Model Card for Model ID
This is the checkpoint for the model from the paper V-FLUTE: Visual Figurative Language Understanding with Textual Explanations. Specifically, it is the best performing fine-tuned model on a combination of V-FLUTE and e-ViL (e-SNLI-VE) datasets with early stopping based on the V-FLUTE validation set.
Model Details
Model Description
See more on LLaVA 1.5 here: https://github.com/haotian-liu/LLaVA V-FLUTE dataset: https://huggingface.co/datasets/ColumbiaNLP/V-FLUTE V-FLUTE paper: https://arxiv.org/abs/2405.01474 Citation:
@misc{saakyan2024vflute,
title={V-FLUTE: Visual Figurative Language Understanding with Textual Explanations},
author={Arkadiy Saakyan and Shreyas Kulkarni and Tuhin Chakrabarty and Smaranda Muresan},
year={2024},
eprint={2405.01474},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Arkadiy Saakyan (ColumbiaNLP)
- Model type: Vision-Language Model
- Language(s) (NLP): English
- Finetuned from model [optional]: LLaVA-v1.5
Model Sources [optional]
- Repository: https://github.com/asaakyan/V-FLUTE
- Paper [optional]: https://arxiv.org/abs/2405.01474
Uses
The model's intended use is limited to interpreting multimodal figurative inputs such as metaphors, similes, idioms, sarcasm, and humor.
Out-of-Scope Use
The model may not work well for other general instruction-following usecases.
[More Information Needed]
Bias, Risks, and Limitations
The V-FLUTE dataset or its source datasets may contain bias, especially in datasets reflecting user-generated distributions (memecap and muse).
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Install LLaVA as described here: https://github.com/asaakyan/LLaVA/tree/6f595efcf2699884f18957ee603986cebfaa9df7
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava_mod import eval_model
model_base = "llava-v1.5-7b"
model_dir = "llava-v1.5-7b-evil-vflue-v2-lora"
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path=model_path,
model_base=model_base,
model_name=model_name,
load_4bit=False
)
prompt = """Does the illustration affirm or contest the claim "Feeling motivated and energetic after only cleaning a room minimally."? Provide your argument and choose a label: entailment or contradiction."""
image_file = f"{image_path}/27.png"
infer_args = type('Args', (), {
"model_name": model_name,
"model": model,
"tokenizer": tokenizer,
"image_processor": image_processor,
"query": prompt,
"conv_mode": None,
"image_file": image_file,
"sep": ",",
"temperature": 0,
"top_p": None,
"num_beams": 3,
"max_new_tokens": 512
})()
output = eval_model(infer_args)
print(output)
Training Details
Training Data
https://huggingface.co/datasets/ColumbiaNLP/V-FLUTE
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