readme: ---
  language: en
  license: cc-by-nc-sa-4.0
  datasets: 
    - ClaimRev
  ---
  
  # Model
  This model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset.
  
  Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/)
  Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth
  
  # Claim Quality Classification
  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. 
  
  # Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  tokenizer = AutoTokenizer.from_pretrained("gabski/bert-relative-claim-quality")
  model = AutoModelForSequenceClassification.from_pretrained("gabski/bert-relative-claim-quality")
  claim_1 = 'Smoking marijuana is less harmfull then smoking cigarettes.'
  claim_2 = 'Smoking marijuana is less harmful than smoking cigarettes.'
  model_input = tokenizer(claim_1,claim_2, return_tensors='pt')
  model_outputs = model(**model_input)
  
  outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1)
  print(outputs)
  ```
  
  