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/52d9d90bc731cefae7b7a1206337c2df2a4624f3/pytorch_model.bin
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@52d9d90bc731cefae7b7a1206337c2df2a4624f3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/52d9d90bc731cefae7b7a1206337c2df2a4624f3/pytorch_model.bin
852 MB
- Xet hash:
- 49414cd1c74c204958ff0244e723b4638bb5ef4b318f249ed63ae7c9297c4f04
- Size of remote file:
- 852 MB
- SHA256:
- 46cea30b79e59c2f051bd7dba0855749e14737d1639e66a49593d1915f71d19e
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