Instructions to use bobox/DeBERTaV3-base-GeneralSentenceTransformer-checkpoints-tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bobox/DeBERTaV3-base-GeneralSentenceTransformer-checkpoints-tmp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bobox/DeBERTaV3-base-GeneralSentenceTransformer-checkpoints-tmp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bobox/DeBERTaV3-base-GeneralSentenceTransformer-checkpoints-tmp") model = AutoModel.from_pretrained("bobox/DeBERTaV3-base-GeneralSentenceTransformer-checkpoints-tmp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a656efb496feb49b86135f1c43e413f2fe28da649321a93a8b7331426436dda6
- Size of remote file:
- 735 MB
- SHA256:
- dd2f3f68ba49d47cbec07de1aa03058e8d378b613393580878612d075ed121ae
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