metadata
language:
- en
license: mit
datasets:
- cardiffnlp/x_sensitive
metrics:
- f1
widget:
- text: Call me today to earn some money mofos!
pipeline_tag: text-classification
twitter-roberta-base-sensitive-binary
This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for detecting sensitive content (multilabel classification) on the X-Sensitive dataset. The original Twitter-based RoBERTa model can be found here.
Labels
"id2label": {
"0": "conflictual",
"1": "profanity",
"2": "sex",
"3": "drugs",
"4": "selfharm",
"5": "spam",
"6": "not-sensitive"
}
Full classification example
from transformers import pipeline
pipe = pipeline(model='cardiffnlp/twitter-roberta-large-sensitive-multilabel')
text = "Call me today to earn some money mofos!"
pipe(text)
Output:
[[{'label': 'conflictual', 'score': 0.03700090944766998},
{'label': 'profanity', 'score': 0.9770461916923523},
{'label': 'sex', 'score': 0.01981434039771557},
{'label': 'drugs', 'score': 0.017757439985871315},
{'label': 'selfharm', 'score': 0.008804548531770706},
{'label': 'spam', 'score': 0.07784222811460495},
{'label': 'not-sensitive', 'score': 0.010364986956119537}]]
BibTeX entry and citation info
TBA