File size: 1,165 Bytes
ce2db7d cae583f ce2db7d cae583f 3ebd0ae ce2db7d 5b81ab3 7fc0e86 5b81ab3 c303448 5b81ab3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
---
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)
```
|