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 # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="TIGER-Lab/VideoScore")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore") model = AutoModelForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- MantisScore also beat the best baselines on other three benchmarks EvalCrafter, GenAI-Bench and VBench, showing high alignment with human evaluations.
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### Evaluation Results
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We test our video evaluation model MantisScore on VideoEval-test, EvalCrafter, GenAI-Bench and VBench.
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For the first two benchmarks, we take Spearman corrleation between model's output and human ratings
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- MantisScore also beat the best baselines on other three benchmarks EvalCrafter, GenAI-Bench and VBench, showing high alignment with human evaluations.
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## Evaluation Results
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We test our video evaluation model MantisScore on VideoEval-test, EvalCrafter, GenAI-Bench and VBench.
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For the first two benchmarks, we take Spearman corrleation between model's output and human ratings
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