Instructions to use kadasterdst/t5-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kadasterdst/t5-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kadasterdst/t5-pretrained")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kadasterdst/t5-pretrained") model = AutoModel.from_pretrained("kadasterdst/t5-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45f364811a649372ccd56f073daae4f75c48c460e45f9a18873b6c0a0b947a7d
- Size of remote file:
- 36.9 MB
- SHA256:
- 45c60318d672b8c21371fa0a14cddb66345e375448f7800e6e9e8a2946b42c69
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