21 datasets found

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

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...

The dataset used in the paper to evaluate attribution scores.

The Penn Tree Bank dataset is a corpus split into a training, validation and testing set of 929k words, a validation set of 73k words, and a test set of 82k words. The...

The Quora Question Pairs (QQP) dataset consists of 50,000 question pairs labeled with paraphrase or non-paraphrase.

Bilingual word embeddings from parallel and non-parallel corpora for cross-language text classification

The authors evaluate the performance of their proposed conformal prediction methods for multistep feedback covariate shift (MFCS) on synthetic black-box optimization and active...

The TEL-NLP dataset is a collection of Telugu text data for four NLP tasks: sentiment analysis, emotion identification, hate speech detection, and sarcasm detection.

The dataset is used to evaluate the performance of the xLSTM architecture on various tasks, including language modeling, question answering, and text classification.

The dataset used in this paper is a pre-trained BERT model trained on English Wikipedia and Books datasets.

The dataset used in the paper is a multiple-choice reading comprehension dataset, which includes a passage, question, and answer. The passage is a script, and the question is a...

The Natural Questions dataset consists of questions extracted from web queries, with each question accompanied by a corresponding Wikipedia article containing the answer.

The TriviaQA dataset is a collection of questions sourced from Quiz League websites, with sentence-level supporting facts annotation.

The dataset used for the experiments across ten models– ranging from bag-of-words models to pre-trained transformers– and find that a model having higher AUC does not necessarily...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10 and CIFAR-100 datasets for image classification, and ImageNet-100...

The dataset used in the paper is a language domain dataset, specifically for sentiment classification, named AG News. The dataset is used to evaluate the performance of...

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

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 dataset used in the paper is the Stanford Natural Language Inference (SNLI) dataset, which consists of 549,367 premise-hypothesis pairs for train/dev/test sets and target...