reddit_disentanglement

• Description:

This dataset contains ~3M messages from reddit. Every message is labeled with metadata. The task is to predict the id of its parent message in the corresponding thread. Each record contains a list of messages from one thread. Duplicated and broken records are removed from the dataset.

Features are:

• id - message id
• text - message text
• author - message author
• created_utc - message UTC timestamp
• link_id - id of the post that the comment relates to

Target:

• parent_id - id of the parent message in the current thread

• Homepage: https://github.com/henghuiz/MaskedHierarchicalTransformer

• Source code: tfds.datasets.reddit_disentanglement.Builder

• Versions:

  • 2.0.0 (default): No release notes.

• Download size: Unknown size

• Dataset size: Unknown size

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  Download https://github.com/henghuiz/MaskedHierarchicalTransformer, decompress raw_data.zip and run generate_dataset.py with your reddit api credentials. Then put train.csv, val.csv and test.csv from the output directory into the manual folder.

• Auto-cached (documentation): Unknown

• Splits:

• Feature structure:

FeaturesDict({
    'thread': Sequence({
        'author': Text(shape=(), dtype=string),
        'created_utc': Text(shape=(), dtype=string),
        'id': Text(shape=(), dtype=string),
        'link_id': Text(shape=(), dtype=string),
        'parent_id': Text(shape=(), dtype=string),
        'text': Text(shape=(), dtype=string),
    }),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe): Missing.

• Citation:

@article{zhu2019did,
  title={Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer},
  author={Zhu, Henghui and Nan, Feng and Wang, Zhiguo and Nallapati, Ramesh and Xiang, Bing},
  journal={arXiv preprint arXiv:1911.10666},
  year={2019}
}