Instructions to use nlpie/clinical-mobilebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpie/clinical-mobilebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpie/clinical-mobilebert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlpie/clinical-mobilebert") model = AutoModelForMaskedLM.from_pretrained("nlpie/clinical-mobilebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a1c2f5fc6885b94f08eea99c5aaa6ea31a4615d255e1f9162c759655c09314e
- Size of remote file:
- 147 MB
- SHA256:
- edb3d004af0fe9c05d17ccdf6a94fa7a508e46892e7707595ce20509699693e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.