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---
license: cc-by-nc-4.0
datasets:
- APauli/Persuasive-Pairs
language:
- en
pipeline_tag: sentence-similarity
---
# Model to score relative persuasive language between pairs
More info about training, evaluation, and use in paper is here: https://arxiv.org/abs/2406.17753
Python:
```python
from transformers import AutoModelForSequenceClassification,AutoTokenizer
import torch
modelname='APauli/Persuasive_language_in_pairs'
model = AutoModelForSequenceClassification.from_pretrained(modelname)
tokenizer = AutoTokenizer.from_pretrained(modelname)
def predict(textA, textB, model,tokenizer):
encoded_input = tokenizer(textA, textB, padding=True, truncation=True,max_length=256, return_tensors="pt")
with torch.no_grad():
logits = model(**encoded_input).logits
score1=logits.detach().cpu().numpy()
#flipped
encoded_input = tokenizer(textB, textA, padding=True, truncation=True,max_length=256, return_tensors="pt")
with torch.no_grad():
logits = model(**encoded_input).logits
score2=logits.detach().cpu().numpy()*(-1)
score = (score1+score2)/2
return score
```
## Citation
If you find our dataset helpful, kindly refer to us in your work using the following citation:
```
@misc{pauli2024measuringbenchmarkinglargelanguage,
title={Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language},
author={Amalie Brogaard Pauli and Isabelle Augenstein and Ira Assent},
year={2024},
eprint={2406.17753},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.17753},
}
``` |