Instructions to use davanstrien/clip-roberta-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davanstrien/clip-roberta-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="davanstrien/clip-roberta-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("davanstrien/clip-roberta-finetuned") model = AutoModel.from_pretrained("davanstrien/clip-roberta-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from davanstrien/clip-roberta-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 852 MB
-
https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/pytorch_model.bin
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/pytorch_model.bin
852 MB
- Xet hash:
- 2222a9bb5a0eb40d4a39bbf90cda8cea9623f1d76550a9b97e5797ef769b7d95
- Size of remote file:
- 852 MB
- SHA256:
- 9bdc2184cbac4263ea076f1e6980079942b627dca2b4ca83ef00bd0ba834f6db
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