Model Card for appropriateness-classifier-binary
This model classifies an argument as appropriate (0) or inappropriate (1). For further details on (in)appropriateness, we refer to the paper below and the corpora used for training.
For a more fine-grained reasoning towards inappropriateness, we refer to our multilabel model found here
Model Details
Model Sources
- Repository: [https://github.com/timonziegenbein/appropriateness-corpus]
- Paper [optional]: Modeling Appropriate Language in Argumentation
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import pipeline
pipe = pipeline("text-classification", model="timonziegenbein/appropriateness-classifier-binary", device='cuda:0')
argument = ''''Towed three times and impounded for 30 days each time? Man, you're just not getting the message, are you? If you are in California, you bet the police can forfeit your vehicle and it doesn't take three times to make it a charm. Technically, your vehicle could be subject to forfeiture proceedings after your first suspended license beef. Someone like you is exactly the reason the legislature designed that law, because your privilege to drive has been taken away from you and yet you obviously continue to drive. People like you are involved in an exponentially higher than average number of traffic accidents so the legislature figured maybe people like you should have your vehicles forfeited to the state if you just didn't go along with the game plan. Voila - I give you California Vehicle Code section 14607.6...and a link to it below. It would also be worth your time to review 14607.4, whether or not you live in California. You really need to stop driving. Really.'''
out = pipe(argument)
print(out)
# [{'label': 'LABEL_1', 'score': 0.9970095157623291}] = Inappropriate
Citation
If you are interested in using the corpus, please cite the following paper: Modeling Appropriate Language in Argumentation (Ziegenbein et al., ACL 2023)