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/52d9d90bc731cefae7b7a1206337c2df2a4624f3/training_args.bin
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@52d9d90bc731cefae7b7a1206337c2df2a4624f3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/52d9d90bc731cefae7b7a1206337c2df2a4624f3/training_args.bin
3.31 kB
- Xet hash:
- 1294caf67ca0263a47f93a4ba2f2adc04910802cc757eb2b10a574c8f66fbd8b
- Size of remote file:
- 3.31 kB
- SHA256:
- b214cad3e9b60aca7ab5d88286713c207d7b47a4c7b0388e84fec6439186a585
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