gabski/deberta-claim-improvement-suggestion-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

Claim Improvement Suggestion

We cast this task as a multi-class classification task, where the objective is given an argumentative claim and some contextual information (in this case, the main thesis of the debate), select all types of quality issues from a defined set that should be improved when revising the claim.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("gabski/deberta-claim-improvement-suggestion-with-thesis-context")
model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-claim-improvement-suggestion-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)