Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
multimodal
video embedding
ncsoft
ncai
varco
Instructions to use NCSOFT/GME-VARCO-VISION-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NCSOFT/GME-VARCO-VISION-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NCSOFT/GME-VARCO-VISION-Embedding")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding") model = AutoModelForMultimodalLM.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Performance
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Our model achieves **state-of-the-art (SOTA) performance** on the MultiVENT2.0 dataset in zero-shot settings. See the [official leaderboard](https://eval.ai/web/challenges/challenge-page/2507/leaderboard/6262) for detailed results.
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## Release Schedule
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## Performance
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Our model achieves **state-of-the-art (SOTA) performance** on the MultiVENT2.0 dataset in zero-shot settings. See the [official leaderboard](https://eval.ai/web/challenges/challenge-page/2507/leaderboard/6262) for detailed results.
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## Release Schedule
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