249 | 32.3% |
+| **3** | Word | 0.1163 | 1.084 | 1.24 | 72,420,142 | 88.4% |
+| **3** | Subword | 0.7664 | 1.701 | 4.71 | 432,153 | 23.4% |
+| **4** | Word | 0.0401 🏆 | 1.028 | 1.06 | 89,534,275 | 96.0% |
+| **4** | Subword | 0.7484 | 1.680 | 3.95 | 2,032,871 | 25.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a filozófiában jeleskedők élete vácon igazgatótanár aszalón bőcsön borsod abaúj zemplén vármegye hon...`
+2. `az elfelejtett kísérlet singer berta fia némó elbűvölő szörnyeteg régebben megszűnt a európa bajnoki...`
+3. `és dániai roskilde dánia szoroson át kellett találni a királyi engedélyt egy bizonyos ágakra elosztó...`
+
+**Context Size 2:**
+
+1. `és a helybeli hazafiak köztük władysław gomułka lett akkor több fellépést is a neve és a razorblade`
+2. `hogy a gerjesztést követően 0 0 0 0 0 0 0 fe7 lépéssorozatból áll a leendő űrkísérletekhez`
+3. `és az egy évvel korábban halt meg csak hallomásból ismerik a kantonmozdony mivel az ember annak érde...`
+
+**Context Size 3:**
+
+1. `született személyek elhunyt személyek drámaírók novogyevicsi temetőben eltemetett személyek erdélyi ...`
+2. `madarai madarai madarai északi mariana szigetek madarai madarai madarai madarai amerikai egyesült ál...`
+3. `jegyzetek további információk labdarúgók river plate labdarúgói cruz azul labdarúgói atlas labdarúgó...`
+
+**Context Size 4:**
+
+1. `madarai madarai madarai madarai madarai madarai tomé és príncipe madarai madarai madarai seychelle s...`
+2. `született személyek elhunyt személyek úszók olimpiai ezüstérmesek nők`
+3. `kisbolygók listája jegyzetek naprendszer kisbolygói vec lista de aënna`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_tén.krermi_k_ho`
+2. `emi_ra_vaiszegyő`
+3. `ajat_(gola,_jena`
+
+**Context Size 2:**
+
+1. `_a_törül_ingmukiv`
+2. `a_katt,_fő_sztünc`
+3. `szvetibelyek_mika`
+
+**Context Size 3:**
+
+1. `_a_„tusábang_napil`
+2. `_szócikkel_a_bolyg`
+3. `_az_a4_március_211`
+
+**Context Size 4:**
+
+1. `_az_eszit_lasszultá`
+2. `_és_galéria_szereti`
+3. `_egyik_mária_foglal`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (2,032,871 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 1,458,224 |
+| Total Tokens | 103,537,627 |
+| Mean Frequency | 71.00 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 7231.61 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 7,834,400 |
+| 2 | az | 2,443,698 |
+| 3 | és | 2,194,407 |
+| 4 | is | 733,028 |
+| 5 | egy | 579,913 |
+| 6 | hogy | 494,341 |
+| 7 | volt | 474,789 |
+| 8 | nem | 446,048 |
+| 9 | 1 | 405,757 |
+| 10 | magyar | 343,055 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | léggömbzár | 2 |
+| 2 | elterelővel | 2 |
+| 3 | hajófelderítő | 2 |
+| 4 | szűrőrendszerrel | 2 |
+| 5 | 801ml | 2 |
+| 6 | 801tp | 2 |
+| 7 | sorosmotort | 2 |
+| 8 | stammkennzeichen | 2 |
+| 9 | schindleréről | 2 |
+| 10 | kapraraszműlesiklásnem | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9296 |
+| R² (Goodness of Fit) | 0.996343 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 25.8% |
+| Top 1,000 | 45.6% |
+| Top 5,000 | 61.9% |
+| Top 10,000 | 69.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9963 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 25.8% of corpus
+- **Long Tail:** 1,448,224 words needed for remaining 30.8% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7886 | 0.3560 | N/A | N/A |
+| **mono_64d** | 64 | 0.7831 | 0.2846 | N/A | N/A |
+| **mono_128d** | 128 | 0.7114 | 0.2337 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7886 🏆 | 0.3516 | 0.3600 | 0.7520 |
+| **aligned_64d** | 64 | 0.7831 | 0.2765 | 0.5320 | 0.8560 |
