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:
- e45ac2eb1774b447b3dcbb320985285ea1126146ff87f5264ce9ed8ce026873f
- Size of remote file:
- 443 MB
- SHA256:
- 6bf99d3a3c56e417ed3d4fb10d51a4fba9fbc008545b296336dbfe911c981cfc
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