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