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---
license: cc-by-nc-sa-4.0
language:
- en
library_name: transformers
pipeline_tag: text-classification
datasets:
- ClaimRev
---
# 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](https://arxiv.org/abs/2305.16799)
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 **parent claim** in the debate, which is opposed or supported by the claim in question), select all types of quality issues from a defined set that should be improved when revising the claim.
# Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("gabski/deberta-claim-improvement-suggestion-with-parent-context")
model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-claim-improvement-suggestion-with-parent-context")
claim = 'Teachers are likely to educate children better than parents.'
parent_claim = 'Homeschooling should be banned.'
model_input = tokenizer(claim,parent_claim, return_tensors='pt')
model_outputs = model(**model_input)
outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1)
print(outputs)
``` |