Omkar2118/datasets huggingface dataset:
LeQua2024
Full name: Learning to Quantify Dataset 2024

This is the dataset used in the [2nd data challenge on Learning to Quantify](https://lequa2024.github.io/). 
It is designed for the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) are required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set.

The dataset has data for four different tasks:

* Task T1: This task is concerned with evaluating binary quantifiers, i.e., quantifiers that must only predict the relative frequencies of a class and its complement; the data used are affected by prior probability shift (a.k.a. “label shift”). This task is akin to Task T1A of LeQua 2022.

* Task T2: This task is concerned with evaluating single-label multi-class quantifiers, i.e., quantifiers that operate on datapoints each belonging to exactly one among a set of n>2 classes; here too, the data used are affected by prior probability shift. This task is akin to Task T1B of LeQua 2022.

* Task T3: This task is concerned with evaluating ordinal quantifiers, i.e., quantifiers that operate on a set of n>2 totally ordered classes; here too, the data used are affected by prior probability shift. This task is new to LeQua 2024.
* Task T4: Like Task T1, this task is concerned with evaluating binary quantifiers; unlike in Task T1, the data used are affected by covariate shift. This task is new to LeQua 2024.

[Challenge website](https://lequa2024.github.io/)

[Info on data and formats](https://github.com/HLT-ISTI/LeQua2024_scripts)
LeQua2022
Full name: Learning to Quantify Dataset 2024

This is the dataset used in the [1st data challenge on Learning to Quantify](https://lequa2022.github.io/). 
It is designed for the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) are required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set.

The dataset has data for four different tasks:

 * T1A: This task is concerned with evaluating binary quantifiers, i.e., quantifiers that must only predict the relative frequencies of a class and its complement. Participants in this task will be provided with documents already converted into vector form; the task is thus suitable for participants who do not wish to engage in generating representations for the textual documents, but want instead to concentrate on optimizing the methods for learning to quantify.

* T1B: This task is concerned with evaluating single-label multi-class quantifiers, i.e., quantifiers that operate on documents that each belong to exactly one among a set of n>2 classes. Like in Task T1A, participants will be provided with documents already converted in vector form.

* T2A: Like Task T1A, this task is concerned with evaluating binary quantifiers. Unlike in Task T1A, participants will be provided with the raw text of the documents; the task is thus suitable for participants who also wish to engage in generating suitable representations for the textual documents, or to train end-to-end systems.

* T2B: Like Task T1B, this task is concerned with evaluating single-label multi-class quantifiers; like in Task T2A, participants will be provided with the raw text of the documents.

[Challenge website](https://lequa2022.github.io/)

[Info on data and formats](https://github.com/HLT-ISTI/LeQua2022_scripts)

Introducing paper: LeQua@CLEF2022: Learning to Quantify