Instructions to use wooseoko/clip-roberta-finetuned_GQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wooseoko/clip-roberta-finetuned_GQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wooseoko/clip-roberta-finetuned_GQA")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("wooseoko/clip-roberta-finetuned_GQA") model = AutoModel.from_pretrained("wooseoko/clip-roberta-finetuned_GQA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d5f129c545c831a9908638c6b166a73d7f2c7aa3e032aea360d92e34afe0d1e
- Size of remote file:
- 3.5 kB
- SHA256:
- 1dbc27d7d790414390cab924d5a91d85b89e88985d9aff97f6ebc7488e6af957
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.