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README.md

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+---
+language: is
+language_name: Icelandic
+language_family: germanic_north
+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-germanic_north
+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: 4.556
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8275
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Icelandic - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Icelandic** 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
+
+![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** | 3.538x | 3.54 | 0.0547% | 1,307,527 |
+| **16k** | 3.917x | 3.92 | 0.0605% | 1,181,053 |
+| **32k** | 4.268x | 4.27 | 0.0660% | 1,083,827 |
+| **64k** | 4.556x 🏆 | 4.56 | 0.0704% | 1,015,400 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ensími getur átt við: Ensím Íslensku hljómsveitina Ensími`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁en sí mi ▁getur ▁átt ▁við : ▁en sí m ... (+5 more)` | 15 |
+| 16k | `▁ensí mi ▁getur ▁átt ▁við : ▁ensí m ▁íslensku ▁hljómsveitina ... (+2 more)` | 12 |
+| 32k | `▁ensí mi ▁getur ▁átt ▁við : ▁ensím ▁íslensku ▁hljómsveitina ▁ensí ... (+1 more)` | 11 |
+| 64k | `▁ensími ▁getur ▁átt ▁við : ▁ensím ▁íslensku ▁hljómsveitina ▁ensími` | 9 |
+
+**Sample 2:** `Arís er íslenskt kvenmannsnafn. Dreifing á Íslandi Heimildir kvenmannsnöfn`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ar ís ▁er ▁íslenskt ▁kvenmannsnafn . ▁dreifing ▁á ▁íslandi ▁heimildir ... (+1 more)` | 11 |
+| 16k | `▁ar ís ▁er ▁íslenskt ▁kvenmannsnafn . ▁dreifing ▁á ▁íslandi ▁heimildir ... (+1 more)` | 11 |
+| 32k | `▁ar ís ▁er ▁íslenskt ▁kvenmannsnafn . ▁dreifing ▁á ▁íslandi ▁heimildir ... (+1 more)` | 11 |
+| 64k | `▁ar ís ▁er ▁íslenskt ▁kvenmannsnafn . ▁dreifing ▁á ▁íslandi ▁heimildir ... (+1 more)` | 11 |
+
+**Sample 3:** `Start-Up (Kóreska: 스타트업; Seutateueop) er suður-kóreskur sjónvarpsþáttur. sjónvar...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁st art - up ▁( kó re ska : ▁ ... (+18 more)` | 28 |
+| 16k | `▁st art - up ▁( kóre ska : ▁ 스타트업 ... (+15 more)` | 25 |
+| 32k | `▁start - up ▁( kóreska : ▁ 스타트업 ; ▁se ... (+13 more)` | 23 |
+| 64k | `▁start - up ▁( kóreska : ▁ 스타트업 ; ▁se ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.556x compression
+- **Lowest UNK Rate:** 8k with 0.0547% 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 | 76,323 | 16.22 | 290,201 | 7.5% | 20.3% |
+| **2-gram** | Subword | 360 🏆 | 8.49 | 7,570 | 60.9% | 98.9% |
+| **3-gram** | Word | 187,198 | 17.51 | 409,948 | 3.6% | 11.1% |
+| **3-gram** | Subword | 3,285 | 11.68 | 62,993 | 21.8% | 63.7% |
+| **4-gram** | Word | 412,107 | 18.65 | 661,434 | 2.3% | 6.9% |
+| **4-gram** | Subword | 19,995 | 14.29 | 386,811 | 10.1% | 32.9% |
+| **5-gram** | Word | 284,069 | 18.12 | 418,913 | 3.1% | 8.0% |
+| **5-gram** | Subword | 84,371 | 16.36 | 1,264,141 | 5.6% | 18.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `til að` | 27,637 |
+| 2 | `þar sem` | 24,592 |
+| 3 | `á íslandi` | 18,253 |
+| 4 | `því að` | 15,183 |
+| 5 | `þess að` | 13,286 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `til þess að` | 8,156 |
