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README.md

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+---
+language: it
+language_name: Italian
+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: 4.817
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7834
+  - name: best_alignment_r10
+    type: alignment
+    value: 0.9340
+  - name: vocabulary_size
+    type: vocab
+    value: 511837
+generated: 2026-03-03
+---
+
+# Italian — Wikilangs Models
+
+Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **Italian** Wikipedia by [Wikilangs](https://wikilangs.org).
+
+🌐 [Language Page](https://wikilangs.org/languages/it/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=it) · 📊 [Full Research Report](RESEARCH_REPORT.md)
+
+## Language Samples
+
+Example sentences drawn from the Italian Wikipedia corpus:
+
+> Eventi, invenzioni e scoperte Personaggi nasce Dante Alighieri Altri progetti 07
+
+> Eventi, invenzioni e scoperte Periodo della Grande carestia del Personaggi Giovanni Boccaccio nasce nel luglio Altri progetti 02
+
+> Eventi, invenzioni e scoperte Fine della cattività avignonese A Vicenza venne sparato il primo fuoco d'artificio Europeo. Personaggi Altri progetti 08
+
+> Eventi, invenzioni e scoperte Personaggi ... Altri progetti 09
+
+> Eventi, invenzioni e scoperte Viene inventato il Lapis Benjamin Franklin inventa il Parafulmine. Personaggi Wolfgang Amadeus Mozart Altri progetti 06
+
+## Quick Start
+
+### Load the Tokenizer
+
+```python
+import sentencepiece as spm
+
+sp = spm.SentencePieceProcessor()
+sp.Load("it_tokenizer_32k.model")
+
+text = "Eventi, invenzioni e scoperte Viene inventato il Lapis Benjamin Franklin inventa"
+tokens = sp.EncodeAsPieces(text)
+ids    = sp.EncodeAsIds(text)
+
+print(tokens)  # subword pieces
+print(ids)     # integer ids
+
+# Decode back
+print(sp.DecodeIds(ids))
+```
+
+<details>
+<summary><b>Tokenization examples (click to expand)</b></summary>
+
+**Sample 1:** `Eventi, invenzioni e scoperte Viene inventato il Lapis Benjamin Franklin inventa…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inv entato ▁il ▁la … (+29 more)` | 39 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis … (+21 more)` | 31 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis … (+17 more)` | 27 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis … (+17 more)` | 27 |
+
+**Sample 2:** `Eventi, invenzioni e scoperte Roma - Inaugurazione del Colosseo Personaggi 81 Ro…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugu razione ▁del … (+19 more)` | 29 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugu razione ▁del … (+19 more)` | 29 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugurazione ▁del ▁colo … (+18 more)` | 28 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugurazione ▁del ▁colosseo … (+16 more)` | 26 |
+
+**Sample 3:** `Eventi, invenzioni e scoperte Fine della cattività avignonese A Vicenza venne sp…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca tti vità … (+23 more)` | 33 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca ttività ▁avi … (+22 more)` | 32 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca ttività ▁avi … (+22 more)` | 32 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁cattività ▁avignon ese … (+18 more)` | 28 |
+
+</details>
+
+### Load Word Embeddings
+
+```python
+from gensim.models import KeyedVectors
+
+# Aligned embeddings (cross-lingual, mapped to English vector space)
+wv = KeyedVectors.load("it_embeddings_128d_aligned.kv")
+
+similar = wv.most_similar("word", topn=5)
+for word, score in similar:
+    print(f"  {word}: {score:.3f}")
+```
+
+### Load N-gram Model
+
+```python
