Instructions to use hf-internal-testing/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-internal-testing/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-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10c9968fbebd48cfaef9e1d49f8d7bd14a3abc9e9b55483906662b1f6ff108be
- Size of remote file:
- 905 kB
- SHA256:
- 282dbc4d2c394f18db99700c8271b10bb7a1cbc9d04af68454a2e0858a65976f
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