imdb_reviews
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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.
| Split |
Examples |
'test' |
25,000 |
'train' |
25,000 |
'unsupervised' |
50,000 |
FeaturesDict({
'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
'text': Text(shape=(), dtype=string),
})
| Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
| label |
ClassLabel |
|
int64 |
|
| text |
Text |
|
string |
|
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
imdb_reviews/plain_text (default config)
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Last updated 2024-09-20 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-20 UTC."],[],[],null,["# imdb_reviews\n\n\u003cbr /\u003e\n\n- **Description**:\n\nLarge Movie Review Dataset. This is a dataset for binary sentiment\nclassification containing substantially more data than previous benchmark\ndatasets. We provide a set of 25,000 highly polar movie reviews for training,\nand 25,000 for testing. There is additional unlabeled data for use as well.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/imdb-movie-reviews)\n\n- **Config description**: Plain text\n\n- **Homepage** :\n [http://ai.stanford.edu/\\~amaas/data/sentiment/](http://ai.stanford.edu/%7Eamaas/data/sentiment/)\n\n- **Source code** :\n [`tfds.datasets.imdb_reviews.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/imdb_reviews/imdb_reviews_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): New split API (\u003chttps://tensorflow.org/datasets/splits\u003e)\n- **Download size** : `80.23 MiB`\n\n- **Dataset size** : `129.83 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|------------------|----------|\n| `'test'` | 25,000 |\n| `'train'` | 25,000 |\n| `'unsupervised'` | 50,000 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n 'text': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|---------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| label | ClassLabel | | int64 | |\n| text | Text | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('text', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @InProceedings{maas-EtAl:2011:ACL-HLT2011,\n author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},\n title = {Learning Word Vectors for Sentiment Analysis},\n booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n month = {June},\n year = {2011},\n address = {Portland, Oregon, USA},\n publisher = {Association for Computational Linguistics},\n pages = {142--150},\n url = {http://www.aclweb.org/anthology/P11-1015}\n }\n\nimdb_reviews/plain_text (default config)\n----------------------------------------"]]