+import pyarrow.parquet as pq
+
+df = pq.read_table("it_3gram_word.parquet").to_pandas()
+print(df.head())
+```
+
+## Models Overview
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+| 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 | 3.86x |
+| Tokenizer | 16k BPE | Compression | 4.25x |
+| Tokenizer | 32k BPE | Compression | 4.58x |
+| Tokenizer | 64k BPE | Compression | 4.82x 🏆 |
+| N-gram | 2-gram (subword) | Perplexity | 214 🏆 |
+| N-gram | 2-gram (word) | Perplexity | 204,245 |
+| N-gram | 3-gram (subword) | Perplexity | 1,722 |
+| N-gram | 3-gram (word) | Perplexity | 980,193 |
+| N-gram | 4-gram (subword) | Perplexity | 10,064 |
+| N-gram | 4-gram (word) | Perplexity | 1,937,953 |
+| N-gram | 5-gram (subword) | Perplexity | 43,596 |
+| N-gram | 5-gram (word) | Perplexity | 1,090,157 |
+| Markov | ctx-1 (subword) | Predictability | 0.0% |
+| Markov | ctx-1 (word) | Predictability | 0.0% |
+| Markov | ctx-2 (subword) | Predictability | 32.2% |
+| Markov | ctx-2 (word) | Predictability | 53.2% |
+| Markov | ctx-3 (subword) | Predictability | 27.9% |
+| Markov | ctx-3 (word) | Predictability | 79.8% |
+| Markov | ctx-4 (subword) | Predictability | 32.0% |
+| Markov | ctx-4 (word) | Predictability | 92.6% 🏆 |
+| Vocabulary | full | Size | 511,837 |
+| Vocabulary | full | Zipf R² | 0.9968 |
+| Embeddings | mono_32d | Isotropy | 0.7834 |
+| Embeddings | mono_64d | Isotropy | 0.7465 |
+| Embeddings | mono_128d | Isotropy | 0.6690 |
+| Embeddings | aligned_32d | Isotropy | 0.7834 🏆 |
+| Embeddings | aligned_64d | Isotropy | 0.7465 |
+| Embeddings | aligned_128d | Isotropy | 0.6690 |
+| Alignment | aligned_32d | R@1 / R@5 / R@10 | 39.2% / 64.2% / 74.8% |
+| Alignment | aligned_64d | R@1 / R@5 / R@10 | 60.6% / 81.4% / 85.8% |
+| Alignment | aligned_128d | R@1 / R@5 / R@10 | 67.8% / 88.8% / 93.4% 🏆 |
+
+📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**
+
+---
+
+## About
+
+Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.
+
+A project by **[Wikilangs](https://wikilangs.org)** · Maintainer: [Omar Kamali](https://omarkamali.com) · [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+```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}
+}
+```
+
+### Links
+
+- 🌐 [wikilangs.org](https://wikilangs.org)
+- 🌍 [Language page](https://wikilangs.org/languages/it/)
+- 🎮 [Playground](https://wikilangs.org/playground/?lang=it)
+- 🤗 [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)
+
+**License:** MIT — free for academic and commercial use.
+
+---
+*Generated by Wikilangs Pipeline · 2026-03-03 11:41:08*

RESEARCH_REPORT.md

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+# Italian — Full Ablation Study & Research Report
+
+Detailed evaluation of all model variants trained on **Italian** 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** | 3.856x | 3.86 | 0.1569% | 3,369,266 |
+| **16k** | 4.248x | 4.25 | 0.1729% | 3,057,865 |
+| **32k** | 4.578x | 4.58 | 0.1863% | 2,837,619 |
+| **64k** | 4.817x 🏆 | 4.82 | 0.1960% | 2,696,638 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Eventi, invenzioni e scoperte Viene inventato il Lapis Benjamin Franklin inventa...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inv entato ▁il ▁la ... (+29 more)` | 39 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis ... (+21 more)` | 31 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis ... (+17 more)` | 27 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁viene ▁inventato ▁il ▁la pis ... (+17 more)` | 27 |
+
