
# 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

# Suboptimal Claim Detection
We cast this task as a binary classification task, where the objective is, given an argumentative claim and some contextual information (in this case, the **main thesis** of the debate), to decide whether it is in need of further revision or can be considered to be phrased more or less optimally.

# Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("gabski/deberta-suboptimal-claim-detection-with-thesis-context")
model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-suboptimal-claim-detection-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)
```
{"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.", "context": "Homeschooling should be banned."}]}
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

# Suboptimal Claim Detection
We cast this task as a binary classification task, where the objective is, given an argumentative claim and some contextual information (in this case, the main thesis of the debate), to decide whether it is in need of further revision or can be considered to be phrased more or less optimally.

# 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 # Suboptimal Claim Detection
We cast this task as a binary classification task, where the objective is, given an argumentative claim and some contextual information (in this case, the main thesis of the debate), to decide whether it is in need of further revision or can be considered to be phrased more or less optimally. # 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 # Suboptimal Claim Detection
We cast this task as a binary classification task, where the objective is, given an argumentative claim and some contextual information (in this case, the main thesis of the debate), to decide whether it is in need of further revision or can be considered to be phrased more or less optimally. # Usage