{
 "id": "fancyzhx/yelp_polarity",
 "owner": "fancyzhx",
 "name": "yelp_polarity",
 "downloads": 16171,
 "likes": 23,
 "last_modified": "2024-08-08T05:55:49.000Z",
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 "stated_from": "none",
 "licence_name": null,
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 "file_licence": null,
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 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@article{zhangCharacterlevelConvolutionalNetworks2015,\n  archivePrefix = {arXiv},\n  eprinttype = {arxiv},\n  eprint = {1509.01626},\n  primaryClass = {cs},\n  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},\n  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},\n  journal = {arXiv:1509.01626 [cs]},\n  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},\n  month = sep,\n  year = {2015},\n}",
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