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README.md

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+---
+language: scn
+language_name: Sicilian
+language_family: romance_galloitalic
+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-romance_galloitalic
+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: 4.491
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8559
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Sicilian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Sicilian** 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** | 3.628x | 3.63 | 0.0737% | 333,830 |
+| **16k** | 3.960x | 3.96 | 0.0804% | 305,808 |
+| **32k** | 4.255x | 4.26 | 0.0864% | 284,572 |
+| **64k** | 4.491x 🏆 | 4.49 | 0.0912% | 269,653 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Samo è nu cumuni di 1.005 abbitanti dâ pruvincia di Riggiu Calabbria. dâ pruvinc...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sa mo ▁è ▁nu ▁cumuni ▁di ▁ 1 . 0 ... (+15 more)` | 25 |
+| 16k | `▁samo ▁è ▁nu ▁cumuni ▁di ▁ 1 . 0 0 ... (+14 more)` | 24 |
+| 32k | `▁samo ▁è ▁nu ▁cumuni ▁di ▁ 1 . 0 0 ... (+14 more)` | 24 |
+| 64k | `▁samo ▁è ▁nu ▁cumuni ▁di ▁ 1 . 0 0 ... (+14 more)` | 24 |
+
+**Sample 2:** `è un cumuni talianu dâ pruvincia di Sondriu ntâ Lummardìa. dâ pruvincia di Sondr...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁sondriu ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 16k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁sondriu ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 32k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁sondriu ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 64k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁sondriu ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+
+**Sample 3:** `è un cumuni talianu dâ pruvincia di Cremona ntâ Lummardìa. dâ pruvincia di Cremo...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁cremona ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 16k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁cremona ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 32k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁cremona ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+| 64k | `▁è ▁un ▁cumuni ▁talianu ▁dâ ▁pruvincia ▁di ▁cremona ▁ntâ ▁lummardìa ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.491x compression
+- **Lowest UNK Rate:** 8k with 0.0737% 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 | 16,384 | 14.00 | 58,233 | 15.6% | 34.5% |
+| **2-gram** | Subword | 244 🏆 | 7.93 | 4,404 | 70.4% | 99.0% |
+| **3-gram** | Word | 24,141 | 14.56 | 72,090 | 12.9% | 28.3% |
+| **3-gram** | Subword | 2,050 | 11.00 | 34,045 | 29.2% | 74.5% |
+| **4-gram** | Word | 36,264 | 15.15 | 109,633 | 12.4% | 28.1% |
+| **4-gram** | Subword | 12,335 | 13.59 | 174,994 | 13.1% | 41.0% |
+| **5-gram** | Word | 23,643 | 14.53 | 73,924 | 12.6% | 32.9% |
+| **5-gram** | Subword | 48,581 | 15.57 | 458,275 | 7.8% | 23.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pruvincia di` | 15,198 |
+| 2 | `dâ pruvincia` | 14,084 |
+| 3 | `di l` | 11,236 |
+| 4 | `è un` | 5,502 |
+| 5 | `è nu` | 5,227 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dâ pruvincia di` | 13,755 |
+| 2 | `è un cumuni` | 4,935 |
+| 3 | `talianu dâ pruvincia` | 4,537 |
+| 4 | `cumuni talianu dâ` | 4,533 |
+| 5 | `un cumuni talianu` | 4,497 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `cumuni talianu dâ pruvincia` | 4,533 |
+| 2 | `è un cumuni talianu` | 4,497 |
+| 3 | `un cumuni talianu dâ` | 4,420 |
+| 4 | `talianu dâ pruvincia di` | 4,404 |
+| 5 | `abbitanti dâ pruvincia di` | 1,920 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `è un cumuni talianu dâ` | 4,420 |
+| 2 | `un cumuni talianu dâ pruvincia` | 4,420 |
+| 3 | `cumuni talianu dâ pruvincia di` | 4,400 |
+| 4 | `ntâ lummardìa dâ pruvincia di` | 1,512 |
+| 5 | `nu cumuni dâ pruvincia di` | 1,411 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _` | 648,749 |
+| 2 | `a _` | 449,210 |
+| 3 | `u _` | 428,360 |