+**Sample 2:** `Eventi, invenzioni e scoperte Roma - Inaugurazione del Colosseo Personaggi 81 Ro...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugu razione ▁del ... (+19 more)` | 29 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugu razione ▁del ... (+19 more)` | 29 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugurazione ▁del ▁colo ... (+18 more)` | 28 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁roma ▁- ▁inaugurazione ▁del ▁colosseo ... (+16 more)` | 26 |
+
+**Sample 3:** `Eventi, invenzioni e scoperte Fine della cattività avignonese A Vicenza venne sp...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca tti vità ... (+23 more)` | 33 |
+| 16k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca ttività ▁avi ... (+22 more)` | 32 |
+| 32k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁ca ttività ▁avi ... (+22 more)` | 32 |
+| 64k | `▁eventi , ▁invenzioni ▁e ▁scoperte ▁fine ▁della ▁cattività ▁avignon ese ... (+18 more)` | 28 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.817x compression
+- **Lowest UNK Rate:** 8k with 0.1569% 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 | 204,245 | 17.64 | 1,475,040 | 6.1% | 16.8% |
+| **2-gram** | Subword | 214 🏆 | 7.74 | 16,385 | 74.4% | 99.4% |
+| **3-gram** | Word | 980,193 | 19.90 | 3,253,104 | 3.2% | 7.9% |
+| **3-gram** | Subword | 1,722 | 10.75 | 125,759 | 29.1% | 78.9% |
+| **4-gram** | Word | 1,937,953 | 20.89 | 4,769,877 | 3.5% | 7.0% |
+| **4-gram** | Subword | 10,064 | 13.30 | 693,084 | 14.1% | 42.3% |
+| **5-gram** | Word | 1,090,157 | 20.06 | 2,714,110 | 5.0% | 9.7% |
+| **5-gram** | Subword | 43,596 | 15.41 | 2,255,172 | 7.7% | 24.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `per la` | 97,286 |
+| 2 | `è un` | 96,027 |
+| 3 | `di un` | 94,634 |
+| 4 | `e il` | 87,633 |
+| 5 | `altri progetti` | 84,533 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `altri progetti collegamenti` | 60,396 |
+| 2 | `progetti collegamenti esterni` | 60,395 |
+| 3 | `è un comune` | 49,422 |
+| 4 | `note altri progetti` | 43,464 |
+| 5 | `società evoluzione demografica` | 42,434 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `altri progetti collegamenti esterni` | 60,395 |
+| 2 | `è un comune francese` | 33,569 |
+| 3 | `abitanti situato nel dipartimento` | 33,506 |
+| 4 | `un comune francese di` | 33,266 |
+| 5 | `società evoluzione demografica note` | 32,814 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `è un comune francese di` | 33,067 |
+| 2 | `evoluzione demografica note altri progetti` | 32,558 |
+| 3 | `società evoluzione demografica note altri` | 32,545 |
+| 4 | `note altri progetti collegamenti esterni` | 27,862 |
+| 5 | `demografica note altri progetti collegamenti` | 18,549 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 14,124,940 |
+| 2 | `a _` | 13,040,624 |
+| 3 | `i _` | 10,977,304 |
+| 4 | `o _` | 10,608,684 |
+| 5 | `_ d` | 9,830,426 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d i` | 3,986,152 |
+| 2 | `_ d e` | 3,607,817 |
+| 3 | `l a _` | 3,337,560 |
+| 4 | `d i _` | 3,263,476 |
+| 5 | `_ c o` | 3,099,394 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d i _` | 3,072,491 |
+| 2 | `_ d e l` | 2,506,551 |
+| 3 | `l l a _` | 1,668,082 |
+| 4 | `_ i l _` | 1,519,530 |
+| 5 | `d e l l` | 1,488,470 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e l l` | 1,459,429 |
+| 2 | `e l l a _` | 1,154,387 |
+| 3 | `i o n e _` | 1,144,610 |
+| 4 | `_ d e l _` | 1,020,510 |
+| 5 | `z i o n e` | 936,001 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 214
+- **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 | 1.1017 | 2.146 | 14.31 | 1,066,977 | 0.0% |
+| **1** | Subword | 1.1777 | 2.262 | 7.67 | 8,745 | 0.0% |
+| **2** | Word | 0.4680 | 1.383 | 2.78 | 15,250,485 | 53.2% |
+| **2** | Subword | 0.6777 | 1.600 | 4.48 | 67,098 | 32.2% |
