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 training_args.bin from davanstrien/clip-roberta-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/training_args.bin
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/09d0c80ff62e4b5d79aca4161f23e1d5b18dbd3d/training_args.bin
3.31 kB
- Xet hash:
- 23211959b322480372efce9fea13f559ffd47398fe5bcfe9dded1c4b17bc7547
- Size of remote file:
- 3.31 kB
- SHA256:
- aa9d3535644b4fa02459a9368e20fbdc858f378fd9eea34312874d5b7d22db42
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