https://github.com/orionw/reddithumordetection

Code and datasets for the paper "Humor Detection: A Transformer Gets the Last Laugh"
https://github.com/orionw/reddithumordetection

Last synced: about 1 month ago
JSON representation

Code and datasets for the paper "Humor Detection: A Transformer Gets the Last Laugh"

• Host: GitHub
• URL: https://github.com/orionw/reddithumordetection
• Owner: orionw
• License: mit
• Created: 2019-08-28T19:20:46.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T22:39:20.000Z (over 2 years ago)
• Last Synced: 2023-03-02T22:46:26.201Z (almost 2 years ago)
• Language: Python
• Size: 35.4 MB
• Stars: 64
• Watchers: 3
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
# Humor Detection

## Code and Datasets for the Paper ["Humor Detection: A Transformer Gets the Last Laugh"](https://arxiv.org/abs/1909.00252) by Orion Weller and Kevin Seppi

The repository contains the following:

 - A way to regenerate the results found in the paper, by running `bash run_bert.sh`.  

 - The full datasets referenced in the paper (short jokes, puns, and the reddit dataset) are located in `full_datasets` whereas the `data` folder contains the split files used for training and testing.  The file `create_data.sh` will create the splits (slightly different from the ones used in the paper - see `create_data.sh`).

- pytorch_pretrained_bert contains files used by the model - these files are from the [huggingface repo](https://github.com/huggingface/pytorch-transformers#Training-large-models-introduction,-tools-and-examples) and are NOT up to date with the current `pytorch-transformers` repo.  

**This repository is not maintained and will not be updated.**

## Reference:

If you found this repository helpful, please consider citing the following:

```

@ARTICLE{humorDetection2019,

  title={Humor Detection: A Transformer gets the Last Laugh},

  author={Weller, Orion and Seppi, Kevin},

  journal={"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing"},

  month=Nov,

  year = "2019",

}

```