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 train_results.json from davanstrien/clip-roberta-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 162 Bytes
-
https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/52d9d90bc731cefae7b7a1206337c2df2a4624f3/train_results.json
- Command line
-
hf download hf://davanstrien/clip-roberta-finetuned@52d9d90bc731cefae7b7a1206337c2df2a4624f3/train_results.json
-
curl -L -o train_results.json https://huggingface.co/davanstrien/clip-roberta-finetuned/resolve/52d9d90bc731cefae7b7a1206337c2df2a4624f3/train_results.json
162 Bytes
| { | |
| "epoch": 3.0, | |
| "train_loss": 2.3376487096150718, | |
| "train_runtime": 3.066, | |
| "train_samples_per_second": 9.785, | |
| "train_steps_per_second": 0.978 | |
| } |