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
Update README.md
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README.md
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@@ -129,7 +129,7 @@ all the frames of video are as follows:
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"""
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video_path="examples/video1.mp4"
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video_prompt=""
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processor = AutoProcessor.from_pretrained(f"TIGER-Lab/MantisScore",torch_dtype=torch.bfloat16)
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model = Idefics2ForSequenceClassification.from_pretrained(f"TIGER-Lab/MantisScore",torch_dtype=torch.bfloat16).eval()
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"""
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video_path="examples/video1.mp4"
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video_prompt="Near the Elephant Gate village, they approach the haunted house at night. Rajiv feels anxious, but Bhavesh encourages him. As they reach the house, a mysterious sound in the air adds to the suspense."
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processor = AutoProcessor.from_pretrained(f"TIGER-Lab/MantisScore",torch_dtype=torch.bfloat16)
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model = Idefics2ForSequenceClassification.from_pretrained(f"TIGER-Lab/MantisScore",torch_dtype=torch.bfloat16).eval()
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