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MR

MR Movie Reviews

TextsIntroduced 2004-01-01

MR Movie Reviews is a dataset for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity.

Source: http://www.cs.cornell.edu/people/pabo/movie-review-data/ Image Source: https://storage.googleapis.com/kaggle-competitions/kaggle/3810/media/treebank.png

Benchmarks

Classification/Accuracy Few-Shot Learning/Acc Meta-Learning/Acc Sentiment Analysis/Accuracy Sentiment Analysis/Training Time Text Classification/Accuracy

Related Benchmarks

MRNet/Multi-Label Classification/AUC on ACL Tear (ACL)MRNet/Multi-Label Classification/AUC on Abnormality (ABN)MRNet/Multi-Label Classification/AUC on Meniscus Tear (MEN)MRNet/Multi-Label Classification/Accuracy on ACL Tear (ACL)MRNet/Multi-Label Classification/Accuracy on Abnormality (ABN)MRNet/Multi-Label Classification/Accuracy on Meniscus Tear (MEN)MRNet/Multi-Label Classification/Average AUC MRNet/Multi-Label Classification/Average Accuracy MRPC/Few-Shot Learning/F1-score MRPC/Meta-Learning/F1-score MRPC/Natural Language Inference/Acc MRPC/Semantic Textual Similarity/Accuracy MRPC/Semantic Textual Similarity/F1 MRPC Dev/Semantic Textual Similarity/Accuracy MRQA/Question Answering/Average F1 MRQA out-of-domain/Question Answering/Average F1 MRR-Benchmark/MMR total/Total Column Score Mr. HiSum/Video/MAP (50%)Mr. HiSum/Video Summarization/MAP (50%)

Statistics

Papers: 28
Benchmarks: 6

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Tasks

Classification Few-Shot Learning Meta-Learning Sentiment Analysis Text Classification