Model Card for roberta-large-self-disclosure-sentence-classification
The model is used to classify whether a given sentence contains disclosure or not. It is a binary sentence-level classification where label 1 means containing self-disclosure, and 0 means not containing.
For more details, please read the paper: Reducing Privacy Risks in Online Self-Disclosures with Language Models .
Accessing this model implies automatic agreement to the following guidelines:
- Only use the model for research purposes.
- No redistribution without the author's agreement.
- Any derivative works created using this model must acknowledge the original author.
Model Description
- Model type: A finetuned sentence level classifier that classifies whether a given sentence contains disclosure or not.
- Language(s) (NLP): English
- License: Creative Commons Attribution-NonCommercial
- Finetuned from model: FacebookAI/roberta-large
Example Code
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
config = AutoConfig.from_pretrained("douy/roberta-large-self-disclosure-sentence-classification")
tokenizer = AutoTokenizer.from_pretrained("douy/roberta-large-self-disclosure-sentence-classification")
model = AutoModelForSequenceClassification.from_pretrained("douy/roberta-large-self-disclosure-sentence-classification",
config=config, device_map="cuda:0").eval()
sentences = [
"I am a 23-year-old who is currently going through the last leg of undergraduate school.",
"There is a joke in the design industry about that.",
"My husband and I live in US.",
"I was messing with advanced voice the other day and I was like, 'Oh, I can do this.'",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True).to(model.device)
with torch.no_grad():
logits = model(**inputs).logits
# predicted is the argmax of each row
predicted_class = logits.argmax(dim=-1)
# 1 means the sentence contains self-disclosure
# 0 means the sentence does not contain self-disclosure
# predicted_class: tensor([1, 0, 1, 0], device='cuda:0')
Evaluation
The model achieves 88.6% accuracy.
Citation
@article{dou2023reducing,
title={Reducing Privacy Risks in Online Self-Disclosures with Language Models},
author={Dou, Yao and Krsek, Isadora and Naous, Tarek and Kabra, Anubha and Das, Sauvik and Ritter, Alan and Xu, Wei},
journal={arXiv preprint arXiv:2311.09538},
year={2023}
}
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FacebookAI/roberta-large