|
--- |
|
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) |
|
``` |
