metadata

language:
  - en
widget:
  - text: Call me today to earn some money mofos!
datasets:
  - cardiffnlp/x_sensitive
license: mit
metrics:
  - f1
pipeline_tag: text-classification

twitter-roberta-large-sensitive-binary

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for detecting sensitive content (binary classification) on the X-Sensitive dataset. The original Twitter-based RoBERTa model can be found here.

Labels

"id2label": {
    "0": "non-sensitive",
    "1": "sensitive"
  }

Full classification example

from transformers import pipeline
    
pipe = pipeline(model='cardiffnlp/twitter-roberta-large-sensitive-binary')
text = "Call me today to earn some money mofos!"

pipe(text)

Output:

[{'label': 'sensitive', 'score': 0.999821126461029}]

BibTeX entry and citation info

@article{antypas2024sensitive,
  title={Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation},
  author={Antypas, Dimosthenis and Sen, Indira and Perez-Almendros, Carla and Camacho-Collados, Jose and Barbieri, Francesco},
  journal={arXiv preprint arXiv:2411.19832},
  year={2024}
}