---
language:
- it
license: cc-by-nc-sa-4.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: prompt
    dtype: string
  - name: target
    dtype: string
  - name: class
    dtype:
      class_label:
        names:
          '0': not_follow
          '1': follow
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 9263673
    num_examples: 16000
  - name: test
    num_bytes: 932051
    num_examples: 1600
  download_size: 6727616
  dataset_size: 10195724
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# DisCoTEX - Last Sentence Classification

**Disclaimer: This dataset is not the official DisCoTEX repository from EVALITA. For the official repository and more information, please visit the [EVALITA DiscoTEX page](https://sites.google.com/view/discotex/home) or the [DiscoTEX repository](https://github.com/davidecolla/DisCoTex).**

## Overview

The DisCoTEX dataset is part of the DisCoTEX shared task at EVALITA 2023, aimed at assessing discourse coherence in Italian texts. This dataset focuses on Italian real-world texts and provides resources to model coherence in natural language. The dataset comprises texts from two distinct sources: Italian Wikipedia and the Multilingual TEDx corpus (mTEDx), representing different language varieties and genres.

## Dataset Description

### Structure:
The dataset consists of text passages containing four consecutive sentences, used as the unit of analysis. For Wikipedia, paragraphs were used to select four-sentence segments. For TEDx, transcripts were automatically split into four-sentence passages. The dataset is balanced between `follow` and `not_follow` classes; in the `not_follow` class, 20% of the targets are sentences from a different document, and 80% are the 10th sentence after the prompt in the same document.

### Format: 
The dataset is composed of:
  - `id`: Identifier for the entry.
  - `prompt`: A snippet of text (3 sentences).
  - `target`: The sentence to assess for coherence with the `prompt`.
  - `class`: `follow` if  target is the last sentence of the passage `not_follow` otherwise
  - `source`: Source of the sample, either `mTEDx` or `wikipedia`