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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## Introduction
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- 🤯🤯Try on the new version [VideoScore-v1.1](https://huggingface.co/TIGER-Lab/VideoScore-v1.1), which is a variant from [VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) with better performance in "text-to-video alignment" subscore!
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See more details about this new version [here](https://huggingface.co/TIGER-Lab/VideoScore-v1.1).
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- [VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) series is a video quality evaluation model series, taking [Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) as base-model
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## Introduction
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- 🤯🤯Try on the new version [VideoScore-v1.1](https://huggingface.co/TIGER-Lab/VideoScore-v1.1), which is a variant from [VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) with **better performance in "text-to-video alignment" subscore** and the support for **48 frames** in inference now!
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See more details about this new version [here](https://huggingface.co/TIGER-Lab/VideoScore-v1.1).
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- [VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) series is a video quality evaluation model series, taking [Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) as base-model
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