+| 2 | `með því að` | 4,654 |
+| 3 | `þar sem hann` | 3,445 |
+| 4 | `dreifing á íslandi` | 2,999 |
+| 5 | `á íslandi heimildir` | 2,839 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dreifing á íslandi heimildir` | 2,780 |
+| 2 | `kvenmannsnafn dreifing á íslandi` | 1,520 |
+| 3 | `íslenskt kvenmannsnafn dreifing á` | 1,519 |
+| 4 | `er íslenskt kvenmannsnafn dreifing` | 1,518 |
+| 5 | `á íslandi heimildir kvenmannsnöfn` | 1,509 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `íslenskt kvenmannsnafn dreifing á íslandi` | 1,519 |
+| 2 | `er íslenskt kvenmannsnafn dreifing á` | 1,518 |
+| 3 | `dreifing á íslandi heimildir kvenmannsnöfn` | 1,509 |
+| 4 | `kvenmannsnafn dreifing á íslandi heimildir` | 1,471 |
+| 5 | `íslenskt karlmannsnafn dreifing á íslandi` | 1,309 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `r _` | 1,832,522 |
+| 2 | `a r` | 1,368,870 |
+| 3 | `_ s` | 1,362,774 |
+| 4 | `i n` | 1,140,724 |
+| 5 | `a _` | 1,027,671 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a r _` | 583,858 |
+| 2 | `o g _` | 458,351 |
+| 3 | `_ o g` | 457,248 |
+| 4 | `u r _` | 447,514 |
+| 5 | `_ í _` | 435,363 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ o g _` | 456,555 |
+| 2 | `_ a ð _` | 255,398 |
+| 3 | `s e m _` | 214,724 |
+| 4 | `_ s e m` | 214,407 |
+| 5 | `_ e r _` | 203,790 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ s e m _` | 212,727 |
+| 2 | `_ v a r _` | 160,455 |
+| 3 | `_ t i l _` | 132,778 |
+| 4 | `_ h a n n` | 91,569 |
+| 5 | `_ v i ð _` | 89,262 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 360
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~19% 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 | 0.8991 | 1.865 | 7.58 | 645,450 | 10.1% |
+| **1** | Subword | 0.8434 | 1.794 | 5.91 | 4,305 | 15.7% |
+| **2** | Word | 0.3025 | 1.233 | 1.88 | 4,874,320 | 69.8% |
+| **2** | Subword | 0.7898 | 1.729 | 5.23 | 25,387 | 21.0% |
+| **3** | Word | 0.1108 | 1.080 | 1.21 | 9,119,459 | 88.9% |
+| **3** | Subword | 0.8104 | 1.754 | 4.71 | 132,737 | 19.0% |
+| **4** | Word | 0.0408 🏆 | 1.029 | 1.06 | 11,025,075 | 95.9% |
+| **4** | Subword | 0.7484 | 1.680 | 3.57 | 624,878 | 25.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `og hentar vel stæðir og bornir fram sönnunargögn sem auðmjúkum manni sínum fyrir convention on train`
+2. `í helgafellssveit akureyjar þar sem þau voru í skiftirækt hann var formaður utanríkismálanefndar um ...`
+3. `á suður ítalíu ákvað hópurinn að ráða í þessu nafni sambandsins og er árlega sumarsýningu norræna`
+
+**Context Size 2:**
+
+1. `til að hjálpa til uppáhalds frasinn hans er einkum þekktur fyrir hlutverk sitt í davíð að hann`
+2. `þar sem hann naut mikillar virðingar samtíðarmanna sinna hún var komin í millihýsil þá umbreytast eg...`
+3. `því að þeir þorvaldur og andrea šušnjara lipeja tena 13 33 12 12 12 18 0 31`
+
+**Context Size 3:**
+
+1. `til þess að verða bandamaður michaels í fjórðu seríu er farið yfir launasjóðskenninguna og umfjöllun...`
+2. `með því að stebbi finnur sig fastan á milli steins tóta og sleggju brúnó söguþráður kvikmyndir is le...`
+3. `þar sem hann gerði voru ómerktar eins og venjan var áður núverandi ríkisstjórn er ráðuneyti kristrún...`
