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---
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-base model trained on 154M tweets until the end of December 2022 and finetuned for detecting sensitive content (multilabel classification) on the [_X-Sensitive_](https://huggingface.co/datasets/cardiffnlp/x_sensitive) dataset.
The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m).
## Labels
```
"id2label": {
"0": "conflictual",
"1": "profanity",
"2": "sex",
"3": "drugs",
"4": "selfharm",
"5": "spam"
"6": "not-sensitive"
}
```
## Full classification example
```python
from transformers import pipeline
pipe = pipeline(model='cardiffnlp/twitter-roberta-base-sensitive-multilabel')
text = "Call me today to earn some money mofos!"
pipe(text)
```
Output:
```
[[{'label': 'conflictual', 'score': 0.07463070750236511},
{'label': 'profanity', 'score': 0.9888035655021667},
{'label': 'sex', 'score': 0.0032050721347332},
{'label': 'drugs', 'score': 0.004522938746958971},
{'label': 'selfharm', 'score': 0.0036733713932335377},
{'label': 'spam', 'score': 0.007278479170054197},
{'label': 'not-sensitive', 'score': 0.00972921121865511}]]
```
## BibTeX entry and citation info
TBA |