Instructions to use larskjeldgaard/senda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use larskjeldgaard/senda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="larskjeldgaard/senda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda") model = AutoModelForSequenceClassification.from_pretrained("larskjeldgaard/senda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d006bee381947033923001473c7be69d41cad4ae53ab5f7fed1bdb5b1b558be
- Size of remote file:
- 2.35 kB
- SHA256:
- fde9e18bd59ab66453c2401ac48131b3281dc7cfd5aadb705a53803fc702abef
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