+
+**Context Size 4:**
+
+1. `dreifing á íslandi heimildir karlmannsnöfn millinöfn`
+2. `kvenmannsnafn dreifing á íslandi heimildir karlmannsnöfn kvenmannsnöfn mannanöfn sem notuð eru sem s...`
+3. `íslenskt kvenmannsnafn dreifing á íslandi heimildir karlmannsnöfn karlmannsnöfn karlmannsnöfn karlma...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_alanleft._sist_`
+2. `a_að_mariða_hast`
+3. `r_ng_g_18)._hafr`
+
+**Context Size 2:**
+
+1. `r_og_ver_er_þandu`
+2. `ariðlaráðandurver`
+3. `_skógismeigilsfæd`
+
+**Context Size 3:**
+
+1. `ar_bikarabbí_orian`
+2. `og_heitimennda,_mi`
+3. `_og_lankameríkur_a`
+
+**Context Size 4:**
+
+1. `_og_mannsson,_útgáf`
+2. `_að_innarskógarþrúð`
+3. `sem_juttum_mági_sig`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.9% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (624,878 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 | 287,581 |
+| Total Tokens | 12,356,689 |
+| Mean Frequency | 42.97 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1648.11 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | og | 457,899 |
+| 2 | í | 437,515 |
+| 3 | á | 265,620 |
+| 4 | að | 256,592 |
+| 5 | sem | 214,678 |
+| 6 | er | 205,384 |
+| 7 | var | 161,974 |
+| 8 | til | 134,849 |
+| 9 | við | 91,854 |
+| 10 | af | 91,619 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | 洞 | 2 |
+| 2 | 리 | 2 |
+| 3 | myeongjang | 2 |
+| 4 | hitaþolnir | 2 |
+| 5 | sløttum | 2 |
+| 6 | noregslandi | 2 |
+| 7 | triðja | 2 |
+| 8 | beregszásziová | 2 |
+| 9 | lúóa | 2 |
+| 10 | keníumanna | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9806 |
+| R² (Goodness of Fit) | 0.998336 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 36.0% |
+| Top 1,000 | 56.0% |
+| Top 5,000 | 71.7% |
+| Top 10,000 | 78.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9983 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 36.0% of corpus
+- **Long Tail:** 277,581 words needed for remaining 21.6% 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 | 0.8275 | 0.3448 | N/A | N/A |
+| **mono_64d** | 64 | 0.7798 | 0.2809 | N/A | N/A |
+| **mono_128d** | 128 | 0.7263 | 0.2042 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8275 🏆 | 0.3509 | 0.1760 | 0.5520 |
+| **aligned_64d** | 64 | 0.7798 | 0.2744 | 0.3040 | 0.6540 |
+| **aligned_128d** | 128 | 0.7263 | 0.2020 | 0.3960 | 0.6900 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8275 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2762. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 39.6% 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 | **-0.580** | 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` | skrúðsigling, safamýri, síuna |
+| `-a` | alinu, alfarið, alvarlegar |
+| `-b` | byrlaði, brahes, boðsundssveitar |
+| `-h` | hænis, hryggsúlunnar, heimilisins |
+| `-m` | markúsdóttur, mótmælendunum, málvísindamannsins |
+| `-k` | kesiya, kóngsstaðadalur, kórónaveirufaraldurinn |
+| `-ma` | markúsdóttur, maximine, masterpiece |
+| `-t` | tyrrell, tannþráð, teypaða |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-r` | markúsdóttur, lágmarkar, boðsundssveitar |
+| `-a` | röksemdafærsla, útrýma, síuna |