+| 4 | `_ d` | 299,061 |
+| 5 | `_ c` | 245,777 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d i` | 173,046 |
+| 2 | `d i _` | 150,142 |
+| 3 | `n i _` | 97,798 |
+| 4 | `t i _` | 93,979 |
+| 5 | `i _ d` | 89,842 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d i _` | 140,256 |
+| 2 | `i _ d i` | 52,971 |
+| 3 | `_ l u _` | 51,883 |
+| 4 | `a _ d i` | 47,082 |
+| 5 | `_ l a _` | 44,178 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _ d i _` | 43,124 |
+| 2 | `a _ d i _` | 41,391 |
+| 3 | `u _ d i _` | 29,866 |
+| 4 | `_ d i _ l` | 28,707 |
+| 5 | `i o n i _` | 27,862 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 244
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~24% 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 | 0.8233 | 1.769 | 5.57 | 202,619 | 17.7% |
+| **1** | Subword | 1.0275 | 2.038 | 8.59 | 1,252 | 0.0% |
+| **2** | Word | 0.2739 | 1.209 | 1.68 | 1,121,596 | 72.6% |
+| **2** | Subword | 1.0284 | 2.040 | 6.37 | 10,748 | 0.0% |
+| **3** | Word | 0.0907 | 1.065 | 1.15 | 1,872,306 | 90.9% |
+| **3** | Subword | 0.8687 | 1.826 | 4.42 | 68,346 | 13.1% |
+| **4** | Word | 0.0294 🏆 | 1.021 | 1.04 | 2,146,546 | 97.1% |
+| **4** | Subword | 0.6923 | 1.616 | 3.02 | 301,807 | 30.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `di l utilizzu eni uguali n maduna surmuntatu spissu china occupies a spidercam telecamera e di`
+2. `e in ebbica abbastanza nichi e riazzioni tinta tinta ntô 480 a parallassi dâ prima ranni`
+3. `lu divintaru famusi macari li pupulazzioni di fora dû branu cu la situazzioni 1 cor damaya`
+
+**Context Size 2:**
+
+1. `pruvincia di salernu havi na pupulazzioni di 1 chistu pirmetti ô browser di mozilla firefox sunnu sc...`
+2. `dâ pruvincia di frusinuni havi na vota ntô 147º e na storia assai àutru centru di lu`
+3. `di l aquila havi na pupulazzioni di 1 a 29 annu e ô dramma sacru di la`
+
+**Context Size 3:**
+
+1. `dâ pruvincia di asti ntô piemunti dâ pruvincia di palermu la notti dû 22 di dicèmmiru fu nu`
+2. `è un cumuni talianu dâ pruvincia di cremona ntâ lummardìa havi na pupulazzioni di 2 807 abbitanti dâ`
+3. `talianu dâ pruvincia di sarausa puru si la vilucitati dâ luci sia isutropica ossia ca avi u stissu`
+
+**Context Size 4:**
+
+1. `cumuni talianu dâ pruvincia di carbonia iglesias ntâ sardigna dâ pruvincia di oristanu ntâ sardigna ...`
+2. `è un cumuni talianu dâ pruvincia di nuvara ntô piemunti dâ pruvincia di cuneu ntô piemunti dâ pruvin...`
+3. `un cumuni talianu dâ pruvincia di pavìa ntâ lummardìa dâ pruvincia di bèrgamu ntâ lummardìa dâ pruvi...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_pìu_duvi_nta_dî`
+2. `imprersio_ncidi_`
+3. `a_di_le_antô_–_s`
+
+**Context Size 2:**
+
+1. `i_rinciriglia,_«c`
+2. `a_a_acquersanuota`
+3. `u_acquagnu_do'_va`
+
+**Context Size 3:**
+
+1. `_di_l'asempion:_th`
+2. `di_mai_di_gueva_nz`
+3. `ni_tantironali_a_j`
+
+**Context Size 4:**
+
+1. `_di_giugnu_'n_arban`
+2. `i_di_cuntra_venneme`
+3. `_lu_nùmmuru_nizo_ne`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (301,807 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 | 86,322 |
+| Total Tokens | 2,462,744 |
+| Mean Frequency | 28.53 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 696.15 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | di | 140,762 |
+| 2 | e | 60,379 |
+| 3 | lu | 55,075 |
+| 4 | a | 51,553 |
+| 5 | la | 47,116 |
+| 6 | l | 39,892 |
+| 7 | dâ | 32,478 |
+| 8 | è | 31,791 |
+| 9 | li | 30,388 |
+| 10 | n | 24,459 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | artificaili | 2 |
+| 2 | degeneratu | 2 |
+| 3 | impress | 2 |
+| 4 | escaldes | 2 |
+| 5 | engordany | 2 |
+| 6 | sabigotho | 2 |
+| 7 | reiter | 2 |
+| 8 | homestuck | 2 |
+| 9 | manganelli | 2 |
+| 10 | emiciclu | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0191 |
+| R² (Goodness of Fit) | 0.998927 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 41.6% |
+| Top 1,000 | 63.1% |
+| Top 5,000 | 78.3% |
+| Top 10,000 | 84.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9989 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 41.6% of corpus
