gabski/deberta-claim-improvement-suggestion

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, 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")
model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-claim-improvement-suggestion")
claim = 'Teachers are likely to educate children better than parents.'
model_input = tokenizer(claim, return_tensors='pt')
model_outputs = model(**model_input)

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