Text Classification
Transformers
PyTorch
English
roberta
humor-detection
humor-classification
joke-detection
humor-vs-non-humor
binary-classification
english
nlp
computational-humor
Instructions to use Humor-Research/humor-detection-comb-693 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Humor-Research/humor-detection-comb-693 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Humor-Research/humor-detection-comb-693")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Humor-Research/humor-detection-comb-693") model = AutoModelForSequenceClassification.from_pretrained("Humor-Research/humor-detection-comb-693") - Notebooks
- Google Colab
- Kaggle
File size: 2,252 Bytes
c245275 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | ---
language:
- en
library_name: transformers
pipeline_tag: text-classification
tags:
- humor-detection
- humor-classification
- joke-detection
- humor-vs-non-humor
- binary-classification
- text-classification
- english
- nlp
- computational-humor
- roberta
---
# English Humor Detection Model
This model is part of the **Humor Research** collection of models for **English humor detection**, **humor classification**, and **joke detection**. It can be used for binary text classification tasks such as identifying whether an English text is humorous or non-humorous.
## Model name
The model name indicates the dataset from the paper on which the model was trained. The numbers in the model name correspond to the random seed used for model initialization.
## Recommended model for humor detection
If you need a single recommended model for English humor vs. non-humor classification, please refer to the best model from the project:
[Humor-Research/humor-detection-comb-23](https://huggingface.co/Humor-Research/humor-detection-comb-23)
## Paper
This model was released as part of the study:
[You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models](https://aclanthology.org/2023.emnlp-main.845/)
## GitHub repository
Code, data processing tools, and additional project information are available here:
[Humor-Research/Humor-detection](https://github.com/Humor-Research/Humor-detection)
## Citation
If you use this model, please cite the following paper:
```bibtex
@inproceedings{baranov-etal-2023-told,
title = "You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models",
author = "Baranov, Alexander and
Kniazhevsky, Vladimir and
Braslavski, Pavel",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.845",
doi = "10.18653/v1/2023.emnlp-main.845",
pages = "13701--13715",
}
``` |