---
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)
```