+| `-i` | byrlaði, safamýri, pósthússtræti |
+| `-n` | indverjinn, notodden, rodman |
+| `-um` | mótmælendunum, gjaldmiðlakerfum, stöndum |
+| `-ar` | lágmarkar, boðsundssveitar, hryggsúlunnar |
+| `-ur` | markúsdóttur, ljóstvistur, kóngsstaðadalur |
+| `-s` | brahes, hænis, ekkekrates |
+
+### 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 |
+|------|----------|------------------|----------|
+| `sson` | 2.16x | 82 contexts | arsson, jesson, wesson |
+| `nnar` | 1.68x | 96 contexts | ánnar, innar, unnar |
+| `stjó` | 1.86x | 50 contexts | stjóra, stjórn, stjóri |
+| `maðu` | 2.17x | 28 contexts | maður, ismaður, ármaður |
+| `ngur` | 1.63x | 85 contexts | úngur, ungur, ingur |
+| `ista` | 1.38x | 162 contexts | gista, istar, vista |
+| `ngar` | 1.56x | 71 contexts | angar, ungar, ingar |
+| `ndar` | 1.33x | 133 contexts | undar, andar, endar |
+| `jórn` | 2.04x | 23 contexts | sjórn, stjórn, bjórnum |
+| `egar` | 2.03x | 21 contexts | segar, vegar, þegar |
+| `ndur` | 1.33x | 99 contexts | undur, endur, rindur |
+| `ndir` | 1.41x | 70 contexts | endir, undir, randir |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-r` | 200 words | sjóðríkur, sérkennilegar |
+| `-s` | `-i` | 158 words | stuttskífunni, seyði |
+| `-s` | `-a` | 142 words | saxicola, shimada |
+| `-h` | `-r` | 131 words | hugprýðinnar, hverfisveppur |
+| `-s` | `-n` | 128 words | schliemann, sérútbúin |
+| `-s` | `-m` | 92 words | söderström, sigruðum |
+| `-s` | `-um` | 89 words | sigruðum, stráknum |
+| `-h` | `-a` | 88 words | hálfbræðranna, helga |
+| `-k` | `-r` | 87 words | kýlapestar, knapar |
+| `-b` | `-r` | 83 words | bíldudalur, beaver |
+
+### 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 |
+|------|-----------------|------------|------|
+| læknisins | **`læknis-i-ns`** | 7.5 | `i` |
+| þrumuveðri | **`þrumuveð-r-i`** | 7.5 | `r` |
+| ofbeldisfullra | **`ofbeldisfull-r-a`** | 7.5 | `r` |
+| ketilbjörn | **`ketilbjö-r-n`** | 7.5 | `r` |
+| meðlimina | **`meðlim-i-na`** | 7.5 | `i` |
+| kambódíustjórn | **`kambódíustjó-r-n`** | 7.5 | `r` |
+| óbreyttri | **`óbreytt-r-i`** | 7.5 | `r` |
+| norðurodda | **`norðurod-d-a`** | 7.5 | `d` |
+| jöhannsson | **`jöhanns-s-on`** | 7.5 | `s` |
+| handelman | **`handelm-a-n`** | 7.5 | `a` |
+| steypujárni | **`steypujá-r-ni`** | 7.5 | `r` |
+| konuvísur | **`konuví-s-ur`** | 7.5 | `s` |
+| heittemprað | **`heittempr-a-ð`** | 7.5 | `a` |
+| sororculana | **`sororcu-la-na`** | 7.5 | `la` |
+| hryggdýrum | **`hryggdý-r-um`** | 7.5 | `r` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Icelandic 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 (4.56x) |
+| N-gram | **2-gram** | Lowest perplexity (360) |
+| Markov | **Context-4** | Highest predictability (95.9%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+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](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+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)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### 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)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 06:06:11*

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