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--- |
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language: |
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- en |
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license: mit |
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datasets: |
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- cardiffnlp/x_sensitive |
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metrics: |
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- f1 |
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widget: |
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- text: Call me today to earn some money mofos! |
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pipeline_tag: text-classification |
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--- |
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# twitter-roberta-base-sensitive-binary |
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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. |
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The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m). |
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## Labels |
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``` |
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"id2label": { |
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"0": "conflictual", |
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"1": "profanity", |
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"2": "sex", |
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"3": "drugs", |
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"4": "selfharm", |
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"5": "spam" |
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"6": "not-sensitive" |
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} |
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``` |
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## Full classification example |
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```python |
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from transformers import pipeline |
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pipe = pipeline(model='cardiffnlp/twitter-roberta-base-sensitive-multilabel') |
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text = "Call me today to earn some money mofos!" |
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pipe(text) |
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``` |
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Output: |
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``` |
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[[{'label': 'conflictual', 'score': 0.07463070750236511}, |
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{'label': 'profanity', 'score': 0.9888035655021667}, |
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{'label': 'sex', 'score': 0.0032050721347332}, |
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{'label': 'drugs', 'score': 0.004522938746958971}, |
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{'label': 'selfharm', 'score': 0.0036733713932335377}, |
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{'label': 'spam', 'score': 0.007278479170054197}, |
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{'label': 'not-sensitive', 'score': 0.00972921121865511}]] |
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``` |
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## BibTeX entry and citation info |
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``` |
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@article{antypas2024sensitive, |
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title={Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation}, |
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author={Antypas, Dimosthenis and Sen, Indira and Perez-Almendros, Carla and Camacho-Collados, Jose and Barbieri, Francesco}, |
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journal={arXiv preprint arXiv:2411.19832}, |
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year={2024} |
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} |
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``` |