+| **aligned_128d** | 128 | 0.7114 | 0.2243 | 0.6300 | 0.9180 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7886 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2878. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 63.0% 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 | **-0.625** | 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 |
+|--------|----------|
+| `-s` | schmör, szabadítására, szertárai |
+| `-k` | kelvinator, kürtszavára, keselyűféléket |
+| `-a` | arkopharma, adequate, akszukhosz |
+| `-m` | megyéjébe, mongu, mozilátogatók |
+| `-t` | tlp, terheltségének, tanulhatóak |
+| `-b` | balszárnyának, bhov, beszédfejlődési |
+| `-ma` | mainstoneky, margati, mayrnak |
+| `-e` | elektronszerkezetével, egységnyiek, esztyemirova |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-k` | mozilátogatók, balszárnyának, kóruspadok |
+| `-t` | használókat, felvevőpiacot, keselyűféléket |
+| `-n` | végezetlen, érinthetetlen, állatkertjeiben |
+| `-a` | arkopharma, kürtszavára, vaskarika |
+| `-l` | elektronszerkezetével, versenyeitől, pálinkafőzéssel |
+| `-s` | rombouts, ikszes, gibbins |
+| `-i` | lxviii, desai, vibrálni |
+| `-e` | megyéjébe, jugoslovenske, adequate |
+
+### 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 |
+|------|----------|------------------|----------|
+| `váro` | 2.03x | 219 contexts | város, váron, várod |
+| `embe` | 1.60x | 288 contexts | ember, embed, bembe |
+| `llen` | 1.53x | 361 contexts | llena, illen, lleno |
+| `atás` | 1.48x | 365 contexts | patás, hatás, avatás |
+| `llet` | 1.60x | 226 contexts | allet, ellet, illet |
+| `ítás` | 1.43x | 316 contexts | újítás, ásítás, ámítás |
+| `ítot` | 1.65x | 136 contexts | ított, vított, osított |
+| `erül` | 1.43x | 288 contexts | kerül, terül, derül |
+| `mber` | 1.33x | 400 contexts | ember, imber, amber |
+| `ület` | 1.38x | 311 contexts | fület, szület, őrület |
+| `rtén` | 2.09x | 42 contexts | örtény, értény, kurtén |
+| `örté` | 1.85x | 64 contexts | törté, örtény, körték |
+
+### 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` | `-t` | 115 words | közéletét, kansast |
+| `-k` | `-k` | 112 words | kreatívnak, kiújulnak |
+| `-s` | `-l` | 101 words | szúfizmussal, szinkronstábbal |
+| `-s` | `-k` | 99 words | sírkamrájuk, szülővárosnak |
+| `-s` | `-t` | 95 words | szakmáikat, shiflett |
+| `-m` | `-k` | 86 words | mellékérték, maffiacsoportok |
+| `-k` | `-l` | 83 words | középjel, krízisekkel |
+| `-s` | `-n` | 81 words | szövegírásban, spalatóban |
+| `-k` | `-n` | 76 words | kihalásában, konstruktiven |
+| `-k` | `-a` | 72 words | korongcsiga, kaparinthatja |
+
+### 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 |
+|------|-----------------|------------|------|
+| vercruysse | **`vercruys-s-e`** | 7.5 | `s` |
+| szocialta | **`szoci-al-ta`** | 7.5 | `al` |
+| stílusbanaz | **`stílusban-a-z`** | 7.5 | `a` |
+| részvényeiket | **`részvényei-k-et`** | 7.5 | `k` |
+| hátoldalra | **`hátold-al-ra`** | 7.5 | `al` |
+| fullernek | **`fuller-n-ek`** | 7.5 | `n` |
+| nézhetőnek | **`nézhető-n-ek`** | 7.5 | `n` |
+| napjainkbeli | **`napjainkb-el-i`** | 7.5 | `el` |
+| gyarmataikra | **`gyarmatai-k-ra`** | 7.5 | `k` |
+| mandülion | **`mandül-i-on`** | 7.5 | `i` |
+| robotterheit | **`robotterhe-i-t`** | 7.5 | `i` |
+| albuginea | **`albugin-e-a`** | 7.5 | `e` |
+| elnagyoltak | **`elnagyol-t-ak`** | 7.5 | `t` |
+| minőségileg | **`minőségil-e-g`** | 7.5 | `e` |
+| szárnyacskák | **`szárnyacs-k-ák`** | 7.5 | `k` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Hungarian 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](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.66x) |
+| N-gram | **2-gram** | Lowest perplexity (429) |
+| Markov | **Context-4** | Highest predictability (96.0%) |
+| 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](README.md)
+
+*Generated by Wikilangs Pipeline · 2026-03-04 21:45:03*

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