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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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context: "Homeschooling should be banned." |
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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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# Claim Improvement Suggestion |
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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 **main thesis** of the debate), select all types of quality issues from a defined set that should be improved when revising the claim. |
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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-claim-improvement-suggestion-with-thesis-context") |
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model = AutoModelForSequenceClassification.from_pretrained("gabski/deberta-claim-improvement-suggestion-with-thesis-context") |
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claim = 'Teachers are likely to educate children better than parents.' |
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thesis = 'Homeschooling should be banned.' |
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model_input = tokenizer(claim,thesis, 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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``` |
