Instructions to use IVN-RIN/medBIT-r3-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IVN-RIN/medBIT-r3-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="IVN-RIN/medBIT-r3-plus")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("IVN-RIN/medBIT-r3-plus") model = AutoModelForMaskedLM.from_pretrained("IVN-RIN/medBIT-r3-plus") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f94e5bbb2ba1a405f11502dc3e2c404363066391f243a688797d343483412d37
- Size of remote file:
- 440 MB
- SHA256:
- 50566af3670869afd8718954c536c591e5ded627ab9c469ce9901aebd6f6eebf
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