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/main/training_args.bin
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 0432eb2a3f3f598191292f54ecf93fed28fd6cf2c3c24f1fa77bb6a26130094a
- Size of remote file:
- 3.31 kB
- SHA256:
- d5d4dc75479121f5c73a3a986382a6bba1ded33eef7e419c884c29d8d942b707
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.