+| **3** | Word | 0.2017 | 1.150 | 1.44 | 42,356,757 | 79.8% |
+| **3** | Subword | 0.7209 | 1.648 | 4.10 | 300,612 | 27.9% |
+| **4** | Word | 0.0737 🏆 | 1.052 | 1.12 | 61,123,669 | 92.6% |
+| **4** | Subword | 0.6799 | 1.602 | 3.41 | 1,231,483 | 32.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `di assessore regionale del manto di pilotaggio e la frase di classificazione i dimostranti scendono ...`
+2. `e della borgogna franca contea società evoluzione demografica note book apogeo del jkd è inoltre l`
+3. `il numero di sacco viene riportato che all inizio con le descrizioni matematicamente da cui le`
+
+**Context Size 2:**
+
+1. `per la stirpe più distante e poi per varie statue a delfi in grecia per la prima`
+2. `è un genere teatrale di rendere il suo simbolo è appunto quello di stern gerlach numero quantico`
+3. `di un giovane nero floyd patterson mettendolo anche in alcuni casi come trimble contro gordon brown ...`
+
+**Context Size 3:**
+
+1. `altri progetti collegamenti esterni white teeth a conversation with cary grant che lo portò in testa...`
+2. `è un comune francese di abitanti situato nel dipartimento della valle della politica di per il migli...`
+3. `progetti collegamenti esterni t dell oceano pacifico meridionale polinesia con una superficie per co...`
+
+**Context Size 4:**
+
+1. `altri progetti collegamenti esterni topo gigio all ed sullivan show di elvis presley scatenò i teena...`
+2. `è un comune francese di 75 abitanti situato nella comunità autonoma della navarra altri progetti del...`
+3. `abitanti situato nel dipartimento dell eure nella regione della normandia società evoluzione demogra...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ri_fi_ami_l'agi`
+2. `i_po_mpr_pri_mpa`
+3. `eo_ia,_ltavinafa`
+
+**Context Size 2:**
+
+1. `e_diatuas,_of_spe`
+2. `a_inasa_quo_ca_ar`
+3. `i_nal_re_e_ne_pie`
+
+**Context Size 3:**
+
+1. `_di_anni_di_gioria`
+2. `_della_galle._mali`
+3. `la_perfalcune_pelt`
+
+**Context Size 4:**
+
+1. `_di_fontempi_livini`
+2. `_del_romanzo_è_anch`
+3. `lla_classi_e_l'inse`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 92.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,231,483 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 | 511,837 |
+| Total Tokens | 75,575,358 |
+| Mean Frequency | 147.66 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 7438.35 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | di | 3,083,677 |
+| 2 | e | 1,824,641 |
+| 3 | il | 1,551,962 |
+| 4 | la | 1,467,509 |
+| 5 | in | 1,133,909 |
+| 6 | a | 1,025,836 |
+| 7 | del | 1,022,058 |
+| 8 | che | 801,243 |
+| 9 | un | 799,178 |
+| 10 | della | 790,420 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | strensall | 2 |
+| 2 | towthorpe | 2 |
+| 3 | flamininus | 2 |
+| 4 | etolici | 2 |
+| 5 | riveros | 2 |
+| 6 | kuntur | 2 |
+| 7 | wachana | 2 |
+| 8 | queñua | 2 |
+| 9 | karabotas | 2 |
+| 10 | tveitite | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0088 |
+| R² (Goodness of Fit) | 0.996765 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 39.3% |
+| Top 1,000 | 60.2% |
+| Top 5,000 | 76.8% |
+| Top 10,000 | 83.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9968 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 39.3% of corpus
+- **Long Tail:** 501,837 words needed for remaining 16.6% 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 | 0.7834 | 0.3810 | N/A | N/A |
+| **mono_64d** | 64 | 0.7465 | 0.3030 | N/A | N/A |
+| **mono_128d** | 128 | 0.6690 | 0.2585 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7834 🏆 | 0.3788 | 0.3920 | 0.7480 |
+| **aligned_64d** | 64 | 0.7465 | 0.3134 | 0.6060 | 0.8580 |
