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--- |
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language: en |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- ClaimRev |
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--- |
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# Model |
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This model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. |
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Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) |
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Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth |
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# Claim Quality Classification |
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We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. |
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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/bert-relative-claim-quality") |
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model = AutoModelForSequenceClassification.from_pretrained("gabski/bert-relative-claim-quality") |
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claim_1 = 'Smoking marijuana is less harmfull then smoking cigarettes.' |
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claim_2 = 'Smoking marijuana is less harmful than smoking cigarettes.' |
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model_input = tokenizer(claim_1,claim_2, 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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``` |
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