Instructions to use seldas/rxbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seldas/rxbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="seldas/rxbert-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seldas/rxbert-v1") model = AutoModelForMaskedLM.from_pretrained("seldas/rxbert-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 175997d41bc6933f89d659727fb81a2d3d90e2c87808c036e0e80a29dee41a42
- Size of remote file:
- 2.99 kB
- SHA256:
- 04f8ac93048805d883bb4baa11ba0599279bea1f9f3fd669dd3610d78b9a0299
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