Context **`Stanford Sentiment Treebank V1.0`** `Live Demo :` http://nlp.stanford.edu:8080/sentiment/rntnDemo.html  This is the dataset of the paper: **Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank** Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng and Christopher Potts Conference on Empirical Methods in Natural Language Processing (EMNLP 2013) Content 11,855 sentences from movie reviews Parses generated using Stanford parser Treebank generated from parses 215,154 unique phrases Phrases annotated by Mechanical Turk for sentiment What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements If you use this dataset in your research, please cite the above paper. > @incollection{SocherEtAl2013:RNTN, > title = {{Parsing With Compositional Vector Grammars}}, > author = {Richard Socher and Alex Perelygin and Jean Wu and Jason Chuang and Christopher Manning and Andrew Ng and Christopher Potts}, > booktitle = {{EMNLP}}, > year = {2013} > } `Additional Source of Dataset`: https://github.com/clairett/pytorch-sentiment-classification/tree/master/data/SST2 Inspiration Transformers have been a flashy topic in AI world, good enough to bring anyone's attention. People want to explore about these models and may be they end up with some MAGIC better than these models. Keeping this in mind, I have uploaded this dataset here, to ease people understand the data, Read the research paper, included. and; try their own approaches on getting a kick from my starter kernel.