
# 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, 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")
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)
```
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "datasets": ["ClaimRev"], "pipeline_tag": "text-classification", "widget": [{"text": "Teachers are likely to educate children better than parents."}]}
transformers pytorch deberta text-classification en dataset:ClaimRev arxiv:2305.16799 license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta #text-classification #en #dataset-ClaimRev #arxiv-2305.16799 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# 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


# 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
TAGS
#transformers #pytorch #deberta #text-classification #en #dataset-ClaimRev #arxiv-2305.16799 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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