+| **aligned_128d** | 128 | 0.6690 | 0.2626 | 0.6780 | 0.9340 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7834 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3162. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 67.8% 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 | **-0.603** | 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` | sansavi, sfocianti, santegidiese |
+| `-a` | astrolabes, applicheremo, ancia |
+| `-ma` | madonne, marclay, mastrantuono |
+| `-m` | mistruzzi, medioriente, madonne |
+| `-c` | camminerò, ceraio, coltellacci |
+| `-p` | primaibidem, pohliana, poschiavini |
+| `-b` | brozzo, berlette, bucherer |
+| `-t` | tigerdirect, takahito, tintry |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | medioriente, madonne, lasiocampidae |
+| `-o` | takahito, ceraio, brozzo |
+| `-a` | gialloviola, pohliana, dawa |
+| `-i` | mistruzzi, coltellacci, creatrici |
+| `-s` | astrolabes, glîrs, canids |
+| `-no` | mastrantuono, corrispondano, leprino |
+| `-n` | guédelon, esametilen, cupidon |
+| `-te` | medioriente, berlette, supercorazzate |
+
+### 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 |
+|------|----------|------------------|----------|
+| `rono` | 2.58x | 96 contexts | grono, crono, ronon |
+| `nche` | 1.63x | 202 contexts | anche, nchev, ponche |
+| `ogra` | 1.55x | 226 contexts | fogra, sogra, dogra |
+| `nter` | 1.48x | 287 contexts | enter, inter, nterr |
+| `lmen` | 1.98x | 62 contexts | almen, ulmen, ilmen |
+| `izza` | 1.47x | 191 contexts | vizza, mizza, nizza |
+| `stru` | 1.49x | 158 contexts | strub, strup, strum |
+| `ostr` | 1.37x | 174 contexts | costr, ostro, nostr |
+| `uest` | 1.64x | 65 contexts | quest, fuest, guest |
+| `ntro` | 1.50x | 94 contexts | antro, intro, entro |
+| `ontr` | 1.42x | 115 contexts | contr, contrò, hontra |
+| `ggio` | 1.33x | 157 contexts | iggio, aggio, eggio |
+
+### 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` | `-o` | 141 words | collalto, connettivo |
+| `-c` | `-e` | 139 words | cappelline, clorotiche |
+| `-s` | `-a` | 138 words | stäfa, serratissima |
+| `-s` | `-e` | 136 words | shilke, sommette |
+| `-a` | `-e` | 132 words | acquaforte, accreditabile |
+| `-s` | `-o` | 128 words | sperandiofrancesco, sfido |
+| `-c` | `-i` | 116 words | caseifici, consistenticittadini |
+| `-c` | `-a` | 114 words | cabarga, carapinheira |
+| `-a` | `-o` | 111 words | arcagato, ammandorlato |
+| `-a` | `-i` | 110 words | appaltatrici, angiulli |
+
+### 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 |
+|------|-----------------|------------|------|
+| iannacone | **`ianna-co-ne`** | 7.5 | `co` |
+| frantumata | **`frantum-a-ta`** | 7.5 | `a` |
+| strigosus | **`strigo-s-us`** | 7.5 | `s` |
+| roccatani | **`rocca-ta-ni`** | 7.5 | `ta` |
+| scoppiato | **`scoppi-a-to`** | 7.5 | `a` |
+| archedemo | **`arched-e-mo`** | 7.5 | `e` |
+| pontificem | **`pontific-e-m`** | 7.5 | `e` |
+| approvarono | **`approvar-o-no`** | 7.5 | `o` |
+| millières | **`milliè-re-s`** | 7.5 | `re` |
+| cercheremo | **`cercher-e-mo`** | 7.5 | `e` |
+| sintaxina | **`sintax-i-na`** | 7.5 | `i` |
+| ancorarono | **`ancora-ro-no`** | 7.5 | `ro` |
+| contesero | **`conte-se-ro`** | 7.5 | `se` |
+| wirelessman | **`wirelessm-a-n`** | 7.5 | `a` |
+| granadini | **`granad-i-ni`** | 7.5 | `i` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Italian 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 (4.82x) |
+| N-gram | **2-gram** | Lowest perplexity (214) |
+| Markov | **Context-4** | Highest predictability (92.6%) |
+| 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 · 2026-03-03 12:12:25*

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