Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. An example of 'train' looks as follows. The data fields are the same among all splits. - text: a string feature. - label: a classification label, with possible values including neg (0), pos (1). Thanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding this dataset.
Publicly accessible
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10K<n<100K
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the task of determining the inference relation between two (short, ordered) texts: entailment, contradiction, or neutral (MacCartney and Manning 2008). See the corpus webpage for a list of published results. The language in the dataset is English as spoken by users of the website Flickr and as spoken by…
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cc-by-sa-4.0
100K<n<1M
