Learning to quantify: LeQua 2024 dataset
Datasets of the LeQua 2024 Learning to Quantify Data Challenge
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Datasets of the LeQua 2024 Learning to Quantify Data Challenge
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naïvely” classifying each document via it...
The Interactive Classification System (ICS), is a web-based application that supports the activity of manual text classification, i.e., labeling documents according to their content.
This data set comprises a labelled training set used in the experimentation of the paper "Binary Quantification and Dataset Shift: An Experimental Investigation".
Word-Class Embeddings (WCEs) are a form of supervised embeddings specially suited for multiclass text classification. WCEs are meant to be used as extensions (i.e., by concatenation) to pre-trained embeddings (e.g., GloVe or word2vec) embeddings in order ...
ql4facct is a software for replicating experiments concerning the evaluation of estimators of classifier "fairness".
This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "Ordinal Quantification Through Regularization", which we have published at ECML-PKDD 2022.
The aim of the LeQua 2022 dataset is to allow 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 ...
This record consists of several scripts and files designed to calculate and visualize the efficiency of a burner, as well as predict the burner power using a neural network model. This model consists of a sequential neural network with multiple layers. Bo...