Create README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+datasets: 
+  - ClaimRev
+widget:
+- text: "Teachers are likely to educate children better than parents."
+  context: "Homeschooling should be banned."
+---
+
+# 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")
+model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-suboptimal-claim-detection-with-thesis")
+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)
+```