Instructions to use digit82/dialog-sbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digit82/dialog-sbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="digit82/dialog-sbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("digit82/dialog-sbert-base") model = AutoModelForSequenceClassification.from_pretrained("digit82/dialog-sbert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51e4523273f9785d514a64698ff955d3533623362d7a2a37371d4342ac4229d8
- Size of remote file:
- 443 MB
- SHA256:
- 3c008cd40166319311d4b8d6e8e4e8ff95aea59b27b83d8715d668a346232ea7
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