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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README.md
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print(aspect_scores)
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"""
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# model output on visual quality, temporal consistency, dynamic degree,
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[2.2969, 2.4375, 2.8281, 2.5, 2.4688]
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"""
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print(aspect_scores)
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"""
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# model output on visual quality, temporal consistency, dynamic degree,
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# text-to-video alignment, factual consistency, respectively
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[2.2969, 2.4375, 2.8281, 2.5, 2.4688]
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"""
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