Instructions to use salsabiilashifa11/sbert-cv-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salsabiilashifa11/sbert-cv-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="salsabiilashifa11/sbert-cv-2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("salsabiilashifa11/sbert-cv-2") model = AutoModel.from_pretrained("salsabiilashifa11/sbert-cv-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e09c71965076572847597757b2d0a2bfbbf7e208753249ab5085f705af87ca2
- Size of remote file:
- 328 MB
- SHA256:
- 6bebbcc7ead5534be3f4dee3e5a1883e2a7e185f1c610256eaf5d804332fc4f9
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