Instructions to use texturejc/texture-frames-de-frame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use texturejc/texture-frames-de-frame with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="texturejc/texture-frames-de-frame")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("texturejc/texture-frames-de-frame", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6aaedfc948dff6d932115389296ed5a7e7c9fd51e3d744c2bd0dc4a3cd402fd
- Size of remote file:
- 1.36 GB
- SHA256:
- ab550317a7cf5e754d847b3627c29171fac37a1a358a18a8c2f01d494e2f026b
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