---
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*