110 datasets found

The dataset used for testing the sLDA model [16].

The dataset used in this study for image and text classification tasks.

The dataset used in this paper is not explicitly described, but it is mentioned that it is a large language model (LLM) and that the authors used it to train and evaluate their...

The dataset used in the paper is the Multitask Language Understanding (MMLU) dataset, which consists of 57 tasks from Science, Technology, Engineering, and Math (STEM),...

The dataset is used for multilabel learning tasks. It contains 7395 documents, each labeled with 159 relevant tickers.

The dataset used in this paper is SST-2, Irony, IronyB, TREC6, and SNIPS.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a variety of datasets for semi-supervised learning tasks.

Two datasets used for multi-task learning, CIFAR-100 and AGNews.

The dataset used for Aspect-Based Sentiment Analysis (ABSA) experiments.

The dataset used for Aspect-Based Sentiment Analysis (ABSA) experiments.

The dataset used for Aspect-Based Sentiment Analysis (ABSA) experiments.

The dataset used for Aspect-Based Sentiment Analysis (ABSA) experiments.

The dataset is a combination of 3 singular datasets: A Million News Headlines, Fake and real news, Getting Real about Fake News.

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

A multilingual corpus with a collection of 487,000 news stories.

The dataset used in the paper is a concatenation of the original Harry Potter books and synthetic discussions, blog posts, and wiki-like entries about the books.

The dataset used in the paper for instruction tuning of Large Language Models (LLMs) during instruction tuning.

The dataset used in the paper for instruction tuning of Large Language Models (LLMs) during instruction tuning.

The dataset used in the paper is the Stanford Sentiment Treebank (SST) dataset, which contains standard train/dev/test sets and two subtasks: binary sentence classification or...

Deep learning-based text classification models need abundant labeled data to obtain competitive performance. To tackle this, multiple researches try to use data augmentation to...