Visual Question Answering
Transformers
Safetensors
English
idefics2
text-classification
text-generation-inference
Instructions to use TIGER-Lab/VideoScore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/VideoScore with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TIGER-Lab/VideoScore")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore") model = AutoModelForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore") - Notebooks
- Google Colab
- Kaggle
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@@ -193,3 +193,13 @@ see [MantisScore/training](https://github.com/TIGER-AI-Lab/MantisScore/tree/main
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see [MantisScore/benchmark](https://github.com/TIGER-AI-Lab/MantisScore/tree/main/benchmark) for details
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## Citation
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see [MantisScore/benchmark](https://github.com/TIGER-AI-Lab/MantisScore/tree/main/benchmark) for details
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## Citation
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```bibtex
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@article{he2024mantisscore,
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title = {MantisScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation},
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author = {He, Xuan and Jiang, Dongfu and Zhang, Ge and Ku, Max and Soni, Achint and Siu, Sherman and Chen, Haonan and Chandra, Abhranil and Jiang, Ziyan and Arulraj, Aaran and Wang, Kai and Do, Quy Duc and Ni, Yuansheng and Lyu, Bohan and Narsupalli, Yaswanth and Fan, Rongqi and Lyu, Zhiheng and Lin, Yuchen and Chen, Wenhu},
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journal = {ArXiv},
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year = {2024},
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volume={abs/2406.15252},
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url = {https://arxiv.org/abs/2406.15252},
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}
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```
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