114 datasets found

Movie Review (MR) dataset is a binary sentiment classification dataset with movie reviews from IMDB, consisting of 1000 positive and 1000 negative movie reviews. Product Review...

The CNN news articles dataset is a collection of news articles crawled from the CNN website.

Stanford Sentiment Treebank Binary (SST-2) and Internet Movie Database (IMDb) datasets for sentiment classification.

The dataset used in the paper is a collection of text samples from the Offensive Language Identification Dataset (OLID).

The benchmark ride-sharing comment user experience data set was constructed from the real comments in the main city zone of ride-sharing orders within the time period from Mar...

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

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

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

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

The AdQA dataset is a question answering dataset.

The RTE dataset is a reading comprehension dataset.

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.