Instructions to use pruas/BENT-PubMEdBERT-NER-Variant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruas/BENT-PubMEdBERT-NER-Variant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pruas/BENT-PubMEdBERT-NER-Variant")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pruas/BENT-PubMEdBERT-NER-Variant") model = AutoModelForTokenClassification.from_pretrained("pruas/BENT-PubMEdBERT-NER-Variant") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5b927848544ab099bc05bd1634060486b612808239094e20ee8ef5d73190ef2
- Size of remote file:
- 436 MB
- SHA256:
- 9be83d00ead645587088fa8bd37634f4f21f955d7bfc6e95ced3835772974fc8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.