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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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datasets: |
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- ClaimRev |
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widget: |
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- text: "Teachers are likely to educate children better than parents." |
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--- |
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# Model |
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This model was obtained by fine-tuning `microsoft/deberta-base` on the extended ClaimRev dataset. |
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Paper: [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://arxiv.org/abs/2305.16799) |
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Authors: Gabriella Skitalinskaya and Henning Wachsmuth |
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# Suboptimal Claim Detection |
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We cast this task as a binary classification task, where the objective is, given an argumentative claim, to decide whether it is in need of further revision or can be considered to be phrased more or less optimally. |
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# Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("gabski/deberta-suboptimal-claim-detection") |
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model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-suboptimal-claim-detection") |
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claim = 'Teachers are likely to educate children better than parents.' |
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model_input = tokenizer(claim, return_tensors='pt') |
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model_outputs = model(**model_input) |
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outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1) |
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print(outputs) |
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``` |
