metadata
license: mit
pipeline_tag: text-classification
inference: false
Official ICC model
The official checkpoint of ICC model, introduced in ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation
Usage
The ICC model is used to quantify the concreteness of image captions (and sentences in general).
Running the model
Click to expand
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("moranyanuka/icc")
model = AutoModelForSequenceClassification.from_pretrained("moranyanuka/icc").to("cuda")
captions = ["a great method of quantifying concreteness", "a man with a white shirt"]
text_ids = tokenizer(captions, padding=True, return_tensors="pt", truncation=True).to('cuda')
with torch.inference_mode():
icc_scores = model(**text_ids)['logits']
# tensor([[0.0339], [1.0068]])
bibtex:
@misc{yanuka2024icc,
title={ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation},
author={Moran Yanuka and Morris Alper and Hadar Averbuch-Elor and Raja Giryes},
year={2024},
eprint={2403.01306},
archivePrefix={arXiv},
primaryClass={cs.LG}
}