Instructions to use Posos/ClinicalNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Posos/ClinicalNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Posos/ClinicalNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Posos/ClinicalNER") model = AutoModelForTokenClassification.from_pretrained("Posos/ClinicalNER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad2bb67d6f28b7e2e4f15a61000133882355d3904c0807741d567ebbbae96dcb
- Size of remote file:
- 1.11 GB
- SHA256:
- fcc765a21c9bd4c0ab77ed6bbaeccd69b8e5a6a38f83588e6aefce43c5ff7d8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.