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library_name: transformers
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:**
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:**
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- **Paper [optional]:**
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- **Demo [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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## How to Get Started with the Model
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[More Information Needed]
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## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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tags:
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- art
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datasets:
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- ColumbiaNLP/V-FLUTE
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language:
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- en
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metrics:
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- f1
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# Model Card for Model ID
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This is the checkpoint for the model from the paper [V-FLUTE: Visual Figurative Language Understanding with Textual Explanations](https://arxiv.org/abs/2405.01474).
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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.
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## Model Details
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### Model Description
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See more on LLaVA 1.5 here: https://github.com/haotian-liu/LLaVA
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V-FLUTE dataset: https://huggingface.co/datasets/ColumbiaNLP/V-FLUTE
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V-FLUTE paper: https://arxiv.org/abs/2405.01474
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Citation:
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```
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@misc{saakyan2024vflute,
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title={V-FLUTE: Visual Figurative Language Understanding with Textual Explanations},
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author={Arkadiy Saakyan and Shreyas Kulkarni and Tuhin Chakrabarty and Smaranda Muresan},
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year={2024},
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eprint={2405.01474},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** Arkadiy Saakyan (ColumbiaNLP)
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- **Model type:** Vision-Language Model
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** LLaVA-v1.5
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/asaakyan/V-FLUTE
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- **Paper [optional]:** https://arxiv.org/abs/2405.01474
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## Uses
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The model's intended use is limited to interpreting multimodal figurative inputs such as metaphors, similes, idioms, sarcasm, and humor.
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### Out-of-Scope Use
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The model may not work well for other general instruction-following usecases.
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[More Information Needed]
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## Bias, Risks, and Limitations
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The V-FLUTE dataset or its source datasets may contain bias, especially in datasets reflecting user-generated distributions (memecap and muse).
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### Recommendations
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## How to Get Started with the Model
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Install LLaVA as described here: https://github.com/asaakyan/LLaVA/tree/6f595efcf2699884f18957ee603986cebfaa9df7
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```
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from llava.model.builder import load_pretrained_model
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from llava.mm_utils import get_model_name_from_path
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from llava.eval.run_llava_mod import eval_model
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model_base = "llava-v1.5-7b"
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model_dir = "llava-v1.5-7b-evil-vflue-v2-lora"
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model_name = get_model_name_from_path(model_path)
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tokenizer, model, image_processor, context_len = load_pretrained_model(
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model_path=model_path,
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model_base=model_base,
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model_name=model_name,
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load_4bit=False
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)
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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."""
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image_file = f"{image_path}/27.png"
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infer_args = type('Args', (), {
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"model_name": model_name,
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"model": model,
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"tokenizer": tokenizer,
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"image_processor": image_processor,
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"query": prompt,
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"conv_mode": None,
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"image_file": image_file,
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"sep": ",",
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"temperature": 0,
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"top_p": None,
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"num_beams": 3,
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"max_new_tokens": 512
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})()
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output = eval_model(infer_args)
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print(output)
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```
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## Training Details
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See [here](https://github.com/asaakyan/LLaVA/tree/6f595efcf2699884f18957ee603986cebfaa9df7/scripts/vflute)
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or [here](https://github.com/asaakyan/V-FLUTE)
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### Training Data
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https://huggingface.co/datasets/ColumbiaNLP/V-FLUTE
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## Model Card Contact
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