Instructions to use hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a1a808762ef7502a5df4d37b51cff2d7f2e367b7e4818888cec724b4e1cc989
- Size of remote file:
- 679 kB
- SHA256:
- b49e841baa931ab3329ce70017a058af713c5967c8c5b1fe370137efab0ea536
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