3 datasets found
  1. T

    imdb_reviews

    • tensorflow.org
    Updated Sep 20, 2024
    + more versions
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    (2024). imdb_reviews [Dataset]. https://www.tensorflow.org/datasets/catalog/imdb_reviews
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    Dataset updated
    Sep 20, 2024
    Description

    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.

    To use this dataset:

    import tensorflow_datasets as tfds
    
    ds = tfds.load('imdb_reviews', split='train')
    for ex in ds.take(4):
     print(ex)
    

    See the guide for more informations on tensorflow_datasets.

  2. P

    MovieLens Dataset

    • paperswithcode.com
    Updated Feb 7, 2021
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    F. Maxwell Harper; Joseph A. Konstan (2021). MovieLens Dataset [Dataset]. https://paperswithcode.com/dataset/movielens
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    Dataset updated
    Feb 7, 2021
    Authors
    F. Maxwell Harper; Joseph A. Konstan
    Description

    The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations.

  3. Biggest Netflix libraries in the world 2024

    • statista.com
    Updated Oct 21, 2024
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    Statista (2024). Biggest Netflix libraries in the world 2024 [Dataset]. https://www.statista.com/statistics/1013571/netflix-library-size-worldwide/
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    Dataset updated
    Oct 21, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jul 2024
    Area covered
    World
    Description

    Industry data revealed that Slovakia had the most extensive Netflix media library worldwide as of July 2024, with over 8,500 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2024, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 21.19 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 6,500 and 6,900 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs at around 2.90 to 4.65 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of mid-2024, "Red Notice" and "Don't Look Up" were the most popular English-language movies on Netflix, with over 230 million views in its first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

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Share
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Click to copy link
Link copied
Close
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(2024). imdb_reviews [Dataset]. https://www.tensorflow.org/datasets/catalog/imdb_reviews

imdb_reviews

Explore at:
33 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 20, 2024
Description

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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

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