13 datasets found

PG-19 is a well-established benchmark for long-form language modeling.

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 this paper is a collection of regular expressions and grammars for constraining language models.

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...

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

The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used the Wikitext-2 dataset for text generation tasks.

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 for pre-training language models, containing a large collection of text documents.

The Chinese Poetry dataset is a dataset of Chinese poems used for language modeling.

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 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 Penn Treebank (PTB) dataset is used for word ordering task. The dataset is used to evaluate the performance of different models for word ordering.