---
language:
- it
dataset_info:
  features:
  - name: volume
    dtype: string
  - name: canto
    dtype: string
  - name: tercet
    dtype: int64
  - name: verse_number
    dtype: int64
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1182296
    num_examples: 14233
  download_size: 458870
  dataset_size: 1182296
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Divina Commedia Dataset

## Overview

The **Divina Commedia** (Divine Comedy) is an epic poem by Dante Alighieri, widely considered one of the greatest works of world literature. This dataset contains the text of the poem, organized into volumes, cantos, and verses. It is suitable for various natural language processing (NLP) tasks, such as text analysis, machine learning, and linguistic research.

## Dataset Structure

The dataset is structured in a hierarchical format, with the following attributes:

- **volume**: The name of the volume (e.g., Inferno, Purgatorio, Paradiso).
- **canto**: The name of the canto (e.g., Canto I).
- **tercet**: The number of the tercet (a group of three verses).
- **verse_number**: The number of the verse within the tercet.
- **text**: The actual text of the verse.
- **text_length**: The length of the verse text in characters.

### Example Entry

| volume   | canto     | tercet  | verse_number | text                                    
|----------|-----------|---------|--------------|-----------------------------------------
| Inferno  | Canto I   | 1       | 1            | Nel mezzo del cammin di nostra vita     
| Inferno  | Canto I   | 1       | 2            | mi ritrovai per una selva oscura,       
| Inferno  | Canto I   | 1       | 3            | ché la diritta via era smarrita.        

## Usage

To load the dataset using the `datasets` library from Hugging Face, you can use the following code:

```python
from datasets import load_dataset

dataset = load_dataset("maiurilorenzo/divina-commedia")

# Display the first few entries
print(dataset["train"].to_pandas().head())