Instructions to use ProdicusII/ZeroShotBioNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProdicusII/ZeroShotBioNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ProdicusII/ZeroShotBioNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ProdicusII/ZeroShotBioNER") model = AutoModelForTokenClassification.from_pretrained("ProdicusII/ZeroShotBioNER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb49603e271b952060a8e8c12a23f5c55ceff903e26a0fe95f34ebe74f1df60e
- Size of remote file:
- 431 MB
- SHA256:
- ddaf0a5e877717ff24b33dc662d573458133dd1080adedab8d90eaa01b86b276
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.