182 datasets found

Word2Vec is a distributed word embedding generator that uses an artificial neural network to learn dense vector representations of words.

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

The dataset used in the paper is PubMed, ArXiv, and Movies. PubMed is a medical dataset consisting of research articles from the PubMed repository. The articles' subheadings...

The dataset used in this paper is a collection of news articles from Google News.

The dataset used in this paper is a collection of English Wikipedia abstracts from DBpedia.

The dataset used in this paper is a collection of documents from various domains, including news, articles, and emails.

The Penn Treebank dataset contains one million words of 1989 Wall Street Journal material annotated in Treebank II style, with 42k sentences of varying lengths.

The FDU-MTL dataset spans 16 domains: 14 Amazon review domains and two movie review domains. The textual content within this dataset remains in its pristine form, tokenized by...

The Amazon review dataset is used for multi-source domain adaptation. It contains review texts and ratings of bought products. Products are grouped into categories. Following...

The dataset used in this paper is Wikitext-103, a general English language corpus containing good and featured Wikipedia articles.

Two text classification data sets for evaluating the quality of interpretability methods.

The dataset used in this paper for unsupervised sentence representation learning, consisting of paragraphs from unlabeled text.

The Reuters RCV1-v2 contains 804,414 newswire articles. There are 103 topics which form a tree hierarchy. Thus documents typically have multiple labels. The data was randomly...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested the proposed method on three real data sets for the most relevant security...

The dataset used in the paper is the Penn Treebank dataset, which is a large-scale text classification dataset.

The MNIST-SVHN-Text dataset is a multi-modal dataset consisting of images, text, and labels.

The dataset used for sentiment analysis, question type classification, and subjectivity classification tasks.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large-scale captioned image dataset (LAION) used to train the Stable Diffusion model.

A dataset for training language models to perform tasks such as question answering and text classification.

Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have...