+- **Long Tail:** 76,322 words needed for remaining 15.5% 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 | 0.8559 🏆 | 0.3184 | N/A | N/A |
+| **mono_64d** | 64 | 0.8517 | 0.2317 | N/A | N/A |
+| **mono_128d** | 128 | 0.7450 | 0.1848 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8559 | 0.3200 | 0.0900 | 0.3440 |
+| **aligned_64d** | 64 | 0.8517 | 0.2295 | 0.1380 | 0.4440 |
+| **aligned_128d** | 128 | 0.7450 | 0.1777 | 0.2000 | 0.5420 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8559 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2437. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.0% 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 | **0.381** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | schianari, straputiri, spirimintàvanu |
+| `-a` | aglabita, abbunata, azzurri |
+| `-c` | chidja, calculatu, catenanuova |
+| `-m` | mladic, medioevo, mintennu |
+| `-p` | presu, puvuredda, puacu |
+| `-ca` | calculatu, catenanuova, calabbra |
+| `-n` | negroponte, nnustri, ntitulata |
+| `-ma` | magistri, majistrìa, maladzečna |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-i` | schianari, liberi, itali |
+| `-u` | calculatu, mintennu, presu |
+| `-a` | chidja, aglabita, catenanuova |
+| `-ti` | acuti, disignati, fimmati |
+| `-ni` | moroni, littoni, valanzuni |
+| `-ri` | schianari, liberi, azzurri |
+| `-tu` | calculatu, scuraggiatu, nzignatu |
+| `-nu` | mintennu, infantinu, spirimintàvanu |
+
+### 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 |
+|------|----------|------------------|----------|
+| `azzi` | 2.30x | 147 contexts | tazzi, yazzi, mazzi |
+| `izzi` | 2.03x | 166 contexts | pizzi, rizzi, nizzi |
+| `itat` | 2.12x | 103 contexts | citat, itati, vitatu |
+| `zion` | 2.21x | 73 contexts | zione, zioni, azione |
+| `zzio` | 2.32x | 44 contexts | zzioni, azziona, azzioni |
+| `vinc` | 2.14x | 39 contexts | vinci, vincì, vince |
+| `iggi` | 1.65x | 109 contexts | siggi, liggi, figgi |
+| `nali` | 1.91x | 43 contexts | anali, linali, fanali |
+| `ncia` | 1.61x | 80 contexts | uncia, ancia, lancia |
+| `lian` | 1.58x | 70 contexts | julian, alianu, eliana |
+| `inci` | 1.46x | 96 contexts | jinci, vinci, linci |
+| `ilia` | 1.52x | 77 contexts | dilia, filia, iliadi |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-i` | 299 words | crudili, ciampi |
+| `-a` | `-i` | 257 words | arrisbigghiari, avvrazzari |
+| `-a` | `-u` | 247 words | albergu, arrinneru |
+| `-c` | `-u` | 225 words | colledimenzu, chiu |
+| `-c` | `-a` | 203 words | catilina, caldea |
+| `-s` | `-u` | 201 words | sassòfunu, suffru |
+| `-p` | `-i` | 200 words | pinitrari, picciriddi |
+| `-s` | `-i` | 200 words | sumenzi, sanguinari |
+| `-a` | `-a` | 157 words | adriatica, amatura |
+| `-m` | `-i` | 153 words | mustri, matrici |
+
+### 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 |
+|------|-----------------|------------|------|
+| eremitaggiu | **`eremitagg-i-u`** | 7.5 | `i` |
+| intellighentsia | **`intellighents-i-a`** | 7.5 | `i` |
+| impiegati | **`impieg-a-ti`** | 7.5 | `a` |
+| gghiùnciri | **`gghiùnc-i-ri`** | 7.5 | `i` |
+| cunsidiratu | **`cunsidir-a-tu`** | 7.5 | `a` |
+| melitensis | **`melitens-i-s`** | 7.5 | `i` |
+| nfruinzatu | **`nfruinz-a-tu`** | 7.5 | `a` |
+| fortificata | **`fortific-a-ta`** | 7.5 | `a` |
+| agghìunciri | **`agghìunc-i-ri`** | 7.5 | `i` |
+| madeleine | **`madele-i-ne`** | 7.5 | `i` |
+| munarchii | **`munarch-i-i`** | 7.5 | `i` |
+| baudelaire | **`baudela-i-re`** | 7.5 | `i` |
+| ndividuari | **`ndividu-a-ri`** | 7.5 | `a` |
+| ncintivati | **`ncintiv-a-ti`** | 7.5 | `a` |
+| vintagghiu | **`vintaggh-i-u`** | 7.5 | `i` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Sicilian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.49x) |
+| N-gram | **2-gram** | Lowest perplexity (244) |
+| Markov | **Context-4** | Highest predictability (97.1%) |
+| 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 19:53:23*

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