103 datasets found

The Clickbait Challenge 2017 dataset, a collection of social media posts and their corresponding article titles, used for clickbait detection.

The FNC-1 dataset is a supervised classification task for stance detection, where the goal is to automatically predict the labels in a supervised classification task.

Text categorization, a document may be associated with a range of topics, such as science, entertainment, and news.

Semeval-2016 Task 6: Detecting stance in tweets.

The Out Of Scope dataset (Larson et al., 2019) is an intent detection dataset containing 150 equally-distributed classes.

The Rotten Tomatoes dataset has 5331 positive and 5331 negative review sentences.

The SST2 dataset has 6920/872/1821 example sentences in the train/dev/test sets. The task is binary classification into positive/negative sentiment. The IMDB dataset has...

The dataset used for toxicity and stereotype mitigation task, which consists of 25 thousand examples of positive and negative movie reviews.

The dataset used in the paper is a collection of unstructured text data from social networks, news sites, and forums.

The Microsoft Academic Graph (MAG) dataset is used to construct Maple, a multi-field benchmark for evaluating scientific literature tagging.

The Fake News Detection dataset contains text documents with fake news labels.

The Spam Detection dataset contains text documents with spam labels.

The Polarity dataset contains text documents with sentiment labels.

The IMDb review dataset is used for positive generation task.

The dataset used in the LightDXML paper for extreme multi-label classification.

The Pile dataset is a large-scale dataset containing 800GB of text data.

The LM-Extraction benchmark is derived from The Pile (Gao et al., 2020) dataset, which contains 15,000 pairs of prefixes and suffixes derived from The Pile dataset (Gao et al.,...

The dataset used in the paper is the TREC05 spam corpus, which contains 39,999 real ham and 52,790 spam emails.

The dataset consists of 108 news headlines, 72 of which are true and 36 of which are false.

The gisette dataset is a collection of 20,000 text documents, each containing a single sentence.