gabski/deberta-suboptimal-claim-detection-with-thesis-context

Model

This model was obtained by fine-tuning microsoft/deberta-base on the extended ClaimRev dataset.

Paper: To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support

Authors: Gabriella Skitalinskaya and Henning Wachsmuth

Suboptimal Claim Detection

We cast this task as a binary classification task, where the objective is, given an argumentative claim and some contextual information (in this case, the main thesis of the debate), to decide whether it is in need of further revision or can be considered to be phrased more or less optimally.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("gabski/deberta-suboptimal-claim-detection-with-thesis-context")
model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-suboptimal-claim-detection-with-thesis-context")
claim = 'Teachers are likely to educate children better than parents.'
thesis = 'Homeschooling should be banned.'
model_input = tokenizer(claim, thesis, return_tensors='pt')
model_outputs = model(**model_input)

outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1)
print(outputs)